title: Cryogenic Temperature Characterization of a 28-nm FD-SOI Dedicated Structure for Advanced CMOS and Quantum Technologies Co-Integration

abstract: Silicon co-integration offers compelling scale-up opportunities for quantum computing. In this framework, cryogenic temperature is required for the coherence of solid-state quantum devices. This paper reports the characterization of an nMOS quantum-dot dedicated structure below 100 mK. The device under test is built in thin silicon film fabricated with 28 nm high- $k$ metal gate ultra-thin body and ultra-thin buried oxide advanced CMOS technology. The MOS structure is functional with improved performances at cryogenic temperature. The results open new research avenues in CMOS co-integration for quantum computing applications within the FD-SOI platform.


title: Comparing the performance of different x86 SIMD instruction sets for a medical imaging application on modern multi- and manycore chips

abstract: Single Instruction, Multiple Data (SIMD) vectorization is a major driver of performance in current architectures, and is mandatory for achieving good performance with codes that are limited by instruction throughput. We investigate the efficiency of different SIMD-vectorized implementations of the RabbitCT benchmark. RabbitCT performs 3D image reconstruction by back projection, a vital operation in computed tomography applications. The underlying algorithm is a challenge for vectorization because it consists, apart from a streaming part, also of a bilinear interpolation requiring scattered access to image data. We analyze the performance of SSE (128 bit), AVX (256 bit), AVX2 (256 bit), and IMCI (512 bit) implementations on recent Intel x86 systems. A special emphasis is put on the vector gather implementation on Intel Haswell and Knights Corner microarchitectures. Finally we discuss why GPU implementations perform much better for this specific algorithm.


title: Probabilistic Adaptive Computation Time

abstract: We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables expresses the preference for faster computation. The amount of computation for an input is determined via amortized maximum a posteriori (MAP) inference. MAP inference is performed using a novel stochastic variational optimization method. The recently proposed Adaptive Computation Time mechanism can be seen as an ad-hoc relaxation of this model. We demonstrate training using the general-purpose Concrete relaxation of discrete variables. Evaluation on ResNet shows that our method matches the speed-accuracy trade-off of Adaptive Computation Time, while allowing for evaluation with a simple deterministic procedure that has a lower memory footprint.


title: Sangam: a distributed pair programming plug-in for Eclipse

abstract: In pair programming, two programmers traditionally work side-by-side at one computer. However, in globally distributed organizations, long-distance collaboration is frequently necessary. Sangam is an Eclipse plug-in that allows Eclipse users in different locations to share a workspace so that they may work as if they were using the same computer. In this paper, we discuss the Sangam plug-in, and our experience developing it via distributed and collocated pair programming.


title: Perceptions of Agriculture Science Teachers Regarding Negative Deviant Behaviours of Students in Senior Secondary Schools in Botswana

abstract: This descriptive survey study covers perceptions of agriculture science teachers regarding negative deviant behaviours of students in schools in the southern district of Botswana. A questionnaire was administered to gather the views of thirty (30) agriculture science teachers in six purposively selected secondary schools. The results showed that the majority (67%) of the respondents were males while only 33% were females. Teacher respondents' perceptions indicated behaviours such as "regular missing agriculture lessons, bullying other students in the garden over the use of tools, vandalism of school agriculture tools, disrespecting technical staff members, fighting other students during class, and failure to wear protective clothing" with a mean of 2.50-3.49. Lack of parental guidance and peer pressure were revealed as primary factors influencing students' behaviours.


title: Interpersonal and social rhythm therapy: managing the chaos of bipolar disorder

abstract: Abstract Interpersonal and social rhythm therapy is an individual psychotherapy designed specifically for the treatment for bipolar disorder. Interpersonal and social rhythm therapy grew from a chronobiological model of bipolar disorder postulating that individuals with bipolar disorder have a genetic predisposition to circadian rhythm and sleep–wake cycle abnormalities that may be responsible, in part, for the symptomatic manifestations of the illness. In our model, life events (both negative and positive) may cause disruptions in patients' social rhythms that, in turn, perturb circadian rhythms and sleep–wake cycles and lead to the development of bipolar symptoms. Administered in concert with medications, interpersonal and social rhythm therapy combines the basic principles of interpersonal psychotherapy with behavioral techniques to help patients regularize their daily routines, diminish interpersonal problems, and adhere to medication regimens. It modulates both biological and psychosocial factors to mitigate patients' circadian and sleep–wake cycle vulnerabilities, improve overall functioning, and better manage the potential chaos of bipolar disorder symptomatology.


title: Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization

abstract: Sequence generative models with RNN variants, such as LSTM, GRU, show promising performance on abstractive document summarization. However, they still have some issues that limit their performance, especially while deal-ing with long sequences. One of the issues is that, to the best of our knowledge, all current models employ a unidirectional decoder, which reasons only about the past and still limited to retain future context while giving a prediction. This makes these models suffer on their own by generating unbalanced outputs. Moreover, unidirec-tional attention-based document summarization can only capture partial aspects of attentional regularities due to the inherited challenges in document summarization. To this end, we propose an end-to-end trainable bidirectional RNN model to tackle the aforementioned issues. The model has a bidirectional encoder-decoder architecture; in which the encoder and the decoder are bidirectional LSTMs. The forward decoder is initialized with the last hidden state of the backward encoder while the backward decoder is initialized with the last hidden state of the for-ward encoder. In addition, a bidirectional beam search mechanism is proposed as an approximate inference algo-rithm for generating the output summaries from the bidi-rectional model. This enables the model to reason about the past and future and to generate balanced outputs as a result. Experimental results on CNN / Daily Mail dataset show that the proposed model outperforms the current abstractive state-of-the-art models by a considerable mar-gin.


: A Cognitive-Affective Model of Organizational Communication for Designing


title: What are developers talking about? An analysis of topics and trends in Stack Overflow

abstract: Programming question and answer (QA questions in some topics lead to discussions in other topics; and the topics gaining the most popularity over time are web development (especially jQuery), mobile applications (especially Android), Git, and MySQL.


title: CMT-bone: A Mini-App for Compressible Multiphase Turbulence Simulation Software

abstract: Designed with the goal of mimicking key features of real HPC workloads, mini-apps have become an important tool for co-design. An investigation of mini-app behavior can provide system designers with insight into the impact of architectures, programming models, and tools on application performance. Mini-apps can also serve as a platform for fast algorithm design space exploration, allowing the application developers to evaluate their design choices before significantly redesigning the application codes. Consequently, it is prudent to develop a mini-app alongside the full blown application it is intended to represent. In this paper, we present CMT-bone a mini-app for the compressible multiphase turbulence (CMT) application, CMT-nek, being developed to extend the physics of the CESAR Nek5000 application code. CMT-bone consists of the most computationally intensive kernels of CMT-nek and the communication operations involved in nearest-neighbor updates and vector reductions. The mini-app represents CMT-nek in its most mature state and going forward it will be developed in parallel with the CMT-nek application to keep pace with key new performance impacting changes. We describe these kernels and discuss the role that CMT-bone has played in enabling interdisciplinary collaboration by allowing application developers to work with computer scientists on performance optimization on current architectures and performance analysis on notional future systems.


title: How do the brain's time and space mediate consciousness and its different dimensions? Temporo‐spatial theory of consciousness (TTC)

abstract: Abstract Time and space are the basic building blocks of nature. As a unique existent in nature, our brain exists in time and takes up space. The brain’s activity itself also constitutes and spreads in its own (intrinsic) time and space that is crucial for consciousness. Consciousness is a complex phenomenon including different dimensions: level/state, content/form, phenomenal aspects, and cognitive features. We propose a Temporo-spatial Theory of Consciousness (TTC) focusing primarily on the temporal and spatial features of the brain activity. We postulate four different neuronal mechanisms accounting for the different dimensions of consciousness: (i) “temporo-spatial nestedness” of the spontaneous activity accounts for the level/state of consciousness as neural predisposition of consciousness (NPC); (ii) “temporo-spatial alignment” of the pre-stimulus activity accounts for the content/form of consciousness as neural prerequisite of consciousness (preNCC); (iii) “temporo-spatial expansion” of early stimulus-induced activity accounts for phenomenal consciousness as neural correlates of consciousness (NCC); (iv) “temporo-spatial globalization” of late stimulus-induced activity accounts for the cognitive features of consciousness as neural consequence of consciousness (NCCcon).


title: Count-Based Exploration in Feature Space for Reinforcement Learning

abstract: We introduce a new count-based optimistic exploration algorithm for Reinforcement Learning (RL) that is feasible in environments with high-dimensional state-action spaces. The success of RL algorithms in these domains depends crucially on generalisation from limited training experience. Function approximation techniques enable RL agents to generalise in order to estimate the value of unvisited states, but at present few methods enable generalisation regarding uncertainty. This has prevented the combination of scalable RL algorithms with efficient exploration strategies that drive the agent to reduce its uncertainty. We present a new method for computing a generalised state visit-count, which allows the agent to estimate the uncertainty associated with any state. Our \phi-pseudocount achieves generalisation by exploiting same feature representation of the state space that is used for value function approximation. States that have less frequently observed features are deemed more uncertain. The \phi-Exploration-Bonus algorithm rewards the agent for exploring in feature space rather than in the untransformed state space. The method is simpler and less computationally expensive than some previous proposals, and achieves near state-of-the-art results on high-dimensional RL benchmarks.


title: A Deep Recurrent Framework for Cleaning Motion Capture Data

abstract: We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained to denoise a heterogeneous mix of action types, under substantial amounts of noise. A signal that has both noise and gaps is preprocessed with a second bidirectional network that synthesizes missing frames from surrounding context. The approach handles a wide variety of noise types and long gaps, does not rely on knowledge of the noise distribution, and operates in a streaming setting. We validate our approach through extensive evaluations on noise both in joint angles and in joint positions, and show that it improves upon various alternatives.


Analysis and Visualization of Network Data using JUNG


title: General planar quadrilateral mesh design using conjugate direction field

abstract: We present a novel method to approximate a freeform shape with a planar quadrilateral (PQ) mesh for modeling architectural glass structures. Our method is based on the study of conjugate direction fields (CDF) which allow the presence of ±κ/4(κ e Z) singularities. Starting with a triangle discretization of a freeform shape, we first compute an as smooth as possible conjugate direction field satisfying the user's directional and angular constraints, then apply mixed-integer quadrangulation and planarization techniques to generate a PQ mesh which approximates the input shape faithfully. We demonstrate that our method is effective and robust on various 3D models.


Deep Learning-A Technology With the Potential to Transform Health Care.


title: Reducing the data center electricity costs through participation in smart grid programs

abstract: To accommodate the increasing presence of intermittent renewable energy sources in power generation, electricity providers offer incentives for demand response so as to stabilize the grid load. This paper argues that data centers have the ability to provide large capacity reserves to emerging smart grid programs, and thus, provide opportunities for sustainable data center growth and cost savings as well as more flexibility for the grid. The paper first reviews the emerging smart grid opportunities, and then proposes policies to deliver data center demand response for peak shaving, regulation services, and frequency control prorgrams. Experimental results indicate substantial cost saving potential compared to solely applying energy management strategies at the data centers.


title: Towards trusted execution of multi-modal continuous authentication schemes

abstract: The emergence of powerful, sensor-rich devices has led to the development of continuous authentication (CA) schemes using off-the-shelf hardware, where user behaviour is compared to past experience to produce an authentication decision with the aim of addressing challenges with traditional authentication schemes. Current CA proposals, however, have largely neglected adversaries present in a real-world deployment, namely the ubiquity of mal ware and software attacks. This has particular importance when a device cannot be trusted by a third-party, such as a corporation, that controls access to assets based on that decision. A software compromise, either on the scheme implementation or platform, may enable an adversary to modify authentication scores to alter the status of the device in reality, give insights into user behaviour, or gain unauthorised access to restricted assets. Hence, for the first time, we examine two standardised constructs that offer isolated and trusted execution - Secure Elements (SEs) and Trusted Execution Environments (TEEs) - even when an adversary has root-level privileges, and propose measures for providing trusted CA while retaining deployability. Based on these, we implement the first system for evaluating TEE-based CA on a consumer mobile device using Intel SGX, thus providing confidentiality, integrity and trust while removing the main platform from the TCB. We present an empirical evaluation of TEE-and non-TEE performance using methods proposed in related CA schemes. Our results indicate that trusted CA can be provided with no significant performance penalty, and may even offer performance benefits.


title: [Reliability of quantifying vascular white matter brain lesions -- a contribution to reproducible quantitative diagnosis].

abstract: PURPOSE: Microangiopathic lesions of the brain tissue correlate with the clinical diagnosis of vascular subcortical dementia. The "experience-based" evaluation is insufficient. Rating scales may contribute to reproducible quantification. MATERIALS AND METHODS: In MRI studies of 10 patients, 9 neuroradiologists quantified vascular white matter lesions (WMLs) at two different points in time for 12 anatomically defined regions with respect to number, size and localization (score). For 9 observers and 10 studies, 90 intra-observer differences were obtained for each of the 12 WML scores. To calculate the inter-observer reliability, rating pairs were formed. Furthermore, 360 differences were computed for each score and rating for 12 anatomically defined WML scores, and the intraclass correlation (ICC) was calculated as a measure of agreement (reliability). RESULTS: As to the intra-observer reliability, the median of the differences was 1.5 for the entire brain as opposed to 0 for defined brain regions. The corresponding values for the inter-observer reliability were 3 and 1, respectively. The mean intra-class correlation coefficient for the 10 studies was 0.88, whereas the mean interclass correlation concerning the inter-observer reliability was 0.70, with the first and second rating being averaged. The rating of each study took about 6 minutes. CONCLUSION: The rating scale with high intra- and inter-observer reliability can dependably quantify WMLs and correlates with the clinical diagnosis of vascular dementia. Using a reliable rating scale, the diagnostic distinction of age-associated physiological vs. pathological size of the WML can make a contribution to the reproducible quantifiable diagnostic evaluation of vascular brain tissue lesions within the framework of dementia diagnostics.


Single case studies vs. multiple case studies: A comparative study


title: Exploiting FPGA-based techniques for fault injection campaigns on VLSI circuits

abstract: Proposes an FPGA-based system to speed-up fault injection campaigns for the evaluation of the fault-tolerant capabilities of VLSI circuits. An environment is described, relying on FPGA-based emulation of the circuit. Suitable techniques are described, allowing one to emulate the effects of faults and to observe faulty behavior. The proposed approach allows the combination of the speed of hardware-based techniques, and the flexibility of simulation-based techniques. Experimental results are provided showing that significant speed-up figures with respect to state-of-the-art simulation-based techniques can be achieved.


title: Accelerated mineralization of dense collagen-nano bioactive glass hybrid gels increases scaffold stiffness and regulates osteoblastic function.

abstract: Plastically compressed dense collagen (DC) gels mimic the microstructural, mechanical, and biological properties of native osteoid. This study investigated the effect of hybridizing DC with osteoinductive nano-sized bioactive glass (nBG) particles in order to potentially produce readily implantable, and mineralizable, cell seeded hydrogel scaffolds for bone tissue engineering. Due to the high surface area of nBG and increased reactivity, calcium phosphate formation was immediately detected within as processed DC-nGB hybrid gel scaffolds. By day 3 in simulated body fluid, accelerated mineralization was confirmed through the homogeneous growth of carbonated hydroxylapatite on the nanofibrillar collagen framework. At day 7, there was a 13 fold increase in the hybrid gel scaffold compressive modulus. MC3T3-E1 pre-osteoblasts, three-dimensionally seeded at the point of nanocomposite self-assembly, were viable up to day 28 in culture. In the absence of osteogenic supplements, MC3T3-E1 metabolic activity and alkaline phosphatase production were affected by the presence of nBG, indicating accelerated osteogenic differentiation. Additionally, no cell-induced contraction of DC-nBG gel scaffolds was detected. The accelerated mineralization of rapidly produced DC-nBG hybrid gels indicates their potential suitability as osteoinductive cell delivery scaffolds for bone regenerative therapy.


title: ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.


title: Thin skin elastodynamics

abstract: We present a novel approach for simulating thin hyperelastic skin. Real human skin is only a few millimeters thick. It can stretch and slide over underlying body structures such as muscles, bones, and tendons, revealing rich details of a moving character. Simulating such skin is challenging because it is in close contact with the body and shares its geometry. Despite major advances in simulating elastodynamics of cloth and soft bodies for computer graphics, such methods are difficult to use for simulating thin skin due to the need to deal with non-conforming meshes, collision detection, and contact response. We propose a novel Eulerian representation of skin that avoids all the difficulties of constraining the skin to lie on the body surface by working directly on the surface itself. Skin is modeled as a 2D hyperelastic membrane with arbitrary topology, which makes it easy to cover an entire character or object. Unlike most Eulerian simulations, we do not require a regular grid and can use triangular meshes to model body and skin geometry. The method is easy to implement, and can use low resolution meshes to animate high-resolution details stored in texture-like maps. Skin movement is driven by the animation of body shape prescribed by an artist or by another simulation, and so it can be easily added as a post-processing stage to an existing animation pipeline. We provide several examples simulating human and animal skin, and skin-tight clothes.


Predicting Maintainability using Object- Oriented Metrics


title: A probabilistic methodology for multilabel classification

abstract: Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an exponential number of alternatives), a very common solution (the binary method) is frequently used, learning a binary classifier for every category, and combining them all afterwards. The assumption taken in this solution is not realistic, and in this work we give examples where the decisions for all the labels are not taken independently, and thus, a supervised approach should learn those existing relationships among categories to make a better classification. Therefore, we show here a generic methodology that can improve the results obtained by a set of independent probabilistic binary classifiers, by using a combination procedure with a classifier trained on the co-occurrences of the labels. We show an exhaustive experimentation in three different standard corpora of labeled documents (Reuters-21578, Ohsumed-23 and RCV1), which present noticeable improvements in all of them, when using our methodology, in three probabilistic base classifiers.


Comparative advantages. Roman slavery and imperialism


Breaking up is hard to do.


title: Ultrathin glass wafer lamination and laser debonding to enable glass interposer fabrication

abstract: Interposer fabrication processes are applied in three-dimensional (3-D) integrated circuit (IC) integration to shorten the interconnection among different stacked chips and substrates. Because Si is a common material in semiconductor technology, Si interposers have been widely studied in many research activities. Compared with a Si wafer, glass substrates have the advantages of high resistivity, low dielectric constant, low insertion loss, adjustable coefficient of thermal expansion (CTE), and the possibility to use panel-size substrates as well as thin glass substrates (100 µm) to avoid the costly thinning process for realization of low-cost 2.5-D ICs. Thus, glass interposer fabrication is studied thoroughly in this paper.


title: Comparative study of copy-move forgery detection techniques

abstract: Digital images and their applications gained a huge interest around the world in several fields like newspapers, social media, defaming persons, and courts. There are two types of digital image authentication. The first type is active authentication, which uses digital signature and image watermarks. These techniques have certain constraints such as knowing the content of the digital image. They need special equipment like cameras and development software. The second type is passive authentication, which is used to detect digital image forgeries represented in image cloning, image splicing, image resampling, image retouching, and image morphing. Passive authentication has an advantage of not needing any previous knowledge of the image content to detect the forgery. Copy-move forgery is the most famous type, and it is widespread in all image forgeries. Copy-move forgery is easy to perform and the forged part has the same properties of the whole image that makes it difficult to detect. There are many algorithms used to detect copy-move forgery attacks depending on different techniques. This paper covers the directions of copy-move forgery detection and gives a wide coverage of earlier copy-move forgery detection algorithms and techniques.


title: Polysaccharide extraction from Spirulina sp. and its antioxidant capacity

abstract: Abstract To optimize polysaccharide extraction from Spirulina sp., the effect of solid-to-liquid ratio, extraction temperature and time were investigated using Box–Behnken experimental design and response surface methodology. The results showed that extraction temperature and solid-to-liquid ratio had a significant impact on the yield of polysaccharides. A polysaccharides yield of around 8.3% dry weight was obtained under the following optimized conditions: solid-to-liquid ratio of 1:45, temperature of 90 °C, and time of 120 min. The polysaccharide extracts contained rhamnose, which accounted for 53% of the total sugars, with a phenolic content of 45 mg GAE/g sample.


title: The neuroprotective effects of bilingualism upon the inferior parietal lobule: A Structural Neuroimaging Study in Aging Chinese Bilinguals

abstract: Abstract It is a timely issue to understand the impact of bilingualism upon brain structure in healthy aging and upon cognitive decline given evidence of its neuroprotective effects. Plastic changes induced by bilingualism were reported in young adults in the left inferior parietal lobule (LIPL) and its right counterpart (RIPL) (Mechelli et al., 2004). Moreover, both age of second language (L2) acquisition and L2 proficiency correlated with increased grey matter (GM) in the LIPL/RIPL. However it is unknown whether such findings replicate in older bilinguals. We examined this question in an aging bilingual population from Hong Kong. Results from our Voxel Based Morphometry study show that elderly bilinguals relative to a matched monolingual control group also have increased GM volumes in the inferior parietal lobules underlining the neuroprotective effect of bilingualism. However, unlike younger adults, age of L2 acquisition did not predict GM volumes. Instead, LIPL and RIPL appear differentially sensitive to the effects of L2 proficiency and L2 exposure with LIPL more sensitive to the former and RIPL more sensitive to the latter. Our data also intimate that such differences may be more prominent for speakers of languages that are linguistically closer such as in Cantonese-Mandarin bilinguals as compared to Cantonese-English bilinguals.


title: Parallel Algorithms for Real-time Motion Planning

abstract: For decades, humans have dreamed of making cars that could drive themselves, so that travel would be less taxing, and the roads safer for everyone. Toward this goal, we have made strides in motion planning algorithms for autonomous cars, using a powerful new computing tool, the parallel graphics processing unit (GPU). We propose a novel five-dimensional search space formulation that includes both spatial and temporal dimensions, and respects the kinematic and dynamic constraints on a typical automobile. With this formulation, the search space grows linearly with the length of the path, compared to the exponential growth of other methods. We also propose a parallel search algorithm, using the GPU to tackle the curse of dimensionality directly and increase the number of plans that can be evaluated by an order of magnitude compared to a CPU implementation. With this larger capacity, we can evaluate a dense sampling of plans combining lateral swerves and accelerations that represent a range of effective responses to more on-road driving scenarios than have previously been addressed in the literature. We contribute a cost function that evaluates many aspects of each candidate plan, ranking them all, and allowing the behavior of the vehicle to be fine-tuned by changing the ranking. We show that the cost function can be changed on-line by a behavioral planning layer to express preferred vehicle behavior without the brittleness induced by top-down planning architectures. Our method is particularly effective at generating robust merging behaviors, which have traditionally required a delicate and failure-prone coordination between multiple planning layers. Finally, we demonstrate our proposed planner in a variety of on-road driving scenarios in both simulation and on an autonomous SUV, and make a detailed comparison with prior work.


abstract: Abstract A new action for the gravitational interaction is proposed, which is bounded from below in riemannian spaces and reduces to the standard Einstein-Hilbert action at low energies. The action uses as essential variables the geodesic two-point distance, which gives it a non-local character. It reduces to the Einstein-Hilbert form in the weak coupling limit, plus quartic corrections suppressed by m Pl 1 , which can be put in the (dimensionally continued) Euler characteristic form by field redefinitions.


title: Novel Therapies in Glioblastoma

abstract: Conventional treatment of glioblastoma has advanced only incrementally in the last 30 years and still yields poor outcomes. The current strategy of surgery, radiation, and chemotherapy has increased median survival to approximately 15 months. With the advent of molecular biology and consequent improved understanding of basic tumor biology, targeted therapies have become cornerstones for cancer treatment. Many pathways (RTKs, PI3K/AKT/mTOR, angiogenesis, etc.) have been identified in GBM as playing major roles in tumorigenesis, treatment resistance, or natural history of disease. Despite the growing understanding of the complex networks regulating GBM tumors, many targeted therapies have fallen short of expectations. In this paper, we will discuss novel therapies and the successes and failures that have occurred. One clear message is that monotherapies yield minor results, likely due to functionally redundant pathways. A better understanding of underlying tumor biology may yield insights into optimal targeting strategies which could improve the overall therapeutic ratio of conventional treatments.


title: Supply Chain Inventory Management and the Value of Shared Information

abstract: In traditional supply chain inventory management, orders are the only information firms exchange, but information technology now allows firms to share demand and inventory data quickly and inexpensively. We study the value of sharing these data in a model with one supplier, N identical retailers, and stationary stochastic consumer demand. There are inventory holding costs and back-order penalty costs. We compare a traditional information policy that does not use shared information with a full information policy that does exploit shared information. In a numerical study we find that supply chain costs are 2.2% lower on average with the full information policy than with the traditional information policy, and the maximum difference is 12.1%. We also develop a simulation-based lower bound over all feasible policies. The cost difference between the traditional information policy and the lower bound is an upper bound on the value of information sharing: In the same study, that difference is 3.4% on average, and no more than 13.8%. We contrast the value of information sharing with two other benefits of information technology, faster and cheaper order processing, which lead to shorter lead times and smaller batch sizes, respectively. In our sample, cutting lead times nearly in half reduces costs by 21% on average, and cutting batches in half reduces costs by 22% on average. For the settings we study, we conclude that implementing information technology to accelerate and smooth the physical flow of goods through a supply chain is significantly more valuable than using information technology to expand the flow of information.


title: Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors

abstract: In this paper, a new sensing device that can simultaneously monitor traffic congestion and urban flash floods is presented. This sensing device is based on the combination of passive infrared sensors (PIRs) and ultrasonic rangefinder, and is used for real-time vehicle detection, classification, and speed estimation in the context of wireless sensor networks. This framework relies on dynamic Bayesian Networks to fuse heterogeneous data both spatially and temporally for vehicle detection. To estimate the speed of the incoming vehicles, we first use cross correlation and wavelet transform-based methods to estimate the time delay between the signals of different sensors. We then propose a calibration and self-correction model based on Bayesian Networks to make a joint inference by all sensors about the speed and the length of the detected vehicle. Furthermore, we use the measurements of the ultrasonic and the PIR sensors to perform vehicle classification. Validation data (using an experimental dual infrared and ultrasonic traffic sensor) show a 99% accuracy in vehicle detection, a mean error of 5 kph in vehicle speed estimation, a mean error of 0.7m in vehicle length estimation, and a high accuracy in vehicle classification. Finally, we discuss the computational performance of the algorithm, and show that this framework can be implemented on low-power computational devices within a wireless sensor network setting. Such decentralized processing greatly improves the energy consumption of the system and minimizes bandwidth usage.


title: Orthogonal and Idempotent Transformations for Learning Deep Neural Networks

abstract: Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training. In this paper, we introduce two alternative linear transforms, orthogonal transformation and idempotent transformation. According to the definition and property of orthogonal and idempotent matrices, the product of multiple orthogonal (same idempotent) matrices, used to form linear transformations, is equal to a single orthogonal (idempotent) matrix, resulting in that information flow is improved and the training is eased. One interesting point is that the success essentially stems from feature reuse and gradient reuse in forward and backward propagation for maintaining the information during flow and eliminating the gradient vanishing problem because of the express way through skip-connections. We empirically demonstrate the effectiveness of the proposed two transformations: similar performance in single-branch networks and even superior in multi-branch networks in comparison to identity transformations.


title: Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition

abstract: Three dimensional convolutional neural networks (3D CNNs) have been established as a powerful tool to simultaneously learn features from both spatial and temporal dimensions, which is suitable to be applied to video-based action recognition. In this work, we propose not to directly use the activations of fully-connected layers of a 3D CNN as the video feature, but to use selective convolutional layer activations to form a discriminative descriptor for video. It pools the feature on the convolutional layers under the guidance of body joint positions. Two schemes of mapping body joints into convolutional feature maps for pooling are discussed. The body joint positions can be obtained from any off-the-shelf skeleton estimation algorithm. The helpfulness of the body joint guided feature pooling with inaccurate skeleton estimation is systematically evaluated. To make it end-to-end and do not rely on any sophisticated body joint detection algorithm, we further propose a two-stream bilinear model which can learn the guidance from the body joints and capture the spatio-temporal features simultaneously. In this model, the body joint guided feature pooling is conveniently formulated as a bilinear product operation. Experimental results on three real-world datasets demonstrate the effectiveness of body joint guided pooling which achieves promising performance.


title: Geographical Text Analysis: A new approach to understanding nineteenth-century mortality

abstract: This paper uses a combination of Geographic Information Systems (GIS) and corpus linguistic analysis to extract and analyse disease related keywords from the Registrar-General's Decennial Supplements. Combined with known mortality figures, this provides, for the first time, a spatial picture of the relationship between the Registrar-General's discussion of disease and deaths in England and Wales in the nineteenth and early twentieth centuries. Techniques such as collocation, density analysis, the Hierarchical Regional Settlement matrix and regression analysis are employed to extract and analyse the data resulting in new insight into the relationship between the Registrar-General's published texts and the changing mortality patterns during this time.


title: Using electronic health records to drive discovery in disease genomics

abstract: Increasing efforts are being made to integrate electronic health records into disease genetics and genomics studies and to use the biological 'by-products' of health-care for research. Such efforts promise to decrease the cost of genomics studies and increase their clinical relevance.


title: Accuracy of Monoclonal Stool Antigen Test for the Diagnosis of H. pylori Infection: A Systematic Review and Meta-Analysis

abstract: OBJECTIVE ::: To perform a systematic review and a meta-analysis of accuracy of monoclonal stool antigen test (SAT) for the diagnosis of Helicobacter pylori infection. ::: ::: ::: METHODS ::: ::: ::: ::: SELECTION OF STUDIES ::: assessing the accuracy of monoclonal SAT for the diagnosis of H. pylori infection. ::: ::: ::: SEARCH STRATEGY ::: electronic and manual bibliographical searches. ::: ::: ::: DATA EXTRACTION ::: independently done by two reviewers. ::: ::: ::: DATA SYNTHESIS ::: meta-analyses combining the sensitivities, specificities, and likelihood ratios (LRs) of the individual studies. ::: ::: ::: RESULTS ::: Twenty-two studies, including 2,499 patients, evaluated the monoclonal SAT before eradication therapy. Pooled sensitivity, specificity, LR+, and LR- were: 0.94 (95% CI 0.93-0.95), 0.97 (0.96-0.98), 24 (15-41), and 0.07 (0.04-0.12). The accuracy of both monoclonal and polyclonal SAT was evaluated together in 13 pretreatment studies, and higher pooled sensitivity was demonstrated with the monoclonal technique (0.95 vs 0.83). Twelve studies, including 957 patients, assessed the monoclonal SAT to confirm eradication after therapy. Pooled sensitivity, specificity, LR+, and LR- were 0.93 (0.89-0.96), 0.96 (0.94-0.97), 17 (12-23), and 0.1 (0.07-0.15). Both tests were evaluated together in eight post-treatment studies and, again, the monoclonal technique showed higher sensitivity (0.91 vs 0.76). Heterogeneity among studies disappeared when a single outlier study was excluded. Subanalysis depending on the reference method, the study population, or the study quality showed similar results. ::: ::: ::: CONCLUSION ::: Monoclonal SAT is an accurate noninvasive method both for the initial diagnosis of H. pylori infection and for the confirmation of its eradication after treatment. The monoclonal technique has higher sensitivity than the polyclonal one, especially in the post-treatment setting.


title: MAESTRO: An Open-source Infrastructure for Modeling Dataflows within Deep Learning Accelerators.

abstract: We present MAESTRO, a framework to describe and analyze CNN dataflows, and predict performance and energy-efficiency when running neural network layers across various hardware configurations. This includes two components: (i) a concise language to describe arbitrary dataflows and (ii) and analysis framework that accepts the dataflow description, hardware resource description, and DNN layer description as inputs and generates buffer requirements, buffer access counts, network-on-chip (NoC) bandwidth requirements, and roofline performance information. We demonstrate both components across several dataflows as case studies.


title: Working and Assembly Modes of the Agile Eye

abstract: This paper deals with the in-depth kinematic analysis of a special spherical parallel wrist, called the Agile Eye. The Agile Eye is a three-legged spherical parallel robot with revolute joints in which all pairs of adjacent joint axes are orthogonal. Its most peculiar feature, demonstrated in this paper for the first time, is that its (orientation) workspace is unlimited and flawed only by six singularity curves (rather than surfaces). Furthermore, these curves correspond to self-motions of the mobile platform. This paper also demonstrates that, unlike for any other such complex spatial robots, the four solutions to the direct kinematics of the Agile Eye (assembly modes) have a simple geometric relationship with the eight solutions to the inverse kinematics (working modes).


title: MIMO-NOMA Design for Small Packet Transmission in the Internet of Things

abstract: A feature of the Internet of Things (IoT) is that some users in the system need to be served quickly for small packet transmission. To address this requirement, a new multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) scheme is designed in this paper, where one user is served with its quality of service requirement strictly met, and the other user is served opportunistically by using the NOMA concept. The novelty of this new scheme is that it confronts the challenge that the existing MIMO-NOMA schemes rely on the assumption that users’ channel conditions are different, a strong assumption which may not be valid in practice. The developed precoding and detection strategies can effectively create a significant difference between the users’ effective channel gains, and therefore, the potential of NOMA can be realized even if the users’ original channel conditions are similar. Analytical and numerical results are provided to demonstrate the performance of the proposed MIMO-NOMA scheme.


title: DataSpotting: offloading cellular traffic via managed device-to-device data transfer at data spots

abstract: Mobile broadband demand keeps growing at an overwhelming pace. Though emerging wireless technologies will provide more bandwidth, the increase in demand may easily consume the extra bandwidth. To alleviate this problem, we propose using the content available on individual devices as caches. Particularly, when a user reaches areas with dense clusters of mobile devices, "data spots", the operator can instruct the user to connect with other users sharing similar interests and serve the requests locally. This paper presents feasibility study as well as prototype implementation of this idea.


title: A Prioritization Based Congestion Control Protocol for Healthcare Monitoring Application in Wireless Sensor Networks

abstract: Recent developments in biosensor and wireless technology have led to a rapid progress in wearable real time health monitoring. Unlike wired networks, wireless networks are subject to more packet loss and congestion. In this paper, we propose a congestion control and service prioritization protocol for real time monitoring of patients' vital signs using wireless biomedical sensor networks. The proposed system is able to discriminate between physiological signals and assign them different priorities. Thus, it would be possible to provide a better quality of service for transmitting highly important vital signs. Congestion control is performed by considering both the congestion situation in the parent node and the priority of the child nodes in assigning network bandwidth to signals from different patients. Given the dynamic nature of patients' health conditions, the proposed system can detect an anomaly in the received vital signs from a patient and hence assign more priority to patients in need. Simulation results confirm the superior performance of the proposed protocol. To our knowledge, this is the first attempt at a special-purpose congestion control protocol specifically designed for wireless biosensor networks.


title: A 60 GHz Broadband Low-Noise Amplifier With Variable-Gain Control in 65 nm CMOS

abstract: A 60 GHz low-noise amplifier (LNA) implemented in a 65 nm CMOS process is presented. Due to the use of a gain-boosted input stage and binary controlled attenuators, the LNA exhibits a broadband response and four programmable gain levels from 18.9 to 7.9 dB while maintaining impedance matching at the 60 GHz frequency band. The fabricated circuit consumes a dc current of 25 mA from a 1.8 V supply.


title: Memory Prefetching Using Adaptive Stream Detection

abstract: We present Adaptive Stream Detection, a simple technique for modulating the aggressiveness of a stream prefetcher to match a workload?s observed spatial locality. We use this concept to design a prefetcher that resides on an on-chip memory controller. The result is a prefetcher with small hardware costs that can exploit workloads with low amounts of spatial locality. Using highly accurate simulators for the IBM Power5+, we show that this prefetcher improves performance of the SPEC2006fp benchmarks by an average of 32.7% when compared against a Power5+ that performs no prefetching. On a set of 5 commercial benchmarks that have low spatial locality, this prefetcher improves performance by an average of 15.1%. When compared against a typical Power5+ that does perform processor-side prefetching, the average performance improvement of these benchmark suites is 10.2% and 8.4%. We also evaluate the power and energy impact of our technique. For the same benchmark suites, DRAM power consumption increases by less than 3%, while energy usage decreases by 9.8% and 8.2%, respectively. Moreover, the power consumption of the prefetcher itself is low; it is estimated to increase the power consumption of the Power5+ chip by 0.06%.


title: The Contribution of Social Media to Body Dissatisfaction, Eating Disorder Symptoms, and Anabolic Steroid Use Among Sexual Minority Men

abstract: Abstract Social media has been associated with body dissatisfaction and eating disorder symptoms among young women and adolescent girls. However, despite notable evidence of susceptibility to body image pressures, it remains unknown whether these associations generalize to sexual minority men. A nationwide sample of 2,733 sexual minority men completed an online survey advertised to Australian and New Zealand users of a popular dating app. Participants answered questions about how frequently they used 11 different social media platforms in addition to questions about their dating app use, body image, eating disorder symptoms, and anabolic steroids. Facebook, Youtube, Instagram, and Snapchat were the most frequently used social media platforms. A pattern of small-sized and positive associations emerged between social media use and body dissatisfaction, eating disorder symptoms, and thoughts about using anabolic steroids. Facebook, Instagram, and Snapchat evidenced the strongest associations. The association...


title: A bio-impedance measurement system for portable monitoring of heart rate and pulse wave velocity using small body area

abstract: Continuous monitoring of cardiovascular system can prevent death and disabilities due to heart failure. In this paper, a bio-impedance measurement system that operates on a small local area of the forearm is presented for portable and continuous monitoring of the heart rate and pulse wave velocity (PWV). The impedance variation caused by the heart pump is measured by analog circuitries that consist of amplifiers, mixers and filters. The overall gain, CMRR, and input impedance of the implemented system using discrete components are 80dB, 72dB and 477kΩ at 100kHz, respectively, which results in a system sensitivity of 21V/Ω. Experimental results show that the heart rate and PWV can be measured by using only 1.5cm × 7cm area of the body. The measured heart rate is in the range of 65∼110 beats/min, and PWV is in 6.51±2.39 (m/s), which fits well with a reference device and normal physiological range. The optimum location for small electrodes in terms of signal quality is also investigated, and the results show that the location at the lower forearm along the radial artery has the best signal quality.


title: Dynamic TXOP HCCA reclaiming scheduler with transmission time estimation for IEEE 802.11e real-time networks

abstract: IEEE 802.11e HCCA reference scheduler guarantees Quality of Service only for Constant Bit Rate traffic streams, whereas its assignment of scheduling parameters (transmission time TXOP and polling period) is too rigid to serve Variable Bit Rate (VBR) traffic. This paper presents a new scheduling algorithm, Dynamic TXOP HCCA (DTH). Its scheduling scheme, integrated with the centralized scheduler, uses both a statistical estimation of needed transmission duration and a bandwidth reclaiming mechanism with the aim of improving the resource management and providing an instantaneous dynamic Transmission Opportunity (TXOP), tailored to multimedia applications with variable bit rate. Performance evaluation through simulation, confirmed by the scheduling analysis, shows that DTH is suitable to reduce the transmission queues length. This positively impacts on the delay and on packets drop rate experienced by VBR traffic streams.


title: Unsupervised Simultaneous Orthogonal basis Clustering Feature Selection

abstract: In this paper, we propose a novel unsupervised feature selection method: Simultaneous Orthogonal basis Clustering Feature Selection (SOCFS). To perform feature selection on unlabeled data effectively, a regularized regression-based formulation with a new type of target matrix is designed. The target matrix captures latent cluster centers of the projected data points by performing orthogonal basis clustering, and then guides the projection matrix to select discriminative features. Unlike the recent unsupervised feature selection methods, SOCFS does not explicitly use the pre-computed local structure information for data points represented as additional terms of their objective functions, but directly computes latent cluster information by the target matrix conducting orthogonal basis clustering in a single unified term of the proposed objective function. It turns out that the proposed objective function can be minimized by a simple optimization algorithm. Experimental results demonstrate the effectiveness of SOCFS achieving the state-of-the-art results with diverse real world datasets.


title: Graphical user interface concepts for tactical augmented reality

abstract: Applied Research Associates and BAE Systems are working together to develop a wearable augmented reality system ::: under the DARPA ULTRA-Vis program † . Our approach to achieve the objectives of ULTRAVis, called iLeader, ::: incorporates a full color 40° field of view (FOV) see-thru holographic waveguide integrated with sensors for full ::: position and head tracking to provide an unobtrusive information system for operational maneuvers. iLeader will enable ::: warfighters to mark-up the 3D battle-space with symbologic identification of graphical control measures, friendly force ::: positions and enemy/target locations. Our augmented reality display provides dynamic real-time painting of symbols on ::: real objects, a pose-sensitive 360° representation of relevant object positions, and visual feedback for a variety of system ::: activities. The iLeader user interface and situational awareness graphical representations are highly intuitive, nondisruptive, ::: and always tactically relevant. ::: We used best human-factors practices, system engineering expertise, and cognitive task analysis to design effective ::: strategies for presenting real-time situational awareness to the military user without distorting their natural senses and ::: perception. We present requirements identified for presenting information within a see-through display in combat ::: environments, challenges in designing suitable visualization capabilities, and solutions that enable us to bring real-time ::: iconic command and control to the tactical user community.


title: Influence of Battery/Ultracapacitor Energy-Storage Sizing on Battery Lifetime in a Fuel Cell Hybrid Electric Vehicle

abstract: Combining high-energy-density batteries and high-power-density ultracapacitors in fuel cell hybrid electric vehicles (FCHEVs) results in a high-performance, highly efficient, low-size, and light system. Often, the battery is rated with respect to its energy requirement to reduce its volume and mass. This does not prevent deep discharges of the battery, which are critical to the lifetime of the battery. In this paper, the ratings of the battery and ultracapacitors are investigated. Comparisons of the system volume, the system mass, and the lifetime of the battery due to the rating of the energy storage devices are presented. It is concluded that not only should the energy storage devices of a FCHEV be sized by their power and energy requirements, but the battery lifetime should also be considered. Two energy-management strategies, which sufficiently divide the load power between the fuel cell stack, the battery, and the ultracapacitors, are proposed. A charging strategy, which charges the energy-storage devices due to the conditions of the FCHEV, is also proposed. The analysis provides recommendations on the design of the battery and the ultracapacitor energy-storage systems for FCHEVs.


title: Front-of-pack nutrition labelling: Testing effectiveness of different nutrition labelling formats front-of-pack in four European countries

abstract: In two studies, the impact of eight front-of-pack nutrition labelling formats that differed in complexity was investigated across four European countries. In total 1630 men and women (18-55 yrs) were recruited from Internet panels in the United Kingdom, Germany, Italy and the Netherlands for study 1 and 776 in Italy and the United Kingdom for study 2. Participants evaluated several products (healthier and less healthy variants of the same product category) with a front-of-pack nutrition labelling format. The first study evaluated different labelling formats on consumer friendliness (comprehension, liking and credibility) and the second study measured the effect of the different labelling formats on decision-making (usage intention and process time). The results indicated minor differences in consumer friendliness and usage intention between simpler (such as Healthier Choice Tick, Smileys and Stars) and more complex front-of-pack nutrition labelling formats (such as Multiple Traffic Light, Wheel of Health and GDA scores). Endorsement by national and international health organisations strongly increased the labelling formats' credibility. Participants needed significantly less time to evaluate simpler front-of-pack labelling compared to the more complex labelling format. Thus simpler front-of-pack labelling formats seem more appropriate in a shopping environment where quick decisions are made.


title: Smart Textiles and Wearable Technology

abstract: This report gives an overview of projects combining smart textiles and clothing as a basis for discussions of how smart textiles could be introduced to in fashion. The overview covers different projects, research as well as commercial projects within smart textiles and clothing, with a certain focus on European activities.


title: A Secured and Trusted Demand Response system based on Blockchain technologies

abstract: The aim of the proposed work is to introduce a secure and interoperable Demand Response (DR) management platform that will assist Aggregators (or other relevant Stakeholders involved in DR business scenarios) in their decision making mechanisms over their portfolios of prosumers. This novel architecture incorporates multiple strategies and policies provided from energy market stakeholders, establishing a more modular and future-proof DR solution. By employing an innovative multi-agent decision making system and self-learning algorithms to enable aggregation, segmentation and coordination of several diverse clusters, consisting of supply and demand assets, a fully autonomous design will be delivered. This DR framework is further fortified in terms of data security by not only implementing cutting-edge blockchain infrastructure, but also by making use of Smart Contracts and Decentralized Applications (dApps) which will further secure and facilitate Aggregators-to-Prosumers transactions. The blockchain technologies will be combined with well-known open protocols (i.e. OpenADR) towards also supporting interoperability in terms of information exchange.


title: Moving object classification using horizontal laser scan data

abstract: Motivated by two potential applications, i.e. enhancing driving safety and traffic data collection, a system has been developed using a single-layer horizontal laser scanner as the major sensor for both localization and perception of the surroundings in a large dynamic urban environment. This research focuses on a classification method, that given a stream of laser measurements, classify the moving object into either a person, a group of people, a bicycle or a car. In this research, a number of features are defined after examining the property of data appearance. A classification method is proposed after examining the likelihood measures between each pair of feature and class. Experimental results are presented, demonstrating that the algorithm has efficiency with respect to both driving safety and traffic data collection in highly dynamic environment.


title: Distance Visualization for Interactive 3D Implant Planning

abstract: An instant and quantitative assessment of spatial distances between two objects plays an important role in interactive applications such as virtual model assembly, medical operation planning, or computational steering. While some research has been done on the development of distance-based measures between two objects, only very few attempts have been reported to visualize such measures in interactive scenarios. In this paper we present two different approaches for this purpose, and we investigate the effectiveness of these approaches for intuitive 3D implant positioning in a medical operation planning system. The first approach uses cylindrical glyphs to depict distances, which smoothly adapt their shape and color to changing distances when the objects are moved. This approach computes distances directly on the polygonal object representations by means of ray/triangle mesh intersection. The second approach introduces a set of slices as additional geometric structures, and uses color coding on surfaces to indicate distances. This approach obtains distances from a precomputed distance field of each object. The major findings of the performed user study indicate that a visualization that can facilitate an instant and quantitative analysis of distances between two objects in interactive 3D scenarios is demanding, yet can be achieved by including additional monocular cues into the visualization.


title: Number Theoretic Transforms for Secure Signal Processing

abstract: Multimedia contents are inherently sensitive signals that must be protected whenever they are outsourced to an untrusted environment. This problem becomes a challenge when the untrusted environment must perform some processing on the sensitive signals; a paradigmatic example is Cloud-based signal processing services. Approaches based on Secure Signal Processing (SSP) address this challenge by proposing novel mechanisms for signal processing in the encrypted domain and interactive secure protocols to achieve the goal of protecting signals without disclosing the sensitive information they convey. ::: This work presents a novel and comprehensive set of approaches and primitives to efficiently process signals in an encrypted form, by using Number Theoretic Transforms (NTTs) in innovative ways. This usage of NTTs paired with appropriate signal pre- and post-coding enables a whole range of easily composable signal processing operations comprising, among others, filtering, generalized convolutions, matrix-based processing or error correcting codes. The main focus is on unattended processing, in which no interaction from the client is needed; for implementation purposes, efficient lattice-based somewhat homomorphic cryptosystems are used. We exemplify these approaches and evaluate their performance and accuracy, proving that the proposed framework opens up a wide variety of new applications for secured outsourced-processing of multimedia contents.


title: The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects

abstract: The Berlin brain-computer interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensional features from multichannel EEG; and 3) advanced machine-learning techniques. Spatio-spectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, and foot). A previous feedback study [M. Krauledat, K.-R. Muller, and G. Curio. (2007) The non-invasive Berlin brain-computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage. [Online]. 37(2), pp. 539--550. Available: http://dx.doi.org/10.1016/j.neuroimage.2007.01.051] with ten subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than five prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naive subjects that 8 out of 14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further four subjects at >70%. Thus, 12 out of 14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine-learning algorithms.


Extracting structure from human-readable semistructured text


title: Data Management and Transfer in High-Performance Computational Grid Environments

abstract: Abstract An emerging class of data-intensive applications involve the geographically dispersed extraction of complex scientific information from very large collections of measured or computed data. Such applications arise, for example, in experimental physics, where the data in question is generated by accelerators, and in simulation science, where the data is generated by supercomputers. So-called Data Grids provide essential infrastructure for such applications, much as the Internet provides essential services for applications such as e-mail and the Web. We describe here two services that we believe are fundamental to any Data Grid: reliable, high-speed transport and replica management. Our high-speed transport service, GridFTP, extends the popular FTP protocol with new features required for Data Grid applications, such as striping and partial file access. Our replica management service integrates a replica catalog with GridFTP transfers to provide for the creation, registration, location, and management of dataset replicas. We present the design of both services and also preliminary performance results. Our implementations exploit security and other services provided by the Globus Toolkit.


title: DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

abstract: Traditional1 salient object detection models often use hand-crafted features to formulate contrast and various prior knowledge, and then combine them artificially. In this work, we propose a novel end-to-end deep hierarchical saliency network (DHSNet) based on convolutional neural networks for detecting salient objects. DHSNet first makes a coarse global prediction by automatically learning various global structured saliency cues, including global contrast, objectness, compactness, and their optimal combination. Then a novel hierarchical recurrent convolutional neural network (HRCNN) is adopted to further hierarchically and progressively refine the details of saliency maps step by step via integrating local context information. The whole architecture works in a global to local and coarse to fine manner. DHSNet is directly trained using whole images and corresponding ground truth saliency masks. When testing, saliency maps can be generated by directly and efficiently feedforwarding testing images through the network, without relying on any other techniques. Evaluations on four benchmark datasets and comparisons with other 11 state-of-the-art algorithms demonstrate that DHSNet not only shows its significant superiority in terms of performance, but also achieves a real-time speed of 23 FPS on modern GPUs.


A level set method for thin film epitaxial growth


title: An overview of pipeline leak detection and location systems

abstract: Leak detection in transmission pipelines is crucially important for safe operation. Delay in detecting leaks leads to loss of property and human life in fire hazards and loss of valuable material. Leaking of methane and hydrocarbon gas causes negative impacts on the eco system such as global warming and air pollution. Pipeline leak detection systems play a key role in minimization of the probability of occurrence of leaks and hence their impacts. Today there are many available technologies in the domain of leak detection. This paper provides an overview on external and internal leak detection and location systems and a summary of comparison regarding performance of each system.


title: Flavanols and Anthocyanins in Cardiovascular Health: A Review of Current Evidence

abstract: Nowadays it is accepted that natural flavonoids present in fruits and plant-derived-foods are relevant, not only for technological reasons and organoleptic properties, but also because of their potential health-promoting effects, as suggested by the available experimental and epidemiological evidence. The beneficial biological effects of these food bioactives may be driven by two of their characteristic properties: their affinity for proteins and their antioxidant activity. Over the last 15 years, numerous publications have demonstrated that besides their in vitro antioxidant capacity, certain phenolic compounds, such as anthocyanins, catechins, proanthocyanidins, and other non coloured flavonoids, may regulate different signaling pathways involved in cell survival, growth and differentiation. In this review we will update the knowledge on the cardiovascular effects of anthocyanins, catechins and proanthocyanidins, as implied by the in vitro and clinical studies on these compounds. We also review the available information on the structure, distribution and bioavailability of flavanols (monomeric catechins and proanthocyanidins) and anthocyanins, data necessary in order to understand their role in reducing risk factors and preventing cardiovascular health problems through different aspects of their bioefficacy on vascular parameters (platelet agregation, atherosclerosis, blood pressure, antioxidant status, inflammation-related markers, etc.), myocardial conditions, and whole-body metabolism (serum biochemistry, lipid profile), highlighting the need for better-designed clinical studies to improve the current knowledge on the potential health benefits of these flavonoids to cardiovascular and metabolic health.


title: The active NPC converter and its loss-balancing control

abstract: The three-level neutral-point-clamped voltage-source converter (NPC VSC) is widely used in high-power medium-voltage applications. The unequal loss distribution among the semiconductors is one major disadvantage of this popular topology. This paper studies the loss distribution problem of the NPC VSC and proposes the active NPC VSC to overcome this drawback. The switch states and commutations of the converter are analyzed. A loss-balancing scheme is introduced, enabling a substantially increased output power and an improved performance at zero speed, compared to the conventional NPC VSC.


title: Metamorphic Testing and its Applications

abstract: An “oracle” in software testing is a procedure by which testers can decide whether the output of the program under testing is correct. In some situations, however, the oracle is no t available or too difficult to apply. This is known as the “oracle problem”. In other situations, the oracle is often the human tester who checks the testing result manually. The manual prediction and verification of program output greatl y decreases the efficiency and increases the cost of testing. A metamorphic testing method has been proposed to test programs without the involvement of an oracle. It employs properties of the target function, known as metamorphic relations, to generate follow-up test cases and verify the outputs auto∗ c 2004 Software Engineering Association, Japan. This material is presented to ensure timely dissemination of scholarly and technical work. Personal use of this material is permitted. Copyright and all r ights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works ma y not be reposted without the explicit permission of the copyright holder. Permission to reprint/republish this material for advertising or promot ional purposes or for creating new collective works for resale or redistribut ion to servers or lists, or to reuse anyrcopyrighted component of this work in other works must be obtained from the Software Engineers Association, Japan. † This research is supported in part by a discovery grant of the Aus


title: A split-and-conquer approach for analysis of extraordinarily large data

abstract: If there are datasets, too large to fit into a single computer or too expen- sive for a computationally intensive data analysis, what should we do? We propose a split-and-conquer approach and illustrate it using several computationally inten- sive penalized regression methods, along with a theoretical support. We show that the split-and-conquer approach can substantially reduce computing time and com- puter memory requirements. The proposed methodology is illustrated numerically using both simulation and data examples.


title: The Toyota Way in Services: The Case of Lean Product Development

abstract: Executive Overview Toyota's Production System (TPS) is based on “lean” principles including a focus on the customer, continual improvement and quality through waste reduction, and tightly integrated upstream and downstream processes as part of a lean value chain. Most manufacturing companies have adopted some type of “lean initiative,” and the lean movement recently has gone beyond the shop floor to white-collar offices and is even spreading to service industries. Unfortunately, most of these efforts represent limited, piecemeal approaches—quick fixes to reduce lead time and costs and to increase quality—that almost never create a true learning culture. We outline and illustrate the management principles of TPS that can be applied beyond manufacturing to any technical or service process. It is a true systems approach that effectively integrates people, processes, and technology—one that must be adopted as a continual, comprehensive, and coordinated effort for change and learning across the organization.


title: Early clinical predictors and progression of irreversible disability in multiple sclerosis: an amnesic process

abstract: Prognosis of multiple sclerosis is highly variable. Clinical variables have been identified that are assessable early in the disease and are predictors of the time from the disease onset to the onset of irreversible disability. Our objective was to determine if these clinical variables still have an effect after the first stages of disability have been reached. We determined the dates of disease onset and assignment of scores of irreversible disability in 1844 patients with multiple sclerosis. We used three scores on the Kurtzke Disability Status Scale as benchmarks of disability accumulation: 4 (limited walking but without aid); 6 (walking with unilateral aid); and 7 (wheelchair bound). We used Kaplan–Meier analyses and Cox regression models to determine the influence of the clinical variables on the time to disability onset. Median times from onset of multiple sclerosis to assignment of a score of 4, 6 and 7 were significantly influenced by gender, age, symptoms and course (relapsing–remitting or progressive) at onset of the disease, degree of recovery from the first relapse, time to a second neurological episode, and the number of relapses in the first 5 years of the disease. Similarly, times from onset of multiple sclerosis to a score of 6 and 7 were influenced by time to a score of 4. In contrast, none of the variables substantially affected the time from a score of 4 to a score of 6 or 7, or from a score of 6 to a score of 7. Early assessable clinical variables significantly influence the time from the onset of multiple sclerosis to the assignment of a disability score of 4, but not the subsequent progression of irreversible disability.


title: Semantic segmentation with multi-path refinement and pyramid pooling dilated-resnet

abstract: Recently, fully convolutional network (FCN) and dilated convolution have shown significantly improvment in semantic segmentation task. Deep residual network (ResNet) has shown strong ability in object recognition. However, FCN-based methods utilize intermediate layers and spatial context information ineffectively. Repeated 2-step striding in ResNet is harmful for segmentation tasks. In this paper, we propose a new semantic segmentation method based on FCN and ResNet. Here, we combine the dilated convolution designed for semantic segmentation with residual unit to enlarge receptive field of ResNet. Meanwhile, “pre-activation” method is used in dilated residual unit. We propose a new segmentation architecture which intergrate multiple intermediate layers and global context information. We extract low-level features of intermediate layers with multi-path refinement which consists of relu-conv unit and chained residual pooling. Global context is gathered by a pyramid pooling method which is connected to the final output of ResNet. Outputs of these 2 modules are then fused to reach high-resolution prediction. In this way, global and local information from pyramid pooling can be enhanced by multi-path refinement. Fully-connected conditional random field is added as a “post-processing” after fusion to receive accurate boundary performance. Our proposed approach achieves 74.7% IoU on Cityscapes benchmark.


title: Artificial Intelligence and Legal Liability

abstract: A recent issue of a popular computing journal asked which laws would apply if a self-driving car killed a pedestrian. This paper considers the question of legal liability for artificially intelligent computer systems. It discusses whether criminal liability could ever apply; to whom it might apply; and, under civil law, whether an AI program is a product that is subject to product design legislation or a service to which the tort of negligence applies. The issue of sales warranties is also considered. A discussion of some of the practical limitations that AI systems are subject to is also included.


title: Deep Reader: Information extraction from Document images via relation extraction and Natural Language

abstract: Recent advancements in the area of Computer Vision with state-of-art Neural Networks has given a boost to Optical Character Recognition (OCR) accuracies. However, extracting characters/text alone is often insufficient for relevant information extraction as documents also have a visual structure that is not captured by OCR. Extracting information from tables, charts, footnotes, boxes, headings and retrieving the corresponding structured representation for the document remains a challenge and finds application in a large number of real-world use cases. In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image. The model schema allows for an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. DeepReader has a suite of state-of-the-art vision algorithms which are applied to recognize handwritten and printed text, eliminate noisy effects, identify the type of documents and detect visual entities like tables, lines and boxes. Deep Reader maps the extracted entities into a rich relational schema so as to capture all the relevant relationships between entities (words, textboxes, lines etc.) detected in the document. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added on top of the relationship schema so that a non-technical user, specifying the queries in natural language, can fetch the information with minimal effort. In this paper, we also demonstrate many different capabilities of Deep Reader and report results on a real-world use case.


title: Bandwidth Enhancement of Flip-Flops Using Feedback for High-Speed Integrated Circuits

abstract: This brief presents a high-speed inductorless D flip-flop (DFF) architecture that works on the principle of equalization using two feedbacks. Feedback from the first latch output to the input effectively results in a linear equalizer, whereas the feedback of the flip-flop output to the input culminates in decision feedback equalization. The proposed feedback-based DFF designed in a 90-nm CMOS technology shows 37.5% improvement in speed, as compared with the conventional flip-flops (conv-DFFs) without feedbacks. Post-layout circuit simulations also show an overall 33.7% improvement in the speed of a high-speed pseudorandom binary sequence generator when such feedbacks are added to conv-DFFs in it. Additionally, when this technique is used in a Hogge phase detector for clock and data recovery, 89% improvement in vertical eye opening and 73% reduction in pattern-dependent jitter at the input of the phase detector are observed. In essence, the technique effectively achieves distributed equalization in high-speed communication circuits.


title: Stretchable Heater Using Ligand-Exchanged Silver Nanowire Nanocomposite for Wearable Articular Thermotherapy.

abstract: Thermal therapy is one of the most popular physiotherapies and it is particularly useful for treating joint injuries. Conventional devices adapted for thermal therapy including heat packs and wraps have often caused discomfort to their wearers because of their rigidity and heavy weight. In our study, we developed a soft, thin, and stretchable heater by using a nanocomposite of silver nanowires and a thermoplastic elastomer. A ligand exchange reaction enabled the formation of a highly conductive and homogeneous nanocomposite. By patterning the nanocomposite with serpentine-mesh structures, conformal lamination of devices on curvilinear joints and effective heat transfer even during motion were achieved. The combination of homogeneous conductive elastomer, stretchable design, and a custom-designed electronic band created a novel wearable system for long-term, continuous articular thermotherapy.


title: Medical Imaging of Oral and Oropharyngeal Cancer

abstract: Oral cancer is associated with documented risk factors, yet no comprehensive screening program is in place in the United States for early detection of the disease. Oral cancer often is diagnosed in more advanced stages, resulting in a poor prognosis. Dental practitioners and radiographers play an important role in the management of the disease and in helping to improve the quality of life for people who have oral cancer. This article discusses types of oral and oropharyngeal cancer, their diagnosis, treatment options, and the role of dental imaging in patients with these cancers.


title: A Character-Word Compositional Neural Language Model for Finnish

abstract: Inspired by recent research, we explore ways to model the highly morphological Finnish language at the level of characters while maintaining the performance of word-level models. We propose a new Character-to-Word-to-Character (C2W2C) compositional language model that uses characters as input and output while still internally processing word level embeddings. Our preliminary experiments, using the Finnish Europarl V7 corpus, indicate that C2W2C can respond well to the challenges of morphologically rich languages such as high out of vocabulary rates, the prediction of novel words, and growing vocabulary size. Notably, the model is able to correctly score inflectional forms that are not present in the training data and sample grammatically and semantically correct Finnish sentences character by character.


title: Processing Of Sentences With Intra-Sentential Code-Switching

abstract: Speakers of certain bilingual communities systematically produce utterances in which they switch from one language to another, suggesting that the two language systems systematically interact with each other in the production (and recognition) of these sentences. We have investigated this phenomenon in a formal or computational framework which consists of two grammatical systems and a mechanism for switching between the two systems. A variety of constraints apparent in these sentences are then explained in terms of constraints on the switching mechanism, especially, those on closed class items.


title: Loop-mediated isothermal amplification (LAMP): recent progress in research and development

abstract: Loop-mediated isothermal amplification (LAMP) is an established technology that continues to attract the attention of researchers in many fields. Research and development efforts on LAMP technology in recent years have focused on two major areas; first, the study of its clinical application as an approved in vitro diagnostics tool in Japan and certain other countries; and second, research aimed at further simplifying the LAMP test process. This review provides an overview of the status of LAMP on these two topics by summarizing research work conducted, in the main, after our previous review article.


title: Identity-Preserving Face Recovery from Portraits

abstract: Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, a recovery process that can preserve identity is challenging because the fine details of real faces can be distorted or lost in stylized images. In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) to recover latent photorealistic faces from unaligned stylized portraits. Our IFRP method consists of two components: Style Removal Network (SRN) and Discriminative Network (DN). The SRN is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. By embedding spatial transformer networks into the SRN, our method can compensate for misalignments of stylized faces automatically and output aligned realistic face images. The role of the DN is to enforce recovered faces to be similar to authentic faces. To ensure the identity preservation, we promote the recovered and ground-truth faces to share similar visual features via a distance measure which compares features of recovered and ground-truth faces extracted from a pre-trained VGG network. We evaluate our method on a large-scale synthesized dataset of real and stylized face pairs and attain state of the art results. In addition, our method can recover photorealistic faces from previously unseen stylized portraits, original paintings and human-drawn sketches.


title: Towards surround stereo vision: Analysis of a new surround view camera configuration for driving assistance applications

abstract: The authors propose a new camera configuration for automotive surround view systems where the criteria for which the camera mountings are optimized is derived not from visualization, but optimal depth measurements. The authors show that based on special algorithmic processing it is feasible to achieve three dimensional (3D) reconstruction in almost the complete surrounding of the car. In order to accomplish this, more than four (4) cameras are required and the positions have to be carefully selected. An analysis of the proposed configuration is performed with regard to the field of view overlaps and the authors show that the estimated depth is good compared to reference measurements conducted with a lidar sensor.


title: Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

abstract: Learning deeper convolutional neural networks has become a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be attained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, which encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015 Scene Classification Challenge. Extensive experiments on two large scale challenging datasets demonstrate the effectiveness of our method is not restricted to a specific dataset or network architecture.


title: Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): a co-word analysis

abstract: The study utilized co-word analysis to explore papers in the field of Internet of Things to examine the scientific development in the area. The research data were retrieved from the WOS database from the period between 2000 and 2014, which consists of 758 papers. By using co-word analysis, this study found 7 clusters that represent the intellectual structure of IoT, including `IoT and Security', `Middleware', `RFID', `Internet', `Cloud computing', `Wireless sensor networks' and `6LoWPAN'. To understand these intellectual structures, this study used a co-occurrence matrix based on Pearson's correlation coefficient to create a clustering of the words using the hierarchical clustering technique. To visualize these intellectual structures, this study carried out a multidimensional scaling analysis, to which a PROXCAL algorithm was applied.


title: The Alzheimer's Disease Neuroimaging Initiative positron emission tomography core

abstract: Abstract Background This is a progress report of the Alzheimer's Disease Neuroimaging Initiative (ADNI) positron emission tomography (PET) Core. Methods The Core has supervised the acquisition, quality control, and analysis of longitudinal [ 18 F]fluorodeoxyglucose PET (FDG-PET) data in approximately half of the ADNI cohort. In an "add on" study, approximately 100 subjects also underwent scanning with [ 11 C] Pittsburgh compound B PET for amyloid imaging. The Core developed quality control procedures and standardized image acquisition by developing an imaging protocol that has been widely adopted in academic and pharmaceutical industry studies. Data processing provides users with scans that have identical orientation and resolution characteristics despite acquisition on multiple scanner models. The Core labs have used many different approaches to characterize differences between subject groups (Alzheimer's disease, mild cognitive impairment, controls), to examine longitudinal change over time in glucose metabolism and amyloid deposition, and to assess the use of FDG-PET as a potential outcome measure in clinical trials. Results ADNI data indicate that FDG-PET increases statistical power over traditional cognitive measures, might aid subject selection, and could substantially reduce the sample size in a clinical trial. Pittsburgh compound B PET data showed expected group differences, and identified subjects with significant annual increases in amyloid load across the subject groups. The next activities of the PET core in ADNI will entail developing standardized protocols for amyloid imaging using the [ 18 F]-labeled amyloid imaging agent AV45, which can be delivered to virtually all ADNI sites. Conclusions ADNI has demonstrated the feasibility and utility of multicenter PET studies and is helping to clarify the role of biomarkers in the study of aging and dementia.


title: Multi-camera visual SLAM for autonomous navigation of micro aerial vehicles

abstract: In this paper, we present a visual simultaneous localization and mapping (SLAM) system which integrates measurements from multiple cameras to achieve robust pose tracking for autonomous navigation of micro aerial vehicles (MAVs) in unknown complex environments. We analyze the iterative optimizations for pose tracking and map refinement of visual SLAM in multi-camera cases. The analysis ensures the soundness and accuracy of each optimization update. A well-known monocular visual SLAM system is extended to utilize two cameras with non-overlapping fields of view (FOVs) in the final implementation. The resulting visual SLAM system enables autonomous navigation of an MAV in complex scenarios. The theory behind this system can easily be extended to multi-camera configurations, when the onboard computational capability allows this. For operations in large-scale environments, we modify the resulting visual SLAM system to be a constant-time robust visual odometry. To form a full visual SLAM system, we further implement an efficient back-end for loop closing. The back-end maintains a keyframe-based global map, which is also used for loop-closure detection. An adaptive-window pose-graph optimization method is proposed to refine keyframe poses of the global map and thus correct pose drift that is inherent in the visual odometry. We demonstrate the efficiency of the proposed visual SLAM system for applications onboard of MAVs in experiments with both autonomous and manual flights. The pose tracking results are compared with ground truth data provided by an external tracking system. A SLAM system integrating measurements from multiple cameras for MAVs is proposed.No overlap in the respective fields of view of the multiple cameras is required.Robust pose-tracking can be achieved in complex environments.Mathematical analysis on the iterative optimizations in visual SLAM is provided.The efficiency of the proposed visual SLAM system is demonstrated onboard of MAVs.


title: Design of Permanent Magnet-Assisted Synchronous Reluctance Motor for Maximized Back-EMF and Torque Ripple Reduction

abstract: This paper proposes the optimization of the shape and size of a ferrite magnet and the arrangements necessary to reduce torque ripple and maximize back electromotive force (B-EMF) in a permanent-magnet-assisted synchronous reluctance motor. First, four different models of general magnet arrangements were selected, and analysis using the finite-element method was performed under open circuit and load operations for each of the four models. Next, the relationship between the cogging torque and the torque ripple was analyzed, considering the segment saturation phenomenon. Then, based on the initial model, the design was optimized for maximum B-EMF and minimum torque ripple, and the total harmonic distortion was measured. Finally, the validity and superiority of the optimized design were confirmed by manufacturing a prototype motor and performing the experiment.


title: Intestinal permeability – a new target for disease prevention and therapy

abstract: Data are accumulating that emphasize the important role of the intestinal barrier and intestinal permeability for health and disease. However, these terms are poorly defined, their assessment is a matter of debate, and their clinical significance is not clearly established. In the present review, current knowledge on mucosal barrier and its role in disease prevention and therapy is summarized. First, the relevant terms 'intestinal barrier' and 'intestinal permeability' are defined. Secondly, the key element of the intestinal barrier affecting permeability are described. This barrier represents a huge mucosal surface, where billions of bacteria face the largest immune system of our body. On the one hand, an intact intestinal barrier protects the human organism against invasion of microorganisms and toxins, on the other hand, this barrier must be open to absorb essential fluids and nutrients. Such opposing goals are achieved by a complex anatomical and functional structure the intestinal barrier consists of, the functional status of which is described by 'intestinal permeability'. Third, the regulation of intestinal permeability by diet and bacteria is depicted. In particular, potential barrier disruptors such as hypoperfusion of the gut, infections and toxins, but also selected over-dosed nutrients, drugs, and other lifestyle factors have to be considered. In the fourth part, the means to assess intestinal permeability are presented and critically discussed. The means vary enormously and probably assess different functional components of the barrier. The barrier assessments are further hindered by the natural variability of this functional entity depending on species and genes as well as on diet and other environmental factors. In the final part, we discuss selected diseases associated with increased intestinal permeability such as critically illness, inflammatory bowel diseases, celiac disease, food allergy, irritable bowel syndrome, and--more recently recognized--obesity and metabolic diseases. All these diseases are characterized by inflammation that might be triggered by the translocation of luminal components into the host. In summary, intestinal permeability, which is a feature of intestinal barrier function, is increasingly recognized as being of relevance for health and disease, and therefore, this topic warrants more attention.


title: If You Can't Beat Them, Join Them: Learning with Noisy Data

abstract: Vision capabilities have been significantly enhanced in recent years due to the availability of powerful computing hardware and sufficiently large and varied databases. However, the labelling of these image databases prior to training still involves considerable effort and is a roadblock for truly scalable learning. For instance, it has been shown that tag noise levels in Flickr images are as high as 80%. In an effort to exploit large images datasets therefore, extensive efforts have been invested to reduce the tag noise of the data by refining the image tags or by developing robust learning frameworks. In this work, we follow the latter approach, where we propose a multi-layer neural network-based noisy learning framework that incorporates noise probabilities of a training dataset. These are then utilized effectively to perform learning with sustained levels of accuracy, even in the presence of significant noise levels. We present results on several datasets of varying sizes and complexity and demonstrate that the proposed mechanism is able to outperform existing methods, despite often employing weaker constraints and assumptions.