title: Pose Estimation from Line Correspondences: A Complete Analysis and a Series of Solutions

abstract: In this paper we deal with the camera pose estimation problem from a set of 2D/3D line correspondences, which is also known as PnL (Perspective-n-Line) problem. We carry out our study by comparing PnL with the well-studied PnP (Perspective-n-Point) problem, and our contributions are three-fold: (1) We provide a complete 3D configuration analysis for P3L, which includes the well-known P3P problem as well as several existing analyses as special cases. (2) By exploring the similarity between PnL and PnP, we propose a new subset-based PnL approach as well as a series of linear-formulation-based PnL approaches inspired by their PnP counterparts. (3) The proposed linear-formulation-based methods can be easily extended to deal with the line and point features simultaneously.


title: Seq2SQL: Generating Structured Queries From Natural Language Using Reinforcement Learning

abstract: Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model uses rewards from in the loop query execution over the database to learn a policy to generate the query, which contains unordered parts that are less suitable for optimization via cross entropy loss. Moreover, Seq2SQL leverages the structure of SQL to prune the space of generated queries and significantly simplify the generation problem. In addition to the model, we release WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets. By applying policy based reinforcement learning with a query execution environment to WikiSQL, Seq2SQL outperforms a state-of-the-art semantic parser, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%.


title: Core Stability Exercises On and Off a Swiss Ball

abstract: Abstract Marshall PW, Murphy BA. Core stability exercises on and off a Swiss ball. Objectives To assess lumbopelvic muscle activity during different core stability exercises on and off a Swiss ball. Design Prospective comparison study. Setting Research laboratory. Participants Eight healthy volunteers from a university population. Intervention Subjects performed 4 exercises on and off a Swiss ball: inclined press-up, upper body roll-out, single-leg hold, and quadruped exercise. Main outcome measures Surface electromyography from selected lumbopelvic muscles, normalized to maximum voluntary isometric contraction, and median frequency analysis of electromyography power spectrum. Visual analog scale for perception of task difficulty. Results There was a significant increase in the activation of the rectus abdominus with performance of the single-leg hold and at the top of the press-up on the Swiss ball. This led to changes in the relation between the activation levels of the lumbopelvic muscles measured. Conclusions Although there was evidence to suggest that the Swiss ball provides a training stimulus for the rectus abdominus, the relevance of this change to core stability training requires further research because the focus of stabilization training is on minimizing rectus abdominus activity. Further support has also been provided about the quality of the quadruped exercise for core stability.


title: Telephone Problem-Solving Treatment Improves Sleep Quality in Service Members With Combat-Related Mild Traumatic Brain Injury: Results From a Randomized Clinical Trial.

abstract: OBJECTIVE ::: Evaluate sleep quality, its correlates, and the effect of telephone-based problem-solving treatment (PST) in active duty postdeployment service members with mild traumatic brain injury (mTBI) SETTING:: Randomized clinical trial. ::: ::: ::: PARTICIPANTS ::: Active duty service members with combat-related mTBI. ::: ::: ::: STUDY DESIGN ::: Education-only (EO) and PST groups (N = 178 each) received printed study materials and 12 educational brochures. The PST group additionally received up to 12 PST telephone calls addressing participant-selected issues. Outcomes were evaluated postintervention (6 months) and at 12 months. ::: ::: ::: MAIN MEASURE ::: Pittsburgh Sleep Quality Index. ::: ::: ::: RESULTS ::: Sleep quality was manifestly poor in both groups at baseline (Pittsburgh Sleep Quality Index = 12.5 ± 4). Overall sleep quality was significantly different between the PST and EO groups at 6 months (P = .003) but not at 12 months. Longitudinally, PST significantly improved sleep quality at 6 months (P = .001) but not over the follow-up. Low sleep quality was associated with concussion symptoms, pain, depression, and posttraumatic stress disorder at all time points (P < .0001). ::: ::: ::: CONCLUSIONS ::: Sleep disorders, common in postdeployment service members with mTBI, are strongly associated with the presence of pain, posttraumatic stress disorder, and depression. Telephone-based PST may be an effective therapeutic approach for reducing sleep disorders in this population. Research should focus on maintenance of treatment gains.


title: High-Speed Parallel Hardware Architecture for Galois Counter Mode

abstract: Parallel hardware architecture for an authenticated encryption mode GCM (Galois counter mode) capable of a throughput higher than 100 Gbps is proposed. In GCM, the CTR mode can encrypt multiple data blocks independently, but a hash function performs multiply-add operation with the cipher-text blocks sequentially. Therefore, a parallel multiply-adder was designed and integrated into the GCM hardware with a 14-stage pipelined AES circuit and two kinds of S-box, composite field and BDD (binary decision tree) versions. Performance of a 4-parallel version was evaluated by using a 0.13-mum CMOS standard cell library, and very high throughputs of 102 Gbps with 600 Kgates and 162 Gbps with 979 Kgates were obtained by the composite and BDD S-boxes, respectively. Higher hardware efficiency (throughput/gate) in comparison with prior art was also achieved. The critical path of the design is the multiply-adder in each local processing block, and is not affected by the number of the parallel blocks. Therefore the proposed architecture has almost linear scalability in terms of hardware speed versus size.


title: Psychiatric morbidity and domestic violence: a survey of married women in Lahore

abstract: OBJECTIVE ::: A number of studies have looked at the prevalence rates of psychiatric disorders in the community in Pakistan over the last two decades. However, a very little information is available on psychiatric morbidity in primary health care. We therefore decided to measure prevalence of psychiatric disorders and their correlates among women from primary health care facilities in Lahore. ::: ::: ::: METHODS ::: We interviewed 650 women in primary health care settings in Lahore. We used a semi-structured interview and questionnaires to collect information during face-to-face interviews. ::: ::: ::: RESULTS ::: Nearly two-third of the women (64.3%) in our study were diagnosed to have a psychiatric problem, while one-third (30.4%) suffered with Major Depressive Disorder. Stressful life events, verbal violence and battering were positively correlated with psychiatric morbidity and social support, using reasoning to resolve conflicts and education were negatively correlated with psychiatric morbidity. ::: ::: ::: CONCLUSION ::: The prevalence of psychiatric disorders is in line with the prevalence figures found in community studies. Domestic violence is an important correlate which can be the focus of interventions.


title: Jamming sensor networks: attack and defense strategies

abstract: Wireless sensor networks are built upon a shared medium that makes it easy for adversaries to conduct radio interference, or jamming, attacks that effectively cause a denial of service of either transmission or reception functionalities. These attacks can easily be accomplished by an adversary by either bypassing MAC-layer protocols or emitting a radio signal targeted at jamming a particular channel. In this article we survey different jamming attacks that may be employed against a sensor network. In order to cope with the problem of jamming, we discuss a two-phase strategy involving the diagnosis of the attack, followed by a suitable defense strategy. We highlight the challenges associated with detecting jamming. To cope with jamming, we propose two different but complementary approaches. One approach is to simply retreat from the interferer which may be accomplished by either spectral evasion (channel surfing) or spatial evasion (spatial retreats). The second approach aims to compete more actively with the interferer by adjusting resources, such as power levels and communication coding, to achieve communication in the presence of the jammer.


title: ASLR-Guard: Stopping Address Space Leakage for Code Reuse Attacks

abstract: A general prerequisite for a code reuse attack is that the attacker needs to locate code gadgets that perform the desired operations and then direct the control flow of a vulnerable application to those gadgets. Address Space Layout Randomization (ASLR) attempts to stop code reuse attacks by making the first part of the prerequisite unsatisfiable. However, research in recent years has shown that this protection is often defeated by commonly existing information leaks, which provides attackers clues about the whereabouts of certain code gadgets. In this paper, we present ASLR-Guard, a novel mechanism that completely prevents the leaks of code pointers, and render other information leaks (e.g., the ones of data pointers) useless in deriving code address. The main idea behind ASLR-Guard is to render leak of data pointer useless in deriving code address by separating code and data, provide a secure storage for code pointers, and encode the code pointers when they are treated as data. ASLR-Guard can either prevent code pointer leaks or render their leaks harmless. That is, ASLR-Guard makes it impossible to overwrite code pointers with values that point to or will hijack the control flow to a desired address when the code pointers are dereferenced. We have implemented a prototype of ASLR-Guard, including a compilation toolchain and a C/C++ runtime. Our evaluation results show that (1) ASLR-Guard supports normal operations correctly; (2) it completely stops code address leaks and can resist against recent sophisticated attacks; (3) it imposes almost no runtime overhead (


title: A measurement-based analysis of multihoming

abstract: Multihoming has traditionally been employed by stub networks to enhance the reliability of their network connectivity. With the advent of commercial "intelligent route control" products, stubs now leverage multihoming to improve performance. Although multihoming is widely used for reliability and, increasingly for performance, not much is known about the tangible benefits that multihoming can offer, or how these benefits can be fully exploited. In this paper, we aim to quantify the extent to which multihomed networks can leverage performance and reliability benefits from connections to multiple providers. We use data collected from servers belonging to the Akamai content distribution network to evaluate performance benefits from two distinct perspectives of multihoming: high-volume content-providers which transmit large volumes of data to many distributed clients, and enterprises which primarily receive data from the network. In both cases, we find that multihoming can improve performance significantly and that not choosing the right set of providers could result in a performance penalty as high as 40%. We also find evidence of diminishing returns in performance when more than four providers are considered for multihoming. In addition, using a large collection of measurements, we provide an analysis of the reliability benefits of multihoming. Finally, we provide guidelines on how multihomed networks can choose ISPs, and discuss practical strategies of using multiple upstream connections to achieve optimal performance benefits.


title: Field Path Design Assessments of a High-Performance Small-Power Synchronous-Reluctance Motor

abstract: With the ever rigorous operational efficiency requirements, effective copper loss reduction is one of the viable approaches for small-power alternative current motors. Among those cost-effective schemes, by eliminating the rotor windings while keeping the same stator structures, the synchronous-reluctance motor (SynRM) offers a potential substitute for those small-power applications that the induction motors are commonly adopted. Since the key factor that affects the SynRM-generated torque will be the difference between its direct-axis and quadrature-axis inductances, by arranging the rotor field paths aligned to the direct axis and by considering the possible cross-coupling magnetic saturations on the rotor ribs and segments, the performance impacts due to field path arrangements on the rotor will be thoroughly evaluated by 3-D finite-element analyses. From the experimental measurements, it is clear that the operational objectives can be achieved, and the adequacy of the proposed design guidance can then be confirmed.


title: PPEDNet: Pyramid Pooling Encoder-Decoder Network for Real-Time Semantic Segmentation

abstract: Image semantic segmentation is a fundamental problem and plays an important role in computer vision and artificial intelligence. Recent deep neural networks have improved the accuracy of semantic segmentation significantly. Meanwhile, the number of network parameters and floating point operations have also increased notably. The real-world applications not only have high requirements on the segmentation accuracy, but also demand real-time processing. In this paper, we propose a pyramid pooling encoder-decoder network named PPEDNet for both better accuracy and faster processing speed. Our encoder network is based on VGG16 and discards the fully connected layers due to their huge amounts of parameters. To extract context feature efficiently, we design a pyramid pooling architecture. The decoder is a trainable convolutional network for upsampling the output of the encoder, and fine-tuning the segmentation details. Our method is evaluated on CamVid dataset, achieving 7.214% mIOU accuracy improvement while reducing 17.9% of the parameters compared with the state-of-the-art algorithm.


title: Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

abstract: The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.


title: General Stability of Stepwise Waveform of an Adiabatic Charge Recycling Circuit With Any Circuit Topology

abstract: The stability of a stepwise waveform of an adiabatic charge recycling circuit with tank capacitors is investigated. We propose a new tank capacitor circuit with two-string capacitor arrays. We experimentally confirmed the stability of the five-step waveform from the tank capacitors. The voltage changes of tank capacitors in the experiment are consistent with the simulation. The power consumption is reduced to one-fifth due to the five-step waveform. We analyze the stability using matrix theory. The results prove that the step waveform is stable for any circuit topology. Moreover, we consider the effects of conductors fixed at a certain voltage and floating conductors and confirm the system is stable using matrix theory.


MANUFACTURABILITY ANALYSIS TOOL FOR ADDITIVE MANUFACTURING USING VOXEL-BASED GEOMETRIC MODELING


title: Visual tracking based on flower pollination algorithm

abstract: Flower pollination algorithm (FPA) is a new meta-heuristic optimization algorithm that mimics the real life processes of the flower pollination. In this study, visual tracking is regard as a process of optimal reproduction of flowering plants. An FPA-based tracking architecture is proposed and its tracking accuracy and speed are compared with particle filter and mean shift. Comparative results indicate that the other two trackers.


PRAMOD SANKAR, K. et al.: SUBTITLE-FREE MOVIE TO SCRIPT ALIGNMENT 1 Subtitle-free Movie to Script Alignment


title: Camp CyberGirls: using a virtual world to introduce computing concepts to middle school girls

abstract: In this paper we report on the design and results of a one-week, residential summer camp experience that introduced computing concepts to middle school girls in the context of an online, multiplayer, virtual world known as the Curiosity Grid. In contrast to programming environments designed specifically as teaching tools to introduce children to programming, virtual world programming exposes novice learners to a more representative computer science experience. Students write real code and get real syntax errors when their code is not correct. They also design objects in a three-dimensional world where knowledge of mathematical concepts such as 3-D global and local coordinate systems, and 3-D transformations are important to the creation of objects and behaviors. Programming artifacts from the camp and feedback from the camp participants provide a strong argument that middle school girls can handle the challenge of this approach and even be enthusiastic about it.


title: Tap water versus sterile saline solution in the colonisation of skin wounds

abstract: Irrigating wounds with tap water does not increase colonisation, but controlled studies are required for further evidence. Microbial colonisation was assessed in skin wounds, before and after irrigation with tap water, and was compared with irrigation using 0·9% sodium chloride sterile solution. The study included 120 subjects with chronic, traumatic, vascular, pressure or neuropathic wounds. A total of 60 wounds were randomly assigned to be irrigated with tap water (tap water group) and another 60 to be irrigated with 0·9% sodium chloride sterile solution (saline group), at a pressure of 0·46–0·54 PSI. Samples were collected from the centre of each wound using Levine's technique, before and after irrigation, and cultivated in thioglycollate, hypertonic mannitol agar, eosin methylene blue (EMB) agar, blood agar and Sabouraud agar at 37°C for 72 hours. There was concordance (kappa test) and discordance (McNemar test) regarding the count of positive and/or negative samples before and after irrigation in each group. The proportion of reduction of positive samples was similar for both groups in all cultures. Colony-forming unit count before and after irrigation was similar in both groups and in all cultures, except for the culture in hypertonic mannitol agar from the tap water group, for which the count was lower after irrigation (Wilcoxon z = 2·05, P = 0·041). It is concluded that skin wound irrigation with tap water leads to further reduction of Gram-positive bacteria compared with 0·9% sodium chloride sterile solution, with no difference in colonisation of haemolytic bacteria, Gram-negative bacteria and fungi.


title: Manufacturing human mesenchymal stem cells at clinical scale: process and regulatory challenges

abstract: Human mesenchymal stem cell (hMSC)-based therapies are of increasing interest in the field of regenerative medicine. As economic considerations have shown, allogeneic therapy seems to be the most cost-effective method. Standardized procedures based on instrumented single-use bioreactors have been shown to provide billion of cells with consistent product quality and to be superior to traditional expansions in planar cultivation systems. Furthermore, under consideration of the complex nature and requirements of allogeneic hMSC-therapeutics, a new equipment for downstream processing (DSP) was successfully evaluated. This mini-review summarizes both the current state of the hMSC production process and the challenges which have to be taken into account when efficiently producing hMSCs for the clinical scale. Special emphasis is placed on the upstream processing (USP) and DSP operations which cover expansion, harvesting, detachment, separation, washing and concentration steps, and the regulatory demands.


title: Drive quality analysis of lane change maneuvers for naturalistic driving studies

abstract: Analysis of naturalistic driving data provides a rich set of semantics which can be used to determine the driving characteristics that could lead to crashes and near-crashes. In this paper, we introduce “drive quality” analysis as part of the drive analysis process of naturalistic driving studies (NDSs) that we have previously introduced in [1]. In this first work on drive quality analysis for NDS data reduction, lane change maneuvers that are reduced from naturalistic driving data are further analyzed in a detailed manner in order to characterize them. Visual data from multiple perspectives and the data from in-vehicle sensors are used to characterize lane changes based on both the ego-vehicle kinematics and ego-vehicle surround dynamics. According to available literature on NDS and data reduction, this is the first work that presents an analysis of visual data from multiple perspectives to characterize and extract semantics related to ego-vehicle maneuvers in NDSs such as SHRP2.


title: Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation

abstract: Most recent approaches to monocular 3D human pose estimation rely on Deep Learning. They typically involve training a network to regress from an image to either 3D joint coordinates directly, or 2D joint locations from which the 3D coordinates are inferred by a model-fitting procedure. The former takes advantage of 3D cues present in the images but rarely models uncertainty. By contrast, the latter often models 2D uncertainty, for example in the form of joint location heatmaps, but discards all the image information, such as texture, shading and depth cues, in the fitting step. ::: In this paper, we therefore propose to jointly model 2D uncertainty and leverage 3D image cues in a regression framework for monocular 3D human pose estimation. To this end, we introduce a novel two-stream deep architecture. One stream focuses on modeling uncertainty via probability maps of 2D joint locations and the other exploits 3D cues by directly acting on the image. We then study different approaches to fusing their outputs to obtain the final 3D prediction. Our experiments evidence in particular that our late-fusion mechanism improves upon the state-of-the-art by a large margin on standard 3D human pose estimation benchmarks.


title: Randomized controlled pilot study of a SystemCHANGE™ weight management intervention in stroke survivors: rationale and protocol

abstract: Over 65% of stroke survivors are either overweight or obese and have multiple cardiovascular risk factors. However, few studies have examined the effects of comprehensive lifestyle behavior interventions to promote weight loss and control cardiovascular risk factors in stroke survivors. Thus, the purpose of this study is to examine a novel behavior change approach - SystemCHANGE™ - to promote weight loss and improve health and function in stroke survivors. SystemCHANGE™ focuses on redesigning the social environment to achieve a specific goal. We will conduct a randomized controlled pilot study to examine the efficacy, feasibility, and safety of the SystemCHANGE™ weight management program in overweight and obese stroke survivors. The central hypothesis of the study is that the SystemCHANGE™ intervention will help overweight and obese stroke survivors lose 5% of their body weight, thereby improving health and function. Thirty-five stroke survivors will be randomized into either the 6-month SystemCHANGE™ intervention or a contact-control intervention. Outcome measures will be assessed at baseline and again at 3 and 6 months after the interventions. Body composition will be assessed using a Bod Pod. Patient-reported outcomes will be the Stroke Impact Scale and Reintegration to Normal Living Index. Objective outcomes will include the 6-Minute Walking Test and Rivermead Motor Assessment. This study will be the first randomized controlled trial to evaluate the efficacy and safety of a weight management intervention in stroke survivors using the SystemCHANGE™ approach. Furthermore, it will be the first empirically-examined comprehensive lifestyle intervention designed to target physical activity, nutrition, and sleep to promote weight loss in stroke survivors. ClinicalTrials.gov Identifier: NCT01776034


title: Adaptive Neuro-Fuzzy Inference System for Health Monitoring at Home

abstract: Healthcare is approaching a critical situation. The ageing of population is increasing the prevalence of chronic diseases. Cardiovascular and respiratory diseases not only kill hundreds of thousands of people each year around the globe but also cost billions of dollars. Patients have to make frequent visits to their doctor to get their vital signs measured. People in remote places are deprived of proper healthcare. Hence, there is a need to develop a system which will help in reducing the frequent visits to the clinic and also help in early diagnosis of dangerous diseases. A system must be targeted both for monitoring elderly and for monitoring rehabilitation after hospitalization period and at the same time economically efficient. This paper presents our initial attempts to develop such a system with the help of Adaptive Neuro-Fuzzy Inference System (ANFIS) by adaptive learning mechanism. The MATLAB simulation results indicate that the performance of the ANFIS approach is much important and at the same time easy to implement. The study results are based on the ranges of diagnostic health parameters and the corresponding opinion of the expert. The developed healthcare system can be useful for the elderly and terminally ill patients confined within their homes and at the same time helpful to the pregnant women for their regular checkups without personally visiting to the clinic.


title: Simultaneous Electric and Magnetic Two-Dimensionally Tuned Parameter-Agile SIW Devices

abstract: A concept of simultaneous electric (E) and magnetic ( H) 2-D tuning is presented, demonstrated, and applied for the first time through the theoretical and experimental studies of parameter-agile substrated integrated waveguide (SIW) devices. First of all, a two-dimensionally tuned SIW cavity is introduced as a building block. Considering only electric tuning with varactor diodes, no more than 1.3% of total tuning range is accomplished, while for simultaneous electric and magnetic tuning, it is extended to 7.9% with an unloaded Q factor of better than 130. Using 0.05-0.1-pF surface mount capacitors, a total tuning range of as much as 20% is experimentally achieved at 12 GHz. Another characteristic of significant interest is that the proposed 2-D tuning not only allows changing frequency, but also simultaneously optimizing other key parameters such as the return loss or unloaded Q factor. Second, the dual-tuned SIW cavity resonator is further accommodated to demonstrate parameter agile two-dimensionally tuned bandpass filter at 12 GHz. The proposed concept of 2-D tuning theoretically and experimentally demonstrates a simultaneous frequency and bandwidth tuning of bandpass filter. This filter can perform both frequency-tunable constant bandwidth, and constant-frequency variable-bandwidth operations. Third, a cavity backed slot antenna using the proposed dual tuning is demonstrated. The tuning not only achieves a higher frequency range, but the antenna return loss is optimized to improve the overall efficiency.


title: Improved website fingerprinting on Tor

abstract: In this paper, we propose new website fingerprinting techniques that achieve a higher classification accuracy on Tor than previous works. We describe our novel methodology for gathering data on Tor; this methodology is essential for accurate classifier comparison and analysis. We offer new ways to interpret the data by using the more fundamental Tor cells as a unit of data rather than TCP/IP packets. We demonstrate an experimental method to remove Tor SENDMEs, which are control cells that provide no useful data, in order to improve accuracy. We also propose a new set of metrics to describe the similarity between two traffic instances; they are derived from observations on how a site is loaded. Using our new metrics we achieve a higher success rate than previous authors. We conduct a thorough analysis and comparison between our new algorithms and the previous best algorithm. To identify the potential power of website fingerprinting on Tor, we perform open-world experiments; we achieve a recall rate over 95% and a false positive rate under 0.2% for several potentially monitored sites, which far exceeds previous reported recall rates. In the closed-world experiments, our accuracy is 91%, as compared to 86-87% from the best previous classifier on the same data.


title: Developing A First Course on Cyber-Physical Systems

abstract: Effective and creative cyber-physical systems (CPS) development requires expertise in disparate fields that have traditionally been taught in several distinct disciplines. At the same time, students seeking a CPS education generally come from diverse educational backgrounds. In this paper, we report on our recent experience developing and teaching a course on CPS. The course addresses the following three questions: What are the core elements of CPS? How should these core concepts be integrated in the CPS design process? What types of modeling tools can assist in the design of cyber-physical systems? Our experience with the first three offerings of the course has been positive overall. We also discuss the lessons we learned from some issues that were not handled well. All material including lecture notes and software used for the course are openly available online.


title: Distance Functions and Geodesics on Points Clouds

abstract: Abstract : An algorithm for computing intrinsic distance functions and geodesics on sub-manifolds vector r(sup d) given by point clouds is introduced in this paper. The basic idea is that, as shown in this paper intrinsic distance functions and geodesics on general co-dimension sub-manifolds vector r(sup d) can be accurately approximated by the extrinsic Euclidean ones computed in a thin offset band surrounding the manifold. This permits the use of computationally optimal algorithms for computing distance functions in Cartesian grids. We then use these algorithms, modified to deal with spaces with boundaries, and obtain also for the case of intrinsic distance functions on sub-manifolds of vector r(sup d), a computationally optimal approach. For point clouds. the offset band is constructed without the need to explicitly find the underlying manifold, thereby computing intrinsic distance functions and geodesics on point clouds while skipping the manifold reconstruction step. The case of point clouds representing noisy samples of a sub-manifold of Euclidean space is studied as well. All the underlying theoretical results are presented. together with experimental examples, and comparisons to graph-based distance algorithms.


title: Pose-timeline for propagating motion edits

abstract: Motion editing often requires repetitive operations for modifying similar action units to give a similar effect or impression. This paper proposes a system for efficiently and flexibly editing the sequence of iterative actions by a few intuitive operations. Our system visualizes a motion sequence on a summary timeline with editable pose-icons, and drag-and-drop operations on the timeline enable intuitive controls of temporal properties of the motion such as timing, duration, and coordination. This graphical interface is also suited to transfer kinematical and temporal features between two motions through simple interactions with a quick preview of the resulting poses. Our method also integrates the concept of edit propagation by which the manual modification of one action unit is automatically transferred to the other units that are robustly detected by similarity search technique. We demonstrate the efficiency of our pose-timeline interface with a propagation mechanism for the timing adjustment of mutual actions and for motion synchronization with a music sequence.


title: Optimal randomized classification in adversarial settings

abstract: The problem of learning to distinguish good inputs from malicious has come to be known as adversarial classification emphasizing the fact that, unlike traditional classification, the adversary can manipulate input instances to avoid being so classified. We offer the first general theoretical analysis of the problem of adversarial classification, resolving several important open questions in the process. First, we significantly generalize previous results on adversarial classifier reverse engineering (ACRE), showing that if a classifier can be efficiently learned, it can subsequently be efficiently reverse engineered with arbitrary precision. We extend this result to randomized classification schemes, but now observe that reverse engineering is imperfect, and its efficacy depends on the defender's randomization scheme. Armed with this insight, we proceed to characterize optimal randomization schemes in the face of adversarial reverse engineering and classifier manipulation. What we find is quite surprising: in all the model variations we consider, the defender's optimal policy tends to be either to randomize uniformly (ignoring baseline classification accuracy), which is the case for targeted attacks, or not to randomize at all, which is typically optimal when attacks are indiscriminate.


title: Geometric descent method for convex composite minimization

abstract: In this paper, we extend the geometric descent method recently proposed by Bubeck, Lee and Singh to tackle nonsmooth and strongly convex composite problems. We prove that our proposed algorithm, dubbed geometric proximal gradient method (GeoPG), converges with a linear rate $(1-1/\sqrt{\kappa})$ and thus achieves the optimal rate among first-order methods, where $\kappa$ is the condition number of the problem. Numerical results on linear regression and logistic regression with elastic net regularization show that GeoPG compares favorably with Nesterov's accelerated proximal gradient method, especially when the problem is ill-conditioned.


title: Exercising Your Brain: A Review of Human Brain Plasticity and Training-Induced Learning

abstract: Human beings have an amazing capacity to learn new skills and adapt to new environments. However, several obstacles remain to be overcome in designing paradigms to broadly improve quality of life. Arguably, the most notable impediment to this goal is that learning tends to be quite specific to the trained regimen and does not transfer to even qualitatively similar tasks. This severely limits the potential benefits of learning to daily life. This review discusses training regimens that lead to the acquisition of new knowledge and strategies that can be used flexibly across a range of tasks and contexts. Possible characteristics of training regimens are proposed that may be responsible for augmented learning, including the manner in which task difficulty is progressed, the motivational state of the learner, and the type of feedback the training provides. When maximally implemented in rehabilitative paradigms, these characteristics may greatly increase the efficacy of training.


title: Reliable Behavioural Factors in the Information Security Context

abstract: Users do often not behave securely when using information technology. Many studies have tried to identify the factors of behavioural theories which can increase secure behaviour. The goal of this work is to identify which of the factors are reliably associated with secure behaviour across multiple studies. Those factors are of interest to information security professionals since addressing them in security awareness and education campaigns can help improving security related processes of users. To attain our goal, we conducted a systematic literature review and assessed the reliability of the factors based on the effect sizes reported in the literature. Our results indicate that 11 out of the 14 factors from well established behavioural theories can be associated with reliable effects in the information security context. These factors cover very different aspects: influence of the users skills, whether the environment makes it possible to exhibit secure behaviour, the influence of friends or co-workers, and the perceived properties of the secure behaviour (e.g. response cost). Also, we identify areas, where more studies are needed to increase the confidence of the factors' reliability assessment.


title: Effective Professional Development for E-Learning: What Do the Managers Think?.

abstract: Introducing new methods of teaching and learning requires an institutional approach to professional development in order to cater for the different levels and requirements of staff. The increase in e-learning use has prompted many institutions to adopt a whole organisation approach to professional development for lecturers. This paper proposes to answer three related questions. How do institutions of vocational education within New Zealand structure their institutional provision of e-learning professional development? What training or other development opportunities are provided by institutions? What do e-learning managers feel are the types of e-learning professional development that work best in terms of lecturer development and support? A literature review was completed and interviews were held with e-learning managers from 13 institutions. The data collected from the interviews were then analysed using a grounded analysis approach. The analysis process yielded concepts that were related to different types of professional development training, information and support. The analysis provided a structure of professional development. Furthermore, the efficiency of the types of e-learning professional development was analysed based on e-learning managers' perception and evaluation models. Professional development with opportunities for skill acquisition and collaboration was deemed the most effective. Practitioner Notes What is already known about this topic Academic staff need new skills to teach e-learning., There are different levels of professional development, which vary in duration and scope., Professional development needs to be relevant for academic staff to benefit., What this paper adds This paper discusses the types of professional development made available at tertiary institutions for staff new to e-learning., This paper explains what e-learning managers feel is the most effective professional development format., This paper analyses why the e-learning managers feel this format is the most effective., Implications for practice and/or policy Managers will be better informed why particular types of professional development for e-learning work well., Academic staff will be better informed about what professional development might best suit them., Academic staff will be better informed about what managers feel about effective professional development and be able to have a greater role in the evaluation feedback cycle. [ABSTRACT FROM AUTHOR]


title: A hybrid mars ascent vehicle design and FY 2016 technology development

abstract: Hybrid propulsion is currently favored for a Mars Ascent Vehicle (MAV) concept from a thermal performance and Gross Lift Off Mass standpoint. However, it is at a relatively low level of maturity compared to conventional propulsion options. Technology development efforts are currently underway to bring hybrid propulsion to a technology readiness level that would enable its infusion into potential Mars Sample Return. A new propellant combination is being considered for this design that has excellent low temperature behavior. Preliminary results of two ground test campaigns are currently underway to characterize this propellant combination. Hotfire testing is being carried out in parallel at Parabilis Space Technologies and Space Propulsion Group. In addition to the new propellant combination, several other technologies are being pursued for a potential hybrid MAV: hypergolic ignition and Liquid Injection Thrust Vector Control. Both of these technologies have been applied in other rocket applications, e.g. liquid propulsion commonly uses hypergolic propellants and missiles, such as the Minuteman II, have used LITVC in the past. Hypergolic ignition, when oxidizer and fuel combust upon contact, is highly desirable for multiple starts required by the MAV concept. Therefore, testing at Penn State and Purdue is being completed in this area. An updated hybrid propulsion system design for a Mars Ascent Vehicle concept based on JPL's current understanding of potential Mars Sample Return requirements will be presented, leveraging the advances in technology development as well as updated understanding of how requirements may evolve.


title: Backtrack search algorithms and the maximal common subgraph problem

abstract: SUMMARY Backtrack algorithms are applicable to a wide variety of problems. An efficient but readable version of such an algorithm is presented and its use in the problem of finding the maximal common subgraph of two graphs is described. Techniques available in this application area for ordering and pruning the backtrack search are discussed. This algorithm has been used successfully as a component of a program for analysing chemical reactions and enumerating the bond changes which have taken place. Graph matching algorithms have been extensively studied for use in the analysis of chemical molecules and chemical reactions.' -3 The methods discussed in the present paper were developed for use in a program for classifying chemical reactions. Given structural descriptions of the molecules present before and after the reaction, the program identifies the chemical bonds changed by the reaction. The present paper is not concerned with chemical details, which are given el~ewhere,~~ ' and instead concentrates on the underlying abstract problem of determining the maximal common subgraph of two graphs. Relatively efficient solutions to this fundamental problem may find practical applications in many areas besides chemistry. For example, various police forces have attempted to match descriptions of crimes: when the descriptions, which may be in the form of graphs, are sufficiently similar, then the same gang may have committed the crimes. This and other such applications are not yet clearly established because the maximal common subgraph problem has hitherto appeared to be combinatorially prohibitive. As well as having the prospect of usefulness in many areas, the present work provides a case-study in combinatorial computing. Levi6 describes an algorithm for deriving the maximal common subgraph of two graphs and the use of this algorithm in comparing molecular structures has been discussed by Cone.7 Levi's definition of the term 'maximal common subgraph' is inappropriate for the reaction classification problem. The present paper uses a more appropriate and generally useful measure of the similarity between two graphs, for which Levi's methods do not work. We will introduce a maximal common subgraph algorithm that uses a backtrack search' as its basic component. Backtrack search, if used alone in a problem of this nature, is grotesquely inefficient. If, however, other techniques can be found for


title: Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

abstract: Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.


title: Severe adolescent female stress urinary incontinence (SAFSUI): case report and literature review

abstract: Introduction ::: Severe adolescent female stress urinary incontinence (SAFSUI) can be defined as female adolescents between the ages of 12 and 17 years complaining of involuntary loss of urine multiple times each day during normal activities or sneezing or coughing rather than during sporting activities. An updated review of its likely prevalence, etiology, and management is required.


Deconfounded Lexicon Induction for Interpretable Social Science


title: Cost Effective Network Flow Measurement for Software Defined Networks: A Distributed Controller Scenario

abstract: Software-defined networking (SDN) has emerged as an evolutionary paradigm in datacenter networks by separating data from control plane and centralizing network decision making. Traffic flow measurement in SDN is relatively lightweight in comparison to the traditional methods. It enables flow measurement system to overcome the issues of traditional measurement systems, such as cost and accuracy by employing a centralized controller. Nevertheless, a full physically centralized controller introduces negative impacts on the network as well as the measurement system (i.e., introducing extra overhead or accuracy issues). However, few efforts have been devoted to measurement techniques in SDN distributed controller architecture, where every controller pulls its corresponding flow statistics, and these statistics are required to expose by only one single expression as if they are collected by one controller. Moreover, the imposed costs of flow measurement in distributed controller architecture are still an issue that remains unsolved. In this paper, we attempt to fill in this gap and present a novel and a practical solution for a cost-effective measurement system in SDN distributed controller deployment. We also propose a synchronization mechanism for aggregating traffic statistics in the multiple controller model. We evaluate our method through extensive emulations in a datacenter topology and present our findings to demonstrate the impact of multiple controllers on overhead and accuracy.


title: Neural Machine Translation

abstract: Draft of textbook chapter on neural machine translation. a comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges. Written as chapter for the textbook Statistical Machine Translation. Used in the JHU Fall 2017 class on machine translation.


title: Study of Cooling System in I.C. Engine Improving Performance with Reduction of Cost

abstract: Background/Objectives : Cooling system of the engine is one of main essential systems for automobiles, which maintains the engine temperature to the sustain conditions. Hence the main objective of this project work is to propose a simple piping design with less number of connecting hoses and also to reduce coolant leakage. It is also proposed to change the piping material from aluminium alloy to mild steel which reduces the overall cost of the engine cooling system. Findings: The proposed design addresses the coolant leakage problem and reduces the number of parts used in coolant pipe connections hence leading to cost reduction and simplified design. Application/Improvements: The proposed design would benefit with coolant leakage elimination by reduction in hose defects, reduced head loss, cost reduction and improved life of piping system in automobiles heavy vehicles.


title: Functional cultures and health benefits

abstract: A number of health benefits have been claimed for probiotic bacteria such as Lactobacillus acidophilus, Bifidobacterium spp., and L. casei. These benefits include antimutagenic effects, anticarcinogenic properties, improvement in lactose metabolism, reduction in serum cholesterol, and immune system stimulation. Because of the potential health benefits, these organisms are increasingly being incorporated into dairy foods, particularly yoghurt. In addition to yoghurt, fermented functional foods with health benefits based on bioactive peptides released by probiotic organisms, including Evolus® and Calpis®, have been introduced in the market. To maximize effectiveness of bifidus products, prebiotics are used in probiotic foods. Synbiotics are products that contain both prebiotics and probiotics.


title: Map-based driving profile simulation for energy consumption estimation of electric vehicles

abstract: Estimating the energy consumption of electric vehicles accurately along a given route is an important prerequisite for increasing their acceptance. In this paper, an algorithmic framework for interpolating a track described by a set of waypoints using sub-splines is presented. Furthermore, the velocity and acceleration a driver would choose is simulated along the course. The results are evaluated by comparing them to real consumption measurements of an electric vehicle during a test drive.


title: Collaborative filtering with privacy

abstract: Server-based collaborative filtering systems have been very successful in e-commerce and in direct recommendation applications. In future, they have many potential applications in ubiquitous computing settings. But today's schemes have problems such as loss of privacy, favoring retail monopolies, and with hampering diffusion of innovations. We propose an alternative model in which users control all of their log data. We describe an algorithm whereby a community of users can compute a public "aggregate" of their data that does not expose individual users' data. The aggregate allows personalized recommendations to be computed by members of the community, or by outsiders. The numerical algorithm is fast, robust and accurate. Our method reduces the collaborative filtering task to an iterative calculation of the aggregate requiring only addition of vectors of user data. Then we use homomorphic encryption to allow sums of encrypted vectors to be computed and decrypted without exposing individual data. We give verification schemes for all parties in the computation. Our system can be implemented with untrusted servers, or with additional infrastructure, as a fully peer-to-peer (P2P) system.


title: Congenital agminated melanocytic nevus - case report*

abstract: Agminated nevus is a cluster group of melanocytic nevi confined to a localized area of the body. There are many pigmented lesions described in the literature as agminated, such as blue nevi, multiple lentigines and Spitz nevi, but only a few cases of congenital agminated melanocytic nevi have been described. We report a case of a male child who presented with congenital agminated nevi, emphasizing the importance of physical examination, dermoscopy, histopathological evaluation, differential diagnosis and follow up to rule out the possibility of dysplastic or malignant changes.


title: An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy

abstract: Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists? diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert. A novel lung segmentation algorithm is proposed, focusing on juxtapleural nodules.A bidirectional chain coding method is proposed to detect border inflection points.A support vector machine classifier is used to selectively smooth the lung border.The method is evaluated on 233 CT studies with 403 juxtapleural nodules.


title: A Maximum Entropy Approach To Natural Language Processing

abstract: The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. In this paper, we describe a method for statistical modeling based on maximum entropy. We present a maximum-likelihood approach for automatically constructing maximum entropy models and describe how to implement this approach efficiently, using as examples several problems in natural language processing.


title: Human-competitive results produced by genetic programming

abstract: Genetic programming has now been used to produce at least 76 instances of results that are competitive with human-produced results. These human-competitive results come from a wide variety of fields, including quantum computing circuits, analog electrical circuits, antennas, mechanical systems, controllers, game playing, finite algebras, photonic systems, image recognition, optical lens systems, mathematical algorithms, cellular automata rules, bioinformatics, sorting networks, robotics, assembly code generation, software repair, scheduling, communication protocols, symbolic regression, reverse engineering, and empirical model discovery. This paper observes that, despite considerable variation in the techniques employed by the various researchers and research groups that produced these human-competitive results, many of the results share several common features. Many of the results were achieved by using a developmental process and by using native representations regularly used by engineers in the fields involved. The best individual in the initial generation of the run of genetic programming often contains only a small number of operative parts. Most of the results that duplicated the functionality of previously issued patents were novel solutions, not infringing solutions. In addition, the production of human-competitive results, as well as the increased intricacy of the results, are broadly correlated to increased availability of computing power tracked by Moore's law. The paper ends by predicting that the increased availability of computing power (through both parallel computing and Moore's law) should result in the production, in the future, of an increasing flow of human-competitive results, as well as more intricate and impressive results.


title: Trace element concentrations in blood and hair of young apprentices of a technical-professional school.

abstract: Abstract The concentrations of total Fe, Cu, Mn, Zn, Pb, Cr, Ni and Ca in the whole blood of young male apprentices of a technical-professional school, who are exposed to low doses of fumes from nanual metal arc welding of mild steel, were monitored over their 2 years of apprenticeship in order to evaluate the influence of occupational exposure on biological metal levels. The results were compared with those from a control group of the same sex and age and living in the same geographic area. For comparison, monitoring of the same metal levels in the hair of both groups of individuals were also carried out. In the apprentices, the mean metal concentrations in blood at the end of the study were statistically significantly higher for Cu, lower for Fe and Mn and similar for the remaining metals. The levels of Fe significantly decreased whereas the levels of Cu were significantly increased during the study. A systematic influence of the exposure period on the levels of Mn was not observed. All the metal concentrations measured in the blood and hair of both apprentices and controls fell in the very large range of published reference levels. Seasonal variation (higher levels in the summer) of the hair metal concentrations were observed for Mn, Cu, Pb, Ni, Zn and Ca.


title: Privacy-preserving deep learning

abstract: Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into the foundation of new AI-based services on the Internet. Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for training.


title: Secure Quantum Steganography Protocol for Fog Cloud Internet of Things

abstract: The security of sensitive information is an urgent need in today’s communication, principally in cloud and Internet of Things (IoT) environments. Therefore, a well-designed security mechanism should be carefully considered. This paper presents a new framework for secure information in fog cloud IoT. In the framework, the user in one location embeds his/her valuable data via the proposed quantum steganography protocol and uploads the covered data to the fog cloud. The intended receiver in another location accesses the data from the fog cloud and extracts the intended content via the proposed extraction approach. This paper also presents a novel quantum steganography protocol based on hash function and quantum entangled states. To the best of our knowledge, there is no prior quantum steganography protocol that authenticates an embedded secret message. In the suggested protocol, the hash function is utilized to authenticate embedded secret messages. The presented protocol is secure against well-known attacks, such as message, man-in-the-middle, and no-message attacks. In addition, it does not consume additional channels besides the proposed one to send a secret message or verify security. The proposed approach is nominated for use in fog and mobile edge computing.


title: BLADYG: A Novel Block-Centric Framework for the Analysis of Large Dynamic Graphs

abstract: Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, GraphLab, and Trinity. These systems can be divided into two categories: (1) vertex-centric and (2) block-centric approaches. In vertex-centric approaches, each vertex corresponds to a process, and message are exchanged among vertices. In block-centric approaches, the unit of computation is a block, a connected subgraph of the graph, and message exchanges occur among blocks. In this paper, we are considering the issues of scale and dynamism in the case of block-centric approaches. We present BLADYG, a block-centric framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of BLADYG on top of AKKA framework. We experimentally evaluate the performance of the proposed framework.


ICT, skills, and organizational change: evidence from Italian manufacturing firms


title: R-Crank: Amphibious all terrain mobile robot

abstract: An amphibious all terrain mobile robot is required for disaster response tasks. In this paper, we propose “R-Crank”, which has a simple four wheels driving configuration with crank links, connecting to ipsilateral front and rear wheels so as to form parallel four-bar linkage systems. The crank links act as legs because their trajectory with respect to the ground become trochoid curve, which is commonly used as leg trajectories for a legged robot. These links achieve high terrain adaptability while maintaining simple structure of the wheeled robot. We developed a prototype model and carried out basic experiments. The robot achieved to climb 0.3 m of a step(76.9% of the wheel diameter), stairs and a snowy slope. In addition, we propose amphibious structure of crank links by installing multiple rigid paddles. The propulsive force generated by the crank links was measured and empirically derived the optimum parameters by using a small-sized experimental apparatus. Finally the robot equipped with the amphibious crank links achieved propulsion on water at 0.13 m/s. We also verified that the robot achieved going up and into water through stairs.


title: UAV-Based Autonomous Image Acquisition with Multi-view Stereo Quality Assurance by Confidence Prediction

abstract: In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without any externally recorded ground truth. We use the confidence prediction for on-site quality assurance and for planning further views that are tailored for a specific multi-view stereo approach with respect to the given scene. We demonstrate the capabilities of our approach with an autonomous Unmanned Aerial Vehicle (UAV) in a challenging outdoor scenario.


title: Shape from Defocus via Diffusion

abstract: Defocus can be modeled as a diffusion process and represented mathematically using the heat equation, where image blur corresponds to the diffusion of heat. This analogy can be extended to nonplanar scenes by allowing a space-varying diffusion coefficient. The inverse problem of reconstructing 3D structure from blurred images corresponds to an "inverse diffusion" that is notoriously ill posed. We show how to bypass this problem by using the notion of relative blur. Given two images, within each neighborhood, the amount of diffusion necessary to transform the sharper image into the blurrier one depends on the depth of the scene. This can be used to devise a global algorithm to estimate the depth profile of the scene without recovering the deblurred image using only forward diffusion.


title: Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks

abstract: Dynamic scene understanding is a challenging problem and motion segmentation plays a crucial role in solving it. Incorporating semantics and motion enhances the overall perception of the dynamic scene. For applications of outdoor robotic navigation, joint learning methods have not been extensively used for extracting spatio-temporal features or adding different priors into the formulation. The task becomes even more challenging without stereo information being incorporated. This paper proposes an approach to fuse semantic features and motion clues using CNNs, to address the problem of monocular semantic motion segmentation. We deduce semantic and motion labels by integrating optical flow as a constraint with semantic features into dilated convolution network. The pipeline consists of three main stages i.e Feature extraction, Feature amplification and Multi Scale Context Aggregation to fuse the semantics and flow features. Our joint formulation shows significant improvements in monocular motion segmentation over the state of the art methods on challenging KITTI tracking dataset.


title: Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation

abstract: We propose a novel end-to-end trainable, deep, encoder-decoder architecture for single-pass semantic segmentation. Our approach is based on a cascaded architecture with feature-level long-range skip connections. The encoder incorporates the structure of ResNeXt's residual building blocks and adopts the strategy of repeating a building block that aggregates a set of transformations with the same topology. The decoder features a novel architecture, consisting of blocks, that (i) capture context information, (ii) generate semantic features, and (iii) enable fusion between different output resolutions. Crucially, we introduce dense decoder shortcut connections to allow decoder blocks to use semantic feature maps from all previous decoder levels, i.e. from all higher-level feature maps. The dense decoder connections allow for effective information propagation from one decoder block to another, as well as for multi-level feature fusion that significantly improves the accuracy. Importantly, these connections allow our method to obtain state-of-the-art performance on several challenging datasets, without the need of time-consuming multi-scale averaging of previous works.


title: Basic kinematics and biomechanics of the patello-femoral joint. Part 1: The native patella.

abstract: The patello-femoral joint (PFJ) enhances our ability of knee flexion and extension and is assumed to have evolved through men's ability of having adopted a bipedal gait. This articulation between patella and femur is relatively complex and displays intricate biomechanical behaviour. Forces in the patello-femoral joint are a function of the quadriceps force, and the angle of flexion of the knee. They are highly dependent on the distance between the patello-femoral joint and the centre of gravity, which explains why different activities despite equivalence in tibio-femoral angle may exert wide variations in patello-femoral reaction forces and contact pressures. During normal daily activities the PFJ becomes exposed to force values between 0.5 to 9.7 x body weight, whilst sporting activities create force values that approach up to 20 x body weight. Those forces are considerable and it is therefore not surprising that the PFJ is particularly susceptible to degenerative disease especially if its mechanical equilibrium is disturbed through injury, muscle weakness and congenital or developmental abnormalities. The clinician must be aware of biomechanical and kinematic specifics, the high patellofemoral load values, small patellofemoral contact areas, and resultant high stress magnitudes when trying to remedy such abnormalities. Language: en


title: An ID-Based Linearly Homomorphic Signature Scheme and Its Application in Blockchain

abstract: Identity-based cryptosystems mean that public keys can be directly derived from user identifiers, such as telephone numbers, email addresses, and social insurance number, and so on. So they can simplify key management procedures of certificate-based public key infrastructures and can be used to realize authentication in blockchain. Linearly homomorphic signature schemes allow to perform linear computations on authenticated data. And the correctness of the computation can be publicly verified. Although a series of homomorphic signature schemes have been designed recently, there are few homomorphic signature schemes designed in identity-based cryptography. In this paper, we construct a new ID-based linear homomorphic signature scheme, which avoids the shortcomings of the use of public-key certificates. The scheme is proved secure against existential forgery on adaptively chosen message and ID attack under the random oracle model. The ID-based linearly homomorphic signature schemes can be applied in e-business and cloud computing. Finally, we show how to apply it to realize authentication in blockchain.


title: Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features

abstract: Abstract Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on a selection of exemplar image patches from a convolution neural network (CNN) and patch classification by quantifying the divergence between the PHPs of exemplars and the input image patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for image patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperform competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet, and healthy cases. Collectively, the accurate tumor segmentation produces the highest average patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology image analysis.


title: Fast Feature Extraction with CNNs with Pooling Layers

abstract: In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features. However, not much effort was taken so far to extract local features efficiently for a whole image. In this paper, we present an approach to compute patch-based local feature descriptors efficiently in presence of pooling and striding layers for whole images at once. Our approach is generic and can be applied to nearly all existing network architectures. This includes networks for all local feature extraction tasks like camera calibration, Patchmatching, optical flow estimation and stereo matching. In addition, our approach can be applied to other patch-based approaches like sliding window object detection and recognition. We complete our paper with a speed benchmark of popular CNN based feature extraction approaches applied on a whole image, with and without our speedup, and example code (for Torch) that shows how an arbitrary CNN architecture can be easily converted by our approach.


title: 'Big Data' collaboration: Exploring, recording and sharing enterprise knowledge

abstract: As data sources and data size proliferate, knowledge discovery from 'Big Data' is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis inference and decision-making lifecycle. We motivate the implementation of an enterprise data discovery and knowledge recorder tool called SEEKER based on a real-world case study. We motivate the implementation of an enterprise data discovery and knowledge recorder tool called SEEKER Schema Exploration and Evolving Knowledge Entity Recorder based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as a digital record of institutional domain knowledge and as documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by 1 avoiding analytic “silos” i.e., a separate set of data that is not included in an enterprise's data administration, 2 expediting the process of multi-source data integration and 3 intelligently documenting discoveries from collaborating analysts.


title: Privacy and security concerns as major barriers for e-commerce : a survey study

abstract: The public lack of confidence in online information technology (IT) is not merely about security of value, but also about trust in the information society. Privacy and security concerns are the number one reason Web users are not purchasing over the Web. Proposes to investigate the privacy and security concerns of IT users in order to establish a consensus among them. Uses data from 158 participants to come to a conclusion that the following major concerns (in the descending of importance) exist: privacy, security and threats, impersonation and forged identity, children protection, e‐mail safety, and censorship. The results also show that privacy and security concerns are the main impediment to shopping on the Internet. The implication is that the successful organizations will be those who expend their resources and efforts to ensure that IT users’ concerns are adequately addressed.


title: Bacopa monnieri extract increases rat coronary flow and protects against myocardial ischemia/reperfusion injury

abstract: This study explored Bacopa monnieri, a medicinal Ayurvedic herb, as a cardioprotectant against ischemia/reperfusion injury using cardiac function and coronary flow as end-points. In normal isolated rat hearts, coronary flow, left ventricular developed pressure, heart rate, and functional recovery were measured using the Langendorff preparation. Hearts were perfused with either (i) Krebs-Henseleit (normal) solution, (control), or with 30, 100 μg/ml B. monnieri ethanolic extract (30 min), or (ii) with normal solution or extract for 10 min preceding no-perfusion ischemia (30 min) followed by reperfusion (30 min) with normal solution. Infarct volumes were measured by triphenyltetrazolium staining. L-type Ca2+-currents (ICa, L) were measured by whole-cell patching in HL-1 cells, a mouse atrial cardiomyocyte cell line. Cytotoxicity of B. monnieri was assessed in rat isolated ventricular myocytes by trypan blue exclusion. In normally perfused hearts, B. monnieri increased coronary flow by 63 ± 13% (30 μg/ml) and 216 ± 21% (100 μg/ml), compared to control (5 ± 3%) (n = 8–10, p < 0.001). B. monnieri treatment preceding ischemia/reperfusion improved left ventricular developed pressure by 84 ± 10% (30 μg/ml), 82 ± 10% (100 μg/ml) and 52 ± 6% (control) compared to pre- ischemia/reperfusion. Similarly, functional recovery showed a sustained increase. Moreover, B. monnieri (100 μg/ml) reduced the percentage of infarct size from 51 ± 2% (control) to 25 ± 2% (n = 6-8, p < 0.0001). B. monnieri (100 μg/ml) reduced ICa, L by 63 ± 4% in HL-1 cells. Ventricular myocyte survival decreased at higher concentrations (50–1000 μg/ml) B. monnieri. B. monnieri improves myocardial function following ischemia/reperfusion injury through recovery of coronary blood flow, contractile force and decrease in infarct size. Thus this may lead to a novel cardioprotectant strategy.


title: Learning From the Experts: An Analysis of Résumé Writers' Self-Presentation on LinkedIn

abstract: Research problem: In light of the growing popularity of LinkedIn as a self-promotional tool for the job search process, this paper examines the rhetorical structure of LinkedIn Summaries. Research questions: (1) Which rhetorical moves are found in the Summary sections of LinkedIn profiles? (2) Do the Summary sections display similar patterns of textual organization? (3) What strategies do the writers of the Summary sections deploy to achieve these rhetorical moves? Literature review: This paper has a genre analytic theoretical orientation of approach, whose underlying assumption is that genres sharing the same purposes and context of use are likely to have similar organizational patterns and to deploy similar strategies to achieve these purposes. Previous research on LinkedIn has focused on characteristics of the service and its impact on interactions, but not as a medium used in the classroom. A much deeper literature exists on job application letters, which concludes that a job application uses similar rhetorical moves, or discoursal units that perform distinct communicative functions, to a sales promotion letter. The moves include: introducing candidature, establishing credentials, essential detailing of candidature, indicating value of candidature, offering incentives, enclosing documents, using pressure tactics, soliciting response, and ending politely. Methodology: It is a study that analyzed the rhetorical structure of LinkedIn Summaries of 50 professional resume writers in the US from a genre analytic perspective. Results and conclusions: The LinkedIn Summary shares many moves in common with the job application letter. Moves that were found in all or nearly all of the texts were establishing credentials, identifying target market and detailing service; an additional move, not seen in job application letters, was also identified—that of personal branding. However, a rigid organizational structure was not seen. The results and the methodology used to obtain them will be of use to students and others wishing to use LinkedIn to promote themselves to potential employers. The study is limited to one particular professional group in one region, but there is ample scope for further research looking at LinkedIn profiles in other professions and other parts of the world.


title: Timeout Strategy-based Mobility Management for Software Defined Satellite Networks

abstract: Software Defined Satellite Network (SDSN) is a novel framework which brings Software Defined Network (SDN) technologies in the satellite networks. It has great potential to achieve effective and flexible management in the satellite networks. However, the frequent handovers will lead to an increase in the flow table size in SDSN. Due to the limited flow table space, a lot of flows will be dropped if the flow table is full during the handover. This is a burning issue to be solved for mobility management in SDSN. In this paper, we propose a heuristic Timeout Strategy-based Mobility Management algorithm for SDSN, named TSMM. TSMM aims to reduce the drop-flows during handover by considering two key points, the limited flow table space and satellite link handover. We implement TSMM mechanism and conduct contrast experiments. The experimental results verify the good performance in terms of transmission quality, an 8.2%-9.9% decrease in drop-flow rate, and a 6.9%–11.18% decrease in flow table size during the handover.


title: Modelling complementary-split-rings-resonator (CSRR) left-handed lines with inter-resonator's coupling

abstract: A circuit model for the accurate description of left handed microstrip lines based on complementary split ring resonators (CSRRs) is proposed in this work. The main relevant contribution is the inclusion of inter-resonator's coupling in the model. From the analysis of several structures, where the shape and distance between adjacent CSRRs differs, it is concluded that electric coupling between CSRRs can be significant if these resonators are rectangular and they are tiny spaced. For circular-shape CSRRs, coupling can be neglected regardless of the distance between resonators. Several prototype devices have been fabricated and measured in order to validate the model and the previous conclusions. The proposed analysis is of interest for the design of planar microwave circuits based on CSRR left handed lines.


title: Characterization of Polymeric Microneedle Arrays for Transdermal Drug Delivery

abstract: Microfabrication of dissolvable, swellable, and biodegradable polymeric microneedle arrays (MNs) were extensively investigated based in a nano sensitive fabrication style known as micromilling that is then combined with conventional micromolding technique. The aim of this study was to describe the polymer selection, and optimize formulation compounding parameters for various polymeric MNs. Inverse replication of micromilled master MNs reproduced with polydimethylsiloxane (PDMS), where solid out of plane polymeric MNs were subsequently assembled, and physicochemically characterized. Dissolvable, swellable, and biodegradable MNs were constructed to depth of less than 1 mm with an aspect ratio of 3.6, and 1/2 mm of both inter needle tip and base spacing. Micromolding step also enabled to replicate the MNs very precisely and accurate. Polymeric microneedles (MN) precision was ranging from ±0.18 to ±1.82% for microneedle height, ±0.45 to ±1.42% for base diameter, and ±0.22 to ±0.95% for interbase spacing. Although dissolvable sodium alginate MN showed less physical robustness than biodegradable polylactic-co-glycolic acid MN, their thermogravimetric analysis is of promise for constructing these polymeric types of matrix devices.


Incorporating Failure-to-Success Transitions in Imitation Learning for a Dynamic Pouring Task


title: Feasibility Study of Mobile Phone WiFi Detection in Aerial Search and Rescue Operations

abstract: In this paper, we show that it is feasible for a mobile phone to be used as an SOS beacon in an aerial search and rescue operation. We show with various experiments that we can reliably detect WiFi-enabled mobile phones from the air at distances up to 200 m. By using a custom mobile application that triggers WiFi scanning with the display off, we can simultaneously extend battery life and increase WiFi scanning frequency, compared to keeping the phone in the default scanning mode. Even if an application is not installed or used, our measurement study suggests that it may be possible to detect mobile devices from their background WiFi emissions alone.


title: The impact of channel-width on threshold voltage for short channel devices

abstract: Technology advances has enabled reduction in transistor dimensions to almost nm scale dimensions as low as 25 nm. CMOS circuits designed for deep submicron technology have imposed new design challenges called as submicron or second order short channel effects (SCE). These SCE contribute to maximum static power dissipation and are closely related to the aspect ratio of the device. In this paper, we present behavior of a low power multi supply voltage (multi-V DD ) interface circuit with respect to variation in aspect ratio of the sleep transistor. The simulation results show variation of threshold voltage for given technology with respect to the variation in channel width in contrast to previous device theory which says threshold voltage is process dependent. We have tried to optimize the level shifter circuit for high performance and low power by varying channel width. The circuit performance can be improved by increasing channel width of transistors in critical path and decreasing channel width for sleep transistor resulting in high threshold voltage. This reduces sub threshold, short circuit and leakage currents. Simulations have been carried out using Cadence Virtuoso Spectre simulator with 0.18µm technology.


title: An Inductor-Loaded Microstrip Directional Coupler for Directivity Enhancement

abstract: A directivity-enhancement technique is demonstrated for a microstrip directional coupler. By loading the coupler with shunt inductors, the phase velocities of the even and odd modes are altered in such a way that an isolation null is achieved. Compared with other compensation techniques for directivity enhancements, the proposed method provides the advantages of being simple yet easily application to high frequencies. For a 10 dB directional coupler at 0.9 GHz, a maximum directivity improvement of nearly 38 dB is measured over the conventional counterpart.


title: Lack of sleep affects the evaluation of emotional stimuli

abstract: Abstract Sleep deprivation (SD) negatively affects various cognitive performances, but surprisingly evidence about a specific impact of sleep loss on subjective evaluation of emotional stimuli remains sparse. In the present study, we assessed the effect of SD on the emotional rating of standardized visual stimuli selected from the International Affective Picture System. Forty university students were assigned to the sleep group ( n = 20), tested before and after one night of undisturbed sleep at home, or to the deprivation group, tested before and after one night of total SD. One-hundred and eighty pictures (90 test, 90 retest) were selected and categorized as pleasant, neutral and unpleasant. Participants were asked to judge their emotional reactions while viewing pictures by means of the Self-Assessment Manikin. Subjective mood ratings were also obtained by means of Visual Analog Scales. No significant effect of SD was observed on the evaluation of pleasant and unpleasant stimuli. On the contrary, SD subjects perceived the neutral pictures more negatively and showed an increase of negative mood and a decrease of subjective alertness compared to non-deprived subjects. Finally, an analysis of covariance on mean valence ratings of neutral pictures using negative mood as covariate confirmed the effect of SD. Our results indicate that sleep is involved in regulating emotional evaluation. The emotional labeling of neutral stimuli biased toward negative responses was not mediated by the increase of negative mood. This effect can be interpreted as an adaptive reaction supporting the “better safe than sorry” principle. It may also have applied implications for healthcare workers, military and law-enforcement personnel.


title: Anomaly Detection for Skin Disease Images Using Variational Autoencoder

abstract: In this paper, we demonstrate the potential of applying Variational Autoencoder (VAE) [10] for anomaly detection in skin disease images. VAE is a class of deep generative models which is trained by maximizing the evidence lower bound of data distribution [10]. When trained on only normal data, the resulting model is able to perform efficient inference and to determine if a test image is normal or not. We perform experiments on ISIC2018 Challenge Disease Classification dataset (Task 3) and compare different methods to use VAE to detect anomaly. The model is able to detect all diseases with 0.779 AUCROC. If we focus on specific diseases, the model is able to detect melanoma with 0.864 AUCROC and detect actinic keratosis with 0.872 AUCROC, even if it only sees the images of nevus. To the best of our knowledge, this is the first applied work of deep generative models for anomaly detection in dermatology.


title: TRESOR-HUNT: attacking CPU-bound encryption

abstract: Hard disk encryption is known to be vulnerable to a number of attacks that aim to directly extract cryptographic key material from system memory. Several approaches to preventing this class of attacks have been proposed, including Tresor [18] and LoopAmnesia [25]. The common goal of these systems is to confine the encryption key and encryption process itself to the CPU, such that sensitive key material is never released into system memory where it could be accessed by a DMA attack. In this work, we demonstrate that these systems are nevertheless vulnerable to such DMA attacks. Our attack, which we call Tresor-Hunt, relies on the insight that DMA-capable adversaries are not restricted to simply reading physical memory, but can write arbitrary values to memory as well. Tresor-Hunt leverages this insight to inject a ring 0 attack payload that extracts disk encryption keys from the CPU into the target system's memory, from which it can be retrieved using a normal DMA transfer. Our implementation of this attack demonstrates that it can be constructed in a reliable and OS-independent manner that is applicable to any CPU-bound encryption technique, IA32-based system, and DMA-capable peripheral bus. Furthermore, it does not crash the target system or otherwise significantly compromise its integrity. Our evaluation supports the OS-independent nature of the attack, as well as its feasibility in real-world scenarios. Finally, we discuss several countermeasures that might be adopted to mitigate this attack and render CPU-bound encryption systems viable.


title: Supervised Deep Hashing for Hierarchical Labeled Data

abstract: Recently, hashing methods have been widely used in large-scale image retrieval. However, most existing hashing methods did not consider the hierarchical relation of labels, which means that they ignored the rich information stored in the hierarchy. Moreover, most of previous works treat each bit in a hash code equally, which does not meet the scenario of hierarchical labeled data. In this paper, we propose a novel deep hashing method, called supervised hierarchical deep hashing (SHDH), to perform hash code learning for hierarchical labeled data. Specifically, we define a novel similarity formula for hierarchical labeled data by weighting each layer, and design a deep convolutional neural network to obtain a hash code for each data point. Extensive experiments on several real-world public datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.


Gender Identification by Voice K unyu


title: Serotonergic Hallucinogen-Induced Visual Perceptual Alterations

abstract: Serotonergic hallucinogens, such as lysergic acid diethylamide (LSD), psilocybin, and N,N-dimethyltryptamine (DMT), are famous for their capacity to temporally and profoundly alter an individual’s visual experiences. These visual alterations show consistent attributes despite large inter- and intra-individual variances. Many reports document a common perception of colors as more saturated, with increased brightness and contrast in the environment (“Visual Intensifications”). Environmental objects might be altered in size (“Visual illusions”) or take on a modified and special meaning for the subject (“Altered self-reference”). Subjects may perceive light flashes or geometrical figures containing recurrent patterns (“Elementary imagery and hallucinations”) influenced by auditory stimuli (“Audiovisual synesthesia”), or they may envision images of people, animals, or landscapes (“Complex imagery and hallucinations”) without any physical stimuli supporting their percepts. This wide assortment of visual phenomena suggests that one single neuropsychopharmacological mechanism is unlikely to explain such vast phenomenological diversity. Starting with mechanisms that act at the cellular level, the key role of 5-HT2A receptor activation and the subsequent increased cortical excitation will be considered. Next, it will be shown that area specific anatomical and dynamical features link increased excitation to the specific visual contents of hallucinations. The decrease of alpha oscillations by hallucinogens will then be introduced as a systemic mechanism for amplifying internal-driven excitation that overwhelms stimulus-induced excitations. Finally, the hallucinogen-induced parallel decrease of the N170 visual evoked potential and increased medial P1 potential will be discussed as key mechanisms for inducing a dysbalance between global integration and early visual gain that may explain several hallucinogen-induced visual experiences, including visual hallucinations, illusions, and intensifications.


title: Identification of Basic Measurable Security Components for a Distributed Messaging System

abstract: The lack of appropriate information security solutions in software-intensive systems can have serious consequences for businesses and the stakeholders. Carefully designed security metrics can be used to offer evidence of the security behavior of the system under development or operation. This study investigates holistic development of security metrics for a distributed messaging system based on threat analysis, security requirements, decomposition and use case information. Our approach is thus requirement-centric. The high-level security requirements are expressed in terms of lower-level measurable components applying a decomposition approach.


title: Hints on Test Data Selection: Help for the Practicing Programmer

abstract: In many cases tests of a program that uncover simple errors are also effective in uncovering much more complex errors. This so-called coupling effect can be used to save work during the testing process.


title: Surgery Planning and Scheduling

abstract: In 2007, total health-care spending in the United States reached US $2.3 trillion, and continues to rise at the fastest rate in US history. The design and implementation of better planning and scheduling systems is an important area to study in order to reduce high costs and improve access to services in the health-care system. Surgery scheduling, in particular, is an area with significant potential for realizing greater efficiencies. Poor scheduling prevents health-care providers from matching patient demand with available capacity, causing inefficient use of resources, decreased return on investment, and long waiting lists for patients. It has been estimated that surgery accounts for more than 40% of a hospital’s total revenues and expenses. Recent studies indicate that resource utilization, overtime, and on-time start performance within surgical suites could be improved at most hospitals. These important performance measures are influenced in part by the surgery scheduling systems and policiesthat are usedin practice. Surgery scheduling systems impact a variety of expensive resources including operating rooms (ORs), the postanesthesia care unit (PACU), intensive care unit (ICU), hospital beds, equipment resources such as mobile diagnostic imaging devices, and human resources including surgeons, nurses, anesthesiologists, and other staff. The unpredictable nature of surgery results in uncertainty in the duration of surgery and patient recovery. This can be caused by many factors including the varying experience of surgeons and OR teams, the presence of residents or surgical fellows in


title: GazeDirector: Fully Articulated Eye Gaze Redirection in Video

abstract: We present GazeDirector, a new approach for eye gaze redirection that uses model-fitting. Our method first tracks the eyes by fitting a multi-part eye region model to video frames using analysis-by-synthesis, thereby recovering eye region shape, texture, pose, and gaze simultaneously. It then redirects gaze by 1) warping the eyelids from the original image using a model-derived flow field, and 2) rendering and compositing synthesized 3D eyeballs onto the output image in a photorealistic manner. GazeDirector allows us to change where people are looking without person-specific training data, and with full articulation, i.e. we can precisely specify new gaze directions in 3D. Quantitatively, we evaluate both model-fitting and gaze synthesis, with experiments for gaze estimation and redirection on the Columbia gaze dataset. Qualitatively, we compare GazeDirector against recent work on gaze redirection, showing better results especially for large redirection angles. Finally, we demonstrate gaze redirection on YouTube videos by introducing new 3D gaze targets and by manipulating visual behavior.


title: Mood and the reliance on the ease of retrieval heuristic

abstract: Four studies investigate the relationship between individuals´ affective states and their reliance on the ease of retrieval heuristic. Based on prior research, we created conditions that allowed to disentangle whether individuals based their judgments on the ease with which the retrieved information came to mind or on the activated content per se. The results consistently show that happy participants were more likely to rely on the ease of retrieval heuristic, whereas sad participants were more likely to rely on the activated content. Additional analyses indicate that this pattern is not due to a differential recall of happy versus sad participants (Experiment 2), and that happy participants no longer relied on the ease of retrieval when the diagnosticity of this information was called into question (Experiment 3). Experiment 4 shows that relying on the ease of retrieval heuristic resulted in faster judgments than relying on content, with the former but not the latter being a function of the amount of activated information. The results are in line with recent models on affect and cognition suggesting that happy mood increases the reliance on heuristics and general knowledge structures.


Local Indicators of Spatial Association