title: Racial Disparities in Oropharyngeal Cancer Stage at Diagnosis

abstract: AIM ::: To evaluate the impact of race on disease stage at diagnosis in patients with oropharyngeal cancer. ::: ::: ::: PATIENTS AND METHODS ::: The cohort included 18,791 adult patients diagnosed with oropharyngeal squamous cell carcinoma between 2004 and 2012, from the Surveillance, Epidemiology, and End Results 18 Database. ::: ::: ::: RESULTS ::: After adjusting for age, sex, marital status, tumor site, and year of diagnosis, black race was associated with increased risk of presenting with Stage III or IV disease (OR 1.24, p=0.016), T3 or T4 tumors (OR 2.16, <0.001), distant metastasis (OR 2, p<0.001), and unresectable tumors (OR 1.65, p<0.001). Race was not associated with risk of presenting with nodal metastasis diagnosis (OR 0.93, p=0.241). ::: ::: ::: CONCLUSION ::: Black race is associated with increased risk of advanced disease presentation in oropharyngeal cancer.


title: Learning Neural Templates for Text Generation

abstract: While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.


title: Aerial grasping of a moving target with a quadrotor UAV

abstract: For a quadrotor aircraft, we study the problem of planning a trajectory that connects two arbitrary states while allowing the UAV to grasp a moving target at some intermediate time. To this end, two classes of canonical grasping maneuvers are defined and characterized. A planning strategy relying on differential flatness is then proposed to concatenate one or more grasping maneuvers by means of spline-based subtrajectories, with the additional objective of minimizing the total transfer time. The proposed planning algorithm is not restricted to pure hovering-to-hovering motions and takes into account practical constraints, such as the finite duration of the grasping phase. The effectiveness of the proposed approach is shown by means of physically-based simulations.


title: Varieties of Repetitive Behavior in Autism: Comparisons to Mental Retardation

abstract: Systematic study of abnormal repetitive behaviors in autism has been lacking despite the diagnostic significance of such behavior. The occurrence of specific topographies of repetitive behaviors as well as their severity was assessed in individuals with mental retardation with and without autism. The occurrence of each behavior category, except dyskinesias, was higher in the autism group and autistic subjects exhibited a significantly greater number of topographies of stereotypy and compulsions. Both groups had significant patterns of repetitive behavior co-occurrence. Autistic subjects had significantly greater severity ratings for compulsions, stereotypy, and self-injury. Repetitive behavior severity also predicted severity of autism. Although abnormal repetition is not specific to autism, an elevated pattern of occurrence and severity appears to characterize the disorder.


title: Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism

abstract: We introduce the first system towards the novel task of answering complex multisentence recommendation questions in the tourism domain. Our solution uses a pipeline of two modules: question understanding and answering. For question understanding, we define an SQL-like query language that captures the semantic intent of a question; it supports operators like subset, negation, preference and similarity, which are often found in recommendation questions. We train and compare traditional CRFs as well as bidirectional LSTM-based models for converting a question to its semantic representation. We extend these models to a semisupervised setting with partially labeled sequences gathered through crowdsourcing. We find that our best model performs semi-supervised training of BiDiLSTM+CRF with hand-designed features and CCM(Chang et al., 2007) constraints. Finally, in an end to end QA system, our answering component converts our question representation into queries fired on underlying knowledge sources. Our experiments on two different answer corpora demonstrate that our system can significantly outperform baselines with up to 20 pt higher accuracy and 17 pt higher recall.


title: Rigid-Body Dynamics with Friction and Impact

abstract: Rigid-body dynamics with unilateral contact is a good approximation for a wide range of everyday phenomena, from the operation of car brakes to walking to rock slides. It is also of vital importance for simulating robots, virtual reality, and realistic animation. However, correctly modeling rigid-body dynamics with friction is difficult due to a number of discontinuities in the behavior of rigid bodies and the discontinuities inherent in the Coulomb friction law. This is particularly crucial for handling situations with large coefficients of friction, which can result in paradoxical results known at least since Painleve [C. R. Acad. Sci. Paris, 121 (1895), pp. 112--115]. This single example has been a counterexample and cause of controversy ever since, and only recently have there been rigorous mathematical results that show the existence of solutions to his example. ::: The new mathematical developments in rigid-body dynamics have come from several sources: "sweeping processes" and the measure differential inclusions of Moreau in the 1970s and 1980s, the variational inequality approaches of Duvaut and J.-L. Lions in the 1970s, and the use of complementarity problems to formulate frictional contact problems by Lotstedt in the early 1980s. However, it wasn't until much more recently that these tools were finally able to produce rigorous results about rigid-body dynamics with Coulomb friction and impulses.


title: Geographic Routing in Social Networks ∗

abstract: We live in a "small world," where two arbitrary people are likely connected by a short chain of intermediate friends. With scant information about a target individual, people can successively forward a message along such a chain. Experimental studies have verified this property in real social networks, and theoretical models have been advanced to explain it. However, existing theoretical models have not been shown to capture behavior in real-world social networks. Here, we introduce a richer model relating geography and social-network friendship, in which the probability of befriending a particular person is inversely proportional to the number of closer people. In a large social network, we show that one-third of the friendships are independent of geography and the remainder exhibit the proposed relationship. Further, we prove analytically that short chains can be discovered in every network exhibiting the relationship.


title: Prototype of a multispectral and multiwavelength pulse oximeter based on a BQJ sensor

abstract: Pulse oximetry is one of the most commonly employed monitoring modalities in critical care setting. Conventional pulse oximeters, use two leds and a photodiode to estimate the percentage of oxyhemoglobin and deoxyhemoglobin, assuming that those are the only light absorbers in the blood. Nevertheless, the presence of other types of dyshemoglobin may invalidate their calibration. In this study, we employed a Buried Quad Junction photodetector in order to estimate the oxygen saturation using simultaneously lit leds. More precisely, a proof-of-principle was shown for developing an oximeter capable of estimating up to four types of hemoglobin simultaneously with a single led. Results from in vivo measurements obtained with this setup show that we successfully isolated the information of our interest for oxygen saturation estimation. These results were compared with a commercial device yielding acceptable estimations with a mean error of 1.3% of the oxygen saturation estimation.


title: Machine morality: bottom-up and top-down approaches for modelling human moral faculties

abstract: The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches.


title: How we forget may depend on how we remember

abstract: Recent developments reveal that memories relying on the hippocampus are relatively resistant to interference, but sensitive to decay. The hippocampus is vital to recollection, a form of memory involving reinstatement of a studied item within its spatial–temporal context. An additional form of memory known as familiarity does not involve contextual reinstatement, but a feeling of acquaintance with the studied items. Familiarity depends more on extrahippocampal structures that do not have the properties promoting resistance to interference. These notions led to the novel hypothesis that the causes of forgetting depend on the memories’ nature: memories depending on recollection are more vulnerable to decay than interference, whereas for memories depending on familiarity, the reverse is true. This review provides comprehensive evidence for this hypothesis.


Argumentation for machine learning: a survey


title: What Drives a Successful MOOC? An Empirical Examination of Criteria to Assure Design Quality of MOOCs

abstract: Massive Open Online Courses (MOOCs) have gained a lot of attention in the last years as a new technology enhanced learning (TEL) approach in higher education. MOOCs provide more educational opportunities to a massive number of learners to attend free online courses around the globe. Discussions around MOOCs have been focusing on the potential, social, institutional, technological, relevance, and marketing issues and less on the quality design of MOOC environments. Several studies have reported a high drop-out rate in average of 95% of course participants and other pedagogical problems concerning assessment and feedback. Thus, the quality of MOOCs design is worth additional investigation. Although several studies identified a large set of criteria to the successful design of TEL systems in general, not all of them can be used in the MOOC context, due to some unique features of MOOCs. This study is a first step towards identifying specific criteria that need to be considered when designing and implementing MOOCs. The results of this empirical study are based on a large survey targeting learners as well as professors, both with MOOC experience. As a result, we identified and rated 74 indicators classified into our two main dimensions of pedagogical and technological criteria distributed over six categories. From these, the learning analytics and assessment categories were found to be the key features for effective MOOCs.


title: A new modeling methodology for continuous cuffless blood pressure measurement

abstract: Continuous blood pressure (BP) monitoring in a noninvasive and unobtrusive way can significantly improve the awareness, control and treatment rate of prevalent hypertension. Pulse transit time (PTT) has become increasingly popular in recent years for continuous BP measurement without a cuff. However, the accuracy issue of PTT-based method remains to be solved for clinical application. Some previous studies have attempted to estimate BP with only PTT by using linear regression, which is susceptible to arterial regulation and may not reflect the actual relationship between PTT and BP. Furthermore, PTT does not contain all the information of BP variation, thereby resulting in unsatisfactory accuracy. In this paper we establish a cuffless BP estimation model from a physiological perspective by utilizing PTT and photoplethysmogram (PPG) intensity ratio (PIR), an indicator we have recently proposed for evaluation of the change in arterial diameter and the low frequency variation of BP, with the consideration that PIR can track changes in mean BP (MBP) and arterial diameter change. The performance of the proposed BP model was evaluated by comparing the estimated BP with Finapres BP as reference on 10 healthy subjects. The results showed that the mean ± standard deviation (SD) of the estimation error for systolic and diastolic BP were −0.41 ± 5.15 and −0.84 ± 4.05 mmHg, and mean absolute difference (MAD) were 4.18 and 3.43 mmHg, respectively. Furthermore, the proposed modeling method was superior to one contrast PTT-based method, demonstrating the proposed model would be promising for reliable continuous cuffless BP measurement.


title: Application of neural networks to image recognition of plant diseases

abstract: Digital image recognition of plant diseases could reduce the dependence of agricultural production on the professional and technical personnel in plant protection field and is conducive to the development of plant protection informatization. In order to find out a method to realize image recognition of plant diseases, four kinds of neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used to distinguish wheat stripe rust from wheat leaf rust and to distinguish grape downy mildew from grape powdery mildew based on color features, shape features and texture features extracted from the disease images. The results showed that identification and diagnosis of the plant diseases could be effectively achieved using BP networks, RBF neural networks, GRNNs and PNNs based on image processing. For the two kinds of wheat diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 97.50% with the fitting accuracy equal to 100% while RBF neural networks were used. For the two kinds of grape diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 94.29% with the fitting accuracy equal to 100% while RBF neural networks were used.


title: Stanton et al. Behavioral Information Security Behavioral Information Security: Two End User Survey Studies of Motivation and Security Practices ABSTRACT

abstract: Information security is a multibillion-dollar problem faced by commercial and government organizations around the world. Through their adverse effects on organizational information systems, malware, hackers, and malicious insiders jeopardize organizations’ capabilities to pursue their missions effectively. Although technology-based solutions help to mitigate some of the many problems of information security, even the best technology cannot work successfully unless the people in organizations do the right thing. In two national survey studies (N=1167 and N=298) we explored some of the motivational antecedents surrounding the practices of information security by end users. Results revealed that organization type, job role, job satisfaction, and organizational commitment each showed relations to some key security behaviors of end users.


title: Textile Electrodes for EEG Recording — A Pilot Study

abstract: The overall aim of our research is to develop a monitoring system for neonatal intensive care units. Long-term EEG monitoring in newborns require that the electrodes don’t harm the sensitive skin of the baby, an especially relevant feature for premature babies. Our approach to EEG monitoring is based on several electrodes distributed over the head of the baby, and since the weight of the head always will be on some of them, any type of hard electrode will inevitably cause a pressure-point that can irritate the skin. Therefore, we propose the use of soft conductive textiles as EEG electrodes, primarily for neonates, but also for other kinds of unobtrusive long-term monitoring. In this paper we have tested two types of textile electrodes on five healthy adults and compared them to standard high quality electrodes. The acquired signals were compared with respect to morphology, frequency distribution, spectral coherence, correlation and power line interference sensitivity, and the signals were found to be similar in most respects. The good measurement performance exhibited by the textile electrodes indicates that they are feasible candidates for EEG recording, opening the door for long-term EEG monitoring applications.


title: Energy cost of walking of amputees: the influence of level of amputation.

abstract: A comparison of selected gait parameters and the energy cost of prosthetic walking was made in seventy patients with unilateral traumatic and vascular amputations. Amputations above the knee, below the knee, and at the Syme's level were compared in both groups of amputees, and a control group of forty normal subjects also studied. In both groups of amputees performance was significantly better the lower the level of the amputation. When preservation of function is the chief concern, amputation should be performed at the lowest possible level.


title: DNA Steganalysis Using Deep Recurrent Neural Networks

abstract: Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods depend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution variations by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools.


title: Data-driven projection method in fluid simulation

abstract: Physically based fluid simulation requires much time in numerical calculation to solve Navier-Stokes equations. Especially in grid-based fluid simulation, because of iterative computation, the projection step is much more time-consuming than other steps. In this paper, we propose a novel data-driven projection method using an artificial neural network to avoid iterative computation. Once the grid resolution is decided, our data-driven method could obtain projection results in relatively constant time per grid cell, which is independent of scene complexity. Experimental results demonstrated that our data-driven method drastically speeded up the computation in the projection step. With the growth of grid resolution, the speed-up would increase strikingly. In addition, our method is still applicable in different fluid scenes with some alterations, when computational cost is more important than physical accuracy. Copyright © 2016 John Wiley & Sons, Ltd.


title: CHEMICAL COMPOSITION OF ECTOPROCTA (BRYOZOA)

abstract: Published data on the elemental composition of the Ectoprocta (Bryozoa) are supplemented by new chemical analyses of 28 ectoproct samples, distributed among 13 spe- cies, for organic matter, CO2, calcium, magnesium, strontium, barium, phosphorus and iron. The new data, in addition to the 25 fairly complete analyses previously published (distributed among 19 other species) suggest the existence of 3 species groupings that are based on compo- sition and are related to habitat. Group I consists of genera that have more than 50 percent organic matter (dry weight). It includes all species of the entirely freshwater Phylactolaemata and the commonly brackish- water and freshwater Ctenostomata. Group II includes Cheilostomata having from 25 to 50 percent organic matter (dry weight). These genera are bush-like in growth habit and may be anchored to fine-grained sediment bottoms. Genera in Group II contain appreciable amounts of P205 (1.0-2.0 percent) and Fe2Os (0.4-1.0 percent) in the ash. Only calcite has been observed in the mineralized portion of the skeleton of Group II species. Group III in- cludes Cheilostomata and Cyclostomata that have less than 25 percent organic matter. These forms require stable surfaces for colonization. They contain a lesser amount of P205 (0.1-0.6 percent) and of Fe203 (less than 0.4 percent) than Group II genera. About 15 percent of Group III species have aragonitic skeletons. "Pure" aragonitic hard parts contain about 1.0 percent SrO (1.4 wt. percent SrCOa) and 0.3 percent MgO (0.6 wt. percent MgCO3) whereas "pure" calcitic skeletons contain less than 0.4 percent SrO (0.6 wt. percent SrCO3) and more than 3.5 percent MgO (7.4 wt. percent MgCO3). Based on the few species analyzed, some additional taxonomic and ecologic generalizations are suggested. Calcitic cheilostomes contain more magnesium than do calcitic cyclostomes. Warm water calcitic species have higher magnesium concentrations than do cold water calcitic species. Specimens from an estuary contain more P206 and less strontium than do specimens from areas of normal marine salinity, and specimens from nearshore areas have more iron than do specimens from deeper offshore areas. The order of concentration of elements in ectoprocts relative to the composition of sea water is 105 for phosphorus and iron, 103 for barium, 102 for calcium and strontium, and 101 for magnesium. However, the elements that are highly enriched in ectoprocts, especially iron and phosphorus, may be supplied to a considerable extent by sources other than normal sea water, such as food (diatoms in particular), and occluded matter of terrigenous origin. Twenty-eight protein and chitin amino acids were studied in three representative marine ectoproct species: Bugula simplex, a calcitic cheilostome, Parasmittina nitida, an aragonitic cheilostome, and Tubulipora liliacea, a calcitic cyclostome. Similar proportions but widely different amounts (from 0.6 to 11 percent organism dry weight) of amino acids occur in these species. Obvious variations in amino acid proportions relate more directly to degree of calcifi- cation and to mineralogy than to species classification. Chitin amino acids recoverable in residues after leaching with acid form less than 2 percent of the total organic content of the organism.


title: Design and comparison of concentrated and distributed winding synchronous reluctance machines

abstract: This paper presents the design and comparison of two types of synchronous reluctance machines. The compared machines differ in their respective type of winding: One is equipped with a conventional full pitch distributed winding while the other one imparts an integer slot concentrated winding. Challenges for the application of concentrated windings to synchronous reluctance machines are examined. The tradeoff concerning a winding configuration with high winding factor and low space harmonic content is carved out. For the concentrated winding machine a winding configuration which is prioritizing a low space harmonic content over a high winding factor is chosen. Both machines were designed for equivalent performance requirements and design space. Finally, several calculations were done and the individual assets and drawbacks are opposed.


title: Waste to wealth — A novel approach for food waste management

abstract: Food wastage is a huge problem arising in today's world. It has become a serious issue in our society in the last years that affects “poor and rich countries” equally and according to the Food and Agriculture Organization (FAO) almost half of all produced food will never be consumed. By wasting food we also waste the “time and energy” that we have used to produce the food and as well our “natural resources” and the “limited available agricultural land” will be used up which could be handled in a much better and sustainable way. Additionally, waste has a strong financial impact and affects the environment including the overall greenhouse gas emission. To avoid all such situations we are going to implement IOT Based Smart Garbage and Waste Collection bins (SGWC). Our paper idea is based on creating a social awareness to reduce food wastage through measuring and displaying the amount of food wasted and recycling the wasted food using embedded systems. The Food waste disposal machine (FWDM) used here recycles the wasted food to make fertilizer at planting.


title: Reshaping the singularities in 3 D.O.F 3-RRR planar parallel manipulator by varying the position of the actuator

abstract: A parallel manipulator has good payload, greater accuracy, and stiffness; however its workspace has complex kinematics singularities which become a stumbling block in its use. In this paper, a novel approach of changing the base actuator to relocate/reduce the singularities within the workspace is proposed. The proposed approach is implemented in a 3-RRR planar parallel manipulator. Results show that there is a trend in the number of singularities with respect to the change in location of the base actuator and it can be used to reduce or relocate the singularities while planning a singularity free path within its workspace.


title: Task Agnostic Meta-Learning for Few-Shot Learning

abstract: Meta-learning approaches have been proposed to tackle the few-shot learning problem. Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile when it is over-trained on existing tasks during meta-training phase. In other words, the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks, especially when only very few examples are available to update the model. To avoid a biased meta-learner and improve its generalizability, we propose a novel paradigm of Task-Agnostic Meta-Learning (TAML) algorithms. Specifically, we present an entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks. Alternatively, a more general inequality-minimization TAML is presented for more ubiquitous scenarios by directly minimizing the inequality of initial losses beyond the classification tasks wherever a suitable loss can be defined. Experiments on benchmarked datasets demonstrate that the proposed approaches outperform compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.


title: Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions

abstract: Distant supervision for relation extraction is an efficient method to scale relation extraction to very large corpora which contains thousands of relations. However, the existing approaches have flaws on selecting valid instances and lack of background knowledge about the entities. In this paper, we propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases. And we extract entity descriptions from Freebase and Wikipedia pages to supplement background knowledge for our task. The background knowledge not only provides more information for predicting relations, but also brings better entity representations for the attention module. We conduct three experiments on a widely used dataset and the experimental results show that our approach outperforms all the baseline systems significantly.


title: Big Five predictors of academic achievement.

abstract: A large-sample study (N = 717) is described in which two broad Big Five factor measures were compared with two narrow personality trait measures in the prediction of final grades in an undergraduate psychology course. The Big Five factors evaluated as predictors were Conscientiousness and Openness to Experience (or Intellect). The traits evaluated, which were constituents of the respective factors, were need for Achievement and need for Understanding. In each comparison, the lower level trait measure did better than its higher level factor measure in the prediction of course grades. We conclude that the aggregation of narrow trait measures into broad factor measures can be counterproductive from the points of view of both behavior prediction and behavior explanation.


A cognitive radio based solution to coexistence of FH and OFDM signals implemented on USRP N210 platform


title: Testing CPU emulators

abstract: A CPU emulator is a software that simulates a hardware CPU. Emulators are widely used by computer scientists for various kind of activities (e.g., debugging, profiling, and malware analysis). Although no theoretical limitation prevents to develop an emulator that faithfully emulates a physical CPU, writing a fully featured emulator is a very challenging and error-prone task. Modern CISC architectures have a very rich instruction set, some instructions lack proper specifications, and others may have undefined effects in corner-cases. This paper presents a testing methodology specific for CPU emulators, based on fuzzing. The emulator is "stressed" with specially crafted test-cases, to verify whether the CPU is properly emulated or not. Improper behaviours of the emulator are detected by running the same test-case concurrently on the emulated and on the physical CPUs and by comparing the state of the two after the execution. Differences in the final state testify defects in the code of the emulator. We implemented this methodology in a prototype (codenamed EmuFuzzer), analysed four state-of-the-art IA-32 emulators (QEMU, Valgrind, Pin and BOCHS), and found several defects in each of them, some of which can prevent the proper execution of programs.


title: A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation

abstract: In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed and the updating rules for the linear coefficients of the composite kernel are derived as well. The proposed MKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in image-segmentation problems. That is, different pixel information represented by different kernels is combined in the kernel space to produce a new kernel. It is shown that two successful enhanced KFCM-based image-segmentation algorithms are special cases of MKFCM. Several new segmentation algorithms are also derived from the proposed MKFCM framework. Simulations on the segmentation of synthetic and medical images demonstrate the flexibility and advantages of MKFCM-based approaches.


title: A Simple Tactile Probe for Surface Identification by Mobile Robots

abstract: This paper describes a tactile probe designed for surface identification in a context of all-terrain low-velocity mobile robotics. The proposed tactile probe is made of a small metallic rod with a single-axis accelerometer attached near its tip. Surface identification is based on analyzing acceleration patterns induced at the tip of this mechanically robust tactile probe, while it is passively dragged along a surface. A training dataset was collected over ten different indoor and outdoor surfaces. Classification results for an artificial neural network were positive, with an 89.9% and 94.6% success rate for 1- and 4-s time windows of data, respectively. We also demonstrated that the same tactile probe can be used for unsupervised learning of terrains. For 1-s time windows of data, the classification success rate was only reduced to 74.1%. Finally, a blind mobile robot, performing real-time classification of surfaces, demonstrated the feasibility of this tactile probe as a guidance mechanism.


Chatbots and the new world of HCI


title: Optimization , Approximation , and Complexity Classes

abstract: We define a natural variant of NP, MAX NP, and also a subclass called MAX SNP. These are classes of optimization problems, and in fact contain several natural, well-studied ones. We show that problems in these classes can be approximated with some bounded error. Furthermore, we show that a number of common optimization problems are complete for MAX SNP under a kind of careful transformation (called L-reduction) that preserves approximability. It follows that such a complete problem has a polynomial-time approximation scheme iff the whole class does. These results may help explain the lack of progress on the approximability of a host of optimization problems.


title: On-Skin Interaction Using Body Landmarks

abstract: The human skin is a promising surface for input to computing devices but differs fundamentally from existing touch-sensitive devices. The authors propose the use of skin landmarks, which offer unique tactile and visual cues, to enhance body-based user interfaces.


title: Anti-Proliferative Activity of Meroditerpenoids Isolated from the Brown Alga Stypopodium flabelliforme against Several Cancer Cell Lines

abstract: The sea constitutes one of the most promising sources of novel compounds with potential application in human therapeutics. In particular, algae have proved to be an interesting source of new bioactive compounds. In this work, six meroditerpenoids (epitaondiol, epitaondiol diacetate, epitaondiol monoacetate, stypotriol triacetate, 14-ketostypodiol diacetate and stypodiol) isolated from the brown alga Stypopodium flabelliforme were tested for their cell proliferation inhibitory activity in five cell lines. Cell lines tested included human colon adenocarcinoma (Caco-2), human neuroblastoma (SH-SY5Y), rat basophilic leukemia (RBL-2H3), murine macrophages (RAW.267) and Chinese hamster fibroblasts (V79). Antimicrobial activity of the compounds was also evaluated against Staphylococcus aureus, Salmonella typhimurium, Proteus mirabilis, Bacillus cereus, Enterococcus faecalis and Micrococcus luteus. Overall, the compounds showed good activity against all cell lines, with SH-SY5Y and RAW.267 being the most susceptible. Antimicrobial capacity was observed for epitaondiol monoacetate, stypotriol triacetate and stypodiol, with the first being the most active. The results suggest that these molecules deserve further studies in order to evaluate their potential as therapeutic agents.


title: Management Dimension for Assessing Organizations' Readiness toward Business Intelligence Systems

abstract: The improvements and capabilities of Business Intelligence (BI) systems are making them highly required by organizations in many of the fields. Day by day, the demand is increasing on these systems. Figures show that billions of US dollars are spent annually on BI systems. However, half of these BI projects are ending with unrealized benefit, according to some studies. As a participation to reduce this failure rate, the authors have developed a conceptual model, based on the existing studies in the field, which may help organizations to assess their readiness toward BI systems. The conceptual model is tested empirically on the Context of Malaysian Organizations(CMOs) as they are emerging widely to BI systems and they lack for this kind of studies to be done them. The conceptual model is consisting of seven dimensions. Depending on the results of the conducted study, this paper will disclose the results of management dimension, which will help to know the degree of its importance for organizations' to be ready for such systems. A brief introduction on BI systems and the challenges of their implementation will be presented. In Addition, this paper will go through some important studies have been done in the field. The details of the study will be provided as well.


title: Gradient Domain Guided Image Filtering

abstract: Guided image filter (GIF) is a well-known local filter for its edge-preserving property and low computational complexity. Unfortunately, the GIF may suffer from halo artifacts, because the local linear model used in the GIF cannot represent the image well near some edges. In this paper, a gradient domain GIF is proposed by incorporating an explicit first-order edge-aware constraint. The edge-aware constraint makes edges be preserved better. To illustrate the efficiency of the proposed filter, the proposed gradient domain GIF is applied for single-image detail enhancement, tone mapping of high dynamic range images and image saliency detection. Both theoretical analysis and experimental results prove that the proposed gradient domain GIF can produce better resultant images, especially near the edges, where halos appear in the original GIF.


Design of informative tutoring feedback 1 How to design informative tutoring feedback for multi-media learning


title: Evaluating the effect of temporal parameters for vibrotactile saltatory patterns

abstract: Cutaneous saltation provides interesting possibilities for applications. An illusion of vibrotactile mediolateral movement was elicited to a left dorsal forearm to investigate emotional (i.e., pleasantness) and cognitive (i.e., continuity) experiences to vibrotactile stimulation. Twelve participants were presented with nine saltatory stimuli delivered to a linearly aligned row of three vibrotactile actuators separated by 70 mm in distance. The stimuli were composed of three temporal parameters of 12, 24 and 48 ms for both burst duration and inter-burst interval to form all nine possible uniform pairs. First, the stimuli were ranked by the participants using a special three-step procedure. Second, the participants rated the stimuli using two nine-point bipolar scales measuring the pleasantness and continuity of each stimulus, separately. The results showed especially the interval between two successive bursts was a significant factor for saltation. Moreover, the temporal parameters seemed to affect more the experienced continuity of the stimuli compared to pleasantness. These findings encourage us to continue to further study the saltation and the effect of different parameters for subjective experience.


title: Neuroevolution in Games: State of the Art and Open Challenges

abstract: This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyze the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The paper also highlights important open research challenges in the field.


title: Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation

abstract: State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for learning from unlabeled image data in a semi-supervised manner. Assuming a one-to-one mapping between a pose and a depth map, any given point in the shared latent space can be projected into both a hand pose and a corresponding depth map. Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth maps. To improve generalization and to better exploit unlabeled depth maps, we jointly train a generator and a discriminator. At each iteration, the generator is updated with the back-propagated gradient from the discriminator to synthesize realistic depth maps of the articulated hand, while the discriminator benefits from an augmented training set of synthesized and unlabeled samples. The proposed discriminator network architecture is highly efficient and runs at 90 FPS on the CPU with accuracies comparable or better than state-of-art on 3 publicly available benchmarks.


title: A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

abstract: Today’s businesses increasingly rely on cloud computing, which brings both great opportunities and challenges. One of the critical challenges is resiliency: disruptions due to failures (either accidental or because of disasters or attacks) may entail significant revenue losses (e.g., US$ 25.5 billion in 2010 for North America). Such failures may originate at any of the major components in a cloud architecture (and propagate to others): 1) the servers hosting the application; 2) the network interconnecting them (on different scales, inside a data center, up to wide-area connections); or 3) the application itself. We comprehensively survey a large body of work focusing on resilience of cloud computing, in each (or a combination) of the server, network, and application components. First, we present the cloud computing architecture and its key concepts. We highlight both the infrastructure (servers, network) and application components. A key concept is virtualization of infrastructure (i.e., partitioning into logically separate units), and thus we detail the components in both physical and virtual layers. Before moving to the detailed resilience aspects, we provide a qualitative overview of the types of failures that may occur (from the perspective of the layered cloud architecture), and their consequences. The second major part of the paper introduces and categorizes a large number of techniques for cloud computing infrastructure resiliency. This ranges from designing and operating the facilities, servers, networks, to their integration and virtualization (e.g., also including resilience of the middleware infrastructure). The third part focuses on resilience in application design and development. We study how applications are designed, installed, and replicated to survive multiple physical failure scenarios as well as disaster failures.


title: Magnitude-Based Angle-of-Arrival Estimation, Localization, and Target Tracking

abstract: In this paper, we are interested in estimating the angle of arrival (AoA) of all the signal paths arriving at a receiver array using only the corresponding received signal magnitude measurements (or, equivalently, the received power measurements). Typical AoA estimation techniques require phase information, which is not available in some WiFi/Bluetooth receivers, and is further challenging to properly measure in a synthetic antenna array due to synchronization issues. In this paper, we then show that AoA estimation is possible with only the received signal magnitude measurements. More specifically, we first propose a framework, based on the spatial correlation of the received signal magnitude, to estimate the AoA of signal paths from fixed signal sources (both active transmitters and passive objects). Next, we extend our AoA estimation framework to a dual setting, and further utilize a particle filter, to show how a moving target (both active transmitters and passive robots/humans) can be tracked, based on only the received signal magnitude measurements of a small number of fixed receivers. We extensively validate our proposed framework with several experiments (total of 22), in both closed and open areas. More specifically, we first utilize a robot to emulate an antenna array, and estimate the AoA of active transmitters, as well as passive objects using only the received WiFi signal magnitude measurements. We next validate our tracking framework by using only three off-the-shelf WiFi devices as receivers, to track an active transmitter, a passive robot that writes the letters of IPSN on its path, and a walking human. Overall, our results show that AoA can be estimated, with a high accuracy, with only the received signal magnitude measurements, and can be utilized for high quality angular localization and tracking.


title: Antimicrobial peptide expression in a wild tobacco plant reveals the limits of host-microbe-manipulations in the field

abstract: Plant-microbe associations are thought to be beneficial for plant growth and resistance against biotic or abiotic stresses, but for natural ecosystems, the ecological analysis of microbiome function remains in its infancy. We used transformed wild tobacco plants (Nicotiana attenuata) which constitutively express an antimicrobial peptide (Mc-AMP1) of the common ice plant, to establish an ecological tool for plant-microbe studies in the field. Transgenic plants showed in planta activity against plant-beneficial bacteria and were phenotyped within the plants´ natural habitat regarding growth, fitness and the resistance against herbivores. Multiple field experiments, conducted over 3 years, indicated no differences compared to isogenic controls. Pyrosequencing analysis of the root-associated microbial communities showed no major alterations but marginal effects at the genus level. Experimental infiltrations revealed a high heterogeneity in peptide tolerance among native isolates and suggests that the diversity of natural microbial communities can be a major obstacle for microbiome manipulations in nature.


title: PHYSICS OF MAGNETIC PHENOMENA USE OF THE BOHR PRINCIPLE FOR DETECTING NQR SIGNALS FROM MINES

abstract: Principles of message transfer in telecommunication systems are considered when a wide transmission band is required, which can result in tuning away from the transmitting station if the signal-to-noise ratio (SNR) is insufficiently high. Based on a solution of the Fokker−Planck−Kolmogorov equation, it is demonstrated that a signal below noise is broadened 20 times for SNR = 0.05. This makes signal accumulation difficult. The matrix pencil method of information theory is used to demonstrate that the broadening of a signal below noise and shift of its frequency detuning interfere with reliable signal detection for SNR ≤ 0.05. An analog of the Bohr complementarity principle is used to analyze the NQR detector. In addition, performance of the NQR-mine detector used to clear of mines territories of former military actions is examined.


title: Models of incremental concept formation

abstract: Abstract Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling the type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions.


title: Compiler-Agnostic Function Detection in Binaries

abstract: We propose Nucleus, a novel function detection algorithm for binaries. In contrast to prior work, Nucleus is compiler-agnostic, and does not require any learning phase or signature information. Instead of scanning for signatures, Nucleus detects functions at the Control Flow Graph-level, making it inherently suitable for difficult cases such as non-contiguous or multi-entry functions. We evaluate Nucleus on a diverse set of 476 C and C ++ binaries, compiled with gcc, clang and Visual Studio for x86 and x64, at optimization levels O0–O3. We achieve consistently good performance, with a mean F-score of 0.95.


title: Fiber image classification using convolutional neural networks

abstract: Fiber recognition is one of the main tasks in textile quality inspections. It is traditionally carried out manually or by shallow classifiers with feature extraction as a prerequisite step, which can be inefficient. Since the convolutional neural network (CNN) has shown superiority in image recognition tasks, this paper proposes a fiber classification method based on CNN. The CNN model is tailored to classification of 7 different kinds of shapes of fibers using grayscale fiber images. Firstly, the images are collected to construct our dataset. Then, data augmentation techniques are applied to enlarge the dataset, which is to reduce overfitting in training. At last, experiments are conducted to validate the efficacy of our model. The results indicate that the proposed approach achieves higher accuracy than the single-hidden-layer backpropagation (BP) neural network and the support vector machine (SVM) method in fiber recognition.


Organizational Learning with Crowdsourcing: The Revelatory Case of LEGO


title: Understanding complex network attack graphs through clustered adjacency matrices

abstract: We apply adjacency matrix clustering to network attack graphs for attack correlation, prediction, and hypothesizing. We self-multiply the clustered adjacency matrices to show attacker reachability across the network for a given number of attack steps, culminating in transitive closure for attack prediction over all possible number of steps. This reachability analysis provides a concise summary of the impact of network configuration changes on the attack graph. Using our framework, we also place intrusion alarms in the context of vulnerability-based attack graphs, so that false alarms become apparent and missed detections can be inferred. We introduce a graphical technique that shows multiple-step attacks by matching rows and columns of the clustered adjacency matrix. This allows attack impact/responses to be identified and prioritized according to the number of attack steps to victim machines, and allows attack origins to be determined. Our techniques have quadratic complexity in the size of the attack graph


title: Patterns of Play: Play-Personas in User-Centred Game Development

abstract: In recent years certain trends from User-Centered design have been seeping into the practice of designing computer games. The balance of power between game designers and players is being renegotiated in order to find a more active role for players and provide them with control in shaping the experiences that games are meant to evoke. A growing player agency can turn both into an increased sense of player immersion and potentially improve the chances of critical acclaim. This paper presents a possible solution to the challenge of involving the user in the design of interactive entertainment by adopting and adapting the "persona" framework introduced by Alan Cooper in the field of Human Computer Interaction. The original method is improved by complementing the traditional ethnographic descriptions of personas with parametric, quantitative, data-oriented models of patterns of user behaviour for computer games.


title: Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment

abstract: We present our ongoing work building a Raspberry Pi cluster consisting of 300 nodes. The unique characteristics of this single board computer pose several challenges, but also offer a number of interesting opportunities. On the one hand, a single Raspberry Pi can be purchased cheaply and has a low power consumption, which makes it possible to create an affordable and energy-efficient cluster. On the other hand, it lacks in computing power, which makes it difficult to run computationally intensive software on it. Nevertheless, by combining a large number of Raspberries into a cluster, this drawback can be (partially) offset. Here we report on the first important steps of creating our cluster: how to set up and configure the hardware and the system software, and how to monitor and maintain the system. We also discuss potential use cases for our cluster, the two most important being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.


title: Testing: a roadmap

abstract: Testing is an important process that is performed to support quality assurance. Testing activities support quality assurance by gathering information about the nature of the software being studied. These activities consist of designing test cases, executing the software with those test cases, and examining the results produced by those executions. Studies indicate that more than fty percent of the cost of software development is devoted to testing, with the percentage for testing critical software being even higher. As software becomes more pervasive and is used more often to perform critical tasks, it will be required to be of higher quality. Unless we can nd ecient ways to perform eective testing, the percentage of development costs devoted to testing will increase signicantly. This report briefly assesses the state of the art in software testing, outlines some future directions in software testing, and gives some pointers to software testing resources.


title: The tourist loyalty index: A new indicator for measuring tourist destination loyalty?

abstract: Abstract The measurement of loyalty is a topic of great interest for the marketing academic literature. The relation that loyalty has with the results of organizations has been tested by numerous studies and the search to retain profitable customers has become a maxim in firm management. Tourist destinations have not remained oblivious to this trend. However, the difficulty involved in measuring the loyalty of a tourist destination is a brake on its adoption by those in charge of destination management. The usefulness of measuring loyalty lies in being able to apply strategies which enable improving it, but that also impact on the enhancement of the organization's results. The study of tourists’ loyalty to a destination is considered relevant for the literature and from the point of view of the management of the multiple actors involved in the tourist activity. Based on these considerations, this work proposes a synthetic indictor that allows the simple measurement of the tourist's loyalty. To do so, we used as a starting point Best's (2007) customer loyalty index adapted to the case of tourist destinations. We also employed a variable of results – the tourist's overnight stays in the destination – to create a typology of customers according to their levels of loyalty and the number of their overnight stays. The data were obtained from a survey carried out with 2373 tourists of the city of Seville. In accordance with the results attained, the proposal of the synthetic indicator to measure tourist loyalty is viable, as it is a question of a simple index constructed from easily obtainable data. Furthermore, four groups of tourists have been identified, according to their degree of loyalty and profitability, using the number of overnight stays of the tourists in their visit to the destination. The study's main contribution stems from the possibility of simply measuring loyalty and from establishing four profiles of tourists for which marketing strategies of differentiated relations can be put into practice and that contribute to the improvement of the destination's results.


title: Design, fabrication and control of soft robots

abstract: Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.


title: High Directivity Directional Coupler using Metamaterial

abstract: In this paper, a novel uniplanar directional coupler using left-handed transmission lines is proposed. Short microstrip parallel coupled lines and asymmetrical delay line pairs make multi-sectioned coupler enhance the directivity as well as the coupling. Each delay line pair consists of a right-handed transmission line and a lumped left-handed transmission line. Furthermore, a Lambda/12 coupled line for the coupled line section makes the design area compact. The designed 3-section 18 dB coupler is measured at the center frequency of 2.0 GHz, which shows good agreement with the simulation result


title: Association between microdeletion and microduplication at 16p11.2 and autism

abstract: BACKGROUND ::: Autism spectrum disorder is a heritable developmental disorder in which chromosomal abnormalities are thought to play a role. ::: ::: ::: METHODS ::: As a first component of a genomewide association study of families from the Autism Genetic Resource Exchange (AGRE), we used two novel algorithms to search for recurrent copy-number variations in genotype data from 751 multiplex families with autism. Specific recurrent de novo events were further evaluated in clinical-testing data from Children's Hospital Boston and in a large population study in Iceland. ::: ::: ::: RESULTS ::: Among the AGRE families, we observed five instances of a de novo deletion of 593 kb on chromosome 16p11.2. Using comparative genomic hybridization, we observed the identical deletion in 5 of 512 children referred to Children's Hospital Boston for developmental delay, mental retardation, or suspected autism spectrum disorder, as well as in 3 of 299 persons with autism in an Icelandic population; the deletion was also carried by 2 of 18,834 unscreened Icelandic control subjects. The reciprocal duplication of this region occurred in 7 affected persons in AGRE families and 4 of the 512 children from Children's Hospital Boston. The duplication also appeared to be a high-penetrance risk factor. ::: ::: ::: CONCLUSIONS ::: We have identified a novel, recurrent microdeletion and a reciprocal microduplication that carry substantial susceptibility to autism and appear to account for approximately 1% of cases. We did not identify other regions with similar aggregations of large de novo mutations.


JSgraph: Enabling Reconstruction of Web Attacks via Efficient Tracking of Live In-Browser JavaScript Executions


title: Secure Communication Method Based on Encryption and Steganography

abstract: Encryption is a traditional method of scrambling data beyond recognition. For years, encryption has been widely used in various areas and domains where secrecy and confidentiality were required. However, there are certain encryption techniques that are not allowed in some countries. In these cases, steganography may come as a solution for hiding data using an apparently inoffensive and innocent carrier. The purpose of steganography is to deliver secret information from a sender to a receiver by scrambling the communication channel not the information itself. This way, it is very unlikely for someone to identify and extract the secret message without knowing exactly how it was embedded into the carrier. Moreover, this task is more difficult since most steganographic methods rely on file or packet header redundancy, lossy multimedia algorithms or insignificant content alteration. All of these are very hard to detect. In this paper, a combination of encryption with image based steganography is presented in order to offer similar robustness as offered by advanced encryption algorithms such as RSA, AES or DES but with lower resource needs. The most original contribution of this paper consists in how a secret message is embedded into a image carrier generating 3 different but very similar images. These images are sent to the destination using different channels and then used to recover the original (secret) message.


title: UHF RFID in Automatic Vehicle Identification: Analysis and Simulation

abstract: In this paper, the application of UHF RFID in automatic vehicles identification is studied by the means of a new simulation framework. The system under study consists of the stationary readers with antennas mounted over the road lanes and vehicles license plates equipped with passive EPC Class 1 Generation 2 UHF RFID tags. The simulation includes protocol settings, antennas parameters, and propagation media conditions. Propagation analysis considers Doppler shift to compute the path loss of moving tags properly. The estimations of an inventory round duration, the number of rounds the tag participates in and the tag identification probability are provided. The paper presents vehicles identification probabilities computed for different protocol settings and movement speeds. In particular, it was shown that a vehicle could be identified with 0.9 probability by EPC values only when the vehicle speed is 120 km/h or even more. If the vehicle is identified with both EPC and TID banks values, the same 0.9 probability is achievable when the vehicle speed is under 90 km/h. The results of the simulation coincide with the experiment results involving the city buses.


title: Incremental Tube Construction for Human Action Detection

abstract: Current state-of-the-art action detection systems are tailored for offline batch-processing applications. However, for online applications like human-robot interaction, current systems fall short, either because they only detect one action per video, or because they assume that the entire video is available ahead of time. In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging action videos in which different action categories occur concurrently. In contrast to previous methods, we solve the detection-window association and action labelling problems jointly in a single pass. We demonstrate superior online association accuracy and speed (2.2ms per frame) as compared to the current state-of-the-art offline systems. We further demonstrate that the entire action detection pipeline can easily be made to work effectively in real-time using our action tube construction algorithm.


title: A Mobile Robot Path Planning Using Genetic Algorithm in Static Environment

abstract: In this study we present our initial idea for using genetic algorithms to help a controllable mobile robot to find an optimal path between a starting and ending point in a grid environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient based methods. The proposed controlling algorithm allows four-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.


title: Combining Silhouettes, Surface, and Volume Rendering for Surgery Education and Planning

abstract: We introduce a flexible combination of volume, surface, and line rendering. We employ object-based edge detection because this allows a flexible parametrization of the generated lines. Our techniques were developed mainly for medical applications using segmented patient-individual volume datasets. In addition, we present an evaluation of the generated visualizations with 8 medical professionals and 25 laypersons. Integration of lines in conventional rendering turned out to be appropriate.


title: A Computer-Aided Design Process for Optimizing the Size of Inverter-Fed Permanent Magnet Motors

abstract: A process for designing an inverter-fed motor while taking into account the performance of the inverter is proposed. To draw a comparison, the classical approach to machine sizing is discussed and its shortcomings in achieving a more optimal solution to the motor-drive system design problem are pointed out. The results prove that designing the motor as an independent system may give less optimal solutions compared to the case where an integrated system is considered. Therefore, the machines and the inverters should be designed simultaneously. An inverter-fed interior permanent magnet motor is investigated, and the corresponding design guidelines are presented. These guidelines are based on the knowledge extracted from an inclusively modeled, sampled, and simulated design space. The reliability of the design, the inverter efficiency, and the priorities of the transient, rated and flux weakening operations are assigned as the selection criteria. Moreover, various user-package interfaces are proposed to show how the designer should be guided towards a more realistic design choice. Finally, four different design cases are presented and compared to the output of the classical machine sizing process. The proposed approach serves as a proposal for the next generation of processes implemented with the aid of a high-performance cloud-computing service.


title: Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML

abstract: Reward augmented maximum likelihood (RAML) is a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks. RAML incorporates task-specific reward by performing maximum-likelihood updates on candidate outputs sampled according to an exponentiated payoff distribution, which gives higher probabilities to candidates that are close to the reference output. While RAML is notable for its simplicity, efficiency, and its impressive empirical successes, the theoretical properties of RAML, especially the behavior of the exponentiated payoff distribution, has not been examined thoroughly. In this work, we introduce softmax Q-distribution estimation, a novel theoretical interpretation of RAML, which reveals the relation between RAML and Bayesian decision theory. The softmax Q-distribution can be regarded as a smooth approximation of Bayes decision boundary, and the Bayes decision rule is achieved by decoding with this Q-distribution. We further show that RAML is equivalent to approximately estimating the softmax Q-distribution. Experiments on three structured prediction tasks with rewards defined on sequential (named entity recognition), tree-based (dependency parsing) and irregular (machine translation) structures show notable improvements over maximum likelihood baselines.


title: Feature Selection via Dependence Maximization

abstract: We introduce a framework for feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highly dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our feature selector works well in practice.


title: The Test of Functional Health Literacy in Adults : A New Instrument for Measuring Patients ' Literacy Skills

abstract: OBJECTIVE: To develop a valid, reliable instrument to measure the functional health literacy of patients.


Preparation and Characterization of Hybrid Polymer-Inorganic Solar Cells SubModule


title: The Design of Rijndael: AES - The Advanced Encryption Standard

abstract: This book, by the designers of the block cipher, presents Rijndael from scratch. The underlying mathematics and the wide trail strategy as the basic design idea are explained in detail and the basics of differential and linear cryptanalysis are reworked. Subsequent chapters review all known attacks against the Rijndael structure and deal with implementation and optimization issues. In addition, other ciphers related to Rijndael are presented.


title: Information privacy and correlates : an empirical attempt to bridge and distinguish privacy-related concepts

abstract: Privacy is one of the few concepts that has been studied across many disciplines, but is still difficult to grasp. The current understanding of privacy is largely fragmented and discipline-dependent. This study develops and tests a framework of information privacy and its correlates, the latter often being confused with or built into definitions of information privacy per se. Our framework development was based on the privacy theories of Westin and Altman, the economic view of the privacy calculus, and the identity management framework of Zwick and Dholakia. The dependent variable of the model is perceived information privacy. The particularly relevant correlates to information privacy are anonymity, secrecy, confidentiality, and control. We posit that the first three are tactics for information control; perceived information control and perceived risk are salient determinants of perceived information privacy; and perceived risk is a function of perceived benefits of information disclosure, information sensitivity, importance of information transparency, and regulatory expectations. The research model was empirically tested and validated in the Web 2.0 context, using a survey of Web 2.0 users. Our study enhances the theoretical understanding of information privacy and is useful for privacy advocates, and legal, management information systems, marketing, and social science scholars.


title: Knowledge systems for sustainable development

abstract: The challenge of meeting human development needs while protecting the earth's life support systems confronts scientists, technologists, policy makers, and communities from local to global levels. Many believe that science and technology (S&T) must play a more central role in sustainable development, yet little systematic scholarship exists on how to create institutions that effectively harness S&T for sustainability. This study suggests that efforts to mobilize S&T for sustainability are more likely to be effective when they manage boundaries between knowledge and action in ways that simultaneously enhance the salience, credibility, and legitimacy of the information they produce. Effective systems apply a variety of institutional mechanisms that facilitate communication, translation and mediation across boundaries.


title: Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

abstract: In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages.


Development and Evaluation of a Text Recognition Framework using Synthetic Data


title: Comparative study of space-charge effects in polymer light emitting diodes by means of reflection electro-optic and electroabsorption techniques

abstract: We used two nonlinear optical techniques, single wavelength ellipsometry and electroabsorption spectroscopy, to study the creation of space charge distributions in an Al/polymer/ITO organic light emitting diode. The polymer is OXA1-PPV, a derivative of poly(p-phenylene-vinylene) in which oxidiazole groups are grafted as side chains to improve electroluminescence efficiency. Both the techniques are sensitive to the spatial profile of electric field via the nonlinear effect in the bulk of the polymer. Results indicate the creation of an asymmetric charge distribution with depletion region close to the ITO electrode. The comparison between ellipsometry and electroabsorption measurements is an original feature of this work. Such an integrated analysis shows as a further result that the mostly used electroabsorption setup, working with wavelengths in the spectral region of maximum absorption, modifies the spatial charge distribution optically reactivating trapped carriers.


title: Automatic segmentation of latent fingerprints

abstract: Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region.


title: The dielectric properties of biological tissues: I. Literature survey

abstract: The dielectric properties of tissues have been extracted from the literature of the past five decades and presented in a graphical format. The purpose is to assess the current state of knowledge, expose the gaps there are and provide a basis for the evaluation and analysis of corresponding data from an on-going measurement programme.


Building Structured Manuals for Elderly Care as a Computer Interpretable Knowledge Resource


A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking


title: ANFIS: Adaptive-Network-Based Fuzzy Inference System

abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >


title: Claude Bernard and the heart–brain connection: Further elaboration of a model of neurovisceral integration

abstract: Abstract The intimate connection between the brain and the heart was enunciated by Claude Bernard over 150 years ago. In our neurovisceral integration model we have tried to build on this pioneering work. In the present paper we further elaborate our model. Specifically we review recent neuroanatomical studies that implicate inhibitory GABAergic pathways from the prefrontal cortex to the amygdala and additional inhibitory pathways between the amygdala and the sympathetic and parasympathetic medullary output neurons that modulate heart rate and thus heart rate variability. We propose that the default response to uncertainty is the threat response and may be related to the well known negativity bias. We next review the evidence on the role of vagally mediated heart rate variability (HRV) in the regulation of physiological, affective, and cognitive processes. Low HRV is a risk factor for pathophysiology and psychopathology. Finally we review recent work on the genetics of HRV and suggest that low HRV may be an endophenotype for a broad range of dysfunctions.


title: LASS: Local-Activity and Social-Similarity Based Data Forwarding in Mobile Social Networks

abstract: This paper aims to design an efficient data forwarding scheme based on local activity and social similarity (LASS) for mobile social networks (MSNs). Various definitions of social similarity have been proposed as the criterion for relay selection, which results in various forwarding schemes. The appropriateness and practicality of various definitions determine the performances of these forwarding schemes. A popular definition has recently been proven to be more efficient than other existing ones, i.e., the more common interests between two nodes, the larger social similarity between them. In this work, we show that schemes based on such definition ignore the fact that members within the same community, i.e., with the same interest, usually have different levels of local activity, which will result in a low efficiency of data delivery. To address this, in this paper, we design a new data forwarding scheme for MSNs based on community detection in dynamic weighted networks, called Local-Activity and Social-Similarity, taking into account the difference of members’ internal activity within each community, i.e., local activity . To the best of our knowledge, the proposed scheme is the first one that utilizes different levels of local activity within communities. Through extensive simulations, we demonstrate that LASS achieves better performance than state-of-the-art protocols.


title: A close look at the mammalian blastocyst: epiblast and primitive endoderm formation

abstract: During early development, the mammalian embryo undergoes a series of profound changes that lead to the formation of two extraembryonic tissues—the trophectoderm and the primitive endoderm. These tissues encapsulate the pluripotent epiblast at the time of implantation. The current model proposes that the formation of these lineages results from two consecutive binary cell fate decisions. The first controls the formation of the trophectoderm and the inner cell mass, and the second controls the formation of the primitive endoderm and the epiblast within the inner cell mass. While early mammalian embryos develop with extensive plasticity, the embryonic pattern prior to implantation is remarkably reproducible. Here, we review the molecular mechanisms driving the cell fate decision between primitive endoderm and epiblast in the mouse embryo and integrate data from recent studies into the current model of the molecular network regulating the segregation between these lineages and their subsequent differentiation.


title: Heterogeneous Relation Graphs as a Mechanism for Representing Linguistic Information

abstract: Abstract Heterogeneous relation graphs (HRGs) can be used to represent arbitrary linguistic information. Originally designed for use in speech synthesis, HRGs can be used for speech annotation purposes also. In the HRG formalism, atomic linguistic entities such as words, syllables and phones are represented by attribute value matrices known as linguistic items. Using attribute value matrices for items allows them to contain any type or amount of linguistic information. Items are organised into linguistic relations, which take the form of lists, trees or other structures. Items can belong to more than one relation, which allows words to appear in the word relation and, say, the syntax relation. The ability to have items in multiple relations, along with a function ability which can calculate certain values on the fly, eliminates much of the redundancy present in simpler systems. The HRG formalism is not tied to any particular linguistic theory, nor does it impose any preset ideas about what sort of format syntax, prosody or phonology information should have. This paper explains the HRG formalism in detail, and shows why we think this is superior to the types of “multi-level” formats normally used in speech synthesis and database annotation.


title: Medication Management Assessment for Older Adults in the Community

abstract: Purpose: The purpose of this study was to describe the Medication Management Instrument for Deficiencies in the Elderly (MedMalDE) and to provide results of reliability and validity testing. Design and Methods: Participants were 50 older adults, aged 65 and older, who lived in the community, took at least one prescription medication, and were then self-medicating. Non-medical study staff assessed participants in their homes at baseline and 1 week, and a study pharmacist conducted pill counts at baseline and 30 days. The MedMalDE covers three domains important for ensuring medication compliance (knowledge of medications, how to take medications, and procurement) and yields a total deficiency score. We assessed test-retest and interrater reliability. We assessed validity by comparing the MedMalDE deficiency scores to 30-day pill count compliance. Results: The sample was mostly female (72%) and White (56%), with a mean age of 78. Participants were taking an average of 7 prescription drugs, with an average pill count compliance of 70%. The MedMalDE had very good test-retest reliability (intraclass correlation coefficient [ICC] = 0.93) and good interrater reliability (ICC = 0.74). Internal consistency was also strong (Cronbach's alpha = .71). Comparing the MedMalDE to the pill count with those who were compliant (>80%) versus those that were not, the agreement was 75%. The MedMalDE was more highly specific and predictive of compliance compared to the pill count. Implications: The MedMalDE appears to be a reliable and valid instrument for determining if an older adult has deficiencies in managing medications.


title: Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

abstract: We present the first autonomous endoscope for the visual inspection of very small ducts and cavities, up to a 6-mm diameter. The system has been designed, implemented, and tested in a challenging industrial scenario and in strict collaboration with an avionic industry partner. The inspected objects are metallic gearboxes eventually presenting different residuals (e.g., sand, machining swarfs, and metallic dust) inside the oil ducts. The automatic system is actuated by a robotic arm that moves the endoscope with a microcamera inside the gearbox duct, while a deep-learning-based spatio-temporal image analysis module detects, classifies, and localizes defects in real time. Feedback is given to the robotic arm in order to move or extract the endoscope given the detected anomalies. Evaluation provides a detection rate of nearly $98$ % given different tests with different types of residuals and duct structures.


title: Learning how to Active Learn: A Deep Reinforcement Learning Approach

abstract: Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation on one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning.


title: Our perspective of the treatment of naevus of Ota with 1,064-, 755- and 532-nm wavelength lasers

abstract: Naevus of Ota (NO) is a disfiguring pigmentary disorder affecting the face. Q-switched neodymium-doped yttrium aluminium garnet (QS Nd:YAG)-1,064 nm is a standard laser treatment because it causes highly selective destruction of melanin within the aberrant dermal melanocytes. However, not all lesions respond. This study aims to evaluate the efficacy/safety of QS Nd:YAG-1,064 nm and the shorter wavelength QS Alexandrite-755 nm and QS Nd:YAG-532 nm lasers in treating NO. Data were evaluated from 21 patients treated in our laser centre from 2004 to 2012. Lesional skin was irradiated with QS-532 nm/QS-755 nm/QS-1,064 nm, with settings titrated according to responses. All received initial test patches to direct initial wavelength choice, with subsequent treatments at 3-monthly intervals until clearance/lack of further response. Laser modality was switched following repeated test patches if there was no or no sustained improvement. Two thirds of patients had ≥90 % improvement compared to baseline photographs. In 20 % of patients, QS-1,064 nm was most efficacious with 97 % mean improvement. The mean improvement was 80 % for those in whom QS-755 nm was superior, and 90 % for QS-532 nm. Median number of overall laser treatments was 8 (range 4–13). Number of treatments required varied significantly according to lesional colour and site: grey lesions and those on the forehead/temple were most resistant. We confirm successful treatment of NO with QS Nd:YAG-1,064 nm and the shorter wavelength QS-755 nm/QS-532 nm lasers without serious or irreversible side effects. We recommend judicious test patch analysis before treatment and a modality switch if complete clearance is not obtained


title: A clustering-based approach to ontology alignment

abstract: Ontology alignment is an important problem for the linked data web, as more and more ontologies and ontology instances get published for specific domains such as government and healthcare. A number of (semi-)automated alignment systems have been proposed in recent years. Most combine a set of similarity functions on lexical, semantic and structural features to align ontologies. Although these functions work well in many cases of ontology alignments, they fail to capture alignments when terms or structure varies vastly across ontologies. In this case, one is forced to rely on manual alignment. In this paper, we study whether it is feasible to re-use such expert provided ontology alignments for new alignment tasks. We focus in particular on many-to-one alignments, where the opportunity for re-use is feasible if alignments are stable. Specifically, we define the notion of a cluster as being made of multiple entities in the source ontology S that are mapped to the same entity in the target ontology τ. We test the stability hypothesis that the formed clusters of source ontology are stable across alignments to different target ontologies. If this hypothesis is valid, the clusters of an ontology S, built from an existing alignment with an ontology τ, can be effectively exploited to align S with a new ontology τ′. Evaluation on both manual and automated high-quality alignments show remarkable stability of clusters across ontology alignments in the financial domain and the healthcare and life sciences domain. Experimental evaluation also demonstrates the effectiveness of utilizing the stability of clusters in improving the alignment process in terms of precision and recall.


title: A Whole Other Story: Inclusive Victimhood Narratives Reduce Competitive Victimhood and Intergroup Hostility

abstract: Conflict narratives, having at their core the belief that the ingroup suffered more than the outgroup (competitive victimhood), are key in maintaining conflicts. Three experiments conducted with Jewish Israelis (Study 1), Turkish Kurds (Study 2), and Americans (Study 3) tested whether conflict narratives can reduce conflict. Studies 1 and 3 showed that people respond to inclusive victimhood narratives that emphasize both ingroup and outgroup suffering with a reduction in competitive victimhood and, in turn, reduced support for aggressive policies—but only when people were relatively less concerned that acknowledgment of outgroup suffering might risk loss of third-party support. Study 2 further found that inclusive narratives reduce conflict for low-power groups, yet without being moderated by concern. Together, these studies show that inclusive victimhood narratives can reduce conflict when people are not concerned about losing third-party support. The important role of third parties in conflict resolutio...


title: Study of Chip Stacking Process and Electrical Characteristic Evaluation of Cu Pillar Joint Between Chips Including TSV

abstract: Three-dimensional chip stacking is a useful integration technology for improving electrical performance. In this study, we manufactured two different-sized stacked chips and examined the stacking process and electrical properties of a Cu pillar joint with a through-silicon via (TSV). To achieve alignment accuracy, a stacking process using reflow with reduction atmosphere was evaluated. As a result, the reflow process ensured bonding accuracy and the successful joining of a CuSn intermetallic compound. To determine electrical characteristics, we evaluated the electro-migration of the Cu pillar intermetallic compound (IMC) joint, which resulted in problems with small inter-chip connection. It was clarified that there was no damage to the TSV or TSV joint from the current flow, and the lifetime of the Cu pillar joint constructed with a CuSn IMC was determined to have improved by several times compared with a conventional Cu pillar and a solder joint.


title: TL.“Decision making with the analytic hierarchy process

abstract: Decisions involve many intangibles that need to be traded off. To do that, they have to be measured along side tangibles whose measurements must also be evaluated as to, how well, they serve the objectives of the decision maker. The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales. It is these scales that measure intangibles in relative terms. The comparisons are made using a scale of absolute judgements that represents, how much more, one element dominates another with respect to a given attribute. The judgements may be inconsistent, and how to measure inconsistency and improve the judgements, when possible to obtain better consistency is a concern of the AHP. The derived priority scales are synthesised by multiplying them by the priority of their parent nodes and adding for all such nodes. An illustration is included.


title: EmbedJoin: Efficient Edit Similarity Joins via Embeddings

abstract: We study the problem of edit similarity joins, where given a set of strings and a threshold value K, we want to output all pairs of strings whose edit distances are at most K. Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we have observed that all the existing algorithms fall short on long strings and large distance thresholds. In this paper we propose an algorithm named EmbedJoin which scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that EmbedJoin significantly outperforms the previous best algorithms on long strings and large distance thresholds.


title: Socio-Ecological Regionalization of the Urban Sub-Basins in Mexico

abstract: Mexico is a diverse country in terms of culture and natural environments. For this reason, the delimitation of homogeneous basins with similar environmental, social, and economic attributes is important in order to facilitate the elaboration of high-impact regional development strategies. However, this represents an ongoing challenge due to the complexity of the interactions that occur within socio-ecological systems at a regional scale. In the present study, the main objective was to identify the interrelationships among different aspects of the socio-ecological system located within basins, with the goal of utilizing this information to promote the region-specific sustainable development of an Integrated Water Resources Management (IWRM). Therefore, in this study, environmental, social, economic, and institutional variables, relevant to water management and with the capacity to be expressed spatially, were utilized to identify regions with similar characteristics and to regionalize the urban sub-basins of Mexico based on a principal component analysis (PCA) and the k-medoids clustering algorithm. The identification of the most adequate number of regions at the national level was determined by the silhouette method. As a result, five distinct regions for Mexico were generated, which forms the first step in the design of integrated water resources management strategies for these regions.


title: A Contract-Based Methodology for Aircraft Electric Power System Design

abstract: In an aircraft electric power system, one or more supervisory control units actuate a set of electromechanical switches to dynamically distribute power from generators to loads, while satisfying safety, reliability, and real-time performance requirements. To reduce expensive redesign steps, this control problem is generally addressed by minor incremental changes on top of consolidated solutions. A more systematic approach is hindered by a lack of rigorous design methodologies that allow estimating the impact of earlier design decisions on the final implementation. To achieve an optimal implementation that satisfies a set of requirements, we propose a platform-based methodology for electric power system design, which enables independent implementation of system topology (i.e., interconnection among elements) and control protocol by using a compositional approach. In our flow, design space exploration is carried out as a sequence of refinement steps from the initial specification toward a final implementation by mapping higher level behavioral and performance models into a set of either existing or virtual library components at the lower level of abstraction. Specifications are first expressed using the formalisms of linear temporal logic, signal temporal logic, and arithmetic constraints on Boolean variables. To reason about different requirements, we use specialized analysis and synthesis frameworks and formulate assume guarantee contracts at the articulation points in the design flow. We show the effectiveness of our approach on a proof-of-concept electric power system design.


title: A Large-Scale Study of Misophonia.

abstract: Objective ::: We aim to elucidate misophonia, a condition in which particular sounds elicit disproportionally strong aversive reactions. ::: ::: Method ::: A large online study extensively surveyed personal, developmental, and clinical characteristics of over 300 misophonics. ::: ::: Results ::: Most participants indicated that their symptoms started in childhood or early teenage years. Severity of misophonic responses increases over time. One third of participants reported having family members with similar symptoms. Half of our participants reported no comorbid clinical conditions, and the other half reported a variety of conditions. Only posttraumatic stress disorder (PTSD) was related to the severity of the misophonic symptoms. Remarkably, half of the participants reported experiencing euphoric, relaxing, and tingling sensations with particular sounds or sights, a relatively unfamiliar phenomenon called autonomous sensory meridian response (ASMR). ::: ::: Conclusion ::: It is unlikely that another “real” underlying clinical, psychiatric, or psychological disorder can explain away the misophonia. The possible relationship with PTSD and ASMR warrants further investigation.


title: Sequential Sampling for Dynamic Environment Map Illumination

abstract: Sampling complex illumination in the form of environment maps has received a lot of attention in computer graphics. Recent work in this area has demonstrated that drawing samples from the product of light and BRDF produces superior results to other sampling strategies. However, existing methods in this area consider only individual frames, and do not take advantage of coherence in animations. In this paper, we introduce a sequential sampling approach for dynamic environment map illumination. Our algorithm efficiently samples from the product of illumination and BRDF, while exploiting temporal coherence. We demonstrate significant performance benefits over existing methods.