Query: Mitigating Multi-target Attacks in Hash-Based Signatures

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Strengths and Weaknesses of Quantum Computing <=SEP=> Recently a great deal of attention has focused on quantum computation following a sequence of results [4, 16, 15] suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor’s result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of NP can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random, with probability 1, the class NP cannot be solved on a quantum Turing machine in time o(2n/2). We also show that relative to a permutation oracle chosen uniformly at random, with probability 1, the class NP ∩ co–NP cannot be solved on a quantum Turing machine in time o(2n/3). The former bound is tight since recent work of Grover [13] shows how to accept the class NP relative to any oracle on a quantum computer in time O(2n/2). To appear in SIAM Journal on Computing (special issue on quantum computing) ∗ IBM T. J. Watson Research Laboratory, Yorktown Heights, New York, NY 10598, USA. email: bennetc@watson.ibm.com. † 1 Microsoft Way, Redmond, WA 98052 – 6399, USA. email: ethanb@microsoft.com. ‡ Supported in part by Canada’s nserc and Québec’s fcar. §Département IRO, Université de Montréal, C.P. 6128, succursale centre-ville, Montréal (Québec), Canada H3C 3J7. email: brassard@iro.umontreal.ca. ¶ Supported by NSF Grant No. CCR-9310214. Computer Science Division, University of California, Berkeley, CA 94720, USA. email: vazirani@cs.berkeley.edu.

Tight bounds on quantum searching <=SEP=> We provide a tight analysis of Grover’s recent algorithm for quantum database searching. We give a simple closed-form formula for the probability of success after any given number of iterations of the algorithm. This allows us to determine the number of iterations necessary to achieve almost certainty of finding the answer. Furthermore, we analyse the behaviour of the algorithm when the element to be found appears more than once in the table and we provide a new algorithm to find such an element even when the number of solutions is not known ahead of time. Using techniques from Shor’s quantum factoring algorithm in addition to Grover’s approach, we introduce a new technique for approximate quantum counting, which allows to estimate the number of solutions. Finally we provide a lower bound on the efficiency of any possible quantum database searching algorithm and we show that Grover’s algorithm nearly comes within a factor 2 of being optimal in terms of the number of probes required in the table.

XMSS - A Practical Forward Secure Signature Scheme Based on Minimal Security Assumptions <=SEP=> We present the hash-based signature scheme XMSS. It is the first provably (forward) secure and practical signature scheme with minimal security requirements: a pseudorandom and a second preimage resistant (hash) function family. Its signature size is reduced to less than 25% compared to the best provably secure hash based signature scheme.

Algorithms for Quantum Computation: Discrete Logarithms and Factoring <=SEP=> A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factol: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. (We thus give the first examples of quantum cryptanulysis.)

A Fast Quantum Mechanical Algorithm for Database Search <=SEP=> were proposed in the early 1980’s [Benioff80] and shown to be at least as powerful as classical computers an important but not surprising result, since classical computers, at the deepest level, ultimately follow the laws of quantum mechanics. The description of quantum mechanical computers was formalized in the late 80’s and early 90’s [Deutsch85][BB92] [BV93] [Yao93] and they were shown to be more powerful than classical computers on various specialized problems. In early 1994, [Shor94] demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers. This is the problem of integer factorization, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) . ----------------------------------------------

A Study of Cardiovascular Disease Prediction Models Using Discriminant Analysis <=SEP=> Cardiovascular disease is a condition of interest to clinicians around the world and is one of the chief causes of death. In Korea, cardiovascular disease is the second largest cause of death following cancer, and is classified as one of the four major diseases, making an early diagnosis of cardiovascular disease essential for proper and timely treatment. Although the risk of cardiovascular disease is recognized in Korea, there is a lack of quality studies regarding the disease. In the present study, a prediction model was developed which allows for the calculation of the risk of cardiovascular disease among Korean patients. The prediction model for cardiovascular disease was developed using a discriminant analysis based on the data set of Korean National Health and Nutrition Examinations Survey V (KNHANES V).

High-performance polyline intersection based spatial join on GPU-accelerated clusters <=SEP=> The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in gaining insights from large-scale geospatial data. While several distributed spatial join techniques based on spatial partitions have been implemented on top of existing Big Data systems, they are not capable of natively exploiting massively data parallel computing power provided by modern commodity Graphics Processing Units (GPUs). In this study, as an important component of our research initiative in developing high-performance spatial join techniques on GPUs, we have designed and implemented a polyline intersection based spatial join technique that is capable of exploiting massively data parallel computing power on GPUs. The proposed polyline intersection based spatial join technique is integrated into a customized lightweight distributed execution engine that natively supports spatial partitions. We empirically evaluate the performance of the proposed spatial join technique on both a standalone GPU-equipped workstation and Amazon EC2 GPU-accelerated clusters and demonstrate its high performance when comparing with the state-of-the-art.

The Case for Holistic Data Integration <=SEP=> Current data integration approaches are mostly limited to few data sources, partly due to the use of binary match approaches between pairs of sources. We thus advocate for the development of more holistic, clustering-based data integration approaches that scale to many data sources. We outline different use cases and provide an overview of initial approaches for holistic schema/ontology integration and entity clustering. The discussion also considers open data repositories and socalled knowledge graphs.

Comprehensive review of epidemiology, scope, and impact of spinal pain. <=SEP=> Persistent pain interfering with daily activities is common. Chronic pain has been defined in many ways. Chronic pain syndrome is a separate entity from chronic pain. Chronic pain is defined as, "pain that persists 6 months after an injury and beyond the usual course of an acute disease or a reasonable time for a comparable injury to heal, that is associated with chronic pathologic processes that cause continuous or intermittent pain for months or years, that may continue in the presence or absence of demonstrable pathologies; may not be amenable to routine pain control methods; and healing may never occur." In contrast, chronic pain syndrome has been defined as a complex condition with physical, psychological, emotional, and social components. The prevalence of chronic pain in the adult population ranges from 2% to 40%, with a median point prevalence of 15%. Among chronic pain disorders, pain arising from various structures of the spine constitutes the majority of the problems. The lifetime prevalence of spinal pain has been reported as 54% to 80%. Studies of the prevalence of low back pain and neck pain and its impact in general have shown 23% of patients reporting Grade II to IV low back pain (high pain intensity with disability) versus 15% with neck pain. Further, age related prevalence of persistent pain appears to be much more common in the elderly associated with functional limitations and difficulty in performing daily life activities. Chronic persistent low back and neck pain is seen in 25% to 60% of patients, one-year or longer after the initial episode. Spinal pain is associated with significant economic, societal, and health impact. Estimates and patterns of productivity losses and direct health care expenditures among individuals with back and neck pain in the United States continue to escalate. Recent studies have shown significant increases in the prevalence of various pain problems including low back pain. Frequent use of opioids in managing chronic non-cancer pain has been a major issue for health care in the United States placing a significant strain on the economy with the majority of patients receiving opioids for chronic pain necessitating an increased production of opioids, and escalating costs of opioid use, even with normal intake. The additional costs of misuse, abuse, and addiction are enormous. Comorbidities including psychological and physical conditions and numerous other risk factors are common in spinal pain and add significant complexities to the interventionalist's clinical task. This section of the American Society of Interventional Pain Physicians (ASIPP)/Evidence-Based Medicine (EBM) guidelines evaluates the epidemiology, scope, and impact of spinal pain and its relevance to health care interventions.

When Professionals Become Mothers , Warmth Doesn ’ t Cut the Ice <=SEP=> Working moms risk being reduced to one of two subtypes: homemakers—viewed as warm but incompetent, or female professionals—characterized as competent but cold. The current study ( N = 122 college students) presents four important findings. First, when working women become mothers, they trade perceived competence for perceived warmth. Second, working men don’t make this trade; when they become fathers, they gain perceived warmth and maintain perceived competence. Third, people report less interest in hiring, promoting, and educating working moms relative to working dads and childless employees. Finally, competence ratings predict interest in hiring, promoting, and educating workers. Thus, working moms’ gain in perceived warmth does not help them, but their loss in perceived competence does hurt them.

Design of a Wheelchair with Legs for People with Motor Disabilities <=SEP=> Low cost. For example, golf carts, outdoor chairs and special purpose sand buggies. wheelchair. AbstractA proof-of-concept prototype wheelchair with legs for people with motor disabilities is proposed, with the objective of demonstrating the feasibility of a completely new approach to mobility. Our prototype system consists of a chair equipped with wheels and legs, and is capable of traversing uneven terrain and circumventing obstacles. The important design considerations, the system design and analysis, and an experimental prototype of a chair are discussed. The results from the analysis and experimentation show the feasibility of the proposed concept and its advantages.

Uneasy lies the head that wears the crown: the link between guilt proneness and leadership. <=SEP=> We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.

Implementing it Service Management: A Case Study Focussing on Critical Success Factors <=SEP=> Queensland Health, a large Australian government agency, implemented a centralised IT service management model based on the ITIL framework. This paper presents an in-depth case study of the implementation. It sheds light on the challenges and breakthroughs, confirms a set of factors that contributed to the project’s success and offers a learning opportunity for other organisations. The study indicates that the commitment of senior management is crucial to the project’s success as is a project champion and the recognition of the need for an appropriate change management strategy to transform the organisational culture to a service-oriented focus. Maintaining close and forthright relationships with multiple vendors facilitates technology transfer to in-house staff while a benefits realisation plan is a valuable tool for tracking and communicating tangible and intangible project benefits to the project stakeholders. An effective project governance and execution process further contributes to the implementation success.

Efficient super-resolution via image warping <=SEP=> This paper introduces a new algorithm for enhancing image resolution from an image sequence. The approach we propose herein uses the integrating resampler for warping. The method is a direct computation, which is fundamentally different from the iterative back-projection approaches proposed in previous work. This paper shows that image-warping techniques may have a strong impact on the quality of image resolution enhancement. By coupling the degradation model of the imaging system directly into the integrating resampler, we can better approximate the warping characteristics of real sensors, which also significantly improve the quality of super-resolution images. Examples of super-resolutions are given for gray-scale images. Evaluations are made visuallyby comparing the resulting images and those using bi-linear resampling and back-projection and quantitativelyusing OCR as a fundamental measure. The paper shows that even when the images are qualitatively similar, quantitative differences appear in machine processing. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Super-resolution; Imaging-consistent restoration/reconstruction; Integrating resampling; Integrating resampler; Quantitative evaluatio n; OCR; Bilinear resampling; Image reconstruction; Image restoration; Image warping 1. Background and introduction The basic premise of super-resolution is to combine images (and in some work scene/data models) to produce an image that has higher “resolution” than the original image. It is not sufficient to just resample the image—size is not the same as resolution. Increasing resolution could be viewed as either increasing the signal-to-noise ratio while keeping the size fixed and/or approximating the image at a larger size with reasonable approximations for frequencies higher than those representable at the original size. In either case, it requires additional information and assumptions. There are, of course, some fundamental limits on what can be gained; lens and photo-sites cause blur, and the discrete sampling causes frequencies above the Nyquist rate to alias. While in general the aliasing is theoretically impossible to separate, the problem occurs mostly along sharp “intensity edges”, so multiple images with slightly different imaging positions can be used to reduce its impact. There are two different groups of research that use the term super-resolution—one is based on multiple images, which will be considered within this paper; the other uses “scene/object” models. Early research on image-based super-resolution dates back to the work by Huang and Tsai [8]. They solved the problem in the frequency domain, but they disregarded sensor blurring. Furthermore, they assumed only inter-frame translations. Gross [7] solved the problem by first merging the low resolution images by interpolation and then obtaining the high-resolution image by deblurring the merged image. Again, only inter-frame translations were considered. Peleg and Keren [11,14] solved the problem by first estimating an initial guess of the high-resolution image, then simulating the imaging process and minimizing the difference between the observed and simulated low-resolution images. Irani and Peleg [9,10] used a back-projection method similar to that used in tomography to minimize the same difference. Bascle et al. [1] used the same back-projection method to minimize the difference between the predicted and actual images except that a simple motion blur model was included. All previous work ignores the impact of image warping techniques. In this paper, we show that warping techniques may have a strong impact on the quality of image resolution enhancement. Image warping has been around almost as long as image processing, allowing users to reshape the image geometry. Image warping requires the underlying image to be “resampled” at non-integer locations; hence, it requires image reconstruction. When the goal of warping is to produce output for human viewing, only moderately accurate image intensities are needed. In these cases, techniques Image and Vision Computing 18 (2000) 761–771 0262-8856/00/$ see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S0262-8856(99)00044-X www.elsevier.com/locate/imavis * Corresponding author. Tel.: 11-610-7584061. E-mail address:tboult@eecs.lehigh.edu (T.E. Boult). using bi-linear interpolation have been found sufficient. However, as a step for applications such as super-resolution, the precision of the warped intensity values is often important. For these problems, bi-linear image reconstruction may not be sufficient. This paper shows how the integrating resampler can be used to enhance image resolution. This builds upon the ideas of imaging-consistent restoration/reconstruction algorithms proposed in Ref. [2] and the integrating resampler proposed in Ref. [5]. A final contribution of this paper is a quantitative comparison and analysis of our techniques. In this paper, we use a character recognition rate based on a commercial OCR program. We chose this because it is easy to make it quantitative, and we consider this to be a good measure for other applications such as license-plate reading and other pattern recognition problems. In what follows in this paper, the image formation process and the relationship between restoration, reconstruction, and super-resolution is briefly reviewed in Section 2. The integrating resampler—an efficient method for warping using imaging-consistent reconstruction/restoration algorithms—is given in Section 3. This algorithm is well suited for today’s pipelined micro-processors. In addition, the integrating resampler can allow for modifications of the intensity values to better approximate the warping characteristics of real sensors. The super-resolution algorithm where we compare the integrating resampler with bi-linear resampling is presented in Section 4. Quantitative measurement of super-resolution imaging using OCR is shown in Section 5. 2. Image formation and image restoration In this section, we briefly review the image formation process as well as the sensor model proposed in Ref. [2]. Image formation is generally described as a sequence of filtering operations. See Fig. 1. Let f x; y be the intensity distribution of a scene at the front aperture of a lens. That distribution is acted upon by h1x; y; the blurring component of the lens, yielding f1x; y: A geometric distortion functionh2x; y is then applied to produce imagef2u; v: At this point, f2 strikes the image sensor where it undergoes additional blurring by a point spread functionh3u; v to generate image f3u; v: This blurring reflects the limitation of the sensor to accurately resolve each point without the influence of neighboring points. We choose to use a simple model wherein this blurring takes place within one pixel because for CCD and CID cameras, the physical boundaries between photo-sites generally allow only insignificant charge transfer between pixels. Imagef3 undergoes spatial sampling as it hits the discrete photo-sites in a CCD or CID camera. The combination of h3 with sampling is known as areas sampling. It reflects the finite size of the sampling area. If h3 is taken to be an impulse, then we have point sampling. This ideal, often assumed for theoretical considerations, is generally not true in practice. In either case, intensities in the sampled image Is are now defined only for integer values of u and v. The digital imageI u; v is obtained via an analog-todigital converter that quantizes the samples of Is. This completes the image formation process, leaving I u; v as the starting point for subsequent processing, including image reconstruction and restoration. Reconstruction and restoration start with I (and models for one or morehis), and seek to solve for one or more of the fjs. Recoveringf is known as image restoration and is of considerable interest in image processing. Note that recovering a functional form off2 is an intra-pixel restoration. Reconstruction limits itself to the problem of deriving the continuous functionf3. Restoration attempts to estimate the original input functionf2 (or f1). The goal of super-resolution is to recover a discrete approximation, with resolution higher than Is, to either f1 (plain super-resolution) orf (super-resolution with deblurring). Because super-resolution increases the signal-to-noise ratio, it significantly aids in the ill-condition problem of deblurring. 3. Imaging-consistency and the integrating resampler An algorithm is called imaging-consistent if it is the exact solution for some input function, which, according to the imaging model, would have generated the measured input. For image reconstruction, we achieve this by first restoring the image to yieldf2, then blurring it again by the pixel’s PSF. Details can be found in Ref. [2]. Only one-dimensional image models are presented herein; higher dimensions are treated separately. M.-C. Chiang, T.E. Boult / Image and Vision Computing 18 (2000) 761–771 762 Fig. 1. The image formation process and the relationship between restoration, reconstruction, and super-resolution. The first imaging-consistent method to consider is based on a piecewise quadratic model for the image. If we assume a Rect PSF filter (1 inside and 0 outside the pixel), the imaging consistent algorithm is easy to derive. To ensure that the function is continuous and that the method is local, we define the value of the reconstruction at the pixel boundarieski andki11 to be equal toEi andEi11: Any method of approximation could be used to compute Ei, though our examples will only include linear interpolation and cubic convolution. See [3, Section 2.3] for more details. Given the valuesEi at the pixel edges, an additional constraint, that the integral across the pixel must equal Vi, results in exactly three constraints: qi21=2  Ei ; qi1=2  Ei11; Z1=2 2 1=2 qix dx Vi : 1 From Eq. (1), one can det

Simplification of Example Sentences for Learners of Japanese Functional Expressions <=SEP=> Learning functional expressions is one of the difficulties for language learners, since functional expressions tend to have multiple meanings and complicated usages in various situations. In this paper, we report an experiment of simplifying example sentences of Japanese functional expressions especially for Chinese-speaking learners. For this purpose, we developed “Japanese Functional Expressions List” and “Simple Japanese Replacement List”. To evaluate the method, we conduct a small-scale experiment with Chinese-speaking learners on the effectiveness of the simplified example sentences. The experimental results indicate that the simplified sentences are helpful in learning Japanese functional expressions.

Algorithmics of Matching Under Preferences <=SEP=> • Page 17, line 13: “constrast” −→ “contrast”. (Due to Mechthild Opperud.) • Page 21, line 17: “attributes” −→ “attributed”. • Page 21, lines -4 to -3: change “pairs in which either (i) ri is unassigned if she is unassigned in both M and M ′, or (ii)” to “pairs obtained as follows: for each resident ri, ri is unassigned if she is unassigned in both M and M ′, otherwise”. • Page 21, line -1: “join” −→ “meet”. (Due to Didac Busquets.) • Page 22, line 1: “meet” −→ “join”. (Due to Didac Busquets.) • Page 23, line 4: add “In an sm instance, any matching is automatically assumed to have size n.”

SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks <=SEP=> We present SNAP (small-world network analysis and partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for small-world networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph instances on the Sun Fire T2000 multicore system. SNAP also contains parallel implementations of fundamental graph-theoretic kernels and topological analysis metrics (e.g., breadth-first search, connected components, vertex and edge centrality) that are optimized for small- world networks. The SNAP framework is extensible; the graph kernels are modular, portable across shared memory multicore and symmetric multiprocessor systems, and simplify the design of high-level domain-specific applications.

Advances in Very Deep Convolutional Neural Networks for LVCSR <=SEP=> Very deep CNNs with small 3 × 3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we investigate how to efficiently scale these models to larger datasets. Specifically, we address the design choice of pooling and padding along the time dimension which renders convolutional evaluation of sequences highly inefficient. We propose a new CNN design without timepadding and without timepooling, which is slightly suboptimal for accuracy, but has two significant advantages: it enables sequence training and deployment by allowing efficient convolutional evaluation of full utterances, and, it allows for batch normalization to be straightforwardly adopted to CNNs on sequence data. Through batch normalization, we recover the lost peformance from removing the time-pooling, while keeping the benefit of efficient convolutional evaluation. We demonstrate the performance of our models both on larger scale data than before, and after sequence training. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of the 2015 IBM system combination, which was the previous best published result.

Non-orthogonal Multiple Access in a Downlink Multiuser Beamforming System <=SEP=> In this paper, we propose a non-orthogonal multiple access-based multiuser beamforming (NOMA-BF) system designed to enhance the sum capacity. In the proposed NOMA-BF system, a single BF vector is shared by two users, so that the number of supportable users can be increased. However, sharing a BF vector leads to interference from other beams as well as from the other user sharing the BF vector. Therefore, to reduce interference and improve the sum capacity, we additionally propose a clustering and power allocation algorithm. This clustering algorithm, which selects two users with high correlation and a large gain-difference between their channels, can reduce the interference from other beams and from the other user as well. Furthermore, power allocation ensures that each user's transmit power is allocated so as to maximize the sum capacity. Numerical results verify that the proposed NOMA-BF system improves the sum capacity, compared to the conventional multiuser BF system.

On Plant Responses to D-Amino Acids Features of Growth , Root Behaviour and Selection for Plant Transformation <=SEP=> Amino acids have been regarded as potential plant nitrogen sources for more than a century. Some amino acids have been shown to support growth while others have growth-retarding effects. The D-isomers with notably adverse effects on plants’ growth and development include D-serine and D-alanine. Recently, D-serine has been recognised as an endogenous ligand of receptor channels mediating calcium fluxes in plants, but otherwise little is known about endogenous roles of D-amino acids in plants. In the studies underlying this thesis, the negative responses to D-serine and D-alanine were converted to positive effects in Arabidopsis thaliana (Arabidopsis) plants by introducing either of two D-amino acid-metabolising enzymes. Transgenic Arabidopsis lines expressing either the D-serine dehydratase (dsdA) gene from Escherichia coli or the D-amino acid oxidase (DAO1) gene from Rhodotorula gracilis grew with otherwise toxic D-amino acids as the sole nitrogen source. I also expressed a transporter specific for D-amino acids, which further increased the transgenic plants’ growth with D-serine as sole nitrogen source. Hence, both assimilation and uptake restrictions can limit plant growth on D-amino acids. The growth of transgenic lines on D-serine or D-alanine provides an unambiguous and highly visible phenotype, which is essential for a selectable marker. Thus, expressing of either the dsdA or DAO1 genes generated transformants that are easy to screen. Furthermore, the DAO1 gene can be readily used for either positive or negative selection, depending on the substrate, thus it provides a unique conditional, substrate-dependent positive/negative selectable marker for plant transformation. In summary, the presented work demonstrates that introducing the ability to catalyse a single metabolic step can allow plants to exploit an otherwise inaccessible or toxic form of organic nitrogen, and provides a versatile marker based on nitrogen nutrition for selecting transgenic plants. A possible role for D-serine in plants’ touch response is also reviewed in the thesis.

LibD: Scalable and Precise Third-Party Library Detection in Android Markets <=SEP=> With the thriving of the mobile app markets, third-party libraries are pervasively integrated in the Android applications. Third-party libraries provide functionality such as advertisements, location services, and social networking services, making multi-functional app development much more productive. However, the spread of vulnerable or harmful third-party libraries may also hurt the entire mobile ecosystem, leading to various security problems. The Android platform suffers severely from such problems due to the way its ecosystem is constructed and maintained. Therefore, third-party Android library identification has emerged as an important problem which is the basis of many security applications such as repackaging detection and malware analysis. According to our investigation, existing work on Android library detection still requires improvement in many aspects, including accuracy and obfuscation resilience. In response to these limitations, we propose a novel approach to identifying third-party Android libraries. Our method utilizes the internal code dependencies of an Android app to detect and classify library candidates. Different from most previous methods which classify detected library candidates based on similarity comparison, our method is based on feature hashing and can better handle code whose package and method names are obfuscated. Based on this approach, we have developed a prototypical tool called LibD and evaluated it with an update-to-date and large-scale dataset. Our experimental results on 1,427,395 apps show that compared to existing tools, LibD can better handle multi-package third-party libraries in the presence of name-based obfuscation, leading to significantly improved precision without the loss of scalability.

Generating Single Subject Activity Videos as a Sequence of Actions Using 3D Convolutional Generative Adversarial Networks <=SEP=> Humans have the remarkable ability of imagination, where within the human mind virtual simulations are done of scenarios whether visual, auditory or any other senses. These imaginations are based on the experiences during interaction with the real world, where human senses help the mind understand their surroundings. Such level of imagination has not yet been achieved using current algorithms, but a current trend in deep learning architectures known as Generative Adversarial Networks (GANs) have proven capable of generating new and interesting images or videos based on the training data. In that way, GANs can be used to mimic human imagination, where the resulting generated visuals of GANs are based on the data used during training. In this paper, we use a combination of Long Short-Term Memory (LSTM) Networks and 3D GANs to generate videos. We use a 3D Convolutional GAN to generate new human action videos based on trained data. The generated human action videos are used to generate longer videos consisting of a sequence of short actions combined creating longer and more complex activities. To generate the sequence of actions needed we use an LSTM network to translate a simple input description text into the required sequence of actions. The generated chunks are then concatenated using a motion interpolation scheme to form a single video consisting of many generated actions. Hence a visualization of the input text description is generated as a video of a subject performing the activity described.

SLAM-based automatic extrinsic calibration of a multi-camera rig <=SEP=> Cameras are often a good choice as the primary outward-looking sensor for mobile robots, and a wide field of view is usually desirable for responsive and accurate navigation, SLAMand relocalisation. While this can potentially be provided by a single omnidirectional camera, it can also be flexibly achieved by multiple cameras with standard optics mounted around the robot. However, such setups are difficult to calibrate. Here we present a general method for fully automatic extrinsic auto-calibration of a fixed multi camera rig, with no requirement for calibration patterns or other infrastructure, which works even in the case where the cameras have completely non-overlapping views. The robot is placed in a natural environment and makes a set of programmed movements including a full horizontal rotation and captures a synchronized image sequence from each camera. These sequences are processed individually with a monocular visual SLAM algorithm. The resulting maps are matched and fused robustly based on corresponding invariant features, and then all estimates are optimised full joint bundle adjustment, where we constrain the relative poses of the cameras to be fixed. We present results showing accurate performance of the method for various two and four camera configurations.

Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers <=SEP=> The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is proposed. In highway scenarios, our approach is highly suitable to determine the correct number of all existing lanes on the road. Additionally, the output of the laserscannner based lane detection is fused with a production-available vision based system. It is shown that both sensor systems perfectly complement each other to increase the robustness of a lane tracking system. The achieved accuracy of the fusion system, the laserscannner and video based system is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control applications.

Cultural Differences in Collaborative Authoring of Wikipedia <=SEP=> This article explores the relationship between national culture and computer-mediated communication (CMC) in Wikipedia. The articles on the topic game from the French, German, Japanese, and Dutch Wikipedia websites were studied using content analysis methods. Correlations were investigated between patterns of contributions and the four dimensions of cultural influences proposed by Hofstede (Power Distance, Collectivism versus Individualism, Femininity versus Masculinity, and Uncertainty Avoidance). The analysis revealed cultural differences in the style of contributions across the cultures investigated, some of which are correlated with the dimensions identified by Hofstede. These findings suggest that cultural differences that are observed in the physical world also exist in the virtual world. doi:10.1111/j.1083-6101.2006.00316.x

Study of the General Kalman Filter for Echo Cancellation <=SEP=> The Kalman filter is a very interesting signal processing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of echo cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for echo cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for echo cancellation.

Emotion Enhances Learning via Norepinephrine Regulation of AMPA-Receptor Trafficking <=SEP=> Emotion enhances our ability to form vivid memories of even trivial events. Norepinephrine (NE), a neuromodulator released during emotional arousal, plays a central role in the emotional regulation of memory. However, the underlying molecular mechanism remains elusive. Toward this aim, we have examined the role of NE in contextual memory formation and in the synaptic delivery of GluR1-containing alpha-amino-3-hydroxy-5-methyl-4-isoxazoleproprionic acid (AMPA)-type glutamate receptors during long-term potentiation (LTP), a candidate synaptic mechanism for learning. We found that NE, as well as emotional stress, induces phosphorylation of GluR1 at sites critical for its synaptic delivery. Phosphorylation at these sites is necessary and sufficient to lower the threshold for GluR1 synaptic incorporation during LTP. In behavioral experiments, NE can lower the threshold for memory formation in wild-type mice but not in mice carrying mutations in the GluR1 phosphorylation sites. Our results indicate that NE-driven phosphorylation of GluR1 facilitates the synaptic delivery of GluR1-containing AMPARs, lowering the threshold for LTP, thereby providing a molecular mechanism for how emotion enhances learning and memory.

Resource discovery in Internet of Things: Current trends and future standardization aspects <=SEP=> To realize the vision of Internet of Things, there must be mechanisms to discover resources and their capabilities. Thus resource discovery becomes a fundamental requirement of any IoT platform. This paper provides a comprehensive categorization of the current technology landscape for discovery while pointing out their advantages and limitations. Then, a novel search engine based resource discovery framework is proposed. The framework comprises of a proxy layer which includes drivers for various communication technologies and protocols. There is a central registry to store the configurations of the resources and index them based on the configuration parameters. A “lifetime” attribute is introduced which denotes the period through which a resource is discoverable. The search engine ranks the resulting resources of a discovery request and returns an URI to directly access each resource. The functionalities of the proposed framework are exposed using RESTful web services. The framework allows discovery of both smart and legacy devices. We have performed gap analysis of current IoT standards for discovery mechanisms and provide suggestions to improve them to maintain interoperability. The outcomes are communicated to Standard Development Organizations like W3C (in Web of Things Interest Group) and oneM2M.

Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing <=SEP=> We study traces taken from UMass DieselNet, a Disruption-Tolerant Network consisting of WiFi nodes attached to buses. As buses travel their routes, they encounter other buses and in some cases are able to establish pair-wise connections and transfer data between them. We analyze the bus-to-bus contact traces to characterize the contact process between buses and its impact on DTN routing performance. We find that the all-bus-pairs aggregated inter-contact times show no discernible pattern. However, the inter-contact times aggregated at a route level exhibit periodic behavior.Based on analysis of the deterministic inter-meeting times for bus pairs running on route pairs, and consideration of the variability in bus movement and the random failures to establish connections, we construct generative route-level models that capture the above behavior. Through trace-driven simulations of epidemic routing, we find that the epidemic performance predicted by traces generated with this finer-grained route-level model is much closer to the actual performance that would be realized in the operational system than traces generated using the coarse-grained all-bus-pairs aggregated model. This suggests the importance in choosing the rightlevel of model granularity when modelingmobility-related measures such as inter-contact times in DTNs.

Trainable videorealistic speech animation <=SEP=> We describe how to create with machine learning techniques a generative, speech animation module. A human subject is first recorded using a videocamera as he/she utters a predetermined speech corpus. After processing the corpus automatically, a visual speech module is learned from the data that is capable of synthesizing the human subject's mouth uttering entirely novel utterances that were not recorded in the original video. The synthesized utterance is re-composited onto a background sequence which contains natural head and eye movement. The final output is videorealistic in the sense that it looks like a video camera recording of the subject. At run time, the input to the system can be either real audio sequences or synthetic audio produced by a text-to-speech system, as long as they have been phonetically aligned.The two key contributions of this paper are 1) a variant of the multidimensional morphable model (MMM) to synthesize new, previously unseen mouth configurations from a small set of mouth image prototypes; and 2) a trajectory synthesis technique based on regularization, which is automatically trained from the recorded video corpus, and which is capable of synthesizing trajectories in MMM space corresponding to any desired utterance.