title: Is there a trade-off between cognitive and motor recovery after traumatic brain injury due to competition for limited neural resources?

abstract: The status of neurorehabilitation for traumatic brain injury (TBI) is under active debate because of a dearth of research findings demonstrating effectiveness. This may be due, in part, to limitations in our understanding of basic mechanisms of cognitive and motor recovery,including those that might impede recovery. In this regard, we examined whether overall recovery following TBI might be undermined by competition between cognitive and motor functions for finite neural resources during recovery. In this preliminary study, 21 moderately and severely impaired patients were administered cognitive and motor assessments at 1, 4, and 12 months post-TBI, and recovery of cognitive and motor functions was measured using regression residuals. Negative correlations between recovery of cognitive versus motor functions were used as the index of competition. We found suggestive evidence that there may indeed be a trade-off between the recovery of cognitive and motor functions after TBI. Implications for rehabilitation are discussed.


title: Personalizing search via automated analysis of interests and activities

abstract: We formulate and study search algorithms that consider a user's prior interactions with a wide variety of content to personalize that user's current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques that leverage implicit information about the user's interests. This information is used to re-rank Web search results within a relevance feedback framework. We explore rich models of user interests, built from both search-related information, such as previously issued queries and previously visited Web pages, and other information about the user such as documents and email the user has read and created. Our research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search. We show that such personalization algorithms can significantly improve on current Web search.


title: Sub-Nyquist Radar via Doppler Focusing

abstract: We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets, sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem, but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here we describe a sub-Nyquist sampling and recovery approach called Doppler focusing which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys an SNR increase which scales linearly with P, obtaining good detection performance even at SNRs as low as -25dB. The recovery is based on the Xampling framework, which allows reducing the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling prototype.


title: Relations among Children's Social Goals, Implicit Personality Theories, and Responses to Social Failure.

abstract: Two studies examined children's thought patterns in relation to their responses to social challenge. In Study 1, 4th and 5th graders tried out for a pen pal club under either a performance goal (stressing the evaluative nature of the tryout) or a learning goal (emphasizing the potential learning opportunities). In their behavior and attributions following rejection, children who were focused on a performance goal reacted with more helplessness, whereas children given a learning goal displayed a more mastery-oriented response. Study 2 found that in response to hypothetical socially challenging situations, 4th, 5th, and 6th graders who believed personality was nonmalleable (entity theorists) vs. malleable (incremental theorists) were more likely to endorse performance goals. Together, these studies indicate that children's goals in social situations are associated with their responses to social failure and are predicted by their implicit theories about their personality.


title: RGBD Co-saliency Detection via Bagging-Based Clustering

abstract: With the additional depth information, RGBD co-saliency detection, which is an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of RGBD images. This letter proposes a novel RGBD co-saliency model using bagging-based clustering. First, candidate object regions are generated based on RGBD single saliency maps and region pre-segmentation. Then, in order to make regional clustering more robust to different image sets, the feature bagging method is introduced to randomly generate multiple clustering results and the cluster-level weak co-saliency maps. Finally, a clustering quality (CQ) criterion is devised to adaptively integrate the weak co-saliency maps into the final co-saliency map for each image. Experimental results on a public RGBD co-saliency dataset show that the proposed co-saliency model significantly outperforms the state-of-the-art co-saliency models.


title: Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks

abstract: The paper explores the usefulness of hybridizing two distinct nature inspired computational intelligence techniques viz., Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for modeling slump of Ready Mix Concrete (RMC) based on its design mix constituents viz., cement, fly ash, sand, coarse aggregates, admixture and water-binder ratio. The methodology utilizes the universal function approximation ability of ANN for imbibing the subtle relationships between the input and output variables and the stochastic search ability of GA for evolving the initial optimal weights and biases of the ANN to minimize the probability of neural network getting trapped at local minima and slowly converging to global optimum. The performance of hybrid model (ANN-GA) was compared with commonly used back-propagation neural network (BPNN) using six different statistical parameters. The study showed that by hybridizing ANN with GA, the convergence speed of ANN and its accuracy of prediction can be improved. The trained hybrid model can be used for predicting slump of concrete for a given concrete design mix in quick time without performing multiple trials with different design mix proportions.


Energy-Efficient Hash Join Implementations in Hardware-Accelerated MPSoCs


title: Coping with stress during childhood and adolescence : Problems, progress, and potential in theory and research

abstract: Progress and issues in the study of coping with stress during childhood and adolescence are reviewed. Definitions of coping are considered, and the relationship between coping and other aspects of responses to stress (e.g., temperament and stress reactivity) is described. Questionnaire, interview, and observation measures of child and adolescent coping are evaluated with regard to reliability and validity. Studies of the association of coping with symptoms of psychopathology and social and academic competence are reviewed. Initial progress has been made in the conceptualization and measurement of coping, and substantial evidence has accumulated on the association between coping and adjustment. Problems still remain in the conceptualization and measurement of coping in young people, however, and aspects of the development and correlates of coping remain to be identified. An agenda for future research on child-adolescent coping is outlined.


title: Larvicidal activities of chinaberry, neem and Bacillus thuringiensis israelensis (Bti) to an insecticide resistant population of Anopheles arabiensis from Tolay, Southwest Ethiopia

abstract: Abstract Objective To elucidate the larvicidal potency of neem, chinaberry and Bacillus thuringiensis israelensis (Bti) to larvae of Anopheles arabiensis under semi-field condition and adult susceptibility/resistance to the conventionally used insecticides in Tolay, Southwestern Ethiopia. Methods Wild collected 3rd and 4th stage larvae were exposed to neem, and chinaberry seed powder dissolved in water and Bti in artificial containers at three treatment levels: 0.2, 0.1 and 0.05 g/m 2 and controls were free of treatments. Larval and pupal mortalities were monitored daily and residual activities were determined. The experiments were replicated three times. The World Health Organization tube test for all classes of insecticides was conducted on adult Anopheles arabiensis reared from field collected larvae and pupae. Data were analyzed using STATA software version 11. Results In the first application, neem powder caused 88.9%, 87.9% and 79.4% larval and pupal mortality at 0.2, 0.1 and 0.05 g/m 2 after 4.3, 6.0 and 5.7 days, respectively. The corresponding killing effect of chinaberry was 80.3%, 62.1% and 30.3% after 7.0, 7.7 and 8.3 days respectively. Bti at all treatments killed 100% after 24 h except 2.7 days for 0.05 g/m 2 . Adult mosquitoes were susceptible only for fenitrothion and pirimiphos-methyl with 100% mortality while resistant to deltamethrin, alpha-cypermethrin, etofenprox and dichloro-diphenyl-tricgloroethane with only 9.0%, 3.0%, 5.1% and 2.0% mortalities respectively. Conclusions Neem, chinaberry and Bti showed potent larvicidal and pupicidal activities. However, in the area, high level of mosquito resistance to pyrethroids and dichloro-diphenyl-tricgloroethane was seen which will pose serious challenge to vector control in the future. Therefore, using integrated approach including these botanical larvicides is warranted to manage insecticide resistance.


title: Managing Modular Ontology Evolution Under Big Data Integration

abstract: Big Data integration frameworks provide unified view of the data available from heterogeneous data sources. These data sources are continuously evolving, forcing systems that integrate them to adapt their global schema after each change. This gets more challenging when aiming to maintain the global schema always reflecting data sources content. To cope with such complexity, in this paper we describe evolution scenarios and manage modular ontology evolution within Big Data integration framework in an a priori way according to changes performed against the data sources.


title: A systemic and cognitive approach for IoT security

abstract: The Internet of Things (IoT) will enable objects to become active participants of everyday activities. Introducing objects into the control processes of complex systems makes IoT security very difficult to address. Indeed, the Internet of Things is a complex paradigm in which people interact with the technological ecosystem based on smart objects through complex processes. The interactions of these four IoT components, person, intelligent object, technological ecosystem, and process, highlight a systemic and cognitive dimension within security of the IoT. The interaction of people with the technological ecosystem requires the protection of their privacy. Similarly, their interaction with control processes requires the guarantee of their safety. Processes must ensure their reliability and realize the objectives for which they are designed. We believe that the move towards a greater autonomy for objects will bring the security of technologies and processes and the privacy of individuals into sharper focus. Furthermore, in parallel with the increasing autonomy of objects to perceive and act on the environment, IoT security should move towards a greater autonomy in perceiving threats and reacting to attacks, based on a cognitive and systemic approach. In this work, we will analyze the role of each of the mentioned actors in IoT security and their relationships, in order to highlight the research challenges and present our approach to these issues based on a holistic vision of IoT security.


title: Quasi-Homography Warps in Image Stitching

abstract: Naturalness of warping is gaining extensive attention in image stitching. Recent warps such as SPHP, AANAP and GSP, use a global similarity to effectively mitigate projective distortion (which enlarges regions), however, they necessarily bring in perspective distortion (which generates inconsistency). In this paper, we propose a quasi-homography warp, which balances perspective distortion against projective distortion in the non-overlapping region, to create natural-looking mosaics. Our approach formulates the warp as a solution of a system of bivariate equations, where perspective distortion and projective distortion are characterized as slope preservation and scale linearization respectively. Our proposed warp only relies on a global homography thus is totally parameter-free. A comprehensive experiment shows that quasi-homography outperforms some state-of-the-art warps in urban scenes, including homography, AutoStitch and SPHP. A user study demonstrates that quasi-homography wins most users' favor as well, comparing to homography and SPHP.


title: A Common Backend for Hardware Acceleration on FPGA

abstract: Field Programmable Gate Arrays (FPGAs) are configurable integrated circuits able to provide a good trade-off in terms of performance, power consumption, and flexibility with respect to other architectures, like CPUs, GPUs and ASICs. The main drawback in using FPGAs, however, is their steep learning curve. An emerging solution to this problem is to write algorithms in a Domain Specific Language (DSL) and to let the DSL compiler generate efficient code targeting FPGAs. This work proposes FROST, a unified backend that enables different DSL compilers to target FPGA architectures. Differently from other code generation frameworks targeting FPGA, FROST exploits a scheduling co-language that enables users to have full control over which optimizations to apply in order to generate efficient code (e.g. loop pipelining, array partitioning, vectorization). At first, FROST analyzes and manipulates the input Abstract Syntax Tree (AST) in order to apply FPGA-oriented transformations and optimizations, then generates a C/C++ implementation suitable for High-Level Synthesis (HLS) tools. Finally, the output of HLS phase is synthesized and implemented on the target FPGA using Xilinx SDAccel toolchain. The experimental results show a speedup up of 15× with respect to O3-optimized implementations of the same algorithms on CPU.


title: On "shot around a corner" in first-person shooter games

abstract: Lag compensation is widely implemented in shooter games to mitigate the impact of network latency. "Shot around a corner" is an inconsistency introduced by lag compensation that still exists in many recent titles. In this paper, we investigate the influence of network latency and movement speeds on the rollback distances, and how they in turn affect the perceived "shot around a corner" frequencies and fairness. We find that a 250ms limit on lag compensation is reasonable for a movement speed of 4m/s and that experienced FPS players are aware of how frequently they are shot around a corner. We also suggest future experiments on "shot around a corner" to use different game modes and inject latency only to a subset of players.


title: User Intent, Behaviour, and Perceived Satisfaction in Product Search

abstract: As online shopping becomes increasingly popular, users perform more product search to purchase items. Previous studies have investigated people's online shopping behaviours and ways to predict online purchases. However, from a user perspective, there still lacks an in-depth understanding of why users search, how they interact with, and perceive the product search results. In this paper, we conduct both a user study and a log analysis to we address the following three questions: (1) what are the intents of users underlying their search activities? (2) do users behave differently under different search intents? and (3) how does user perceived satisfaction relate to their search behaviour as well as search intents, and can we predict product search satisfaction with interaction signals? Based on an online survey and search logs collected from a major commercial product search engine, we show that user intents in product search fall into three categories: Target Finding (TF), Decision Making (DM) and Exploration (EP). Through a log analysis and a user study, we observe different user interaction patterns as well as perceived satisfaction under these three intents. Using a series of user interaction features, we demonstrate that we can effectively predict user satisfaction, especially for TF and DM intents.


title: Automatic nuclear cataract grading using image gradients

abstract: This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.


title: Context awareness in WBANs: a survey on medical and non-medical applications

abstract: Wireless Body Area Network (WBAN) applications help reduce medical costs and improve people's quality of life by monitoring a user's biological signals via wearable and implantable wireless sensors. In order to satisfy burgeoning requirements, context-aware solutions are needed, thus allowing the system to adapt to changes in the user's mood, mental states, biological signals, and the environment. Thus, contextual information must be measured alongside biological signals in order to characterize and understand the current situation to adapt the system. With these issues in mind, this survey presents an overview of context-aware solutions at the Medium Access Control (MAC) and application layers. A clear distinction between medical and non-medical applications is made and some promising latest commercial WBAN products are highlighted. We show that, despite the importance of context-awareness in WBAN applications, there are limited solutions available, particularly at the MAC layer. The survey concludes with a discussion on open research challenges for future context-aware WBANs.


Ethics and Values


title: System dynamic performance improvement by coordinated modulation control of multi-infeed HVDC for CSG in 2008

abstract: This paper describes part of our recent works on a project of China Southern power grid (CSG), which involves the simultaneous coordinated tuning of the three HVDC modulation controllers, with wide-area signals, in 2008's CSG to damp interarea oscillations. Due to the modeling limitation of HVDC control loops in small signal stability analysis, previous works on similar projects of CSG strongly depended on trial and error method, which tests each set of controller parameters with time-domain simulation to observe how the controllers work. The paper induces a novel HVDC modulation model compatible with NEVA. Using the linearized system model and a hybrid stochastic nonlinear optimization algorithm, combined with genetic algorithm and simulated annealing algorithm, a strong nonlinear modal interaction phenomenon which is so called 'modal resonance' is avoided. Furthermore, the parameters of the three HVDC modulation controllers are optimized simultaneously. Time domain simulations with NETOMAC validate the efficiency of the coordinated control scheme. (7 pages)


title: Learning Where to Classify in Multi-view Semantic Segmentation

abstract: There is an increasing interest in semantically annotated 3D models, e.g. of cities. The typical approaches start with the semantic labelling of all the images used for the 3D model. Such labelling tends to be very time consuming though. The inherent redundancy among the overlapping images calls for more efficient solutions. This paper proposes an alternative approach that exploits the geometry of a 3D mesh model obtained from multi-view reconstruction. Instead of clustering similar views, we predict the best view before the actual labelling. For this we find the single image part that bests supports the correct semantic labelling of each face of the underlying 3D mesh. Moreover, our single-image approach may surprise because it tends to increase the accuracy of the model labelling when compared to approaches that fuse the labels from multiple images. As a matter of fact, we even go a step further, and only explicitly label a subset of faces (e.g. 10%), to subsequently fill in the labels of the remaining faces. This leads to a further reduction of computation time, again combined with a gain in accuracy. Compared to a process that starts from the semantic labelling of the images, our method to semantically label 3D models yields accelerations of about 2 orders of magnitude. We tested our multi-view semantic labelling on a variety of street scenes.


title: Analytical methods for the quantification of volatile aromatic compounds

abstract: Abstract The investigation of odorants is not an easy task, which needs to be undertaken in the context of fit-for-purpose quality systems. To date, great attention has been paid to determination of the volatile fractions of odorants, since they are responsible for the attributes of global flavor [i.e. a combination of olfactory (aroma) and gustatory (taste) sensations produced by chemicals]. This kind of determination can be carried out by analytical techniques [e.g., gas chromatography (GC) combined with mass spectrometry and/or olfactometric GC]. Methods complementary to GC analysis are available, allowing assessment of the olfactory impact by an electronic nose (e-nose) or a panel of selected individuals. Also, we consider some innovative analytical techniques to study the effects of odorants in food during consumption.


title: Tactical Differences in Coping With Rejection Sensitivity: The Role of Prevention Pride

abstract: Prevention pride reflects a person’s subjective history of success in preventing negative outcomes, leading to a strategic avoidance of errors of commission (e.g., explicit mistakes) in new situations. Two studies examined the impact of prevention pride on the strategies that highly rejection sensitive (HRS) people use to cope with the anxiety of anticipated rejection and the negative feelings elicited by perceived rejection. It was hypothesized that prevention pride orientation would lead HRS people toward covert and passive rather than overt and active forms of negative coping. Results indicated that HRS individuals who were also high in prevention pride reported increased use of self-silencing, presumably to prevent rejection. When rejection was perceived, however, they expressed hostility passively, by reducing positive behavior (e.g., withdrawing love and support) while inhibiting direct, active acts of hostility (e.g., yelling).


title: BRAND EQUITY, BRAND LOYALTY AND CONSUMER SATISFACTION

abstract: This study aims to investigate the mediating effects of consumer satisfaction on the relationship between consumer-based brand equity and brand loyalty in the hotel and restau- rant industry. Based on a sample of 378 customers and using structural equation modelling approach, the five dimensions of brand equity—physical quality, staff behaviour, ideal self- congruence, brand identification and lifestyle-congruence—are found to have positive effects on consumer satisfaction. The findings of the study suggest that consumer satisfaction par- tially mediates the effects of staff behaviour, ideal self-congruence and brand identification on brand loyalty. The effects of physical quality and lifestyle-congruence on brand loyalty are fully mediated by consumer satisfaction. Keywords: brand equity, customer satisfaction, brand loyalty. � 2011 Elsevier Ltd. All rights reserved.


title: Extinctions in ancient and modern seas

abstract: In the coming century, life in the ocean will be confronted with a suite of environmental conditions that have no analog in human history. Thus, there is an urgent need to determine which marine species will adapt and which will go extinct. Here, we review the growing literature on marine extinctions and extinction risk in the fossil, historical, and modern records to compare the patterns, drivers, and biological correlates of marine extinctions at different times in the past. Characterized by markedly different environmental states, some past periods share common features with predicted future scenarios. We highlight how the different records can be integrated to better understand and predict the impact of current and projected future environmental changes on extinction risk in the ocean.


title: Not all lies are spontaneous: an examination of deception across different modes of communication

abstract: An online diary study was performed to investigate deception across different media. One hundred and four individuals participated in the study, with 76 completing the diaries. Individuals were most likely to lie on the telephone. Planned lies, which participants also rated the most serious, were more likely told via SMS (short message service) text messaging. Most lies were told to people participants felt closest to. The featurebased model provides a better account of the deceptions reported by participants than do media richness theory or social distance theory. However, the authors propose a reworked feature-based model to explain deception across different media. They suggest that instant messaging should be treated as a near synchronous mode of communication. We suggest that the model needs to distinguish between spontaneous and planned lies.


title: Verification of an implementation of Tomasulo’s algorithm by compositional model checking

abstract: An implementation of an out-of-order processing unit based on Tomasulo's algorithm is formally verified using compositional model checking techniques. This demonstrates that finite-state methods can be applied to such algorithms, without recourse to higher-order proof systems. The paper introduces a novel compositional system that supports cyclic environment reasoning and multiple environment abstractions per signal. A proof of Tomasulo's algorithm is outlined, based on refinement maps, and relying on the novel features of the compositional system. This proof is fully verified by the SMV verifier, using symmetry to reduce the number of assertions that must be verified.


title: An information fidelity criterion for image quality assessment using natural scene statistics

abstract: Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for "human consumption". Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at [1].


title: Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework

abstract: Technologies in autonomous vehicles have seen dramatic advances in recent years; however, it still lacks of robust perception systems for car detection. With the recent development in deep learning research, in this paper, we propose a LIDAR and vision fusion system for car detection through the deep learning framework. It consists of three major parts. The first part generates seed proposals for potential car locations in the image by taking LIDAR point cloud into account. The second part refines the location of the proposal boxes by exploring multi-layer information in the proposal network and the last part carries out the final detection task through a detection network which shares part of the layers with the proposal network. The evaluation shows that the proposed framework is able to generate high quality proposal boxes more efficiently (77.6% average recall) and detect the car at the state of the art accuracy (89.4% average precision). With further optimization of the framework structure, it has great potentials to be implemented onto the autonomous vehicle.


title: Perceived Service Quality in a University Web Portal: Revising the E-Qual Instrument

abstract: Online service quality is a much-studied concept. Despite this, dimensions that make up service quality, and the items used to measure those dimensions have proven unstable. It is widely suggested that service quality measurement scales need to be instantiated differently in different business domains. In addition, the nature of online services is continually changing. Universities have been at the forefront of this change, with university Websites increasingly acting as a portal for a wide range of online transactions for a wide range of stakeholders. In this work-in-progress, qualitative study, we conduct focus groups with a range of stakeholders in university Web portals with a view to adapting the e-qual instrument for use in a university Web portal environment. We find support for a new service quality dimension, and for additional items in existing scales. We conclude by proposing a revised instrument that can form the basis for a more extensive quantitative study


title: A Cross-Reading Approach to Smart City: A European Perspective of Chinese Smart Cities

abstract: The present study, after a literature review of the smart city definitions and ranking tools in Europe and in China, presents a cross-reading approach to the Chinese smart cities concept and implementation. It is indeed nowadays mandatory to re-convert cities in sustainable and smart ecosystems and this can be done with different approaches. In this frame, the role of ICT—the glue of the smart city concept—is central and pervasive. The Smart city model could be a way to reverse the actual trend of cities, re-defining an integrated approach between tangible and intangible infrastructures of cities. Future cities are influenced by two main different visions with different connotations that come along with the planning capacity and with the ability of countries to follow a coherent and sustainable development project. European approach for planning is quite consolidated and based on a long term holistic vision, while Chinese vision is catching up with the dramatic speed of urbanization, deploying critical infrastructures in most cases without a long-term view. On the other hand, Chinese projects are in some cases exemplary for Europe where many constraints and regulatory issues put a strong limitation on the many possible implementations.


title: The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

abstract: Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes. Second, train a model utilizing this data. Toward the goal of solving fine-grained recognition, we introduce an alternative approach, leveraging free, noisy data from the web and simple, generic methods of recognition. This approach has benefits in both performance and scalability. We demonstrate its efficacy on four fine-grained datasets, greatly exceeding existing state of the art without the manual collection of even a single label, and furthermore show first results at scaling to more than 10,000 fine-grained categories. Quantitatively, we achieve top-1 accuracies of \(92.3\,\%\) on CUB-200-2011, \(85.4\,\%\) on Birdsnap, \(93.4\,\%\) on FGVC-Aircraft, and \(80.8\,\%\) on Stanford Dogs without using their annotated training sets. We compare our approach to an active learning approach for expanding fine-grained datasets.


title: An interchangeable surgical instrument system with application to supervised automation of multilateral tumor resection

abstract: Many surgical procedures require a sequence of different end-effectors but switching tools for robot-assisted minimally-invasive surgery (RMIS) requires time-consuming removal and replacement through the trocar port. We present an interchangeable instrument system that can be contained within the body cavity. It is based on a novel mounting mechanism compatible with a standard RMIS gripper and a tool-guide and sleeve to facilitate automated instrument switching. Experiments suggest that an Intuitive Surgical system using these interchangeable instruments can perform a multi-step tumor resection procedure that uses a novel haptic probe to localize the tumor, standard scalpel to expose the tumor, standard grippers to extract the subcutaneous tumor, and a fluid injection tool to seal the wound. Design details and video are available at: http://berkeleyautomation.github.io/surgicaltools.


title: Revisiting Natural Gradient for Deep Networks

abstract: We evaluate natural gradient, an algorithm originally proposed in Amari (1997), for learning deep models. The contributions of this paper are as follows. We show the connection between natural gradient and three other recently proposed methods for training deep models: Hessian-Free (Martens, 2010), Krylov Subspace Descent (Vinyals and Povey, 2012) and TONGA (Le Roux et al., 2008). We describe how one can use unlabeled data to improve the generalization error obtained by natural gradient and empirically evaluate the robustness of the algorithm to the ordering of the training set compared to stochastic gradient descent. Finally we extend natural gradient to incorporate second order information alongside the manifold information and provide a benchmark of the new algorithm using a truncated Newton approach for inverting the metric matrix instead of using a diagonal approximation of it.


title: Coresets and sketches for high dimensional subspace approximation problems

abstract: We consider the problem of approximating a set P of η points in Rd by a j-dimensional subspace under the lp measure, in which we wish to minimize the sum of lp distances from each point of P to this subspace. More generally, the Fq (lp)-subspace approximation problem asks for a j-subspace that minimizes the sum of qth powers of lp-distances to this subspace, up to a multiplicative factor of (1 + e). We develop techniques for subspace approximation, regression, and matrix approximation that can be used to deal with massive data sets in high dimensional spaces. In particular, we develop coresets and sketches, i.e. small space representations that approximate the input point set P with respect to the subspace approximation problem. Our results are: • A dimensionality reduction method that can be applied to Fq (lp)-clustering and shape fitting problems, such as those in [8, 15]. • The first strong coreset for F1 (l2)-subspace approximation in high-dimensional spaces, i.e. of size polynomial in the dimension of the space. This coreset approximates the distances to any j-subspace (not just the optimal one). • A (1 + e)-approximation algorithm for the j-dimensional F1 (l2)-subspace approximation problem with running time ηd(j/e)O(1) + (η + d)2poly(j/e). • A streaming algorithm that maintains a coreset for the F1 (l2)-subspace approximation problem and uses a space of d [EQUATION] (weighted) points. • Streaming algorithms for the above problems with bounded precision in the turnstile model, i.e, when coordinates appear in an arbitrary order and undergo multiple updates. We show that bounded precision can lead to further improvements. We extend results of [7] for approximate linear regression, distances to subspace approximation, and optimal rank-j approximation, to error measures other than the Frobenius norm.


title: Hypervisor Memory Forensics

abstract: Memory forensics is the branch of computer forensics that aims at extracting artifacts from memory snapshots taken from a running system. Even though it is a relatively recent field, it is rapidly growing and it is attracting considerable attention from both industrial and academic researchers. ::: ::: In this paper, we present a set of techniques to extend the field of memory forensics toward the analysis of hypervisors and virtual machines. With the increasing adoption of virtualization techniques both as part of the cloud and in normal desktop environments, we believe that memory forensics will soon play a very important role in many investigations that involve virtual environments. ::: ::: Our approach, implemented in an open source tool as an extension of the Volatility framework, is designed to detect both the existence and the characteristics of any hypervisor that uses the Intel VT-x technology. It also supports the analysis of nested virtualization and it is able to infer the hierarchy of multiple hypervisors and virtual machines. Finally, by exploiting the techniques presented in this paper, our tool can reconstruct the address space of a virtual machine in order to transparently support any existing Volatility plugin - allowing analysts to reuse their code for the analysis of virtual environments.


title: Automatic text extraction and character segmentation using maximally stable extremal regions

abstract: Text detection and segmentation is an important prerequisite for many content based image analysis tasks. The paper proposes a novel text extraction and character segmentation algorithm using Maximally Stable Extremal Regions as basic letter candidates. These regions are then subjected to thresholding and thereafter various connected components are determined to identify separate characters. The algorithm is tested along a set of various JPEG, PNG and BMP images over four different character sets; English, Russian, Hindi and Urdu. The algorithm gives good results for English and Russian character set; however character segmentation in Urdu and Hindi language is not much accurate. The algorithm is simple, efficient, involves no overhead as required in training and gives good results for even low quality images. The paper also proposes various challenges in text extraction and segmentation for multilingual inputs.


title: Irrelevant Features and the Subset Selection Problem

abstract: We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets.


title: A Monte Carlo based energy efficient source localization method for wireless sensor networks

abstract: In this paper, we study the source localization problem in wireless sensor networks where the location of the source is estimated according to the quantized measurements received from sensors in the field. We propose an energy efficient iterative source localization scheme, where the algorithm begins with a coarse location estimate obtained from a set of anchor sensors. Based on the available data at each iteration, we approximate the posterior probability density function (pdf) of the source location using a Monte Carlo method and we use this information to activate a number of non-anchor sensors that minimize the Conditional Posterior Cramer Rao Lower Bound (C-PCRLB). Then we also use the Monte Carlo approximation of the posterior pdf of the source location to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that the proposed iterative method achieves the mean squared error that gets close to the unconditional Posterior Cramer Rao Lower Bound (PCRLB) for a Bayesian estimate based on quantized data from all the sensors within a few iterations. By selecting only the most informative sensors, the iterative approach also reduces the communication requirements significantly and resulting in energy savings.


title: Replication-associated gene dosage effects shape the genomes of fast-growing bacteria but only for transcription and translation genes

abstract: Summary The bidirectional replication of bacterial genomes leads to transient gene dosage effects. Here, we show that such effects shape the chromosome organisation of fast-growing bacteria and that they correlate strongly with maximal growth rate. Surprisingly the predicted maximal number of replication rounds shows little if any phylogenetic inertia, suggesting that it is a very labile trait. Yet, a combination of the- oretical and statistical analyses predicts that dozens of replication forks may be simultaneously present in the cells of certain species. This suggests a strikingly efficient management of the replication apparatus, of replication fork arrests and of chromosome segrega- tion in such cells. Gene dosage effects strongly con- strain the position of genes involved in translation and transcription, but not other highly expressed genes. The relative proximity of the former genes to the origin of replication follows the regulatory depen- dencies observed under exponential growth, as the bias is stronger for RNA polymerase, then rDNA, then ribosomal proteins and tDNA. Within tDNAs we find that only the positions of the previously proposed 'ubiquitous' tRNA, which translate the most frequent codons in highly expressed genes, show strong signs of selection for gene dosage effects. Finally, we pro- vide evidence for selection acting upon genome organisation to take advantage of gene dosage effects by identifying a positive correlation between genome stability and the number of simultaneous rep- lication rounds. We also show that gene dosage effects can explain the over-representation of highly expressed genes in the largest replichore of genomes containing more than one chromosome. Together, these results demonstrate that replication-associated gene dosage is an important determinant of chromo- some organisation and dynamics, especially among fast-growing bacteria.


title: Query suggestion based on user landing pages

abstract: This poster investigates a novel query suggestion technique that selects query refinements through a combination of many users' post-query navigation patterns and the query logs of a large search engine. We compare this technique, which uses the queries that retrieve in the top-ranked search results places where searchers end up after post-query browsing (i.e., the landing pages), with an approach based on query refinements from user search sessions extracted from query logs. Our findings demonstrate the effectiveness of using landing pages for the direct generation of query suggestions, as well as the complementary nature of the suggestions it generates with regard to traditional query log based refinement methodologies.


Dysregulation of Fatty Acid Metabolism in the Etiology of Type 2 Diabetes


A self-adaptive formulation for the Euler/Navier-Stokes coupling


title: A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment

abstract: Aiming at the current problems that most physical hosts in the cloud data center are so overloaded that it makes the whole cloud data center’ load imbalanced and that existing load balancing approaches have relatively high complexity, this paper has focused on the selection problem of physical hosts for deploying requested tasks and proposed a novel heuristic approach called Load Balancing based on Bayes and Clustering (LB-BC). Most previous works, generally, utilize a series of algorithms through optimizing the candidate target hosts within an algorithm cycle and then picking out the optimal target hosts to achieve the immediate load balancing effect. However, the immediate effect doesn’t guarantee high execution efficiency for the next task although it has abilities in achieving high resource utilization. Based on this argument, LB-BC introduces the concept of achieving the overall load balancing in a long-term process in contrast to the immediate load balancing approaches in the current literature. LB-BC makes a limited constraint about all physical hosts aiming to achieve a task deployment approach with global search capability in terms of the performance function of computing resource. The Bayes theorem is combined with the clustering process to obtain the optimal clustering set of physical hosts finally. Simulation results show that compared with the existing works, the proposed approach has reduced the failure number of task deployment events obviously, improved the throughput, and optimized the external services performance of cloud data centers.


title: Client as a first-class citizen: Practical user-centric network MIMO clustering

abstract: Recent advances have demonstrated the potential of network MIMO (netMIMO), which combines a practical number of distributed antennas as a virtual netMIMO AP (nAP) to improve spatial multiplexing of an WLAN. Existing solutions, however, either simply cluster nearby antennas as static nAPs, or dynamically cluster antennas on a per-packet basis so as to maximize the sum rate of the scheduled clients. To strike the balance between the above two extremes, in this paper, we present the design, implementation and evaluation of FlexNEMO, a practical two-phase netMIMO clustering system. Unlike previous per-packet clustering approaches, FlexNEMO only clusters antennas when client distribution and traffic pattern change, as a result being more practical to be implemented. A medium access control protocol is then designed to allow the clients at the center of nAPs to have a higher probability to gain access opportunities, but still ensure long-term fairness among clients. By combining on-demand clustering and priority-based access control, FlexNEMO not only improves antenna utilization, but also optimizes the channel condition for every individual client. We evaluated our design via both testbed experiments on USRPs and trace-driven emulations. The results demonstrate that FlexNEMO can deliver 94.7% and 93.7% throughput gains over static antenna clustering in a 4-antenna testbed and 16-antenna emulation, respectively.


title: Rank conditioned rank selection filters for signal restoration

abstract: A class of nonlinear filters called rank conditioned rank selection (RCRS) filters is developed and analyzed in this paper. The RCRS filters are developed within the general framework of rank selection (RS) filters, which are filters constrained to output an order statistic from the observation set. Many previously proposed rank order based filters can be formulated as RS filters. The only difference between such filters is in the information used in deciding which order statistic to output. The information used by RCRS filters is the ranks of selected input samples, hence the name rank conditioned rank selection filters. The number of input sample ranks used is referred to as the order of the RCRS filter. The order can range from zero to the number of samples in the observation window, giving the filters valuable flexibility. Low-order filters can give good performance and are relatively simple to optimize and implement. If improved performance is demanded, the order can be increased but at the expense of filter simplicity. In this paper, many statistical and deterministic properties of the RCRS filters are presented. A procedure for optimizing over the class of RCRS filters is also presented. Finally, extensive computer simulation results that illustrate the performance of RCRS filters in comparison with other techniques in image restoration applications are presented. >


title: College Female and Male Heavy Internet Users' Profiles of Practices and Their Academic Grades and Psychosocial Adjustment

abstract: Abstract This study presents the profiles of heavy Internet users and provides empirical evidence that it is not how much time university students spend online but what they do online that is associated with academic grades and psychological adjustment. Using a nationally representative sample from Taiwan, we employed K-mean cluster analysis and identified profiles based on nine Internet practices in which users engaged. Female heavy users favoring information seeking and chatting had better academic performance but tended to feel more depressed than nonheavy users, while those favoring information seeking, chatting, and online games had lower academic grades and greater loneliness, physical illness, and depression scores than nonheavy users. In contrast, only male heavy users favoring online games had lower academic grades, whereas those who favored information seeking, chatting, and online games were more likely than nonheavy users to feel physically ill and depressed.


title: Performance Analysis and Algorithm Designs for Transmit Antenna Selection in Linearly Precoded Multiuser MIMO Systems

abstract: This paper investigates transmit antenna selection for linearly precoded multiuser multiple-input-multiple-output (MU-MIMO) systems. First, in some precoded single-user MIMO systems, using all transmit antennas does not always lead to the best performance due to ill-conditioned channel matrices. This condition motivates us to investigate whether a similar result can be obtained in MU-MIMO systems. Based on the derived analytical results, we found that, for a given number of transmit antennas, decreasing the number of active transmit antennas [number of radio frequency (RF) units] always degrades system performance in the linearly precoded MU-MIMO systems. However, in practical systems, RF units are expensive. To reduce the hardware cost, antenna selection is usually used to reduce the number of RF units. Thus, we further analyze the performance loss due to transmit antenna selection (TAS). These analytical results provide good design references for using TAS in practical systems. Moreover, based on the analytical results, we proposed several simple TAS algorithms for linearly precoded MU-MIMO systems. Complexity analysis and simulation results show that the computational complexity of the proposed algorithms can significantly be reduced, whereas the performance is still comparable with the optimal selection scheme. As a result, the analyzed results enable us to better understand how TAS affects the MU-MIMO systems. In addition, the proposed algorithms make TAS more feasible to be used in practical systems.


title: Attribute Extraction from Product Titles in eCommerce

abstract: This paper presents a named entity extraction system for detecting attributes in product titles of eCommerce retailers like Walmart. The absence of syntactic structure in such short pieces of text makes extracting attribute values a challenging problem. We find that combining sequence labeling algorithms such as Conditional Random Fields and Structured Perceptron with a curated normalization scheme produces an effective system for the task of extracting product attribute values from titles. To keep the discussion concrete, we will illustrate the mechanics of the system from the point of view of a particular attribute - brand. We also discuss the importance of an attribute extraction system in the context of retail websites with large product catalogs, compare our approach to other potential approaches to this problem and end the paper with a discussion of the performance of our system for extracting attributes.


title: Blockchain applications in microgrids an overview of current projects and concepts

abstract: Since the release of Bitcoins as crypto currency, Bitcoin has played a prominent part in the media. However, not Bitcoin but the underlying technology blockchain offers the possibility to innovatively change industries. The decentralized structure of the blockchain is particularly suitable for implementing control and business processes in microgrids, using smart contracts and decentralized applications. This paper provides a state of the art survey overview of current blockchain technology based projects with the potential to revolutionize microgrids and provides a first attempt to technically characterize different start-up approaches. The most promising use case from the microgrid perspective is peer-to-peer trading, where energy is exchanged and traded locally between consumers and prosumers. An application concept for distributed PV generation is provided in this promising area.


title: Attentive Systems: A Survey

abstract: Visual saliency analysis detects salient regions/objects that attract human attention in natural scenes. It has attracted intensive research in different fields such as computer vision, computer graphics, and multimedia. While many such computational models exist, the focused study of what and how applications can be beneficial is still lacking. In this article, our ultimate goal is thus to provide a comprehensive review of the applications using saliency cues, the so-called attentive systems. We would like to provide a broad vision about saliency applications and what visual saliency can do. We categorize the vast amount of applications into different areas such as computer vision, computer graphics, and multimedia. Intensively covering 200+ publications we survey (1) key application trends, (2) the role of visual saliency, and (3) the usability of saliency into different tasks.


title: RFID- based supply chain traceability system

abstract: In this paper we propose a real solution for gathering information throughout the entire pig meat supply chain. The architecture consists of a a complex identification system based on RFID tags that transmits data to a distributed database during all phases of the production process. The specific work environment required identifying a suitable technology for implementation in the supply chain and the best possible organization. The aim of this work is to keep track of all the information generated during meat processing, not only for traceability purposes but chiefly for enhancing and optimizing production. All information generated by the traceability system will be collected in a central database accessible by end users thtough a public dedicated web interface.


title: PI controlled Bi-directional DC-DC converter (BDDDC) and highly efficient boost converter for electric vehicles

abstract: Due to the constraint of the fuel, the electric vehicle is designed for the modern world. Bidirectional DC to DC converters (BDDDC) has significantly enlarged the interest in Electric vehicle applications. A bidirectional DC to DC converter for Electric Vehicle (EV) for driving a DC motor is the aim of this present work. In a bi directional converter (conventional) PWM (Pulse Width Modulation) pulse is castoff for the generation of triggering pulse of the switches. We use PI and PID controller for triggering the IGBTs of DC to DC converter. A comparison is done between the three types of bidirectional dc to dc converter. Then a boost converter is designed over conventional boost converter by using parallel combinations of IGBT and MOSFET for electric vehicle applications.


title: Investigating training, transfer and viewpoint effects resulting from recurrent CBT of X-Ray image interpretation

abstract: X-ray screening of passenger bags is an essential task at airport security checkpoints. In this study we investigated how well airport security screeners can detect guns, knives, improvised explosive devices (IEDs) and other threat objects in X-ray images of passenger bags before and after 3 and 6 months of recurrent (about 20 min per week) computer-based training (CBT). Two experiments conducted at different airports gave very similar results. Training with X-ray Tutor (XRT), an individually adaptive CBT, resulted in large performance increases, especially for detecting IEDs. While performance for detecting IEDs was initially substantially lower than for guns, IEDs could be detected as well as guns after several months of training. A large transfer effect was observed as well: Training with XRT helped screeners recognize new threat objects that were similar in shape as the trained objects. Threat recognition was dependent on the rotation of the objects. If depicted from an unusual viewpoint, prohibited items were more difficult to recognize. The results were compared to two conventional (not adaptive) CBT systems. For one system no training and transfer effects were observed whereas small training and transfer effects were found for the other conventional CBT system.


Proposed Architecture of MongoDB-Hive Integration


title: UNDERSTANDING SOFTWARE PRODUCTIVITY UNDERSTANDING SOFTWARE PRODUCTIVITY

abstract: What affects software productivity and how do we improve it? This report examines the current state of the art in software productivity measurement. In turn, it describes a framework for understanding software productivity, some fundamentals of measurement, surveys empirical studies of software productivity, and identifies challenges involved in measuring software productivity. A radical alternative to current approaches is suggested: to construct, evaluate, deploy, and evolve a knowledge-based `software productivity modeling and simulation system' using tools and techniques from the domain of software process engineering.


title: Asset stock accumulation and sustainability of competitive advantage

abstract: Given incomplete factor markets, appropriate time paths of flow variables must be chosen to build required stocks of assets. That is, critical resources are accumulated rather than acquired in "strategic factor markets" Barney [Barney, J. 1986. Strategic factor markets: Expectations, luck, and business strategy. Management Sci. October 1231-1241.]. Sustainability of a firm's asset position hinges on how easily assets can be substituted or imitated. Imitability is linked to the characteristics of the asset accumulation process: time compression diseconomies, asset mass efficiencies, inter-connectedness, asset erosion and causal ambiguity.


title: A miniaturized broadband quasi-Yagi antenna for X- to Ku-band applications

abstract: A miniaturized, broadband quasi-Yagi antenna utilizing a modified ultra-wideband microstrip-to-coplanar stripline (MS-toCPS) balun is proposed. Excellent broadband phase and amplitude balance is possible due to the ultra-wideband MS-to-CPS balun based on field and impedance matching concept. The implemented antenna provides very wide bandwidth from 7 to 15.1GHz (73.3%). The gain of the antenna is from 3.7 to 5.5 dBi, the front-to-back ratio is more than 10 dB, and the nominal radiation efficiency is about 94%.


title: GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations

abstract: Recommending users with their preferred points-of-interest (POIs), e.g., museums and restaurants, has become an important feature for location-based social networks (LBSNs), which benefits people to explore new places and businesses to discover potential customers. However, because users only check in a few POIs in an LBSN, the user-POI check-in interaction is highly sparse, which renders a big challenge for POI recommendations. To tackle this challenge, in this study we propose a new POI recommendation approach called GeoSoCa through exploiting geographical correlations, social correlations and categorical correlations among users and POIs. The geographical, social and categorical correlations can be learned from the historical check-in data of users on POIs and utilized to predict the relevance score of a user to an unvisited POI so as to make recommendations for users. First, in GeoSoCa we propose a kernel estimation method with an adaptive bandwidth to determine a personalized check-in distribution of POIs for each user that naturally models the geographical correlations between POIs. Then, GeoSoCa aggregates the check-in frequency or rating of a user's friends on a POI and models the social check-in frequency or rating as a power-law distribution to employ the social correlations between users. Further, GeoSoCa applies the bias of a user on a POI category to weigh the popularity of a POI in the corresponding category and models the weighed popularity as a power-law distribution to leverage the categorical correlations between POIs. Finally, we conduct a comprehensive performance evaluation for GeoSoCa using two large-scale real-world check-in data sets collected from Foursquare and Yelp. Experimental results show that GeoSoCa achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.


title: R-Apriori: An Efficient Apriori based Algorithm on Spark

abstract: Association rule mining remains a very popular and effective method to extract meaningful information from large datasets. It tries to find possible associations between items in large transaction based datasets. In order to create these associations, frequent patterns have to be generated. The "Apriori" algorithm along with its set of improved variants, which were one of the earliest proposed frequent pattern generation algorithms still remain a preferred choice due to their ease of implementation and natural tendency to be parallelized. While many efficient single-machine methods for Apriori exist, the massive amount of data available these days is far beyond the capacity of a single machine. Hence, there is a need to scale across multiple machines to meet the demands of this ever-growing data. MapReduce is a popular fault-tolerant framework for distributed applications. Nevertheless, heavy disk I/O at each MapReduce operation hinders the implementation of efficient iterative data mining algorithms, such as Apriori, on MapReduce platforms. A newly proposed in-memory distributed dataflow platform called Spark overcomes the disk I/O bottlenecks in MapReduce. Therefore, Spark presents an ideal platform for distributed Apriori. However, in the implementation of Apriori, the most computationally expensive task is the generation of candidate sets having all possible pairs for singleton frequent items and comparing each pair with every transaction record. Here, we propose a new approach which dramatically reduces this computational complexity by eliminating the candidate generation step and avoiding costly comparisons. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of our approach. Our studies show that our approach outperforms the classical Apriori and state-of-the-art on Spark by many times for different datasets.


title: Mining advices from weblogs

abstract: Weblog, one of the fastest growing user generated contents, often contains key learnings gleaned from people's past experiences which are really worthy to be well presented to other people. One of the key learnings contained in weblogs is often vented in the form of advice. In this paper, we aim to provide a methodology to extract sentences that reveal advices on weblogs. We observed our data to discover the characteristics of advices contained in weblogs. Based on this observation, we define our task as a classification problem using various linguistic features. We show that our proposed method significantly outperforms the baseline. The presence or absence of imperative mood expression appears to be the most important feature in this task. It is also worth noting that the work presented in this paper is the first attempt on mining advices from English data.


title: APPLYING COMPOSITE LUTING AGENT ULTRASONICALLY: A SUCCESSFUL ALTERNATIVE

abstract: ABSTRACT The authors conducted research to determine if sonic vibration affected the viscosity of a composite luting agent. They found that using either an ultrasonic or a sonic scaler can thin composite resin. The thickness of the lute is similar to that resulting from manual loading methods, but it is obtained in much shorter time periods and with lower loads.


title: A Verification Platform for SDN-Enabled Applications

abstract: Recent work on integration of SDNs with application-layer systems like Hadoop has created a class of system, SDN-Enabled Applications, which implement application-specific functionality on the network layer by exposing network monitoring and control semantics to application developers. This requires domain-specific knowledge to correctly reason about network behavior and properties, as the SDN is now tightly coupled to the larger system. Existing tools for SDN verification and analysis are insufficiently expressive to capture this composition of network and domain models. Unfortunately, it is exactly this kind of automated reasoning and verification that is necessary to develop robust SDN-enabled applications for real-world systems. In this paper, we present ongoing work on Verificare, a verification platform being built to enable formal verification of SDNs as components of a larger domain-specific system. SLA, safety, and security requirements can selected from a variety of formal libraries and automatically verified using a variety of off-the-shelf tools. This approach not only extends the flexibility of existing SDN verification systems, but can actually provide more fine-grained analysis of possible network states due to extra information supplied by the domain model.


title: Memory Based Online Learning of Deep Representations from Video Streams

abstract: We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.


title: The five‐tier knowledge management hierarchy

abstract: Purpose – Many terms commonly used in the field of knowledge management (KM) have multiple uses and sometimes conflicting definitions because they are adapted from other research streams. Discussions of the various hierarchies of data, information, knowledge, and other related terms, although of value, are limited in providing support for KM. The purpose of this this paper is to define a new set of terminology and develop a five‐tier knowledge management hierarchy (5TKMH) that can provide guidance to managers involved in KM efforts.Design/methodology/approach – The 5TKMH is developed by extending the knowledge hierarchy to include an individual and an innovation tier.Findings – The 5TKMH includes all of the types of KM identified in the literature, provides a tool for evaluating the KM effort in a firm, identifies the relationships between knowledge sources, and provides an evolutionary path for KM efforts within the firm.Research limitations/implications – The 5TKMH has not been formally tested.Practical...


title: The Design and FPGA Implementation of GF(2^128) Multiplier for Ghash

abstract: In this paper, we propose a high-speed parallel 128 bit multiplier for Ghash Function in conjunction with its FPGA implementation. Through the use of Verilog the designs are evaluated by using Xilinx Vertax5 with 65nm technic and 30,000 logic cells. The highest throughput of 30.764Gpbs can be achieved on virtex5 with the consumption of 8864 slices LUT. The proposed design of the multiplier can be utilized as a design IP core for the implementation of the Ghash Function. The architecture of the multiplier can also apply in more general polynomial basis. Moreover it can be used as arithmetic module in other encryption field.


title: A Low-Noise Design Technique for High-Speed CMOS Optical Receivers

abstract: A careful comparison between alternative topologies to realize low-noise wideband TIAs is carried out in this work. In order to break the tradeoff between noise and bandwidth, the proposed front-end uses two stages, i.e. a low-noise narrowband transimpedance interface followed by an equalizer aimed at restoring the required bandwidth. The technique is especially effective for white noise components. The core first-stage amplifier exploits current reuse for minimum power consumption and is optimized for colored noise reduction. A net 4 $\times$ noise power reduction is achieved if compared with a design approach based on a traditional shunt-feedback TIA with the same bandwidth. A complete receiver, interfacing a commercial photodiode, and including the proposed two-stage front-end (TSFE), a limiting amplifier and a wideband output buffer has been realized in 65 nm CMOS. Optical communications tailored to 100GBASE-LR4 standard, which is specified for mid-to-long range transmissions at a channel rate of 25 Gb/s, are targeted. Realized prototypes show a sensitivity of $-$ 11.9 dBm at a BER of $10^{-12}$ with a PRBS31 input pattern and a transimpedance gain of 83 $\hbox{dB}\Omega$ , while tolerating an overall input capacitance of 160 fF. To the best of the authors' knowledge, this is the best sensitivity performance achieved by 25-Gb/s optical receivers in CMOS, comparable to state-of-the-art BiCMOS realizations.


title: Community Size Effects on Epidemic Spreading in Multiplex Social Networks

abstract: The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people’s reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals’ alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals’ risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals’ protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.


title: Every Time You Go Away: Changes in Affect, Behavior, and Physiology Associated With Travel-Related Separations From Romantic Partners

abstract: This study investigated changes in day-to-day affect, behavior, and physiology (hypothalamic–pituitary– adrenocortical axis activity) associated with temporary physical separations from romantic partners (such as those brought about by work-related travel). Daily diaries and measures of salivary cortisol were collected from 42 couples over a 21-day period that was timed to coincide with a naturally occurring 4to 7-day separation. There were significant changes from preseparation to separation and from separation to reunion in the quality of partners’ interactions, their positive and negative affect, sleeping problems, subjective stress, physical symptoms, and cortisol levels. Separation and reunion effects were generally more pronounced in homebound partners, partners with high attachment anxiety, and partners who had less contact with each other during the separation. Separation and reunion effects were not moderated by relationship length, relationship satisfaction, how often couples underwent separations, or the presence of children in the home. The results are discussed with respect to the role of daily proximity and contact between partners for day-to-day affective and physiological regulation.


title: CluWords: Exploiting Semantic Word Clustering Representation for Enhanced Topic Modeling

abstract: In this paper, we advance the state-of-the-art in topic modeling by means of a new document representation based on pre-trained word embeddings for non-probabilistic matrix factorization. Specifically, our strategy, called CluWords, exploits the nearest words of a given pre-trained word embedding to generate meta-words capable of enhancing the document representation, in terms of both, syntactic and semantic information. The novel contributions of our solution include: (i)the introduction of a novel data representation for topic modeling based on syntactic and semantic relationships derived from distances calculated within a pre-trained word embedding space and (ii)the proposal of a new TF-IDF-based strategy, particularly developed to weight the CluWords. In our extensive experimentation evaluation, covering 12 datasets and 8 state-of-the-art baselines, we exceed (with a few ties) in almost cases, with gains of more than 50% against the best baselines (achieving up to 80% against some runner-ups). Finally, we show that our method is able to improve document representation for the task of automatic text classification.


title: Generative agents for player decision modeling in games

abstract: This paper presents a method for modeling player decision making through the use of agents as AI-driven personas. The paper argues that artificial agents, as generative player models, have properties that allow them to be used as psychometrically valid, abstract simulations of a human player’s internal decision making processes. Such agents can then be used to interpret human decision making, as personas and playtesting tools in the game design process, as baselines for adapting agents to mimic classes of human players, or as believable, human-like opponents. This argument is explored in a crowdsourced decision making experiment, in which the decisions of human players are recorded in a small-scale dungeon themed puzzle game. Human decisions are compared to the decisions of a number of a priori defined“archetypical” agent-personas, and the humans are characterized by their likeness to or divergence from these. Essentially, at each step the action of the human is compared to what actions a number of reinforcement-learned agents would have taken in the same situation, where each agent is trained using a different reward scheme. Finally, extensions are outlined for adapting the agents to represent sub-classes found in the human decision making traces.


title: Mapping WordNet synsets to Wikipedia articles

abstract: Lexical knowledge bases (LKBs), such as WordNet, have been shown to be useful for a range of language processing tasks. Extending these resources is an expensive and time-consuming process. This paper describes an approach to address this problem by automatically generating a mapping from WordNet synsets to Wikipedia articles. A sample of synsets has been manually annotated with article matches for evaluation purposes. The automatic methods are shown to create mappings with precision of 87.8% and recall of 46.9%. These mappings can then be used as a basis for enriching WordNet with new relations based on Wikipedia links. The manual and automatically created data is available online.


title: Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs

abstract: We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our methodology exploits the joint sparsity of the mmWave MIMO channel in the angle and delay domains. We formulate the estimation problem as a noisy quantized compressed-sensing problem and solve it using efficient approximate message passing (AMP) algorithms. In particular, we model the angle-delay coefficients using a Bernoulli–Gaussian-mixture distribution with unknown parameters and use the expectation-maximization forms of the generalized AMP and vector AMP algorithms to simultaneously learn the distributional parameters and compute approximately minimum mean-squared error (MSE) estimates of the channel coefficients. We design a training sequence that allows fast, fast Fourier transform based implementation of these algorithms while minimizing peak-to-average power ratio at the transmitter, making our methods scale efficiently to large numbers of antenna elements and delays. We present the results of a detailed simulation study that compares our algorithms to several benchmarks. Our study investigates the effect of SNR, training length, training type, ADC resolution, and runtime on channel estimation MSE, mutual information, and achievable rate. It shows that, in a mmWave MIMO system, the methods we propose to exploit joint angle-delay sparsity allow 1-bit ADCs to perform comparably to infinite-bit ADCs at low SNR, and 4-bit ADCs to perform comparably to infinite-bit ADCs at medium SNR.


title: Predicting Cyber Threats through Hacker Social Networks in Darkweb and Deepweb Forums

abstract: We present an approach that combines social network analysis with machine learning techniques to predict future cyber threats through darkweb/deepweb discussions with hacking-related content. Our approach harnesses features derived from hacker social networks and from online sources of cybersecurity advisories. We address the problem of predicting the exploitability of software vulnerabilities to show that features computed from hacker social networks are important indicators of future cybersecurity incidents. We conduct a suite of experiments on real-world hacker and exploit data and demonstrate that social network data improves recall by about 19%, F1 score by about 6% while maintaining precision. We believe this is because social network structures related to certain exploit authors is indicative of their ability to write exploits that are subsequently employed in an attack.


title: Molecular beam epitaxy-grown ZnSe nanowires

abstract: Molecular beam epitaxy (MBE) via the vapor-liquid-solid (VLS) reaction was used to grow ZnSe nanowires (NWs) on (111), (100), and (110) oriented GaAs substrates. Through detailed transmission electron microscopy (TEM) studies, it was found that 〈111〉 orientation is the growth direction for NWs with size ≥30 nm, while NWs with size around 10 nm prefer to grow along the 〈110〉 direction, with a small portion along the 〈112〉 direction. These observations have led to the realization of vertical ZnSe NWs with size around 10 nm grown on a GaAs (110) substrate. An ordered ZnSe NW array fabricated on a GaAs (111) substrate with a novel prepatterning method associated with plasma etching shows a high degree of ordering and a good size uniformity of the as-grown NWs. The diameter of the NWs in the array is around 80 nm and most of them are found to orient vertically, but some tilt to one of the six possible directions of the 〈111〉 family.


title: Self-Taught Low-Rank Coding for Visual Learning

abstract: The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semisupervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively. Self-taught learning, which is a special type of transfer learning, has fewer restrictions on the choice of auxiliary data. It has shown promising performance in visual learning. However, existing self-taught learning methods usually ignore the structure information in data. In this paper, we focus on building a self-taught coding framework, which can effectively utilize the rich low-level pattern information abstracted from the auxiliary domain, in order to characterize the high-level structural information in the target domain. By leveraging a high quality dictionary learned across auxiliary and target domains, the proposed approach learns expressive codings for the samples in the target domain. Since many types of visual data have been proven to contain subspace structures, a low-rank constraint is introduced into the coding objective to better characterize the structure of the given target set. The proposed representation learning framework is called self-taught low-rank (S-Low) coding, which can be formulated as a nonconvex rank-minimization and dictionary learning problem. We devise an efficient majorization–minimization augmented Lagrange multiplier algorithm to solve it. Based on the proposed S-Low coding mechanism, both unsupervised and supervised visual learning algorithms are derived. Extensive experiments on five benchmark data sets demonstrate the effectiveness of our approach.


title: Privacy Analysis of Android Apps: Implicit Flows and Quantitative Analysis

abstract: A static analysis is presented, based on the theory of abstract interpretation, for verifying privacy policy compliance by mobile applications. This includes instances where, for example, the application releases the user’s location or device ID without authorization. It properly extends previous work on datacentric semantics for verification of privacy policy compliance by mobile applications by (i) tracking implicit information flow, and (ii) performing a quantitative analysis of information leakage. This yields to a novel combination of qualitative and quantitative analyses of information flows in mobile applications.


title: Feed resources assessment, laboratory evaluation of chemical composition of feeds and livestock feed balance in enset (Ensete ventricosum)-based mixed production systems of Gurage zone, southern Ethiopia

abstract: The study was conducted in four districts of Gurage zone, Sothern Nations Nationalities and People’s Region to determine the quantity and quality of available feed resources and to measure the livestock feed balance. A total of 360 households from dega (highland) and weinadega (midaltitude) peasant associations were selected using proportional sample size determination (Cochran, 1909; Thrustfield, 2013). The DM produced from leaf and leaf midribs of enset (Ensete ventricosum), crop reissues and natural grasses in tons, respectively, were 506.4 (52.73%), 312.33 (32.52%) and 141.62 (14.75%) in dega and 662.96 (49.36%), 472.83 (35.2%) and 207.33 (15.44%) in weinadega. The DCP produced from enset leaf and leaf midribs, crop residues and natural pasture, respectively, were 51819.91 kg, 8401.16 kg and 10335.43 kg in dega, 55217.94 kg, 13799.32 kg and 11490.23 kg in weinadega. The ME produced in dega was 4420872, 2296269.8 and 1188191.8 MJ whereas it was 6013047.2, 3430459.7 and 1689739.5 MJ in weinadega from enset parts, crop residues and natural pasture, respectively. The amount of DM, DCP and ME produced by individual household per year in weinadega agroecology were significantly higher (p<0.05) than the amount produced in dega agroecology. The annual feed supply in the study areas met only (76.81%) DM and (69.9%) DCP of the maintenance requirement of livestock in TLU but available ME was (1.67%) surplus. Conversely, the annual feed supply met only (64.98%) DM, (66.24%) DCP and (85.66%) ME of the maintenance requirement of livestock at dega agroecology, whereas in weinadega, agroecology about 88.31% DM and 73.46% DCP of the maintenance requirement were met but the estimate of ME was 17.22% above the requirement. This indicates that the livestock in the study areas of Gurage zone are in serious feed deficit which needs a special attention in supplementing the livestock with concentrates of protein sources for both agroecologies and energy for dega agroecology to overcome deficiency, especially during dry periods for reasonable livestock production. ::: ::: Key words: Chemical composition, Dega, feed balance, feed resource, Gurage zone, Weinadega.


title: Depressor labii inferioris resection: an effective treatment for marginal mandibular nerve paralysis.

abstract: Abstract In patients who show their lower teeth during smiling and facial animation, paralysis of the marginal mandibular nerve (MMN) causes a noticeable asymmetry of the lower lip due to the absence of depressor function. This paper presents a balancing technique for this lower lip asymmetry that involves resection of the depressor labii inferioris (DLI) on the nonparalysed side. The anatomy of the muscle, the operative technique, and the effectiveness of the procedure are outlined. A retrospective chart review was performed for 42 adult patients who were treated for MMN palsy with a DLI resection. Seven cases had only the MMN involved, and 35 cases had unilateral facial nerve paralysis. Thirty-six of these patients were available for a follow-up telephone survey. Of the 42 primary DLI resections performed, 36 cases had successful outcomes. Of the six patients who failed to achieve the expected results, five patients had repeat DLI resection and three of these achieved the desired result; the other two patients required a third resection. One patient continued to have DLI action with smiling and subsequently had a Botox injection into the DLI with good results. Of the 36 survey respondents, 21 patients felt their lower lip was asymmetrical at rest prior to DLI resection and 18 of these patients were improved by the procedure ( P =0.0001). Twenty-nine of the 36 patients reported that their lower lip was more symmetrical when they smiled following the DLI resection ( P P =007), whereas oral continence showed no significant changes ( P =0.147) following the DLI resection. DLI resection is a simple and effective procedure for the treatment of MMN palsy. The results are permanent and predictable. Lower lip symmetry is produced both at rest and with facial animation, without causing a functional deficit. The expected results of surgery can be trialed by local anaesthetic or botulinum toxin to block the activity of the DLI.


title: SUIDroid: An Efficient Hardening-Resilient Approach to Android App Clone Detection

abstract: The huge benefit of mobile application industry has attracted a large number of developers. However, application cloning is becoming a serious threat to the entire Android ecosystem, as it not only compromises the security and privacy of the app users but also robs app developers' revenue. As massive approaches have been proposed to address this issue, plagiarists also fight back through hardening their malicious code with the help of commercial packers. Previous work either not considers the hardening issue or just simply cracks the entire packed app, which may threat the security of legitimate packed app. In this paper, we propose SUIDroid, a novel app clone detection approach that can accurately detect the packed clone app without compromising the app hardening strategy. Another advantage of our detection approach is the accuracy. We propose a counting based technique to effectively filter the noisy-layout that can cause deviation. We evaluate our approach on two sets of app dataset. The experimental results on 160 packed malware samples demonstrate that SUIDroid is resilient to app hardening, and the evaluation on more than 100,000 real-world apps shows the efficiency of SUIDroid in real-world scenario.


title: The fascia: the forgotten structure.

abstract: Summary This paper examines the main characteristics of the human fascial system, considered in its three-dimensional continuity. To better understand the anatomy of the human fascial system, a simple diagram of the subcutaneous tissue must be borne in mind. From the skin to the deepest plane, we find the superficial fascia, dividing the subcutaneous tissue into two fibroadipose layers, superficial and deep, and the deep fascia, which envelops all the muscles of the body, showing different character istics according to region. Under the deep fascia is the epimysium, occurring in the limbs and some regions of the trunk. Skin ligaments connect the superficial fascia to the skin and to the deep fascia, forming a three-dimensional network among the fat lobules. The typical features of the superficial and deep fasciae and their relationships to nerves, vessels and muscles are reported here, highlighting the possible role of the deep fascia in proprioception and peripheral motor coordination. The main features of the fasciae with imaging techniques are also discussed. This knowledge may contribute to clinicians’ understanding of the myofascial system and the role which the deep fasciae may play in musculoskeletal dysfunctions.


title: Efficient representation of local geometry for large scale object retrieval

abstract: State of the art methods for image and object retrieval exploit both appearance (via visual words) and local geometry (spatial extent, relative pose). In large scale problems, memory becomes a limiting factor - local geometry is stored for each feature detected in each image and requires storage larger than the inverted file and term frequency and inverted document frequency weights together. We propose a novel method for learning discretized local geometry representation based on minimization of average reprojection error in the space of ellipses. The representation requires only 24 bits per feature without drop in performance. Additionally, we show that if the gravity vector assumption is used consistently from the feature description to spatial verification, it improves retrieval performance and decreases the memory footprint. The proposed method outperforms state of the art retrieval algorithms in a standard image retrieval benchmark.


title: Preparation of Solid Catalysts

abstract: Developing Industrial Catalysts. Bulk Catalysts and Supports. Supported Catalysts. Zeolites and Related Molecular Sieves. Solid Superacids. Catalyst Forming. Computer-Aided Catalyst Design.


title: An Effective Hierarchical IDS for Wireless Sensor Networks

abstract: This paper emphasizes on safeguarding the hierarchical structure in wireless sensor network by presenting an Intrusion Detection Technique, which is very useful, simple and works effectively in improving and enhancing the security in wireless sensor Network. This IDS works on the combination of anomaly detection algorithm and intrusion detection nodes. Here all the features and numerous architectures of popular IDS(s) along with their confines and benefits are also being described.


title: Do you “like” my photo? Facebook use maintains eating disorder risk

abstract: Objective ::: Social media sites, such as Facebook, merge two factors that influence risk for eating disorders: media and peers. Previous work has identified cross-sectional and temporal associations between Facebook use and disordered eating. This study sought to replicate and extend these findings using an experimental design. ::: ::: Method ::: In Study 1, 960 women completed self-report surveys regarding Facebook use and disordered eating. In Study 2, 84 women were randomly assigned to use Facebook or to use an alternate internet site for 20 min. ::: ::: Results ::: More frequent Facebook use was associated with greater disordered eating in a cross-sectional survey. Facebook use was associated with the maintenance of weight/shape concerns and state anxiety compared to an alternate internet activity. ::: ::: Discussion ::: Facebook use may contribute to disordered eating by maintaining risk for eating pathology. As such, targeting Facebook use may be helpful in intervention and prevention programs. © 2014 Wiley Periodicals, Inc. (Int J Eat Disord 2014; 47:516–523)


title: Comparison of different Linux containers

abstract: The new generation of virtualization is containerization. In this paper we measured the most common container techniques to determinate the performance of these techniques regarding computing power and network performance with industry standard measurement applications. We used these measurement results to compare them with native performance without any container techniques and with the Linux standard virtualization KVM performance. For the first time, this paper also measures the performance of the Singularity container system, which is a new approach of HPC systems container usage.


title: Design of the ARAIG haptic garment for enhanced resilience assessment and development in tactical training serious games

abstract: First person shooter virtual reality games have begun to be used for serious games for military or civilian tactical training for new approaches for resilience assessment and development as part of new approaches for mental health training. However, sensory stimulation has been largely constrained to visual and auditory sensations with limited tactile feedback through haptic controllers. This paper presents a design for the ARAIG haptic garment for enhanced resilience assessment and development in tactical training serious games.


title: The leaking battery: A privacy analysis of the HTML5 Battery Status API

abstract: We highlight privacy risks associated with the HTML5 Battery Status API. We put special focus on its implementation in the Firefox browser. Our study shows that websites can discover the capacity of users’ batteries by exploiting the high precision readouts provided by Firefox on Linux. The capacity of the battery, as well as its level, expose a fingerprintable surface that can be used to track web users in short time intervals. Our analysis shows that the risk is much higher for old or used batteries with reduced capacities, as the battery capacity may potentially serve as a tracking identifier. The fingerprintable surface of the API could be drastically reduced without any loss in the API’s functionality by reducing the precision of the readings. We propose minor modifications to Battery Status API and its implementation in the Firefox browser to address the privacy issues presented in the study. Our bug report for Firefox was accepted and a fix is deployed.


title: Counting with TinyTable: every bit counts!

abstract: Counting Bloom filters (CBF) and their variants are data structures that support membership or multiplicity queries with a low probabilistic error. Yet, they incur a significant memory space overhead when compared to lower bounds as well as to (plain) Bloom filters, which can only represent set membership without removals. This work presents TinyTable, an efficient hash table based algorithm that supports membership queries, removals and multiplicity queries (statistics). TinyTable improves space efficiency by as much as 28% compared to CBF variants and as much as 60% for monitoring flow statistics. When the required false positive rate is smaller than 1%, TinyTable is even slightly more space efficient than (plain) Bloom filters. Our performance study shows that TinyTable has acceptable runtime overheads.