title: LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

abstract: While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset.


title: An autonomous driverless car: an idea to overcome the urban road challenges

abstract: This paper discusses an autonomous robotically handled urban driving vehicle. The approach presents here the number of features as innovations, which are well grounded in past re-search on autonomous driving and mobile robotics, such as mapping, tracking, global and local planning, probabilistic localization. The results of the Urban Challenge, along with prior experiments carried out by the research team, suggest that an autonomous vehicle is capable of navigating in other vehicles and human traffic. It successfully demonstrated merging, intersection handling, parking lot navigation, lane changes, and autonomous U-turns. These innovations include the obstacle/curb detection method, the vehicle tracker, the various motion planners, and the behavioural hierarchy that addresses a broad range of traffic situations. Together, these methods provide for a robust system for urban in-traffic autonomous. Keywords: Self driving Google-Car, LIDAR Sensor, Potion Sensor & Radar Sensor.


title: Executive functions in adolescence: inferences from brain and behavior

abstract: Despite the advances in understanding cognitive improvements in executive function in adolescence, much less is known about the influence of affective and social modulators on executive function and the biological underpinnings of these functions and sensitivities. Here, recent behavioral and neuroscientific studies are summarized that have used different approaches (cognition, emotion, individual differences and training) in the study of adolescent executive functions. The combination of these different approaches gives new insight into this complex transitional phase in life, and marks adolescence as not only a period of vulnerabilities, but also great opportunities in terms of training possibilities and interventions.


title: Bluetooth Network-Based Misuse Detection

abstract: Bluetooth, a protocol designed to replace peripheral cables, has grown steadily over the last five years and includes a variety of applications. The Bluetooth protocol operates on a wide variety of mobile and wireless devices and is nearly ubiquitous. Several attacks exist that successfully target and exploit Bluetooth enabled devices. This paper describes the implementation of a network intrusion detection system for discovering malicious Bluetooth traffic. The work improves upon existing techniques, which only detect a limited set of attacks (based on measuring anomalies in the power levels of the Bluetooth device). The new method identifies reconnaissance, denial of service, and information theft attacks on Bluetooth enabled devices, using signatures of the attacks. Furthermore, this system includes an intrusion response component to detect attacks in progress, based on the attack classification. This paper presents the implementation of the Bluetooth intrusion detection system and demonstrates its detection, analysis, and response capabilities. The tool includes a visualization interface to facilitate the understanding of Bluetooth enabled attacks. The experimental results show that the system can significantly improve the overall security of an organization by identifying and responding to threats posed to the Bluetooth protocol.


Automation of Question Generation From Sentences


title: Mechanism of anovulation in hyperprolactinemic amenorrhea determined by pulsatile gonadotropin-releasing hormone injection combined with human chorionic gonadotropin.

abstract: OBJECTIVE ::: To clarify the mechanism of anovulation in hyperprolactinemic anovulatory women by subcutaneous (SC) pulsatile GnRH injection. ::: ::: ::: DESIGN ::: Prospective clinical study. ::: ::: ::: SETTING ::: Studies were made on at the Department of Obstetrics and Gynecology, the University of Tokushima, School of Medicine. ::: ::: ::: PATIENTS ::: Six hyperprolactinemic (group 1) and 7 normoprolactinemic (group 2) anovulatory patients were studied. ::: ::: ::: INTERVENTIONS ::: After examinations of pulsatile secretion of LH, the GnRH test, thyrotropin-releasing hormone test and estrogen test, pulsatile GnRH treatment (20 micrograms/2 hours SC) was performed. Two protocols were tested on each patient. In the non-hCG protocol, GnRH treatment was continued until ovulation. In the hCG protocol, 5,000 IU of hCG was injected to induce ovulation when follicles were fully mature. ::: ::: ::: MAIN OUTCOME MEASURE ::: The rates of follicular maturation and ovulation, serum E2 and P in the two groups. ::: ::: ::: RESULTS ::: Pulsatile LH secretion was impaired in both groups. LH release 48 hours after estrogen injection was impaired in group 1 but not in group 2. Follicles matured on pulsatile GnRH treatment in all cycles in both groups. However, with the non-hCG protocol, ovulation occurred in only 17% of group 1, but in 89% of group 2. With the hCG protocol ovulation occurred in all cycles in both groups. ::: ::: ::: CONCLUSIONS ::: The main cause of anovulation is impaired gonadotropin pulsatility and derangement of the estrogen-positive feedback effect on LH secretion in hyperprolactinemic patients, their ovarian response to gonadotropin being well maintained. Subcutaneous pulsatile GnRH therapy combined with hCG can be used as an alternative to bromocriptine treatment for induction of ovulation in these patients.


title: FEDERAL RESERVE BANK OF SAN FRANCISCO WORKING PAPER SERIES A Quarterly, Utilization-Adjusted Series on Total Factor Productivity

abstract: This paper describes a real-time, quarterly growth-accounting database for the U.S. business sector. The data on inputs, including capital, are used to produce a quarterly series on total factor productivity (TFP). In addition, the dataset implements an adjustment for variations in factor utilization—labor effort and the workweek of capital. The utilization adjustment follows Basu, Fernald, and Kimball (BFK, 2006). Using relative prices and input/output information, the series are also decomposed into separate TFP and utilization-adjusted TFP series for equipment investment (including consumer durables) and “consumption” (defined as business output less equipment and consumer durables).


title: Shooting a moving target: Motion-prediction-based transmission for 360-degree videos

abstract: Enabled by the rapid development of virtual reality hardware and software, 360-degree video content has proliferated. From the network perspective, 360-degree video transmission imposes significant challenges because it consumes 4 6χ the bandwidth of a regular video with the same resolution. To address these challenges, in this paper, we propose a motion-prediction-based transmission mechanism that matches network video transmission to viewer needs. Ideally, if viewer motion is perfectly known in advance, we could reduce bandwidth consumption by 80%. Practically, however, to guarantee the quality of viewing experience, we have to address the random nature of viewer motion. Based on our experimental study of viewer motion (comprising 16 video clips and over 150 subjects), we found the viewer motion can be well predicted in 100∼500ms. We propose a machine learning mechanism that predicts not only viewer motion but also prediction deviation itself. The latter is important because it provides valuable input on the amount of redundancy to be transmitted. Based on such predictions, we propose a targeted transmission mechanism that minimizes overall bandwidth consumption while providing probabilistic performance guarantees. Real-data-based evaluations show that the proposed scheme significantly reduces bandwidth consumption while minimizing performance degradation, typically a 45% bandwidth reduction with less than 0.1% failure ratio.


title: Performance of 320x240 uncooled bolometer-type infrared focal plane arrays

abstract: The performance of a 320 x 240 bolometer-type uncooled infrared (IR) Focal Plane Array (FPA) is described. Vanadium oxide thin film is adopted for the bolometer material, having a sheet resistance of approximately 10 kohms/square. It is patterned such that the bolometer resistance is by a factor of 10 larger than the sheet resistance. On-chip readout integrated circuit (ROIC) is designed to reduce signal drift, extend dynamic range for object temperature and extend ambient temperature range in which operates non-uniformity correction is carried out with about 1/10 fewer frequency than the former ROIC.The 320 x 240 FPA consists of pixels sensitive to IR radiation and optical black (OB) pixels covered with plate which shuts out IR radiation. Drift is reduced by current mirror circuit, using the OB pixels and digital compensation circuit based on voltage change of OB pixels resulting from change in operation temperature. Both the dynamic range and the ambient temperature range are extended by decreasing integration gain and developing low-noise, low-power and large swing operational amplifier(OP-AMP). Since decrease in integration gain degrades noise equivalent temperature difference (NETD), bias voltage for bolometer is increased by factor of 2 and bandwidth is reduced by route half. Finally, IR image was obtained with prototype camera and NETD value was found to be smaller than 0.1K for F/1 optics at 60Hz frame rate and thermal time constant was measured to be 12 msec.


title: Expanding small UAV capabilities with ANN: A case study for urban areas observation

abstract: Autonomous Unmanned Aerial Vehicles (UAVs) are available alternatives for urban areas inspections due to its cost and safety when compared to other traditional methods. The purpose of this paper is to report the development of a system capable of analyzing digital images of the terrain and identifies potential invasion, unauthorized alterations on the ground and deforestation in some areas of special use. Images are captured by a camera coupled to an autonomous helicopter, which flight around the area. For the processing of the images an artificial neural network technique called Kohonen SOM (Self Organized Map) will be used. The processing is actually a set of steps that seek to collate the final common characteristics of a given image.


title: Data Mining in Clinical Decision Support Systems for Diagnosis, Prediction and Treatment of Heart Disease

abstract: Medical errors are both costly and harmful. Medical errors cause thousands of deaths worldwide each year. A clinical decision support system (CDSS) offers opportunities to reduce medical errors as well as to improve patient safety. One of the most important applications of such systems is in diagnosis and treatment of heart diseases (HD) because statistics have shown that heart disease is one of the leading causes of deaths all over the world. Data mining techniques have been very effective in designing clinical support systems because of its ability discover hidden patterns and relationships in medical data. This paper compares the performance and working of six CDSS systems which use different data mining techniques for heart disease prediction and diagnosis. This paper also finds out that there is no system to identify treatment options for HD patients.


title: NOUS: Construction and Querying of Dynamic Knowledge Graphs

abstract: The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-to-end framework for developing custom knowledge graph driven analytics for arbitrary application domains. The uniqueness of our system lies A) in its combination of curated KGs along with knowledge extracted from unstructured text, B) support for advanced trending and explanatory questions on a dynamic KG, and C) the ability to answer queries where the answer is embedded across multiple data sources.


title: Optical Performance Monitoring in Fiber-Optic Networks Enabled by Machine Learning Techniques

abstract: We review applications of machine learning (ML) in various aspects of optical communications including optical performance monitoring, fiber nonlinearity compensation, and software-defined networking. The future role of ML in optical communications is also discussed.


title: Removal of a migrated dental implant from the maxillary sinus after 7 years: a case report

abstract: Background ::: The accidental displacement of dental implants into the maxillary sinus is an infrequent but possible complication in dental clinical practice. The main cause of implant displacement is the inadequate bone height in the posterior maxilla. This event usually occurs during surgery, and it is more rarely reported in the post-operative period, especially with long-term follow-ups.


title: A Blind-Mixing Scheme for Bitcoin based on an Elliptic Curve Cryptography Blind Digital Signature Algorithm

abstract: To strengthen the anonymity of Bitcoin, several centralized coin-mixing providers (mixers) such as BitcoinFog.com, BitLaundry.com, and Blockchain.info assist users to mix Bitcoins through CoinJoin transactions with multiple inputs and multiple outputs to uncover the relationship between them. However, these mixers know the output address of each user, such that they cannot provide true anonymity. This paper proposes a centralized coin-mixing algorithm based on an elliptic curve blind signature scheme (denoted as Blind-Mixing) that obstructs mixers from linking an input address with an output address. Comparisons among three blind signature based algorithms, Blind-Mixing, BlindCoin, and RSA Coin-Mixing, are conducted. It is determined that BlindCoin may be deanonymized because of its use of a public log. In RSA Coin-Mixing, a user's Bitcoins may be falsely claimed by another. In addition, the blind signature scheme of Blind-Mixing executes 10.5 times faster than that of RSA Coin-Mixing.


title: Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates

abstract: Shunting inhibition, a conductance increase with a reversal potential close to the resting potential of the cell, has been shown to have a divisive effect on subthreshold excitatory postsynaptic potential amplitudes. It has therefore been assumed to have the same divisive effect on firing rates. We show that shunting inhibition actually has a subtractive effect on the firing rate in most circumstances. Averaged over several interspike intervals, the spiking mechanism effectively clamps the somatic membrane potential to a value significantly above the resting potential, so that the current through the shunting conductance is approximately independent of the firing rate. This leads to a subtractive rather than a divisive effect. In addition, at distal synapses, shunting inhibition will also have an approximately subtractive effect if the excitatory conductance is not small compared to the inhibitory conductance. Therefore regulating a cell's passive membrane conductance-for instance, via massive feedback-is not an adequate mechanism for normalizing or scaling its output.


How 2 Sketch : Generating Easy-To-Follow Tutorials for Sketching 3 D Objects


title: The insulated gate rectifier (IGR): A new power switching device

abstract: A new power semiconductor device called the Insulated Gate Rectifier (IGR) is described in this paper. This device has the advantages of operating at high current densities while requiring low gate drive power. The devices exhibit relatively slow switching speeds due to bipolar operation. The results of two dimensional computer modelling of the device structure are compared with measurements taken on devices fabricated with 600 volt forward and reverse blocking capability.


title: Deep Learning Identity-Preserving Face Space

abstract: Face recognition with large pose and illumination variations is a challenging problem in computer vision. This paper addresses this challenge by proposing a new learning based face representation: the face identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining discriminative ness between identities. Moreover, the FIP features extracted from an image under any pose and illumination can be used to reconstruct its face image in the canonical view. This property makes it possible to improve the performance of traditional descriptors, such as LBP [2] and Gabor [31], which can be extracted from our reconstructed images in the canonical view to eliminate variations. In order to learn the FIP features, we carefully design a deep network that combines the feature extraction layers and the reconstruction layer. The former encodes a face image into the FIP features, while the latter transforms them to an image in the canonical view. Extensive experiments on the large MultiPIE face database [7] demonstrate that it significantly outperforms the state-of-the-art face recognition methods.


title: Biased Manifold Embedding: A Framework for Person-Independent Head Pose Estimation

abstract: The estimation of head pose angle from face images is an integral component of face recognition systems, human computer interfaces and other human-centered computing applications. To determine the head pose, face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional feature space. While manifold learning techniques capture the geometrical relationship between data points in the high-dimensional image feature space, the pose label information of the training data samples are neglected in the computation of these embeddings. In this paper, we propose a novel supervised approach to manifold-based non-linear dimensionality reduction for head pose estimation. The Biased Manifold Embedding (BME) framework is pivoted on the ideology of using the pose angle information of the face images to compute a biased neighborhood of each point in the feature space, before determining the low-dimensional embedding. The proposed BME approach is formulated as an extensible framework, and validated with the Isomap, Locally Linear Embedding (LLE) and Laplacian Eigen-maps techniques. A Generalized Regression Neural Network (GRNN) is used to learn the non-linear mapping, and linear multi-variate regression is finally applied on the low-dimensional space to obtain the pose angle. We tested this approach on face images of 24 individuals with pose angles varying from -90deg to +90deg with a granularity of 2. The results showed substantial reduction in the error of pose angle estimation, and robustness to variations in feature spaces, dimensionality of embedding and other parameters.


title: Near Vertical Incidence Skywave Propagation: Elevation Angles and Optimum Antenna Height for Horizontal Dipole Antennas

abstract: Near Vertical Incidence Skywave (NVIS) communication uses the ionosphere as a reflector to cover a continuous area with a radius of at least 150 km around the transmitter, on frequencies typically between 3 and 10 MHz. In developing countries, in areas lacking any other telecommunication infrastructure, it is used on a daily basis for voice and data communication. It may also be used in ad-hoc emergency (disaster) communication in other regions. This paper proposes optimum heights above ground for horizontal dipole antennas for NVIS, based on simulations and empirical data. First, the relationship between elevation angle and skip distance is obtained using ionospheric ray tracing. The high elevation angles found by simulation are confirmed by elevation angle measurements using a professional radio direction finder. The measurements also show the dominance of NVIS over ground wave propagation starting at a short distance. For these elevation angles, the optimum receive and transmit antenna heights above ground are derived using antenna simulations. A distinction is made between optimum transmit signal strength and optimum received signal-to-noise ratio (SNR). These optima are verified experimentally, demonstrating a novel evaluation method that can be used in the presence of the fading typical for ionospheric propagation. For farmland soil ( 20 mS/m, "r 17) the optimum height above ground for the transmit antenna is 0.180.22. If the antenna is lowered to 0.02 a transmit signal loss of 12 dB occurs. This corresponds with the theory. The receive antenna height, however, while appearing uncritical in the simulations, showed a clear optimum at 0.16 and a 27 dB SNR deterioration when lowered to 0.02.


title: Memory-Based Store Price Judgments: The Role of Knowledge and Shopping Experience

abstract: Abstract This paper investigates the processes underlying consumers’ memory-based store price judgments. The numerosity heuristic implies that the greater the number of relatively lower priced products at a store that consumers can recall, the lower will be their overall price image of the store. That is, people use the number of recalled low-price products to judge the overall store price image. We show that this expectation holds only for knowledgeable consumers. Instead, less knowledgeable consumers use the ease with which low-price products are recalled (i.e., the availability heuristic) as a cue to make store price judgments. Therefore, the fewer low-price products they recall, the easier their recall task, and the lower their price perceptions of the store. Field studies using different manipulations tested and confirmed these predictions. Managerial implications for retailers are offered. Theoretical implications for behavioral price perceptions, memory-based judgments, and the use of heuristic cues are also discussed.


title: Partial Copy Detection in Videos: A Benchmark and an Evaluation of Popular Methods

abstract: The goal of partial video copy detection is to find one or more segments of a query video which have (transformed) copies in a large dataset. Previous related research in this field used either small-scale datasets or large datasets with simulated partial copies by imposing several pre-defined transformations (e.g., photometric changes) due to the extremely time-consuming annotation of real copies. It is still unknown how well the techniques developed on simulated datasets perform on real copies, which are much more challenging and too complex to be simulated. In this paper, we introduce a large-scale video copy database (VCDB) with over 100,000 videos, and more than 9,000 copy pairs found by manual annotation. A state-of-the-art system of video copy detection is evaluated on VCDB to show the limitations of existing techniques. We also evaluate deep learning features learned by two neural networks: one is independently trained on a different dataset and the other is tailored to deal with the copy detection task. Our evaluation suggests that all the existing techniques, including the deep learning features, are far from satisfactory in detecting complex real copies.


title: Driver classification for intelligent transportation systems using fuzzy logic

abstract: Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element that affects road safety is driver behavior, because driver errors are usually the principal cause of traffic accidents. Therefore, understanding and modeling human driver behavior is extremely important for the safety of the road transportation. In this paper, a Fuzzy rule-based system to classify the drivers in different profiles according to their behavior is proposed. The system will be integrated in intelligent transportation architecture, which can be used to predict and avoid traffic accidents and to optimize the routing management.


title: Differences in Demographic, Behavioral, and Biological Variables Between Those With Valid and Invalid Accelerometry Data: Implications for Generalizability

abstract: Abstract Statement of problem In the general population, studies demonstrate a beneficial effect of physical activity in reducing depression symptoms; however, few studies have examined this relationship among adults with diabetes with none of these studies using an objective measure (e.g., accelerometer) of physical activity. The purpose of this study was to examine the association between accelerometer-assessed physical activity and depression symptoms among a nationally representative sample of U.S. adults with evidence of diabetes. Methods Data from the 2005–2006 National Health and Nutrition Examination Survey were used. Adult participants with diabetes were included in the present study and wore an ActiGraph 7164 accelerometer. The analytic sample included 372 adults with diabetes. Light and moderate-to-vigorous physical activity (MVPA) intensity was determined from accelerometry. Results Findings showed that MVPA was inversely associated with depression symptoms, with the greatest magnitude of association occurring for those meeting physical activity guidelines. Also of importance was the independent positive association between A1C and depression, highlighting the importance of maintaining glycemic control, which can be accomplished with regular participation in physical activity, along with other strategies. Conclusion Adults with diabetes with greater depression symptoms engaged in less MVPA than those with less depression symptoms. Future studies, particularly longitudinal and experimental designs, are warranted.


title: A pseudo-CCM/DCM SIMO switching converter with freewheel switching

abstract: This paper presents a single-inductor multiple-output (SIMO) converter operating in pseudo-continuous conduction mode (PCCM) and/or discontinuous conduction mode (DCM). With the proposed freewheel switching control, this converter can handle large load currents with a much smaller current ripple, while retaining low cross regulation. It can also work in DCM for high efficiency at light loads. A prototype of a single-inductor dual-output (SIDO) boost converter was fabricated with a standard 0.5-/spl mu/m CMOS n-well process. The two outputs are regulated at 2.5 and 3.0 V, respectively. At an oscillator frequency of 1 MHz, the efficiency reaches 89.4% at a total output power of 320 mW. Compared with prior designs, both current and voltage ripples are reduced. This design can be extended to have multiple outputs and for different types of dc-dc conversions, or be applied to single-output converters for fast transient response.


title: Research on Power Marketing Strategy Based on Intelligent Power Community

abstract: smart power technology is an important part of the smart grid, based on the analysis of the concept of smart grid and smart power technology and research status,the paper propose a new type of two-way interactive customer intelligent power marketing system. With reality, from the surface of the information collection system, intelligent demand-side management, customer knowledge management, load control, customer credit risk management, customer relationship management, customer resource valuation analysis of the basic functions of each sub-system and path, in-depth analysis of smart power technology and power marketing work.


title: Hardware acceleration of genetic sequence alignment

abstract: Next generation DNA sequencing machines have been improving at an exceptional rate; the subsequent analysis of the generated sequenced data has become a bottleneck in current systems. This paper explores the use of reconfigurable hardware to accelerate the short read mapping problem, where the positions of millions of short DNA sequences are located relative to a known reference sequence. The proposed design comprises of an alignment processor based on a backtracking variation of the FM-index algorithm. The design represents a full solution to the short read mapping problem, capable of efficient exact and approximate alignment. We use reconfigurable hardware to accelerate the design and find that an implementation targeting the MaxWorkstation performs considerably faster and more energy efficient than current CPU and GPU based software aligners.


title: Dynamic Assortment Selection under the Nested Logit Models

abstract: We study a stylized dynamic assortment planning problem during a selling season of finite length $T$, by considering a nested multinomial logit model with $M$ nests and $N$ items per nest. Our policy simultaneously learns customers' choice behavior and makes dynamic decisions on assortments based on the current knowledge. It achieves the regret at the order of $\tilde{O}(\sqrt{MNT}+MN^2)$, where $M$ is the number of nests and $N$ is the number of products in each nest. We further provide a lower bound result of $\Omega(\sqrt{MT})$, which shows the optimality of the upper bound when $T>M$ and $N$ is small. However, the $N^2$ term in the upper bound is not ideal for applications where $N$ is large as compared to $T$. To address this issue, we further generalize our first policy by introducing a discretization technique, which leads to a regret of $\tilde{O}(\sqrt{M}T^{2/3}+MNT^{1/3})$ with a specific choice of discretization granularity. It improves the previous regret bound whenever $N>T^{1/3}$. We provide numerical results to demonstrate the empirical performance of both proposed policies.


title: This Looks Like That: Deep Learning for Interpretable Image Recognition

abstract: When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The model thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training without any annotations for parts of images. We demonstrate our method on the CUB-200-2011 dataset and the Stanford Cars dataset. Our experiments show that ProtoPNet can achieve comparable accuracy with its analogous non-interpretable counterpart, and when several ProtoPNets are combined into a larger network, it can achieve an accuracy that is on par with some of the best-performing deep models. Moreover, ProtoPNet provides a level of interpretability that is absent in other interpretable deep models.


title: A New Class of Antenna Array With a Reconfigurable Element Factor

abstract: A parasitic layer-based multifunctional reconfigurable antenna array (MRAA) formed by the linear combination of four (4 $\times$ 1) identical multifunctional reconfigurable antenna (MRA) elements is presented. Each MRA produces eight modes of operation corresponding to three steerable beam directions ( $\theta_{xz}=-30^{\circ}$ , 0 $^{\circ}$ , 30 $^{\circ}$ ) with linear and circular polarizations in $x-z$ plane and another two steerable beam directions ( $\theta_{yz}=-30^{\circ}$ , 30 $^{\circ}$ ) in $y-z$ plane with linear polarization. An individual MRA consists of an aperture-coupled driven patch antenna with a parasitic layer placed above it. The surface of the parasitic layer has a grid of 4 $\times$ 4 electrically-small rectangular-shaped metallic pixels. The adjacent pixels can be connected/disconnected by means of switching resulting in reconfigurability in beam-direction and polarization. A 4 $\times$ 1 linear MRAA operating in the $\sim$ 5.4–5.6 GHz is formed by the optimized MRA elements. MRA and MRAA prototypes have been fabricated and measured. The measured and simulated results agree well indicating $\sim$ 13.5 dB realized array gain and $\sim$ 3% common bandwidth. The MRAA presents some advantages as compared to a standard antenna array: MRAA alleviates the scan loss inherit to standard antenna arrays, provides higher gain, does not need phase shifters for beam steering in certain plane, and is capable of polarization reconfigurability.


Application of dimensionality reduction in recommender systems–a case study


A REVISIONIST MODEL OF SCHOOL LEARNING


title: Physiological effects of hypoxia on metabolism and growth of turtle embryos.

abstract: Hypoxic effects on embryonic metabolism and growth of Florida red-bellied turtle (Pseudemys nelsoni) embryos were studied. Eggs were assigned to either normoxic (21% O2) or hypoxic (10% O2) treatment on day 21. Oxygen consumption (VO2) and embryonic mass were measured periodically. On day 21 and 33, critical oxygen tension (Pc) of embryos and morphometry of blood vessels in chorioallantoic membranes were measured. Although embryos exposed to normoxia increased VO2 and mass at a rate greater than those in the hypoxic condition during incubation, both groups hatched at the same time. Hatchlings from the hypoxic treatment had elevated hematocrit and greater relative dry ventricular mass compared to hatchlings from the normoxic treatment. The Pc of hypoxic embryos was significantly lower than normoxic embryos on day 33 (P < 0.001). Morphometric comparison of blood vessels in chorioallantoic membranes revealed no significant differences except in length density on day 33 (P < 0.05). It is concluded that exposure to chronic hypoxia resulted in retarded growth, depressed metabolism, comparable incubation period, and reduced hatchling mass. In addition, embryos exhibited some physiological plasticity which allowed increased oxygen transport in response to hypoxia.


title: Connections Between Nuclear Norm and Frobenius Norm Based Representation

abstract: A lot of works have shown that frobenius-norm-based representation (FNR) is competitive to sparse representation and nuclear-norm-based representation (NNR) in numerous tasks such as subspace clustering. Despite the success of FNR in experimental studies, less theoretical analysis is provided to understand its working mechanism. In this brief, we fill this gap by building the theoretical connections between FNR and NNR. More specially, we prove that: 1) when the dictionary can provide enough representative capacity, FNR is exactly NNR even though the data set contains the Gaussian noise, Laplacian noise, or sample-specified corruption and 2) otherwise, FNR and NNR are two solutions on the column space of the dictionary.


title: Compilation in the Microsoft SQL Server Hekaton Engine

abstract: Hekaton is a new database engine optimized for memory resident data and OLTP workloads that is fully integrated into Microsoft SQL Server. A key innovation that enables high performance in Hekaton is compilation of SQL stored procedures into machine code.


title: Ear Recognition Based on Statistical Shape Model

abstract: Alfred system suggests that ear shape can be used as a unique and comparable feature for identity recognition. In this paper, we aim to extract ear shape features for recognition. Active Shape Models (ASMs) is applied to model the shape and local appearance of the ear in a statistical manner. In addition, steerable features are extracted from the ear image ahead of ASMs. Steerable features encode rich discriminant information of the local structural texture and provide accurate guidance for shape location. Eigenearshape is used for final classification. Experiments on an ear database show an encouraging performance. Some experiments on double ears combined for recognition are also carried out, and indicate that the fusion outperforms either ear alone.


title: Imaging of Thymic Epithelial Neoplasms

abstract: Thymic epithelial neoplasms are uncommon lesions. Affected patients may be asymptomatic or may present with thoracic complaints or paraneoplastic syndromes. Asymptomatic lesions may be discovered incidentally during chest radiography or during other chest imaging studies. This article addresses the imaging evaluation of patients who have thymic epithelial neoplasms, specifically covering the use of chest radiography, CT, MRI, and positron emission tomography.


title: Nitroglycerin: A Review of Its Use in the Treatment of Vascular Occlusion After Soft Tissue Augmentation

abstract: BACKGROUND ::: Skin necrosis after soft tissue augmentation with dermal fillers is a rare but potentially severe complication. Nitroglycerin paste may be an important treatment option for dermal and epidermal ischemia in cosmetic surgery. ::: ::: ::: OBJECTIVES ::: To summarize the knowledge about nitroglycerin paste in cosmetic surgery and to understand its current use in the treatment of vascular compromise after soft tissue augmentation. To review the mechanism of action of nitroglycerin, examine its utility in the dermal vasculature in the setting of dermal filler-induced ischemia, and describe the facial anatomy danger zones in order to avoid vascular injury. ::: ::: ::: METHODS ::: A literature review was conducted to examine the mechanism of action of nitroglycerin, and a treatment algorithm was proposed from clinical observations to define strategies for impending facial necrosis after filler injection. ::: ::: ::: RESULTS AND CONCLUSIONS ::: Our experience with nitroglycerin paste and our review of the medical literature supports the use of nitroglycerin paste on the skin to help improve flow in the dermal vasculature because of its vasodilatory effect on small-caliber arterioles.


title: Modeling Rainfall Prediction Using Data Mining Method: A Bayesian Approach

abstract: Weather forecasting has been one of the most scientifically and technologically challenging problem around the world. Weather data is one of the meteorological data that is rich with important information, which can be used for weather prediction We extract knowledge from weather historical data collected from Indian Meteorological Department (IMD) Pune. From the collected weather data comprising of 36 attributes, only 7 attributes are most relevant to rainfall prediction. We made data preprocessing and data transformation on raw weather data set, so that it shall be possible to work on Bayesian, the data mining, prediction model used for rainfall prediction. The model is trained using the training data set and has been tested for accuracy on available test data. The meteorological centers uses high performance computing and supercomputing power to run weather prediction model. To address the issue of compute intensive rainfall prediction model, we proposed and implemented data intensive model using data mining technique. Our model works with good accuracy and takes moderate compute resources to predict the rainfall. We have used Bayesian approach to prove our model for rainfall prediction, and found to be working well with good accuracy.


Cross Lingual Transfer Learning for POS tagging


title: Four-Dimensional Discrete-time Lotka-Volterra Models with an Application to Ecology

abstract: This paper presents a study of the two-predators-two-preys discrete-time Lotka-Volterra model with self- inhibition terms for preys with direct applications to ecological problems. Parameters in the model are modified so that each of them has its own biological meaning, enabling more intuitive interpretation of biological conditions to the model . Moreover, the modified version is applicable to simulate a part of a large ecosystem, not only a closed predator-prey system with four species. An easy graphical method of analysis of the conditions on parameters ensuring long persistence under coexistence is presented. As an application, it is explained that a predator specie who feed on relatively small number of preys compared to the other predator species must be selective on the available preys in order for long persistence of the ecosystem. This may be regarded as a theoretical explanation of the existence of flush-pursuer birds, those who uses highly specialized hunting strategy and cross-adapts to the ecosystem relative to the ordinary bird species.


title: Wavelets : Theoretical concepts and vibrations related applications

abstract: In this paper we provide a review of wavelet analysis in the context of applications to vibrations problems. First, we give an introduction to the important concepts and mathematical properties of wavelets within the framework of time-frequency analysis of signals. Next, wavelet analysis is discussed as applied to relevant themes in vibrations, such as time-varying spectra estimation, random field synthesis, system identification, damage detection, and material characterization. In view of the large number of related books and journal articles published in recent decades, the list of selected references in the paper is not meant to be exhaustive. Nevertheless, the cited references aim to point out the salient features of wavelet analysis, and to identify significant contributions in each theme, with the goal of expediting additional research and development efforts.


title: Semantic motion segmentation for urban dynamic scene understanding

abstract: A mount of recent researches on scene parsing and semantic labeling, while few focus on obtaining joint semantic motion labeling. In this paper, we propose an approach to infer both the object class and motion status for each pixel of images. First, we extract and match sparse image features to estimate ego-motion between two consecutive stereo images, the result of feature points grouping is used to segment moving object in U-disparity map. Second, a Fully Convolutional Neural Network is employed for semantic segmentation. Moreover, semantic cues are utilized to remove pixels have no potential to be moved in motion mask. Finally, we use a fully connected CRF to integrate motion into semantic segmentation. To validate the effectiveness of the proposed algorithm, we present experimental results with KITTI stereo images that contain moving objects.


title: The Fable of the Keys

abstract: This paper examines the history of the QWERTY typewriter keyboard, often put forward as the archetypical case of markets choosing the wrong standard. Contrary to the claims made by Paul David and Brian Arthur, we find virtually no evidence to support a view that QWERTY is inferior to DVORAK. Instead, using records of typing experiments, studies by ergonomicists, and examining the historical record of competition among different keyboard designs back when QWERTY first became dominant, we conclude that QWERTY is about as good a design as any alternative.


title: Zipf’s word frequency law in natural language: A critical review and future directions

abstract: The frequency distribution of words has been a key object of study in statistical linguistics for the past 70 years. This distribution approximately follows a simple mathematical form known as Zipf’s law. This article first shows that human language has a highly complex, reliable structure in the frequency distribution over and above this classic law, although prior data visualization methods have obscured this fact. A number of empirical phenomena related to word frequencies are then reviewed. These facts are chosen to be informative about the mechanisms giving rise to Zipf’s law and are then used to evaluate many of the theoretical explanations of Zipf’s law in language. No prior account straightforwardly explains all the basic facts or is supported with independent evaluation of its underlying assumptions. To make progress at understanding why language obeys Zipf’s law, studies must seek evidence beyond the law itself, testing assumptions and evaluating novel predictions with new, independent data.


Disaster risk reduction : a development concern


title: Unsupervised patient representations from clinical notes with interpretable classification decisions

abstract: We have two main contributions in this work: 1. We explore the usage of a stacked denoising autoencoder, and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. We evaluate these representations by using them as features in multiple supervised setups, and compare their performance with those of sparse representations. 2. To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model. Further, we calculate the significance of the input features of the trained classifiers when we use these pretrained representations as input.


title: Piano Transcription With Convolutional Sparse Lateral Inhibition

abstract: This letter extends our prior work on context-dependent piano transcription to estimate the length of the notes in addition to their pitch and onset. This approach employs convolutional sparse coding along with lateral inhibition constraints to approximate a musical signal as the sum of piano note waveforms (dictionary elements) convolved with their temporal activations. The waveforms are pre-recorded for the specific piano to be transcribed in the specific environment. A dictionary containing multiple waveforms per pitch is generated by truncating a long waveform for each pitch to different lengths. During transcription, the dictionary elements are fixed and their temporal activations are estimated and postprocessed to obtain the pitch, onset, and note length estimation. A sparsity penalty promotes globally sparse activations of the dictionary elements, and a lateral inhibition term penalizes concurrent activations of different waveforms corresponding to the same pitch within a temporal neighborhood, to achieve note length estimation. Experiments on the MIDI aligned piano sounds dataset show that the proposed approach significantly outperforms a state-of-the-art music transcription method trained in the same context-dependent setting in transcription accuracy.


title: The neural foundations of aesthetic appreciation

abstract: The study of the cognitive and neural underpinnings of aesthetic appreciation by means of neuroimaging techniques has yielded a wealth of fascinating information. Although the results of these studies have been somewhat divergent, here we provide an integrative view of the early approaches, which identified some of the core mechanisms involved in aesthetic preference. Then, a number of more specific issues under the perspective of recent work are addressed. Finally, we propose a framework to accommodate these findings and we explore future prospects for the emerging field of neuroaesthetics.


NeuroEvolution : The Importance of Transfer Function Evolution and Heterogeneous Networks


title: KnowEdu: A System to Construct Knowledge Graph for Education

abstract: Motivated by the vast applications of knowledge graph and the increasing demand in education domain, we propose a system, called KnowEdu , to automatically construct knowledge graph for education. By leveraging on heterogeneous data (e.g., pedagogical data and learning assessment data) from the education domain, this system first extracts the concepts of subjects or courses and then identifies the educational relations between the concepts. More specifically, it adopts the neural sequence labeling algorithm on pedagogical data to extract instructional concepts and employs probabilistic association rule mining on learning assessment data to identify the relations with educational significance. We detail all the abovementioned efforts through an exemplary case of constructing a demonstrative knowledge graph for mathematics, where the instructional concepts and their prerequisite relations are derived from curriculum standards and concept-based performance data of students. Evaluation results show that the F1 score for concept extraction exceeds 0.70, and for relation identification, the area under the curve and mean average precision achieve 0.95 and 0.87, respectively.


title: Why Do People Need Self-Esteem? A Theoretical and Empirical Review.

abstract: Terror management theory (TMT; J. Greenberg, T. Pyszczynski, & S. Solomon, 1986) posits that people are motivated to pursue positive self-evaluations because self-esteem provides a buffer against the omnipresent potential for anxiety engendered by the uniquely human awareness of mortality. Empirical evidence relevant to the theory is reviewed showing that high levels of self-esteem reduce anxiety and anxiety-related defensive behavior, reminders of one’s mortality increase self-esteem striving and defense of self-esteem against threats in a variety of domains, high levels of self-esteem eliminate the effect of reminders of mortality on both self-esteem striving and the accessibility of death-related thoughts, and convincing people of the existence of an afterlife eliminates the effect of mortality salience on self-esteem striving. TMT is compared with other explanations for why people need self-esteem, and a critique of the most prominent of these, sociometer theory, is provided. Everything cultural is fabricated and given meaning by the mind, a meaning that was not given by physical nature. Culture is in this sense “supernatural,” and all systematizations of culture have in their end the same goal: to raise men above nature to assure them that in some ways their lives count more than merely physical things count. (Becker, 1975, p. 4) They earn this feeling by carving out a place in nature, by building an edifice that reflects human value: a temple, a cathedral, a totem pole, a skyscraper, a family that spans three generations. The hope and belief is that the things that man creates in society are of lasting worth and meaning, that they outlive or outshine death and decay, that man and his products count. (Becker, 1973, p. 5) The idea that people are keenly motivated to maintain high levels of self-esteem and that this motive underlies a great deal of human behavior has been a central theme in psychological theorizing, stretching from the very beginnings of scientific psychol


title: A spiral model of software development and enhancement

abstract: A short description is given of software process models and the issues they address. An outline is given of the process steps involved in the spiral model, an evolving risk-driven approach that provides a framework for guiding the software process, and its application to a software project is shown. A summary is given of the primary advantages and implications involved in using the spiral model and the primary difficulties in using it at its current incomplete level of elaboration. >


title: Simulation optimization: methods and applications

abstract: Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility. The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simulation experiment. The purpose of this paper is to review the area of simulation optimization. A critical review of the methods employed and applications developed in this relatively new area are presented and notable successes are highlighted. Simulation optimization software tools are discussed. The intended audience is simulation practitioners and theoreticians as well as beginners in the field of simulation.


title: Progesterone as a neuroprotective factor in traumatic and ischemic brain injury

abstract: Abstract The search for a “magic bullet” drug targeting a single receptor for the treatment of stroke or traumatic brain injury (TBI) has failed thus far for a variety of reasons. The pathophysiology of ischemic brain injury and TBI involves a number of mechanisms leading to neuronal injury, including excitotoxicity, free radical damage, inflammation, necrosis, and apoptosis. Brain injury also triggers auto-protective mechanisms, including the up-regulation of anti-inflammatory cytokines and endogenous antioxidants. In these conditions an agent with pleiotropic consequences is more likely to provide effective neuroprotection and repair than one operating primarily on a single, or a small number of, injury mechanisms. There is growing evidence, including recently published clinical trials, that progesterone and perhaps its metabolite allopregnanolone exert neuroprotective effects on the injured central nervous system (CNS). Laboratories around the world have shown that progesterone and allopregnanolone act through numerous metabolic and physiological pathways that can affect the injury response in many different tissues and organ systems. Furthermore, progesterone is a natural hormone, synthesized in both males and females, that can act as a pro-drug for other metabolites with their own distinct mode of action in CNS repair. These properties make progesterone a unique and compelling natural agent to consider for testing in clinical trial for CNS injuries including TBI and stroke.


title: Relative scale estimation between two camera motions

abstract: In structure from motion, if two metric structures are given, the unknown scale between them can be resolved by constraining the rigidity of the metric space. There exist two well-known approaches. The first one is the pose estimation which aims to find the pose of a camera for known 3D points. The second one is the scale estimation whose goal is to resolve the scale after estimating the motion between cameras. Recently, the former way is preferred because of the vulnerability of the scale estimation, the weakness to the image noise. In this paper, we thus propose a robust method which can overcome the weakness of the scale estimation by considering the uncertainty of reconstructed 3D points. Additionally, the rotation matrix is directly corrected under the structural consistency constraint. To illustrate the performance of the method, we demonstrate some examples of large-scale reconstructions and compare the results.


title: Scalable process discovery and conformance checking

abstract: Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.


title: A 0.21-V minimum input, 73.6% maximum efficiency, fully integrated voltage boost converter with MPPT for low-voltage energy harvesters

abstract: This paper proposes a fully integrated voltage boost converter with a maximum power point tracking (MPPT) circuit for ultra-low power energy harvesting. The converter is based on a conventional charge pump circuit and can deliver a wide range of load current by using nMOS and pMOS driver circuits for highly efficient switching operation. The MPPT circuit we propose dissipates nano-watt power to extract maximum power regardless of the harvester's power generation conditions and load current. The measurement results demonstrated that the circuit converted a 0.49-V input to a 1.46-V output with 73.6% power conversion efficiency when the output power was 348 µW. The circuit can operate at an extremely low input of 0.21 V. I. INTRODUCTION


title: Estimation of population firing rates and current source densities from laminar electrode recordings

abstract: This model study investigates the validity of methods used to interpret linear (laminar) multielectrode recordings. In computer experiments extracellular potentials from a synaptically activated population of about 1,000 pyramidal neurons are calculated using biologically realistic compartmental neuron models combined with electrostatic forward modeling. The somas of the pyramidal neurons are located in a 0.4 mm high and wide columnar cylinder, mimicking a stimulus-evoked layer-5 population in a neocortical column. Current-source density (CSD) analysis of the low-frequency part (<500 Hz) of the calculated potentials (local field potentials, LFP) based on the 'inverse' CSD method is, in contrast to the 'standard' CSD method, seen to give excellent estimates of the true underlying CSD. The high-frequency part (>750 Hz) of the potentials (multi-unit activity, MUA) is found to scale approximately as the population firing rate to the power 3/4 and to give excellent estimates of the underlying population firing rate for trial-averaged data. The MUA signal is found to decay much more sharply outside the columnar populations than the LFP.


Can Android App Inventor Bring Computational Thinking to K-12 ?


title: Hierarchical Bayesian estimation for MEG inverse problem

abstract: Source current estimation from MEG measurement is an ill-posed problem that requires prior assumptions about brain activity and an efficient estimation algorithm. In this article, we propose a new hierarchical Bayesian method introducing a hierarchical prior that can effectively incorporate both structural and functional MRI data. In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using the Variational Bayesian method. The fMRI information can be imposed as prior information on the variance distribution rather than the variance itself so that it gives a soft constraint on the variance. A spatial smoothness constraint, that the neural activity within a few millimeter radius tends to be similar due to the neural connections, can also be implemented as a hierarchical prior. The proposed method provides a unified theory to deal with the following three situations: (1) MEG with no other data, (2) MEG with structural MRI data on cortical surfaces, and (3) MEG with both structural MRI and fMRI data. We investigated the performance of our method and conventional linear inverse methods under these three conditions. Simulation results indicate that our method has better accuracy and spatial resolution than the conventional linear inverse methods under all three conditions. It is also shown that accuracy of our method improves as MRI and fMRI information becomes available. Simulation results demonstrate that our method appropriately resolves the inverse problem even if fMRI data convey inaccurate information, while the Wiener filter method is seriously deteriorated by inaccurate fMRI information.


title: Physiotherapy Approaches in the Treatment of Ataxic Multiple Sclerosis: A Pilot Study

abstract: Objective: This study was planned to investigate the efficacy of neuromuscular rehabilitation and Johnstone Pressure Splints in the patients who had ataxic multiple sclerosis. Methods: Twenty-six outpatients with multiple sclerosis were the subjects of the study. The control group (n = 13) was given neuromuscular rehabilitation, whereas the study group (n = 13) was treated with Johnstone Pressure Splints in ad dition. Results: In pre- and posttreatment data, significant differences were found in sensation, anterior balance, gait parameters, and Expanded Disability Status Scale (p < 0.05). An important difference was observed in walking-on-two-lines data within the groups (p < 0.05). There also was a statistically significant difference in pendular movements and dysdiadakokinesia (p < 0.05). When the posttreatment values were compared, there was no significant difference between sensation, anterior balance, gait parameters, equilibrium and nonequilibrium coordination tests, Expanded Disability Status Scale, cortical onset latency, and central conduction time of somatosensory evoked potentials and motor evoked potentials (p > 0.05). Comparison of values re vealed an important difference in cortical onset-P37 peak amplitude of somatosen sory evoked potentials (right limbs) in favor of the study group (p < 0.05). Conclu sions : According to our study, it was determined that physiotherapy approaches were effective to decrease the ataxia. We conclude that the combination of suitable phys iotherapy techniques is effective multiple sclerosis rehabilitation. Key Words: Multi ple sclerosis—Ataxia—Physical therapy.


title: Characterization of the intestinal microbiota and its interaction with probiotics and health impacts

abstract: The gastrointestinal tract (GIT) is a dynamic microecosystem containing a diversified microbiota of about 500-1000 different microbial species. Humans depend on their intestinal microbiota to carry out vital functions, and thus, equilibrium among intestinal groups of microorganisms is essential. In this review article, the use of traditional and molecular methods is discussed for the characterization of the intestinal microbiota, as well as its interaction with probiotics and their effects on health. An improved knowledge on intestinal microbiota composition and diversity and how changes in this microecosystem can cause or are associated with diseases remains far from being completely understood. Therefore, a better understanding of the GIT microbial populations is crucial, which will certainly contribute to the development of new strategies for the prevention and/or treatment of several diseases. The manipulation of the GIT microbiota by probiotics consumption is an interesting approach to maintain and restore human health.


title: PID control system analysis and design

abstract: With its three-term functionality offering treatment of both transient and steady-state responses, proportional-integral-derivative (PID) control provides a generic and efficient solution to real-world control problems. The wide application of PID control has stimulated and sustained research and development to "get the best out of PID", and "the search is on to find the next key technology or methodology for PID tuning". This article presents remedies for problems involving the integral and derivative terms. PID design objectives, methods, and future directions are discussed. Subsequently, a computerized simulation-based approach is presented, together with illustrative design results for first-order, higher order, and nonlinear plants. Finally, we discuss differences between academic research and industrial practice, so as to motivate new research directions in PID control.


title: Y.: Comparing two Business Model Ontologies for Designing e-Business Models and Value Constellations

abstract: Business models have been an important topic in various disciplines and particularly ebusiness. Yet, little research has tempted to compare and integrate the different business model approaches. This paper compares two business model ontologies, the Business Model Ontology BMO and the e3value ontology, for the design of business models and value constellations. For that purpose it introduces a framework that allows the comparison of different conceptual approaches to business models. The two ontologies are illustrated through a case study in the domain of rights music management. The outcome of the analysis is twofold. Firstly, it permits a better understanding of business model research. Secondly, it highlights the possible paths to integrate the two ontologies in order to improve the representation, design, and analysis of business models.


title: Building extraction from remote sensing images with deep learning in a supervised manner

abstract: Building extraction from remote sensing images is a longstanding topic in land use analysis and applications of remote sensing. Variations in shape and appearance of buildings, occlusions and other unpredictable factors increase the hardness of automatic building extraction. Numerous methods have been proposed during the last several decays, but most of these works are task oriented and lack of generalization. This paper applys deep learning to building extraction in a supervised manner. A deep deconvolution neural network with 27 Convolution/Deconvolution weight layers is designed to realize building extraction in pixel level. As such a deep network is prone to overfitting, a data augment method that suits pixel-wise prediction tasks in remote sensing is suggested. Moreover, an overall training and inferencing architecture is proposed. Our methods are finally applied to building extraction tasks and get competitive results with other methods published.


title: Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

abstract: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reinforcement tasks, and they do this without explicitly computing gradient estimates or even storing information from which such estimates could be computed. Specific examples of such algorithms are presented, some of which bear a close relationship to certain existing algorithms while others are novel but potentially interesting in their own right. Also given are results that show how such algorithms can be naturally integrated with backpropagation. We close with a brief discussion of a number of additional issues surrounding the use of such algorithms, including what is known about their limiting behaviors as well as further considerations that might be used to help develop similar but potentially more powerful reinforcement learning algorithms.


title: Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks

abstract: A major problem encountered by researchers of dynamic neural networks is the computational complexity increasing the learning time. In this paper the parallel realization of the RTRN and the Elman networks are discussed. Both networks are examples of dynamic neural networks. Inherent parallelism of dynamic neural networks has been employed to accelerate the learning process. The proposed solution is based on a highly parallel three dimensional architecture to speed up the learning performance. The presented structures are suitable for efficient parallel realization in digital hardware or vector processors.


title: A Substrate Integrated Waveguide (SIW) Bandpass Filter in A Box Configuration With Frequency-Dependent Coupling

abstract: This letter presents the design of a microwave bandpass filter with frequency-dependent coupling implemented in substrate integrated waveguide (SIW) technology. The proposed filter implements a four-pole generalized Chebyshev filtering function with two transmission zeros. Resonators are arranged in an extended box configuration with dispersive coupling on a main signal path, which produces an extra zero in comparison to classical approaches. The frequency-dependent coupling is implemented as a shorted stub with an additional septum made from via-holes. Such modification allows better control of the positions of the resonant frequencies of coupled SIW cavities, as well as of the position of the transmission zero. The filter was fabricated in SIW technology and good agreement was achieved between the simulated results and those measured.


title: Enterprise resource planning: Developments and directions for operations management research

abstract: Abstract Enterprise resource planning (ERP) has come to mean many things over the last several decades. Divergent applications by practitioners and academics, as well as by researchers in alternative fields of study, has allowed for considerable proliferation of information on the topic and for a considerable amount of confusion regarding the meaning of the term. In reviewing ERP research two distinct research streams emerge. The first focuses on the fundamental corporate capabilities driving ERP as a strategic concept. A second stream focuses on the details associated with implementing information systems and their relative success and cost. This paper briefly discusses these research streams and suggests some ideas for related future research.


title: Neonatal facial pain detection using NNSOA and LSVM

abstract: We report classification experiments using the pilot Infant COPE database of neonatal facial expressions. Two sets of DCT coeffiecents were used to train a neural network simultaneous algorithm (NNSOA) and a linear support vector machine (LSVM) to classify neonatal expressions into the two categories of pain and nonpain. In the first set (VAR) only 80 of the coefficients with the highest variance were included. In the second set (SFFS), 15 DCT coefficients were selected by applying Sequential Forward Floating Selection (SFFS) [1]. We found that NNSOA+VAR produced the best classification score of 95.38% accuracy, but with no statistical difference compared with the DCT sets. However, NNSOA using the DCT coefficients outperformed with statistical significance previous experiments reported in [2] that used PCA components. It is surmised that NNSOA, an algorithm that eliminates unnecessary weights, is more stable than LSVM and may be better than SFFS at identifying relevant features.


title: Employing Graph Databases as a Standardization Model towards Addressing Heterogeneity

abstract: Schema heterogeneity has been perceived as a major challenge towards data integration and exchange for more than two decades. The advent of big data and NoSQL data stores has further led to proliferation of data models thus exacerbating those challenges. It would be useful to have an approach that allows leveraging both schema-based and schemaless data stores. A graph model provides a solution towards unifying them under a common representation. We present an approach to transform schemas into a homogeneous graph representation so that they can be further integrated with data from other NoSQL stores in an automated and seamless manner. We demonstrate our approach over relational and RDF schemas but the framework is extensible to allow further integration of additional data stores. Through this paper, we contribute to the data complexity (variety) aspect of big data by bridging the gap between different data stores including schema-based and schemaagnostic. We utilize Neo4j as the graph database. We present a proof-of-concept that implements our approach along with an evaluation based on qualitative and quantitative metrics.


title: Improvement of the electrical safe operating area of a DMOS transistor during ESD events

abstract: Electrical safe operating area (SOA) of doublediffused vertical MOSFETs (VDMOS) in smart power ICs is investigated by simulation and experiments. The influence of the layout of VDMOS cells is analyzed. DMOS transistors with circular/oval cell layout exhibit higher ESD robustness in comparison with conventional stripe cells. The effect is related to better current distribution of circular/oval cell devices. It is also observed that appropriate source/body engineering can improve electrical SOA of a VDMOS as well.


title: Movie Analysis Based on Roles' Social Network

abstract: Roles in a movie form a small society and their interrelationship provides clues for movie understanding. Based on this observation, we present a new viewpoint to perform semantic movie analysis. Through checking the co-occurrence of roles in different scenes, we construct a roles' social network to describe their relationships. We introduce the concept of social network analysis to elaborately identify leading roles and the hidden communities. With the results of community identification, we perform storyline detection that facilitates more flexible movie browsing and higher-level movie analysis. The experimental results show that the proposed community identification method is accurate and is robust to errors.


title: Frequency Based Chunking for Data De-Duplication

abstract: A predominant portion of Internet services, like content delivery networks, news broadcasting, blogs sharing and social networks, etc., is data centric. A significant amount of new data is generated by these services each day. To efficiently store and maintain backups for such data is a challenging task for current data storage systems. Chunking based deduplication (dedup) methods are widely used to eliminate redundant data and hence reduce the required total storage space. In this paper, we propose a novel Frequency Based Chunking (FBC) algorithm. Unlike the most popular Content-Defined Chunking (CDC) algorithm which divides the data stream randomly according to the content, FBC explicitly utilizes the chunk frequency information in the data stream to enhance the data deduplication gain especially when the metadata overhead is taken into consideration. The FBC algorithm consists of two components, a statistical chunk frequency estimation algorithm for identifying the globally appeared frequent chunks, and a two-stage chunking algorithm which uses these chunk frequencies to obtain a better chunking result. To evaluate the effectiveness of the proposed FBC algorithm, we conducted extensive experiments on heterogeneous datasets. In all experiments, the FBC algorithm persistently outperforms the CDC algorithm in terms of achieving a better dedup gain or producing much less number of chunks. Particularly, our experiments show that FBC produces 2.5 ~ 4 times less number of chunks than that of a baseline CDC which achieving the same Duplicate Elimination Ratio (DER). Another benefit of FBC over CDC is that the FBC with average chunk size greater than or equal to that of CDC achieves up to 50% higher DER than that of a CDC algorithm.


Intrinsic dimensionality estimation of submanifolds in Euclidean space


title: Scene Parsing by Integrating Function, Geometry and Appearance Models

abstract: Indoor functional objects exhibit large view and appearance variations, thus are difficult to be recognized by the traditional appearance-based classification paradigm. In this paper, we present an algorithm to parse indoor images based on two observations: i) The functionality is the most essential property to define an indoor object, e.g. "a chair to sit on", ii) The geometry (3D shape) of an object is designed to serve its function. We formulate the nature of the object function into a stochastic grammar model. This model characterizes a joint distribution over the function-geometry-appearance (FGA) hierarchy. The hierarchical structure includes a scene category, functional groups, functional objects, functional parts and 3D geometric shapes. We use a simulated annealing MCMC algorithm to find the maximum a posteriori (MAP) solution, i.e. a parse tree. We design four data-driven steps to accelerate the search in the FGA space: i) group the line segments into 3D primitive shapes, ii) assign functional labels to these 3D primitive shapes, iii) fill in missing objects/parts according to the functional labels, and iv) synthesize 2D segmentation maps and verify the current parse tree by the Metropolis-Hastings acceptance probability. The experimental results on several challenging indoor datasets demonstrate the proposed approach not only significantly widens the scope of indoor scene parsing algorithm from the segmentation and the 3D recovery to the functional object recognition, but also yields improved overall performance.


title: Cluster Sampling Filters for Non-Gaussian Data Assimilation

abstract: This paper presents a fully non-Gaussian version of the Hamiltonian Monte Carlo (HMC) sampling filter. The Gaussian prior assumption in the original HMC filter is relaxed. Specifically, a clustering step is introduced after the forecast phase of the filter, and the prior density function is estimated by fitting a Gaussian Mixture Model (GMM) to the prior ensemble. Using the data likelihood function, the posterior density is then formulated as a mixture density, and is sampled using a HMC approach (or any other scheme capable of sampling multimodal densities in high-dimensional subspaces). The main filter developed herein is named"cluster HMC sampling filter"(ClHMC). A multi-chain version of the ClHMC filter, namely MC-ClHMC is also proposed to guarantee that samples are taken from the vicinities of all probability modes of the formulated posterior. The new methodologies are tested using a quasi-geostrophic (QG) model with double-gyre wind forcing and bi-harmonic friction. Numerical results demonstrate the usefulness of using GMMs to relax the Gaussian prior assumption in the HMC filtering paradigm.


A Study on the Use of the kNN Algorithm for Prognosis of Breast Cancer


title: Applications of Recurrent Neural Networks in Environmental Factor Forecasting: A Review

abstract: Analysis and forecasting of sequential data, key problems in various domains of engineering and science, have attracted the attention of many researchers from different communities. When predicting the future probability of events using time series, recurrent neural networks (RNNs) are an effective tool that have the learning ability of feedforward neural networks and expand their expression ability using dynamic equations. Moreover, RNNs are able to model several computational structures. Researchers have developed various RNNs with different architectures and topologies. To summarize the work of RNNs in forecasting and provide guidelines for modeling and novel applications in future studies, this review focuses on applications of RNNs for time series forecasting in environmental factor forecasting. We present the structure, processing flow, and advantages of RNNs and analyze the applications of various RNNs in time series forecasting. In addition, we discuss limitations and challenges of applications based on RNNs and future research directions. Finally, we summarize applications of RNNs in forecasting.


title: Health and Safety Education in Auto Body Collision and Machine Tool Technology Programs in Vocational Colleges: Challenges and Opportunities

abstract: Collision repair, machining, and metal manufacturing are industries with a large percentage of small businesses whose owners face unique challenges implementing health and safety regulatory requirements. Previous research found that 72% of collision repair technicians and 47% of machinists attended some classes or graduated from vocational colleges. Although health and safety is a mandatory part of the curricula for post-secondary vocational education, little is known about what, how, and when health and safety is taught and if teaching is effective. Surveys and discussion groups were used to evaluate health and safety education in two vocational colleges in Minnesota. Six instructors and 76 students in collision repair, and 6 instructors and 130 students in machine tool technology programs participated. Instructors had no formal training in health and safety, few teaching materials, and lacked opportunities to learn about safety in their trade. Teaching was unscripted and heavily influenced by each instructor's™ industry experience, knowledge, perceptions and attitude towards safety, with little or no guidance from school administration, or safety professionals. Student survey results show that graduates have significant gaps in safety and health knowledge. Standardized trade-specific curricula and instructor training are needed to ensure students receive adequate health and safety education.


Design Structure Matrix and Project Management: bibliometric analysis


title: How Would You Feel versus How Do You Think She Would Feel? A Neuroimaging Study of Perspective-Taking with Social Emotions

abstract: Perspective-taking is a complex cognitive process involved in social cognition. This positron emission tomography (PET) study investigated by means of a factorial design the interaction between the emotional and the perspective factors. Participants were asked to adopt either their own (first person) perspective or the (third person) perspective of their mothers in response to situations involving social emotions or to neutral situations. The main effect of third-person versus first-person perspective resulted in hemodynamic increase in the medial part of the superior frontal gyrus, the left superior temporal sulcus, the left temporal pole, the posterior cingulate gyrus, and the right inferior parietal lobe. A cluster in the postcentral gyrus was detected in the reverse comparison. The amygdala was selectively activated when subjects were processing social emotions, both related to self and other. Interaction effects were identified in the left temporal pole and in the right postcentral gyrus. These results support our prediction that the frontopolar, the somatosensory cortex, and the right inferior parietal lobe are crucial in the process of self/other distinction. In addition, this study provides important building blocks in our understanding of social emotion processing and human empathy.


title: KnowledgeScope: managing knowledge in context

abstract: Knowledge repositories have been implemented in many organizations, but they often suffer from non-use. This research considers two key design factors that cause non-use: the extra burden on users to document knowledge in the repository, and the lack of a standard knowledge structure that facilitates knowledge sharing among users with different perspectives. We propose a design of a knowledge management system called KnowledgeScope that addresses these problems through (1) an integrated workflow support capability that captures and retrieves knowledge as an organizational process proceeds, i.e., within the context in which it is created and used, and (2) a process meta-model that organizes that knowledge and context in a knowledge repository. In this paper, we describe this design and report the results from implementing it in a real-life organization.


title: Experimental assessment of battery lifetime for commercial off-the-shelf NB-IoT module

abstract: Low Power Wide Area (LPWA) connectivity, a wireless wide area technology that is characterized for interconnecting devices with low bandwidth connectivity and focusing on range and power efficiency, is seen as one of the fastest-growing components of Internet-of-Things (IoT). The LPWA connectivity is used to serve a diverse range of vertical applications, including agriculture, consumer, industrial, logistic, smart building, smart city and utilities. 3GPP has defined the maiden Narrowband IoT (NB-IoT) specification in Release 13 (Rel-13) to accommodate the LPWA demand. Several major cellular operators, such as China Mobile, Deutsch Telekom and Vodafone, have announced their NB-IoT trials or commercial network in year 2017. In Telekom Malaysia, we have setup a NB-IoT trial network for End-to-End (E2E) integration study. Our experimental assessment showed that the battery lifetime target for NB-IoT devices as stated by 3GPP utilizing latest-to-date Commercial Off-The-Shelf (COTS) NB-IoT modules is yet to be realized. Finally, several recommendations on how to optimize the battery lifetime while designing firmware for NB-IoT device are also provided.


title: The iMpact on practice, oUtcomes and costs of New roles for health pROfeSsionals: a study protocol for MUNROS

abstract: Introduction The size and composition of the European Union healthcare workforce are key drivers of expenditure and performance; it now includes new health professions and enhanced roles for established professions. This project will systematically analyse how this has contributed to health service redesign, integration and performance in 9 European countries (Scotland, England, Netherlands, Germany, Italy, Czech Republic, Poland, Norway, and Turkey i ). This paper describes the protocol for collection of survey data in 3 distinct care pathways, and sets it in the context of the wider programme. Methods Questionnaires will be distributed to healthcare professionals (n=14 580), managers (n=3564) and patients (n=19 440) in 3 care pathways (breast cancer; type 2 diabetes; and coronary heart disease) within 12 hospitals and associated primary care settings in each country. Questionnaire topics will include demography, the different professionals working on the care pathway, the tasks they do and the time taken, their decision-making abilities when considering skill mix, specialisation and integration of care. Patient satisfaction, healthcare utilisation and preferences will be explored. In later work, register data and data from patient records will be used to record clinical outcomes. Data will also be collected on workforce and procedure costs. Descriptive analysis will identify the different models of care and multivariate analysis will establish the most clinically and cost-effective models. Ethics and dissemination This protocol was approved by ethical committees in each country. Findings will be disseminated through national/international clinical, health services research and health workforce conferences, and publications in national/international peer-reviewed journals.


title: Looking for the Best Historical Window for Assessing Semantic Similarity Using Human Literature

abstract: We describe the way to get benefit from broad cultural trends through the quantitative analysis of a vast digital book collection representing the digested history of humanity. Our research work has revealed that appropriately comparing the occurrence patterns of words in some periods of human literature can help us to accurately determine the semantic similarity between these words by means of computers without requiring human intervention. Preliminary results seem to be promising.


title: Which robot am I thinking about? The impact of action and appearance on people's evaluations of a moral robot

abstract: In three studies we found further evidence for a previously discovered Human-Robot (HR) asymmetry in moral judgments: that people blame robots more for inaction than action in a moral dilemma but blame humans more for action than inaction in the identical dilemma (where inaction allows four persons to die and action sacrifices one to save the four). Importantly, we found that people's representation of the "robot" making these moral decisions appears to be one of a mechanical robot. For when we manipulated the pictorial display of a verbally described robot, people showed the HR asymmetry only when making judgments about a mechanical-looking robot, not a humanoid robot. This is the first demonstration that robot appearance affects people's moral judgments about robots.


Optimization of Examination Timetable Using Harmony Search Hyper-Heuristics ( HSHH ) Ms


title: DICOM Image Secure Communications With Internet Protocols IPv6 and IPv4

abstract: Image-data transmission from one site to another through public network is usually characterized in term of privacy, authenticity, and integrity. In this paper, we first describe a general scenario about how image is delivered from one site to another through a wide-area network (WAN) with security features of data privacy, integrity, and authenticity. Second, we give the common implementation method of the digital imaging and communication in medicine (DICOM) image communication software library with IPv6/IPv4 for high-speed broadband Internet by using open-source software. Third, we discuss two major security-transmission methods, the IP security (IPSec) and the secure-socket layer (SSL) or transport-layer security (TLS), being used currently in medical-image-data communication with privacy support. Fourth, we describe a test schema of multiple-modality DICOM-image communications through TCP/IPv4 and TCP/IPv6 with different security methods, different security algorithms, and operating systems, and evaluate the test results. We found that there are tradeoff factors between choosing the IPsec and the SSL/TLS-based security implementation of IPv6/IPv4 protocols. If the WAN networks only use IPv6 such as in high-speed broadband Internet, the choice is IPsec-based security. If the networks are IPv4 or the combination of IPv6 and IPv4, it is better to use SSL/TLS security. The Linux platform has more security algorithms implemented than the Windows (XP) platform, and can achieve better performance in most experiments of IPv6 and IPv4-based DICOM-image communications. In teleradiology or enterprise-PACS applications, the Linux operating system may be the better choice as peer security gateways for both the IPsec and the SSL/TLS-based secure DICOM communications cross public networks


title: The Theoretical Separation of Brand Equity and Brand Value: Managerial Implications for Strategic Planning

abstract: During the past 15 years, brand equity has been a priority topic for both practitioners and academics. In this article, the authors propose a new framework for conceptualizing brand equity that distinguishes between brand equity, conceived of as an intrapersonal construct that moderates the impact of marketing activities, and brand value, which is the sale or replacement value of a brand. Such a distinction is important because, from a managerial perspective, the ultimate goal of brand management and brand equity research should be to understand how to leverage equity to create value.


title: Real-Time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing

abstract: This study develops a real-time drowsiness detection system based on grayscale image processing and PERCLOS to determine if the driver is fatigued. The proposed system comprises three parts: first, it calculates the approximate position of the driver's face in grayscale images, and then uses a small template to analyze the eye positions, second, it uses the data from the previous step and PERCLOS to establish a fatigue model, and finally, based on the driver's personal fatigue model, the system continuously monitors the driver's state. Once the driver exhibits fatigue, the system alerts the driver to stop driving and take a rest.


title: The slot-coupled hemispherical dielectric resonator antenna with a parasitic patch: applications to the circularly polarized antenna and wide-band antenna

abstract: The aperture-coupled hemispherical dielectric resonator antenna (DRA) with a parasitic patch is studied rigorously. Using the Green's function approach, integral equations for the unknown patch and slot currents are formulated and solved using the method of moments. The theory is utilized to design a circularly polarized (CP) DRA and a wide-band linearly polarized (LP) DRA. In the former, the CP frequency and axial ratio (AR) can easily be controlled by the patch location and patch size, respectively, with the impedance matched by varying the slot length and microstrip stub length. It is important that the AR will not be affected when the input impedance is tuned, and the CP design is therefore greatly facilitated. For the wide-band LP antenna, a maximum bandwidth of 22% can be obtained, which is much wider than the previous bandwidth of 7.5% with no parasitic patches. Finally, the frequency-tuning characteristics of the proposed antenna are discussed. Since the parasitic patch can be applied to any DRAs, the method will find applications in practical DRA designs.


title: VideoMocap: modeling physically realistic human motion from monocular video sequences

abstract: This paper presents a video-based motion modeling technique for capturing physically realistic human motion from monocular video sequences. We formulate the video-based motion modeling process in an image-based keyframe animation framework. The system first computes camera parameters, human skeletal size, and a small number of 3D key poses from video and then uses 2D image measurements at intermediate frames to automatically calculate the "in between" poses. During reconstruction, we leverage Newtonian physics, contact constraints, and 2D image measurements to simultaneously reconstruct full-body poses, joint torques, and contact forces. We have demonstrated the power and effectiveness of our system by generating a wide variety of physically realistic human actions from uncalibrated monocular video sequences such as sports video footage.


title: Characters Who Speak Their Minds: Dialogue Generation in Talk of the Town.

abstract: The Expressive Intelligence Studio is developing a new approach to freeform conversational interaction in playable media that combines dialogue management, natural language generation (NLG), and natural language understanding. In this paper, we present our method for dialogue generation, which has been fully implemented in a game we are developing called Talk of the Town . Eschewing a traditional NLG pipeline, we take up a novel approach that combines human language expertise with computer generativity. Specifically, this method utilizes a tool that we have developed for authoring context-free grammars (CFGs) whose productions come packaged with explicit metadata. Instead of terminally expanding top-level symbols — the conventional way of generating from a CFG — we employ an unusual middle-out procedure that targets mid-level symbols and traverses the grammar by both forward chaining and backward chaining, expanding symbols conditionally by testing against the current game state. In this paper, we present our method, discuss a series of associated authoring patterns, and situate our approach against the few earlier projects in this area.


title: A Comparative Study of Various Routing Protocols in VANET

abstract: Vehicular Ad Hoc Networks (VANET) is a subclass of Mobile ad hoc networks which provides a distinguished approach for Intelligent Transport System (ITS). The survey of routing protocols in VANET is important and necessary for smart ITS. T his paper discusses the advantages / disadvantages and the applications of various routing protocols for vehicular ad hoc networks. It explores the motivation behind the designed, and traces the evolution of these routing protocols. F inally the paper concludes by a tabular comparison of the various routing protocols for VANET.


title: Study on vibration and durability testing for motorcycle frame

abstract: The development was delayed because of the errors and the damage of the reducer box caused by present vibration and durability test platform. To solve this problem, the basic principle and the structure characteristic of the test platform are analyzed. Repeating test and comparison analysis clarify that the cause of the problems. A improved vibration and durability test system is constructed. Moreover, a automatical control system is designed to control the loading, simulate the actual road state, calculate and display the test results. Many kinds of vibration experiments of motorcycle frames have been carried out on the new test system, and the results prove that the test system can satisfy the demands of the vibration and durability test, and also afford a new technique to motorcycle frame vibration test.


title: Decoding as Continuous Optimization in Neural Machine Translation.

abstract: We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. The resulting optimisation problem is then tackled using constrained gradient optimisation. Our powerful decoding framework, enables decoding intractable models such as the intersection of left-to-right and right-to-left (bidirectional) as well as source-to-target and target-to-source (bilingual) NMT models. Our empirical results show that our decoding framework is effective, and leads to substantial improvements in translations generated from the intersected models where the typical greedy or beam search is infeasible.


title: Differential privacy and robust statistics

abstract: We show by means of several examples that robust statistical estimators present an excellent starting point for differentially private estimators. Our algorithms use a new paradigm for differentially private mechanisms, which we call Propose-Test-Release (PTR), and for which we give a formal definition and general composition theorems.


title: Shannon meets Tesla: Wireless information and power transfer

abstract: The problem considered here is that of wireless information and power transfer across a noisy coupled-inductor circuit, which is a frequency-selective channel with additive white Gaussian noise. The optimal tradeoff between the achievable rate and the power transferred is characterized given the total power available. The practical utility of such systems is also discussed.