title: TripTip: a trip planning service with tag-based recommendation

abstract: In this paper we suggest a design for a system, TripTip, the aim of which is to help negotiate their way through the immense amount of information that is often available by recommending a set of choices. TripTip recommends to the users the next place, which they would most likely want to visit given their preference in previous choices. To generate this information, tags that are attached on a given place by users give the characteristics of a place and the reasons for visiting the place.


title: An Ant Colony Optimization approach for Stacking ensemble

abstract: An ensemble in data mining is the strategy that combines a set of different classifiers together to generate an integrated classification system to classify new instances. In the early research, an ensemble outperforms any of its individual components. Stacking is one of the most influential ensemble among the proposed ensemble schemes. Stacking applies a two-level structure: the base-level classifiers output their own predictions and the meta-level classifier takes the outputs as its input to generate final decision. Most of the existing studies focus on the meta-level classifier adoption, and few on the topic about determining the configuration of both base-level classifiers and the meta-level classifier together. This work is inspired by the Ant Colony Optimization which is good at solving combinatorial optimization problems. We propose an ACO-Stacking ensemble approach and also perform some preliminary experiments to compare our approach with some well-known ensembles. The preliminary results show that the performance of the ACO-Stacking is promising.


title: Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

abstract: We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).


title: IMU-Based Joint Angle Measurement for Gait Analysis

abstract: This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.


title: Ingestion of Microplastics by Zooplankton in the Northeast Pacific Ocean

abstract: Microplastics are increasingly recognized as being widespread in the world’s oceans, but relatively little is known about ingestion by marine biota. In light of the potential for microplastic fibers and fragments to be taken up by small marine organisms, we examined plastic ingestion by two foundation species near the base of North Pacific marine food webs, the calanoid copepod Neocalanus cristatus and the euphausiid Euphausia pacifia. We developed an acid digestion method to assess plastic ingestion by individual zooplankton and detected microplastics in both species. Encounter rates resulting from ingestion were 1 particle/every 34 copepods and 1/every 17 euphausiids (euphausiids > copepods; p = 0.01). Consistent with differences in the size selection of food between these two zooplankton species, the ingested particle size was greater in euphausiids (816 ± 108 μm) than in copepods (556 ± 149 μm) (p = 0.014). The contribution of ingested microplastic fibres to total plastic decreased with distance from shore in euphausiids (r 2 = 70, p = 0.003), corresponding to patterns in our previous observations of microplastics in seawater samples from the same locations. This first evidence of microplastic ingestion by marine zooplankton indicate that species at lower trophic levels of the marine food web are mistaking plastic for food, which raises fundamental questions about potential risks to higher trophic level species. One concern is risk to salmon: We estimate that consumption of microplastic-containing zooplankton will lead to the ingestion of 2–7 microplastic particles/day by individual juvenile salmon in coastal British Columbia, and ≤91 microplastic particles/day in returning adults.


title: Regularized clustering for documents

abstract: In recent years, document clustering has been receiving more and more attentions as an important and fundamental technique for unsupervised document organization, automatictopic extraction, and fast information retrieval or filtering. In this paper, we propose a novel method for clustering documents using regularization. Unlike traditional globally regularized clustering methods, our method first construct a local regularized linear label predictor for each document vector, and then combine all those local regularizers with a global smoothness regularizer. So we call our algorithm Clustering with Local and Global Regularization (CLGR). We will show that the cluster memberships of the documents can be achieved by eigenvalue decomposition of a sparse symmetric matrix, which can be efficiently solved by iterative methods. Finally our experimental evaluations on several datasets are presented to show the superiorities of CLGR over traditional document clustering methods.


title: 3-D nFPGA: A Reconfigurable Architecture for 3-D CMOS/Nanomaterial Hybrid Digital Circuits

abstract: In this paper, we introduce a novel reconfigurable architecture, named 3D field-programmable gate array (3D nFPGA), which utilizes 3D integration techniques and new nanoscale materials synergistically. The proposed architecture is based on CMOS nanohybrid techniques that incorporate nanomaterials such as carbon nanotube bundles and nanowire crossbars into CMOS fabrication process. This architecture also has built-in features for fault tolerance and heat alleviation. Using unique features of FPGAs and a novel 3D stacking method enabled by the application of nanomaterials, 3D nFPGA obtains a 4x footprint reduction comparing to the traditional CMOS-based 2D FPGAs. With a customized design automation flow, we evaluate the performance and power of 3D nFPGA driven by the 20 largest MCNC benchmarks. Results demonstrate that 3D nFPGA is able to provide a performance gain of 2.6 x with a small power overhead comparing to the traditional 2D FPGA architecture.


title: Energy and Performance Characterization of Mobile Heterogeneous Computing

abstract: A modern mobile application processor is a heterogeneous multi-core SoC which integrates CPU and application-specific accelerators such as GPU and DSP. It provides opportunity to accelerate other compute-intensive applications, yet mapping an algorithm to such a heterogeneous platform is not a straightforward task and has many design decisions to make. In this paper, we evaluate the performance and energy benefits of utilizing the integrated GPU and DSP cores to offload or share CPU's compute-intensive tasks. The evaluation is conducted on three representative mobile platforms, TI's OMAP3530, Qualcomn's Snapdragon S2, and Nvidia's Tegra2, using common computation tasks in mobile applications. We identify key factors that should be considered in energy-optimized mobile heterogeneous computing. Our evaluation results show that, by effectively utilizing all the computing cores concurrently, an average of 3.7X performance improvement can be achieved with the cost of 33% more power consumption, in comparison with the case of utilizing CPU only. This stands for 2.8X energy saving.


Indoor navigation using Bluetooth Low Energy (BLE) beacons


title: Towards High-Quality Next-Generation Text-to-Speech Synthesis: A Multidomain Approach by Automatic Domain Classification

abstract: This paper is a contribution to the recent advancements in the development of high-quality next generation text-to-speech (TTS) synthesis systems. Two of the hottest research topics in this area are oriented towards the improvement of speech expressiveness and flexibility of synthesis. In this context, this paper presents a new TTS strategy called multidomain TTS (MD-TTS) for synthesizing among different domains. Although the multidomain philosophy has been widely applied in spoken language systems, few research efforts have been conducted to extend it to the TTS field. To do so, several proposals are described in this paper. First, a text classifier (TC) is included in the classic TTS architecture in order to automatically conduct the selection of the most appropriate domain for synthesizing the input text. In contrast to classic topic text classification tasks, the MD-TTS TC should not only consider the contents of text but also its structure. To this end, this paper introduces a new text modeling scheme based on an associative relational network, which represents texts as a directional weighted word-based graph. The conducted experiments validate the proposal in terms of both objective (TC efficiency) and subjective (perceived synthetic speech quality) evaluation criteria.


title: At-speed Test of High-Speed DUT Using Built-Off Test Interface

abstract: This paper presents an efficient test framework to extend a use of low-cost ATE (Automatic Test Equipment) to at-speed test of high-speed DUT (Device Under Test). To bridge the speed gap between the ATE and the DUT, an off-chip test interface circuit, called Built-off Test Interface (BOTI), has been developed. Unlike the previous methods which use on-chip or off-chip self-test circuits, in our method, the ATE plays main role in testing high-speed DUTs by actively controlling the BOTI operation, and monitoring the overall test procedure. This makes the presented method flexible to be applied to various test applications without compromising the test coverage. Also, since the BOTI is implemented off-chip, it does not require hardware modifications of the ATE or the DUT except the DUT load board to accommodate the BOTI module. To maintain reliable off-chip signal communication between the BOTI and the DUT, the BOTI measures off-chip channel skew and compensates the measured skew when communicating signals with the DUT. Currently, the BOTI is configured to do the at-speed test of high-speed memory. The measurement results are presented to validate the functionality of the BOTI, and the effectiveness of the presented test framework.


title: “Dave...I can assure you ...that it’s going to be all right ...” A Definition, Case for, and Survey of Algorithmic Assurances in Human-Autonomy Trust Relationships

abstract: People who design, use, and are affected by autonomous artificially intelligent agents want to be able to trust such agents—that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately. Many techniques have been devised to assess and influence human trust in artificially intelligent agents. However, these approaches are typically ad hoc and have not been formally related to each other or to formal trust models. This article presents a survey of algorithmic assurances, i.e., programmed components of agent operation that are expressly designed to calibrate user trust in artificially intelligent agents. Algorithmic assurances are first formally defined and classified from the perspective of formally modeled human-artificially intelligent agent trust relationships. Building on these definitions, a synthesis of research across communities such as machine learning, human-computer interaction, robotics, e-commerce, and others reveals that assurance algorithms naturally fall along a spectrum in terms of their impact on an agent’s core functionality, with seven notable classes ranging from integral assurances (which impact an agent’s core functionality) to supplemental assurances (which have no direct effect on agent performance). Common approaches within each of these classes are identified and discussed; benefits and drawbacks of different approaches are also investigated.


meets Optimization Fall 2015 Lecture 7 –


title: A gentle introduction to Soar, an architecture for human cognition

abstract: Abstract : Many intellectual disciplines contribute to the field of cognitive science: psychology, linguistics, anthropology, and artificial intelligence, to name just a few. Cognitive science itself originated in the desire to integrate expertise in these traditionally separate disciplines in order to advance our insight into cognitive phenomena - phenomena like problem solving, decision making, language, memory, and learning. Each discipline has a history of asking certain types of questions and accepting certain types of answers. And that, according to Allen Newell, a founder of the field of artificial intelligence, is both an advantage and a problem.


title: Facial Age Estimation With Age Difference

abstract: Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of human photos in the social networks. These images may provide no age label, but it is easy to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data through the deep convolutional neural networks. For each image pair, Kullback–Leibler divergence is employed to embed the age difference information. The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. We also contribute a data set, including more than 100 000 face images attached with their taken dates. Each image is both labeled with the timestamp and people identity. Experimental results on two aging face databases show the advantages of the proposed age difference learning system, and the state-of-the-art performance is gained.


title: Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases

abstract: With the vigorous development of the World Wide Web, many large-scale knowledge bases (KBs) have been generated. To improve the coverage of KBs, an important task is to integrate the heterogeneous KBs. Several automatic alignment methods have been proposed which achieve considerable success. However, due to the inconsistency and uncertainty of large-scale KBs, automatic techniques for KBs alignment achieve low quality (especially recall). Thanks to the open crowdsourcing platforms, we can harness the crowd to improve the alignment quality. To achieve this goal, in this paper we propose a novel hybrid human-machine framework for large-scale KB integration. We rst partition the entities of different KBs into many smaller blocks based on their relations. We then construct a partial order on these partitions and develop an inference model which crowdsources a set of tasks to the crowd and infers the answers of other tasks based on the crowdsourced tasks. Next we formulate the question selection problem, which, given a monetary budget B, selects B crowdsourced tasks to maximize the number of inferred tasks. We prove that this problem is NP-hard and propose greedy algorithms to address this problem with an approximation ratio of 1--1/e. Our experiments on real-world datasets indicate that our method improves the quality and outperforms state-of-the-art approaches.


title: Orthogonal parallel MCMC methods for sampling and optimization

abstract: Monte Carlo (MC) methods are widely used in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called orthogonal MCMC (O-MCMC), where a set of “vertical” parallel MCMC chains share information using some ”horizontal” MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes for reducing the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. We also discuss the application of O-MCMC in a big bata framework. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and parameter choice.


title: Alternative Algorithm for Hilbert's Space-Filling Curve

abstract: An algorithm for generating Hilbert's space-filling curve in a byte-oriented manner is presented. In the context of one application of space-filling curves, the algorithm may be modified so that the results are correct for continua rather than for quantized spaces.


High-Performance Lithium-ion Battery Anode Based on Core-Shell Heterostructure of Silicon-Coated Vertically Aligned Carbon Nanofibers


title: Towards Interpretable Face Recognition

abstract: Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions. Recent works further push the interpretability in the network learning stage to learn more meaningful representations. In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition. We propose a spatial activation diversity loss to learn more structured face representations. By leveraging the structure, we further design a feature activation diversity loss to push the interpretable representations to be discriminative and robust to occlusions. We demonstrate on three face recognition benchmarks that our proposed method is able to achieve the state-of-art face recognition accuracy with easily interpretable face representations.


title: BeatCoin: Leaking Private Keys from Air-Gapped Cryptocurrency Wallets

abstract: Cryptocurrency wallets store the wallets private key(s), and hence, are a lucrative target for attackers. With possession of the private key, an attacker virtually owns all of the currency in the compromised wallet. Managing cryptocurrency wallets offline, in isolated ('air-gapped') computers, has been suggested in order to secure the private keys from theft. Such air-gapped wallets are often referred to as 'cold wallets.' In this paper, we show how private keys can be exfiltrated from air-gapped wallets. In the adversarial attack model, the attacker infiltrates the offline wallet, infecting it with malicious code. The malware can be preinstalled or pushed in during the initial installation of the wallet, or it can infect the system when removable media (e.g., USB flash drive) is inserted into the wallet's computer in order to sign a transaction. These attack vectors have repeatedly been proven feasible in the last decade (e.g., [1],[2],[3],[4],[5],[6],[7],[8],[9],[10]). Having obtained a foothold in the wallet, an attacker can utilize various air-gap covert channel techniques (bridgeware [11]) to jump the airgap and exfiltrate the wallets private keys. We evaluate various exfiltration techniques, including physical, electromagnetic, electric, magnetic, acoustic, optical, and thermal techniques. This research shows that although cold wallets provide a high degree of isolation, it is not beyond the capability of motivated attackers to compromise such wallets and steal private keys from them. We demonstrate how a 256-bit private key (e.g., bitcoin's private keys) can be exfiltrated from an offline, air-gapped wallet of a fictional character named Satoshi within a matter of seconds


title: Coexistence of WiFi and LiFi toward 5G: concepts, opportunities, and challenges

abstract: Smart phones, tablets, and the rise of the Internet of Things are driving an insatiable demand for wireless capacity. This demand requires networking and Internet infrastructures to evolve to meet the needs of current and future multimedia applications. Wireless HetNets will play an important role toward the goal of using a diverse spectrum to provide high quality-of-service, especially in indoor environments where most data are consumed. An additional tier in the wireless HetNets concept is envisioned using indoor gigabit small-cells to offer additional wireless capacity where it is needed the most. The use of light as a new mobile access medium is considered promising. In this article, we describe the general characteristics of WiFi and VLC (or LiFi) and demonstrate a practical framework for both technologies to coexist. We explore the existing research activity in this area and articulate current and future research challenges based on our experience in building a proof-of-concept prototype VLC HetNet.


UO_UPV: Deep Linguistic Humor Detection in Spanish Social Media


Detecting Hate Speech and Insults on Social Commentary using NLP and Machine Learning


title: RadarCat: Radar Categorization for Input & Interaction

abstract: In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and classify different types of materials and objects which we can then recognize in real time. Based on established research designs, we report on the results of three studies, first with 26 materials (including complex composite objects), next with 16 transparent materials (with different thickness and varying dyes) and finally 10 body parts from 6 participants. Both leave one-out and 10-fold cross-validation demonstrate that our approach of classification of radar signals using random forest classifier is robust and accurate. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat. We conclude with a discussion of our results, limitations and outline future directions.


title: Online Learning of Joint-Muscle Mapping Using Vision in Tendon-Driven Musculoskeletal Humanoids

abstract: The body structures of tendon-driven musculoskeletal humanoids are complex, and accurate modeling is difficult, because they are made by imitating the body structures of human beings. For this reason, we have not been able to move them accurately like ordinary humanoids driven by actuators in each axis, and large internal muscle tension and slack of tendon wires have emerged by the model error between its geometric model and the actual robot. Therefore, we construct a joint-muscle mapping (JMM) using a neural network (NN), which expresses a nonlinear relationship between joint angles and muscle lengths, and aim to move tendon-driven musculoskeletal humanoids accurately by updating the JMM online from data of the actual robot. In this study, the JMM is updated online by using the vision of the robot so that it moves to the correct position (Vision Updater). Also, we execute another update to modify muscle antagonisms correctly (Antagonism Updater). By using these two updaters, the error between the target and actual joint angles decrease to about 40% in 5 min, and we show through a manipulation experiment that the tendon-driven musculoskeletal humanoid Kengoro becomes able to move as intended. This novel system can adapt to the state change and growth of robots, because it updates the JMM online successively.


title: vFPGAmanager: A Virtualization Framework for Orchestrated FPGA Accelerator Sharing in 5G Cloud Environments

abstract: Network operators are actively pushing towards the new 5G era and a crucial part to accomplish this is the Network Functions Virtualization (NFV). FPGAs and their hardware accelerators are a promising solution for NFV and 5G cloud environments because of their fast turnaround time and great speedup potential through application parallelism mapping on the reconfigurable fabric. Recently, consolidation reached a plateau in this field with lightweight virtualization techniques, that require a high overcommitment of FPGA accelerator resources to cope with numerous demands of guests. Although FPGAs can play an important role for the future 5G networks their capability to manage and control them from the upper layers of the software stack is inadequate. The lack of such support coupled with cloud integration and programmability issues can repel potential providers from utilizing FPGAs at their data centers. This paper presents the communication mechanism of the vFPGAmanager, an FPGA virtualization framework which can be orchestrated, monitored and enables accelerators overcommitment with direct guest access. These are key features to allow potential adopters of FPGA technology to include them in the next generation of NFV systems. The communication mechanism architecture is detailed and then benchmarked to show that even under heavy load on the system it demonstrates a minimal overhead to orchestrate and monitor the FPGA as a resource.


title: Data-driven modelling: some past experiences and new approaches

abstract: Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system9s physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.


title: Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects

abstract: An image is not just a collection of objects, but rather a graph where each object is related to other objects through spatial and semantic relations. Using relational reasoning modules, such as the non-local module \cite{wang2017non}, can therefore improve object detection. Current schemes apply such dedicated modules either to a specific layer of the bottom-up stream, or between already-detected objects. We show that the relational process can be better modeled in a coarse-to-fine manner and present a novel framework, applying a non-local module sequentially to increasing resolution feature maps along the top-down stream. In this way, information can naturally passed from larger objects to smaller related ones. Applying the module to fine feature maps further allows the information to pass between the small objects themselves, exploiting repetitions of instances of the same class. In practice, due to the expensive memory utilization of the non-local module, it is infeasible to apply the module as currently used to high-resolution feature maps. We redesigned the non local module, improved it in terms of memory and number of operations, allowing it to be placed anywhere along the network. We further incorporated relative spatial information into the module, in a manner that can be incorporated into our efficient implementation. We show the effectiveness of our scheme by improving the results of detecting small objects on COCO by 1-2 AP points over Faster and Mask RCNN and by 1 AP over using non-local module on the bottom-up stream.


title: Voltage- and current-controlled high CMRR instrumentation amplifier using CMOS current conveyors

abstract: A new approach to the design of an instrumentation amplifier using only 2 CMOS current-controlled conveyors and an active resistor is introduced. The new approach, which has no passive components, offers a wide bandwidth and a high common-mode rejection ratio (CMRR). Electronically controllable gain by voltage and current is also obtained by this new circuit. The circuit is simulated using the parameters of a 0.35-μ m TSMC CMOS technology. Theoretical analysis of the circuits was carried out, and the performance of the block circuit was confirmed through PSpice simulation results.


title: Sarcoidosis in strange places

abstract: Three patients with sarcoidosis involving tonsil, muscle and nasal septum respectively, are described. In each case symptoms were confined to one of these tissues. One patient had a selective partial IgA deficiency.


title: Learning computer science concepts with scratch

abstract: Scratch is a visual programming environment that is widely used by young people. We investigated if Scratch can be used to teach concepts of computer science. We developed new learning materials for middle-school students that were designed according to the constructionist philosophy of Scratch and evaluated them in two schools. The classes were normal classes, not extracurricular activities whose participants are self-selected. Questionnaires and a test were constructed based upon a novel combination of the Revised Bloom Taxonomy and the SOLO taxonomy. These quantitative instruments were augmented with a qualitative analysis of observations within the classes. The results showed that in general students could successfully learn important concepts of computer science, although there were some problems with initialization, variables and concurrency; these problems can be overcome by modifications to the teaching process.


Review of classical and heuristic-based navigation and path planning approaches


title: Query-Regression Networks for Machine Comprehension

abstract: We present Query-Regression Network (QRN), a variant of Recurrent Neural Network (RNN) that is suitable for end-to-end machine comprehension. While previous work largely relied on external memory and global softmax attention mechanism, QRN is a single recurrent unit with internal memory and local sigmoid attention. Unlike most RNN-based models, QRN is able to effectively handle long-term dependencies and is highly parallelizable. In our experiments we show that QRN obtains the state-of-the-art result in end-to-end bAbI QA tasks.


title: Adversarial Imitation Learning via Random Search

abstract: Developing agents that can perform challenging complex tasks is the goal of reinforcement learning. The model-free reinforcement learning has been considered as a feasible solution. However, the state of the art research has been to develop increasingly complicated techniques. This increasing complexity makes the reconstruction difficult. Furthermore, the problem of reward dependency is still exists. As a result, research on imitation learning, which learns policy from a demonstration of experts, has begun to attract attention. Imitation learning directly learns policy based on data on the behavior of the experts without the explicit reward signal provided by the environment. However, imitation learning tries to optimize policies based on deep reinforcement learning such as trust region policy optimization. As a result, deep reinforcement learning based imitation learning also poses a crisis of reproducibility. The issue of complex model-free model has received considerable critical attention. A derivative-free optimization based reinforcement learning and the simplification on policies obtain competitive performance on the dynamic complex tasks. The simplified policies and derivative free methods make algorithm be simple. The reconfiguration of research demo becomes easy. In this paper, we propose an imitation learning method that takes advantage of the derivative-free optimization with simple linear policies. The proposed method performs simple random search in the parameter space of policies and shows computational efficiency. Experiments in this paper show that the proposed model, without a direct reward signal from the environment, obtains competitive performance on the MuJoCo locomotion tasks.


title: Creating Stealth Game Interventions for Attitude and Behavior Change: An "Embedded Design" Model

abstract: Persuasive games tackling serious issues in a literal, explicit fashion are far less likely to succeed in changing attitudes or behaviors than are games that take the more “stealthy” approach of embedding persuasive messages within a game’s content or context. The “Embedded Design” model, developed by the design and research team at Tiltfactor Laboratory at Dartmouth College, offers novel, evidence-based strategies for including persuasive content in a game in ways that circumvent players’ psychological defenses, triggering a more receptive mindset for internalizing a game’s intended message, and do so without sacrificing players’ enjoyment or the game’s inherent replayability. Such techniques promise to revolutionize the repertoire of techniques that game developers should consider in broaching and presenting serious topics in games. Three original “Embedded Design” strategies are presented here: (1) Intermixing: balancing “on-message” and “off-message” content to render the former less overt or threatening; (2) Obfuscating: using framing devices or genres that divert expectations or focus away from the game’s persuasive intent; and (3) Distancing: employing fiction and metaphor to increase the psychological gap between players’ identities and beliefs, and the game’s characters and persuasive content.


title: Spatiotemporal Chaos in Coupled Logistic Map Lattice With Dynamic Coupling Coefficient and its Application in Image Encryption

abstract: This paper proposes a new spatiotemporal chaos model, which is a logistic-dynamic coupled logistic map lattice (LDCML). Through a large number of simulation experiments and theoretical analysis, it can be proved that a chaotic region has the larger parameter space, and the system is more chaotic compared with traditional CML by the introduction of a dynamic coupled method, which is more suitable for chaotic encryption and secure communications. So, this paper presents a bit-level adaptive image encryption algorithm based on this system, and further demonstrates the good chaos of the system from the aspect of encryption performance.


title: RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

abstract: Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.


title: A theoretical treatment of related-key attacks: RKA-PRPs, RKAPRFs, and applications

abstract: We initiate a theoretical investigation of the popular block-cipher design-goal of security against related-key attacks (RKAs). We begin by introducing definitions for the concepts of PRPs and PRFs secure against classes of RKAs, each such class being specified by an associated set of related-key deriving (RKD) functions. Then for some such classes of attacks, we prove impossibility results, showing that no block-cipher can resist these attacks while, for other, related classes of attacks that include popular targets in the block cipher community, we prove possibility results that provide theoretical support for the view that security against them is achievable. Finally we prove security of various block-cipher based constructs that use related keys, including a tweakable block cipher given in [14].


title: Shortest-path kernels on graphs

abstract: Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.


CANaLI : A System for Answering Controlled Natural Language Questions on RDF Knowledge Bases UCLA CSD Technical Report Number : 160004


title: Design Optimization of Quasi-Active Gate Control for Series-Connected Power Devices

abstract: This paper presents a new gate drive circuit for driving a series string of insulated-gate bipolar transistors (IGBTs). The proposed quasi-active gate control (QAGC) circuit is simple to implement as it consists of only a few passive components in addition to a standard gate driver. No separate isolation power supply is required for the upper devices in the stack. The proposed QAGC circuit provides an effective way to drive the power devices and control static and dynamic voltage sharing to the devices at the same time. The theoretical switching operation and the oscillation stability analysis allow criteria for component selection to be established. Limitations of the QAGC circuit are also identified. The modification of the circuit to support more power devices in the series stack is discussed with the aid of the simulation results. The switching operation of the circuit is validated from the experimental results using two IGBTs connected in series. The circuit shows an excellent switching operation with well-controlled dynamic and static voltage sharing and comparable gate voltage between the coupled devices.


Professional Development and Teacher Learning : Mapping the Terrain Author ( s ) :


title: Model-Driven Design for the Visual Analysis of Heterogeneous Data

abstract: As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.


title: Classification of Customer Reviews based on Sentiment Analysis

abstract: In this paper we propose a system that performs the classification of customer reviews of hotels by means of a sentiment analysis. We elaborate on a process to extract a domainspecific lexicon of semantically relevant words based on a given corpus (Scharl et al., 2003; Pak & Paroubek, 2010). The resulting lexicon backs the sentiment analysis for generating a classification of the reviews. The evaluation of the classification on test data shows that the proposed system performs better compared to a predefined baseline: if a customer review is classified as good or bad the classification is correct with a probability of about 90%.


title: Streaming video over HTTP with consistent quality

abstract: In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.


title: Trust in new decision aid systems

abstract: One of the main challenges to face concerning the safe utilization of new technologies in complex systems concerns the level of trust the operators have in the system. Danger exists when the operators have a low level of trust in it, as well as it also exists when they overtrust the system. This paper presents an extensive review of theoretical, empirical, and experimental studies on trust in systems. Its goal is to help system designers by proposing a set of design rules and guidelines on how to support appropriate trust tuning in new decision aid systems.


title: A 94-GHz 4TX–4RX Phased-Array FMCW Radar Transceiver With Antenna-in-Package

abstract: A 94-GHz phased-array transceiver IC for frequency modulated continuous wave (FMCW) radar with four transmitters, four receivers, and integrated LO generation has been designed and fabricated in a 130-nm SiGe BiCMOS technology, and integrated into an antenna-in-package module. The transceiver, targeting gesture recognition applications for mobile devices, has been designed using phased-array techniques to reduce the total DC power while still maintaining the required link budget for FMCW operation. The complete array achieves state-of-the-art for W-band per-element power consumption of 106 mW per TX element and 91 mW per RX element, and measurements indicate a per-element output power of 6.4 dBm and single-sideband noise figure of 12.5 dB at 94 GHz. The array is able to achieve a beam steering range of ±20° while maintaining at least 3 dB main lobe to side lobe levels. The complete chip-antenna module has been tested to characterize basic FMCW radar functionality. Initial radar experiments suggest a sub-5-cm range resolution is possible with 3.68 GHz RF sweep bandwidth, which is in line with theoretical predictions.


title: Aging mechanisms of X2 metallized film capacitors in a high temperature and humidity environment

abstract: Safety capacitors (usually denoted as X1, X2 or Y) are metallized film capacitors (MFC). Two kinds of capacitance loss mechanism typically occur in this metallized film structure: (1) caused by self-healing resulting in a very small electrode area loss; (2) caused by electrode oxidation by electrochemical corrosion under ac stress in a humid environment. This study focuses on the aging mechanism of X2 film capacitors working in high temperature and high humidity environments. Two types of X2 film capacitors have been stressed under an applied voltage of 270Vac at 85 ¼C, 78.68%RH. Capacitance and equivalent series resistance (ESR) were monitored during the experiment as parametric parameters that reflect the aging process. The aging mechanism including moisture ingress time were calculated and comprehensively analyzed with the capacitance change characteristics. It was found that capacitors with lower aluminum metallization has a better capacitance stability, the mean corrosion rate of Type2 (9.7% aluminum metallization) is more than 3 times of the Type1(6.9% aluminum metallization).The ingress time calculated by capacitance change and normalized ESR show reasonable agreement.


title: A Model of the Perceptual and Conceptual Processes in Graph Comprehension

abstract: The article proposes that graph comprehension emerges from an integrated sequence of several types of processes: pattern-recognition processes that encode graphic patterns, interpretive processes that operate on those patterns to retrieve or construct qualitative and quantitative meanings, and integrative processes that relate these meanings to the referents inferred from labels and titles. The model is supported by 2 studies that examine the pattern and durations of eye fixations as a person interprets line graphs or answers questions about line graphs that vary in type and complexity. One implication is that graph comprehension might be more accurate and more complete if the graph's format were changed or the audience were educated to lessen the burden of the inferential, interpretive, and integrative processes. In this article, we propose a model of the comprehension of line graphs that emphasizes the close interaction between conceptual processes, such as interpreting labels and scales, and perceptual processes, such as encoding and interpreting the slopes and patterns of the lines themselves. The model accounts for the pattern and duration of the eye fixations of college students who are interpreting line graphs with three dimensions. Moreover, it provides a theoretical framework for analyzing the processing of more complex graphs, more complex tasks, and other types of audiences (novices vs. experts). This focus on the conceptual aspects of graph interpretation contrasts with the implicit assumption of some researchers and graphic designers that graph comprehension is


title: New integrated full-range MEMS speaker for in-ear applications

abstract: This paper reports the concept, fabrication and characterization of a new kind of MEMS loudspeaker for in-ear applications. The piezoelectrically driven device is designed to operate without a closed membrane, which significantly improves the acoustic performance, efficiency, manufacturability and miniaturization capability. Measurements performed on prototypes mounted to an artificial ear simulator have revealed high sound pressure levels (SPL) of about 110 dB. In contrast to standard state-of-the-art in-ear speakers the SPL can be maintained up to 20 kHz leading to an extraordinarily flat frequency response using electronic equalization. Furthermore, low total harmonic distortion (THD) of less than 2 % and good energy efficiency of more than 105 dB/mW have been observed over almost the entire audio range.


title: 3D visual phrases for landmark recognition

abstract: In this paper, we study the problem of landmark recognition and propose to leverage 3D visual phrases to improve the performance. A 3D visual phrase is a triangular facet on the surface of a reconstructed 3D landmark model. In contrast to existing 2D visual phrases which are mainly based on co-occurrence statistics in 2D image planes, such 3D visual phrases explicitly characterize the spatial structure of a 3D object (landmark), and are highly robust to projective transformations due to viewpoint changes. We present an effective solution to discover, describe, and detect 3D visual phrases. The experiments on 10 landmarks have achieved promising results, which demonstrate that our approach provides a good balance between precision and recall of landmark recognition while reducing the dependence on post-verification to reject false positives.


title: Si Photonic Wire Waveguide Devices

abstract: A label cassette is described in which labels releasably adhered to a support strip are stored in a cassette. The cassette is generally rectangular with one corner formed at approximately a 45 DEG angle. A reflective surface is placed on the angular corner to permit a light beam to be reflected from the cassette through holes in the support strip between each label to control the advance of the support strip into the label printer. With this arrangement the proper positioning of the label cassette as well as that of the support strip are simultaneously detected.


title: Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong

abstract: Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently proposed defenses that intentionally combine components designed to work well together. A third defense combines three independent defenses. For all the components of these defenses and the combined defenses themselves, we show that an adaptive adversary can create adversarial examples successfully with low distortion. Thus, our work implies that ensemble of weak defenses is not sufficient to provide strong defense against adversarial examples.


title: Tinmith-Metro: new outdoor techniques for creating city models with an augmented reality wearable computer

abstract: This paper presents new techniques for capturing and viewing on site 3D graphical models for large outdoor objects. Using an augmented reality wearable computer, we have developed a software system, known as Tinmith-Metro. Tinmith-Metro allows users to control a 3D constructive solid geometry modeller for building graphical objects of large physical artefacts, for example buildings, in the physical world. The 3D modeller is driven by a new user interface known as Tinmith-Hand, which allows the user to control the modeller using a set of pinch gloves and hand tracking. These techniques allow user to supply their AR renderers with models that would previously have to be captured with manual, time-consuming, and/or expensive methods.


title: A Survey on Secure Communication Protocols for IoT Systems

abstract: The Internet of Things (IoT) integrates a large number of physical objects that are uniquely identified, ubiquitously interconnected and accessible through the Internet. IoT aims to transform any object in the real-world into a computing device that has sensing, communication and control capabilities. There is a growing number of IoT devices and applications and this leads to an increase in the number and complexity of malicious attacks. It is important to protect IoT systems against malicious attacks, especially to prevent attackers from obtaining control over the devices. A large number of security research solutions for IoT have been proposed in the last years, but most of them are not standardized or interoperable. In this paper, we investigate the security capabilities of existing protocols and networking stacks for IoT. We focus on solutions specified by well-known standardization bodies such as IEEE and IETF, and industry alliances, such as NFC Forum, ZigBee Alliance, Thread Group and LoRa Alliance.


title: Verifying higher-order programs with the dijkstra monad

abstract: Modern programming languages, ranging from Haskell and ML, to JavaScript, C# and Java, all make extensive use of higher-order state. This paper advocates a new verification methodology for higher-order stateful programs, based on a new monad of predicate transformers called the Dijkstra monad. Using the Dijkstra monad has a number of benefits. First, the monad naturally yields a weakest pre-condition calculus. Second, the computed specifications are structurally simpler in several ways, e.g., single-state post-conditions are sufficient (rather than the more complex two-state post-conditions). Finally, the monad can easily be varied to handle features like exceptions and heap invariants, while retaining the same type inference algorithm. We implement the Dijkstra monad and its type inference algorithm for the F* programming language. Our most extensive case study evaluates the Dijkstra monad and its F* implementation by using it to verify JavaScript programs. Specifically, we describe a tool chain that translates programs in a subset of JavaScript decorated with assertions and loop invariants to F*. Once in F*, our type inference algorithm computes verification conditions and automatically discharges their proofs using an SMT solver. We use our tools to prove that a core model of the JavaScript runtime in F* respects various invariants and that a suite of JavaScript source programs are free of runtime errors.


title: Towards Continuous and Passive Authentication via Touch Biometrics: An Experimental Study on Smartphones

abstract: Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user’s touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A onemonth experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication.


title: Shadowing effects on visible light communication channels

abstract: It is well known that the effect of shadowing on a visible light communication (VLC) system is severe due to its line of sight propagation nature. Hence, shadowing can seriously impair the system performance. In this paper, we propose a method to take advantage of the optical diffraction phenomenon to overcome shadowing by employing a receiver with a wide field-of-view (FOV). We have modelled the shadowing effect for visible light by a Bimodal Gaussian distribution and derived the probability of error for low, moderate and severe shadowing conditions. The Bimodal Gaussian distribution effectively represents separate propagation modes caused by different polarizations. This approach is useful in both indoor and outdoor environments.


title: Exome sequencing identifies a recurrent de novo ZSWIM6 mutation associated with acromelic frontonasal dysostosis.

abstract: Acromelic frontonasal dysostosis (AFND) is a rare disorder characterized by distinct craniofacial, brain, and limb malformations, including frontonasal dysplasia, interhemispheric lipoma, agenesis of the corpus callosum, tibial hemimelia, preaxial polydactyly of the feet, and intellectual disability. Exome sequencing of one trio and two unrelated probands revealed the same heterozygous variant (c.3487C>T [p. Arg1163Trp]) in a highly conserved protein domain of ZSWIM6; this variant has not been seen in the 1000 Genomes data, dbSNP, or the Exome Sequencing Project. Sanger validation of the three trios confirmed that the variant was de novo and was also present in a fourth isolated proband. In situ hybridization of early zebrafish embryos at 24 hr postfertilization (hpf) demonstrated telencephalic expression of zswim6 and onset of midbrain, hindbrain, and retinal expression at 48 hpf. Immunohistochemistry of later-stage mouse embryos demonstrated tissue-specific expression in the derivatives of all three germ layers. qRT-PCR expression analysis of osteoblast and fibroblast cell lines available from two probands was suggestive of Hedgehog pathway activation, indicating that the ZSWIM6 mutation associated with AFND may lead to the craniofacial, brain and limb malformations through the disruption of Hedgehog signaling.


Livestock and Local Development: Going to a New Humananimal Relationship


title: Gas field ion source current stability for trimer and single atom terminated W(111) tips

abstract: Tungsten W(111) oriented trimer-terminated tips as well as single atom tips, fabricated by a gas and field assisted etching and evaporation process, were investigated with a view to scanning ion microscopy and ion beam writing applications. In particular, ion current stability was studied for helium and neon imaging gases. Large ion current fluctuations from individual atomic sites were observed when a trimer-terminated tip was used for the creation of neon ion beam. However, neon ion current was stable when a single atom tip was employed. No such current oscillations were observed for either a trimer or a single atom tip when imaged with helium.


title: An on-chip self-test architecture with test patterns recorded in scan chains

abstract: This work proposes a novel test architecture that combines the advantages of both scan-based and built-in self-test (BIST) designs. The main idea is to record (store) all required compressed test data in a novel scan chain structure such that the stored data can be extracted, reconstructed and decompressed into required deterministic patterns using an on-chip test controller with a test pattern decompressor. The recording of test data is achieved by modifying the connections between scan cells. Techniques to extract test data from the modified scan cells and to deliver decompressed test patterns to the modified scan cells are presented. The on-chip test controller can automatically generate all required control signals for the whole test procedure. This significantly reduces the requirements on external ATE. Experimental results on OpenSPARC T2, a publicly accessible 8-core processor containing 5.7M gates, show that all required test data for 100% testable stuck-at fault coverage can be stored in the scan chains of the processor with less than 3% total area overhead for the whole test architecture.


title: Theoretical and experimental study of asymmetry of diffusion permeability of composite membranes

abstract: Theoretical approaches to the description of asymmetry effects of transport characteristics of two-layer membranes are discussed. Based on a uniform finely porous model membrane, a new method is proposed for calculating mass transfer through asymmetric membranes with allowance for the physicochemical properties of their layers. The difference between the effective densities of charges fixed in membrane layers is shown to be the key factor that governs the degree of asymmetry of diffusion permeability. The adequacy of the proposed approach is supported by the comparison between theoretical calculations and experimental data on the diffusion permeability of modified ion-exchange membranes formed on polystyrene and perfluorinated matrices. It is shown that this approach is applicable to the description of the asymmetry effects of diffusion characteristics of MF-4SK perfluorinated membranes, the surface layer of which is modified with polyaniline.


title: Short-circuited CPW multiple-mode resonator for ultra-wideband (UWB) bandpass filter

abstract: An ultra-wideband (UWB: 3.1-10.6 GHz) bandpass filter (BPF) on coplanar waveguide (CPW) is proposed, designed and implemented. A nonuniform CPW multiple-mode resonator with short-circuited ends is constructed and its first three resonant modes are properly allocated around the lower-end, center and higher-end of the specified UWB band. This CPW resonator is then driven at two ends by two parallel-coupled CPW lines with dispersive inductive coupling degree. By properly reallocating the enhanced coupling peak toward the UWB's center, a five-pole CPW BPF with one full-wavelength can be eventually constituted. Its UWB bandpass performance is characterized and optimized on the basis of a simple transmission-line network. Predicted results are confirmed by experiment. Measured results achieve the insertion loss <1.5dB and group delay variation <0.35ns in the realized 3.3 to 10.4GHz UWB passband.


Spontaneity and Systematic Planning in Small Firms: a Grounded Theory Approach


title: Affine invariant scale-space

abstract: A newaffine invariant scale-space for planar curves is presented in this work. The scale-space is obtained from the solution of a novel nonlinear curve evolution equation which admits affine invariant solutions. This flow was proved to be the affine analogue of the well knownEuclidean shortening flow. The evolution also satisfies properties such ascausality, which makes it useful in defining a scale-space. Using an efficient numerical algorithm for curve evolution, this continuous affine flow is implemented, and examples are presented. The affine-invariant progressive smoothing property of the evolution equation is demonstrated as well.


title: CHEILITIS, FIXED DRUG ERUPTION AND GASTROINTESTINAL ALLERGY FROM EOSIN DYE OF LIPSTICK

abstract: Mrs. P. W. W., aged 43, white, was referred to us in March 1936. She complained of itching eyelids, scaling and redness of the skin about the eyes and an eruption at the corner of one eye. The symptoms began in August 1934. The itching had persisted since then with exacerbations and remissions. A physician advised the use of "nonallergic" cosmetics and prescribed solution of potassium arsenite and calcium. She did not improve on this regimen; in fact, her skin became more irritated. One of us (L. B.) diagnosed gastrointestinal allergy in 1935, the symptoms of which were frequent bowel movements (composed largely of mucus), cramps and pencil-like stools. The physical and laboratory examinations at that time revealed no abnormalities. The information obtained during her first visit was that she was using perfumes, toilet waters, talcums, face powder, various creams, liquid nail polish, "henna," rouge and lipstick. On examination the


title: Emergency Event Detection in Twitter Streams Based on Natural Language Processing

abstract: Real-time social media usage is widely adapted today because it encourages quick spreading of news within social networks. New opportunities arise to use social media feeds to detect emergencies and extract crucial information about that event to support rescue operations. A major challenge for the extraction of emergency event information from applications like Twitter is the big mass of data, inaccurate or lacking metadata and the noisy nature of the post text itself. We propose to filter the real-time media stream by analysing posts seriousity, extract facts through natural language processing and group posts using a novel event identification scheme. Based on a manually tagged social media feed corpus we show that false or missed alarms are limited to posts with highly ambiguous information with less value for the rescue units.


title: Motivation as an Enabler for Academic Success.

abstract: Abstract. Student motivation as an academic enabler for school success is discussed. Contrary to many views, however, the authors conceive of student motivation as a multifaceted construct with different components. Accordingly, the article includes a discussion of four key components of student motivation including academic self-efficacy, attributions, intrinsic motivation, and achievement goals. Research on each of these four components is described, research relating these four components to academic achievement and other academic enablers is reviewed, and suggestions are offered for instruction and assessment. ********** Psychologists and educators have long considered the role of motivation in student achievement and learning (for a review see Graham & Weiner, 1996). Much of the early research on student achievement and learning separated cognitive and motivational factors and pursued very distinct lines of research that did not integrate cognition and motivation. However, since at least the 1980s there has been a sustained research focus on how motivational and cognitive factors interact and jointly influence student learning and achievement. In more colloquial terms, there is a recognition that students need both the cognitive skill and the motivational will to do well in school (Pintrich & Schunk, 2002). This miniseries continues in this tradition by highlighting the contribution of both motivational and cognitive factors for student academic success. The integration of motivational and cognitive factors was facilitated by the shift in motivational theories from traditional achievement motivation models to social cognitive models of motivation (Pintrich & Schunk, 2002). One of the most important assumptions of social cognitive models of motivation is that motivation is a dynamic, multifaceted phenomenon that contrasts with the quantitative view taken by traditional models of motivation. In other words, these newer social cognitive models do not assume that students are either "motivated" or "not motivated" or that student motivation can be characterized in some quantitative manner between two endpoints on a single continuum. Rather, social cognitive models stress that students can be motivated in multiple ways and the important issue is understanding how and why students are motivated for school achievement. This change in focus implies that teachers or school psychologists should not label students as "motivated" or "not motivated" in some global fashion. Furthermore, assessment instruments that generate a single global "motivation" score for students may be misleading in terms of a more multifaceted understanding of student motivation. Accordingly, in the discussion of motivation as an academic enabler, many aspects of student motivation including self-efficacy, attributions, intrinsic motivation, and goals are considered. A second important assumption of social cognitive models of motivation is that motivation is not a stable trait of an individual, but is more situated, contextual, and domain-specific. In other words, not only are students motivated in multiple ways, but their motivation can vary depending on the situation or context in the classroom or school. Although this assumption makes it more difficult for research and assessment efforts, it means that student motivation is conceived as being inherently changeable and sensitive to the context. This provides hope for teachers and school psychologists and suggests that instructional efforts and the design of classrooms and schools can make a difference in motivating students for academic achievement. This situated assumption means that student motivation probably varies as a function of subject matter domains and classrooms (e.g., Bong, 2001). For example, within social cognitive models, motivation is usually assessed for a specific subject area such as math, reading, science, or social studies and in reference to a specific classroom or teacher. …


title: Deep High Dynamic Range Imaging with Large Foreground Motions

abstract: This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions. In state-of-the-art deep HDR imaging, input images are first aligned using optical flows before merging, which are still error-prone due to occlusion and large motions. In stark contrast to flow-based methods, we formulate HDR imaging as an image translation problem without optical flows. Moreover, our simple translation network can automatically hallucinate plausible HDR details in the presence of total occlusion, saturation and under-exposure, which are otherwise almost impossible to recover by conventional optimization approaches. Our framework can also be extended for different reference images. We performed extensive qualitative and quantitative comparisons to show that our approach produces excellent results where color artifacts and geometric distortions are significantly reduced compared to existing state-of-the-art methods, and is robust across various inputs, including images without radiometric calibration.


title: The Design and Implementation of the Clouds Distributed Operating System

abstract: Clouds is a native operating system for a distributed environment. The Clouds operating system is built on top of a kernel called Ra. Ra is a second generation kernel derived from our experi- ence with the first version of the Clouds operating system. Ro is a minimal, flexible kernel that pro- vides a framework for implementing a variety of distributed operating systems. This paper presents the Clouds paradigm and a brief overview of its first implementation. We then present the details of the Ro kernel, the rationale for its design, and the system services that consti- tute the Clouds operating system.


title: Text-Independent Writer Identification via CNN Features and Joint Bayesian

abstract: This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is limited in writer identification, a data augmentation technique is first developed to generate thousands of handwriting images for each writer. Then a deep CNN network is designed to extract discriminative features to represent the properties of different writing styles, which is trained by using the generated handwriting images. In the stage of writer identification, the training dataset is used to train the CNN model for feature extraction and the joint Bayesian technique is employed to accomplish the task of writer identification based on the extracted CNN features. The proposed method is tested on two standard benchmark datasets, i.e. ICDAR2013 and CVL dataset. Experimental results demonstrate that the proposed method gets the best performance compared to the state-of-the-art approaches.


title: Adaptive-Latency DRAM (AL-DRAM)

abstract: This paper summarizes the idea of Adaptive-Latency DRAM (AL-DRAM), which was published in HPCA 2015. The key goal of AL-DRAM is to exploit the extra margin that is built into the DRAM timing parameters to reduce DRAM latency. The key observation is that the timing parameters are dictated by the worst-case temperatures and worst-case DRAM cells, both of which lead to small amount of charge storage and hence high access latency. One can therefore reduce latency by adapting the timing parameters to the current operating temperature and the current DIMM that is being accessed. Using an FPGA-based testing platform, our work first characterizes the extra margin for 115 DRAM modules from three major manufacturers. The experimental results demonstrate that it is possible to reduce four of the most critical timing parameters by a minimum/maximum of 17.3%/54.8% at 55C while maintaining reliable operation. AL-DRAM adaptively selects between multiple different timing parameters for each DRAM module based on its current operating condition. AL-DRAM does not require any changes to the DRAM chip or its interface; it only requires multiple different timing parameters to be specified and supported by the memory controller. Real system evaluations show that AL-DRAM improves the performance of memory-intensive workloads by an average of 14% without introducing any errors.


title: Three-Dimensional Printing of Prosthetic Hands for Children.

abstract: Children with hand reductions, whether congenital or traumatic, have unique prosthetic needs. They present a challenge because of their continually changing size due to physical growth as well as changing needs due to psychosocial development. Conventional prosthetics are becoming more technologically advanced and increasingly complex. Although these are welcome advances for adults, the concomitant increases in weight, moving parts, and cost are not beneficial for children. Pediatric prosthetic needs may be better met with simpler solutions. Three-dimensional printing can be used to fabricate rugged, light-weight, easily replaceable, and very low cost assistive hands for children.


title: Refinements in nasal reconstruction: the cross-paramedian forehead flap.

abstract: BACKGROUND ::: Nasal reconstruction can be a challenge for the plastic surgeon. The forehead flap is the workhorse in nasal reconstruction, providing similar skin color, texture, structure, and reliability. There are disadvantages of the forehead flap, including a difficult arc of rotation, which risks displacement of medial eyebrow hair. The vertical design can encroach on the scalp, incorporating hair into the nasal reconstruction. Forehead flap inset results in an inverted V-shaped scar visible in the glabella region. This article describes the authors' modification of the established oblique paramedian forehead flap. ::: ::: ::: METHODS ::: This retrospective study included a consecutive series of patients over an 11-year period (December of 1996 to December of 2007) that underwent nasal reconstruction performed by the primary surgeon (J.A.). Information obtained from chart reviews included age, sex, cause of nasal defect, complications, and revisions. ::: ::: ::: RESULTS ::: This study population consisted of 94 adults: 54 men (57 percent) and 40 women (43 percent) who underwent nasal reconstruction using the cross-paramedian forehead flap. All flaps were performed for skin cancer reconstruction. Partial flap loss was the most common complication. Tobacco use was associated with half of the complications. Further refinements following forehead flap inset were performed in 50 percent of patients. ::: ::: ::: CONCLUSIONS ::: The cross-paramedian forehead flap is a useful design when planning subtotal nasal reconstruction. It provides a smooth arc of rotation and increased length, avoids an inverted V-shaped glabella scar, and minimizes eyebrow distortion. These modifications of the forehead flap maintain the reliability and versatility of the flap and provide an aesthetically pleasing result.


title: Sustainable rural retention of health workforce in Bangladesh - Is a dream or reality?

abstract: The scenario of working health manpower in sub district level is not uniform in relation to doctors and paramedics. Rural retention problem is well marked with doctors1 not significantly with other workers. Frequently print and electronic media covers the existing scenario of upazila health complex in different issues including deficiency and absenteeism of health workforces. Shortage1 and irregular recruitment of health workforce are boosting these challenges. Different initiatives were taken by the competent authority to ensure rural retention of health workforces but little success is achieved.


title: Path-SGD: Path-Normalized Optimization in Deep Neural Networks

abstract: We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights. We argue for a geometry invariant to rescaling of weights that does not affect the output of the network, and suggest Path-SGD, which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization. Path-SGD is easy and efficient to implement and leads to empirical gains over SGD and Ada-Grad.


title: Learning Analytics for English Language Teaching.

abstract: In recent times, online learning platforms get more and more attention and the number of collected data is growing. Learning analytics is a valuable opportunity to gain specific information for a better understanding of student’s learning behaviour and to improve their learning success. In this work, the collected data of the online learning platform www.more-online.at is analysed and first research results are presented. The main objective is to analyse the usage behaviour over a school year and to show the diffusion of the online platform among provinces in Austria, different school types and other characteristics. Furthermore, the content of the online platform is put under closer examination to enable decisions about the efficiency and effectiveness of different types of exercises.


title: A multi-source dataset of urban life in the city of Milan and the Province of Trentino

abstract: The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others.


title: Logico-linguistic Semantic Representation of Documents

abstract: The knowledge behind the gigantic pool of data remains largely unextracted. Techniques such as ontology design, RDF representations, hpernym extraction, etc. have been used to represent the knowledge. However, the area of logic (FOPL) and linguistics (Semantics) has not been explored in depth for this purpose. Search engines suffer in extraction of specific answers to queries because of the absence of structured domain knowledge. The current paper deals with the design of formalism to extract and represent knowledge from the data in a consistent format. The application of logic and linguistics combined greatly eases and increases the precision of knowledge translation from natural language. The results clearly indicate the effectiveness of the knowledge extraction and representation methodology developed providing intelligence to machines for efficient analysis of data. The methodology helps machines to retrieve precise results in an efficient manner.


title: The anatomy of a large-scale social search engine

abstract: We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right document to satisfy a user's information need, the challenge in a social search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.


title: Instagram photos reveal predictive markers of depression

abstract: Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness.


title: Embedding Deep Metric for Person Re-identication A Study Against Large Variations

abstract: Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)’s capability of feature extraction. However, the distribution is unknown, so it is difficult to use the geodesic distance when comparing two samples. In practice, the current deep embedding methods use the Euclidean distance for the training and test. On the other hand, the manifold learning methods suggest to use the Euclidean distance in the local range, combining with the graphical relationship between samples, for approximating the geodesic distance. From this point of view, selecting suitable positive (i.e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations. In this paper, we propose a novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation. In addition, we improve the learning by a metric weight constraint, so that the learned metric has a better generalization ability. Experiments show that these two strategies are effective in learning robust deep metrics for person re-identification, and accordingly our deep model significantly outperforms the state-of-the-art methods on several benchmarks of person re-identification. Therefore, the study presented in this paper may be useful in inspiring new designs of deep models for person re-identification.


abstract: Existed methods of code clones detection have some restrictions. Textual and lexical approaches cannot detect strongly modified fragments of code. Syntactic and metrics based approaches detect strong modifications with low accuracy. On the contrary, semantic approach accurately detects the cloned fragments of code with small changes as well as the strongly modified ones. Methods based on this approach are not scalable for analysis of large projects. This paper describes LLVM-based code clone detection framework, which uses program semantic analysis. It has high accuracy and is scalable for analysis million lines of source code. The tool embeds a testing system, which allows generating code clones for the project automatically. It is used for determining the developed algorithms accuracy. The instrument is applicable for all languages that can be compiled to LLVM bitcode. Proposed method was compared with two widely used tools MOSS and CloneDR. Results show that it has higher accuracy. The tool is scalable for analysis of Linux-2.6 kernel, which has about fourteen millions lines of source code.


title: A Max-Margin Perspective on Sparse Representation-Based Classification

abstract: Sparse Representation-based Classification (SRC) is a powerful tool in distinguishing signal categories which lie on different subspaces. Despite its wide application to visual recognition tasks, current understanding of SRC is solely based on a reconstructive perspective, which neither offers any guarantee on its classification performance nor provides any insight on how to design a discriminative dictionary for SRC. In this paper, we present a novel perspective towards SRC and interpret it as a margin classifier. The decision boundary and margin of SRC are analyzed in local regions where the support of sparse code is stable. Based on the derived margin, we propose a hinge loss function as the gauge for the classification performance of SRC. A stochastic gradient descent algorithm is implemented to maximize the margin of SRC and obtain more discriminative dictionaries. Experiments validate the effectiveness of the proposed approach in predicting classification performance and improving dictionary quality over reconstructive ones. Classification results competitive with other state-of-the-art sparse coding methods are reported on several data sets.


title: A deep learning approach for human behavior prediction with explanations in health social networks: social restricted Boltzmann machine (SRBM+)

abstract: Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems will be actually adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict. In this paper, we study the research problem, human behavior prediction with explanations, for healthcare intervention systems in health social networks. In this work, we propose a deep learning model, named social restricted Boltzmann machine (SRBM), for human behavior modeling over undirected and nodes-attributed graphs. In the proposed SRBM+ model, we naturally incorporate self-motivation, implicit and explicit social influences, and environmental events together. Our model not only predicts human behaviors accurately, but also, for each predicted behavior, it generates explanations. Experimental results on real-world and synthetic health social networks confirm the accuracy of SRBM+ in human behavior prediction and its quality in human behavior explanation.


title: Unsupervised Feature Learning using Self-organizing Maps

abstract: In recent years a great amount of research has focused on algorithms that learn features from unlabeled data. In this work we propose a model based on the Self-Organizing Map (SOM) neural network to learn features useful for the problem of automatic natural images classification. In particular we use the SOM model to learn single-layer features from the extremely challenging CIFAR-10 dataset, containing 60.000 tiny labeled natural images, and subsequently use these features with a pyramidal histogram encoding to train a linear SVM classifier. Despite the large number of images, the proposed feature learning method requires only few minutes on an entry-level system, however we show that a supervised classifier trained with learned features provides significantly better results than using raw pixels values or other handcrafted features designed specifically for image classification. Moreover, exploiting the topological property of the SOM neural network, it is possible to reduce the number of features and speed up the supervised training process combining topologically close neurons, without repeating the feature learning process.


Tensor-Factor Analysis


title: DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

abstract: BackgroundLarge microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.ResultsWe present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset.ConclusionsDiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.


title: Feedforward friction compensation of Bowden-cable transmission via loop routing

abstract: Friction along the Bowden-cable transmission degenerates control performance unless it is properly compensated. Friction is produced when the bending angle of the Bowden-cable changes as the relative position of the actuator and the end-effector changes. This study proposes a method, termed loop routing, to compensate friction along the Bowden-cable. Loop routing involves making a one-round loop along the sheath that continuously maintains the sheath's bending angle at 2π regardless of the end-effector's position in 2-D space. This minimizes the bending angle change of the sheath as the end-effector translates in a 3-D workspace, which minimizes the friction change and enables feedforward friction compensation of the Bowden-cable without employing a sensor. An experiment in open-loop tension control of the Bowden-cable was conducted to evaluate the performance of the proposed method. Results show that the output tension follows the reference tension well, with an RMS error of 4.3% and a peak error of 13.3% of maximum reference.


title: From goals to components: a combined approach to self-management

abstract: Autonomous or semi-autonomous systems are deployed in environments where contact with programmers or technicians is infrequent or undesirable. To operate reliably, such systems should be able to adapt to new circumstances on their own. This paper describes our combined approach for adaptable software architecture and task synthesis from high-level goals, which is based on a three-layer model. In the uppermost layer, reactive plans are generated from goals expressed in a temporal logic. The middle layer is responsible for plan execution and assembling a configuration of domain-specific software components, which reside in the lowest layer. Moreover, the middle layer is responsible for selecting alternative components when the current configuration is no longer viable for the circumstances that have arisen. The implementation demonstrates that the approach enables us to handle non-determinism in the environment and unexpected failures in software components.


Optical Nanoantenna Input Impedance


title: Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost

abstract: Ear detection from a profile face image is an important step in many applications including biometric recognition. But accurate and rapid detection of the ear for real-time applications is a challenging task, particularly in the presence of occlusions. In this work, a cascaded AdaBoost based ear detection approach is proposed. In an experiment with a test set of 203 profile face images, all the ears were accurately detected by the proposed detector with a very low (5 x 10-6) false positive rate. It is also very fast and relatively robust to the presence of occlusions and degradation of the ear images (e.g. motion blur). The detection process is fully automatic and does not require any manual intervention.


title: Cloud-enabled remote health monitoring system

abstract: Sensor Web Enablement (SWE) for health care allows the access of sensor data anytime, anywhere using standard protocol and Application Program Interface (API). In this paper Open Geo-Spatial Consortium (OGC) standard based remote health monitoring system is proposed that allows integration of sensor and web using standard web based interface. The aim is to provide the data in an open & interoperable manner, and reduce data redundancy. Fixed specification is used for exchange of sensor data globally for all sensor networks. OGC SWEis applicable to different sensor systems including medical sensor networks. A standard format is used to document sensor descriptions and encapsulate data. Sensor data is ported on to cloud which provides scalability, centralized user access, persistent data storage and no infrastructure maintenance cost for heavy volumes of sensitive health data. Decision tree pruning algorithm with high confidence factor is proposed for automatic decision making.


title: 3D Shape Segmentation with Projective Convolutional Networks

abstract: This paper introduces a deep architecture for segmenting 3D objects into their labeled semantic parts. Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are used for efficient view-based reasoning about 3D object parts. Through a special projection layer, FCN outputs are effectively aggregated across multiple views and scales, then are projected onto the 3D object surfaces. Finally, a surface-based CRF combines the projected outputs with geometric consistency cues to yield coherent segmentations. The whole architecture (multi-view FCNs and CRF) is trained end-to-end. Our approach significantly outperforms the existing state-of-the-art methods in the currently largest segmentation benchmark (ShapeNet). Finally, we demonstrate promising segmentation results on noisy 3D shapes acquired from consumer-grade depth cameras.


title: Information, Utility&Bounded Rationality

abstract: Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we employ an axiomatic framework for bounded rational decision-making based on a thermodynamic interpretation of resource costs as information costs. This leads to a variational"free utility"principle akin to thermodynamical free energy that trades off utility and information costs. We show that bounded optimal control solutions can be derived from this variational principle, which leads in general to stochastic policies. Furthermore, we show that risk-sensitive and robust (minimax) control schemes fall out naturally from this framework if the environment is considered as a bounded rational and perfectly rational opponent, respectively. When resource costs are ignored, the maximum expected utility principle is recovered.


title: Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV

abstract: For the control of unmanned aerial vehicles UAVs in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit IMU. First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the unknown metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter EKF formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions.