Query: Indexing multi-dimensional data in a cloud system

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Dynamo: amazon's highly available key-value store <=SEP=> Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems.
 This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.

HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads <=SEP=> The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private “clouds”. At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them wellsuited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems.

MapReduce: Simplified Data Processing on Large Clusters <=SEP=> MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

Xen and the art of virtualization <=SEP=> Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

C-Store: A Column-oriented DBMS <=SEP=> This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of columnoriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures. We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.

G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition <=SEP=> Recently, plugging trainable structural layers into deep convolutional neural networks (CNNs) as image representations has made promising progress. However, there has been little work on inserting parametric probability distributions, which can effectively model feature statistics, into deep CNNs in an end-to-end manner. This paper proposes a Global Gaussian Distribution embedding Network (G2DeNet) to take a step towards addressing this problem. The core of G2DeNet is a novel trainable layer of a global Gaussian as an image representation plugged into deep CNNs for end-to-end learning. The challenge is that the proposed layer involves Gaussian distributions whose space is not a linear space, which makes its forward and backward propagations be non-intuitive and non-trivial. To tackle this issue, we employ a Gaussian embedding strategy which respects the structures of both Riemannian manifold and smooth group of Gaussians. Based on this strategy, we construct the proposed global Gaussian embedding layer and decompose it into two sub-layers: the matrix partition sub-layer decoupling the mean vector and covariance matrix entangled in the embedding matrix, and the square-rooted, symmetric positive definite matrix sub-layer. In this way, we can derive the partial derivatives associated with the proposed structural layer and thus allow backpropagation of gradients. Experimental results on large scale region classification and fine-grained recognition tasks show that G2DeNet is superior to its counterparts, capable of achieving state-of-the-art performance.

A grounded theory analysis of modern web applications: knowledge, skills, and abilities for DevOps <=SEP=> Since 2009, DevOps, the combination of development and operation, has been adopted within organizations in industry, such as Netflix, Flickr, and Fotopedia. Configuration management tools have been used to support DevOps. However, in this paper we investigate which Knowledge, Skills, and Abilities (KSA) have been employed in developing and deploying modern web applications and how these KSAs support DevOps. By applying a qualitative analysis approach, namely grounded theory, to three web application development projects, we discover that the KSAs for both Software Development and IT Operator practitioners support the four perspectives of DevOps: collaboration culture, automation, measurement, and sharing.

Reducing restraint use for older adults in acute care. <=SEP=> Although no evidence shows that restraints are effective for maintaining safety, preventing disruption of treatment, or controlling behavior, they're still commonly used in acute care facilities (especially in critical care) in the United States, where the reported prevalence of their use ranges from 7.4% to 17%.[1–3] They may be used to protect patients from falls or to prevent them from inadvertently removing tubes and other devices.[4] Older adults are three times more likely to be restrained during an acute hospital admission than younger patients, even though this practice is associated with poor outcomes.[1] (See Adverse effects of physical restraints.)

The Impact of Information Communication and Technology on Students ' Academic Performance : Evidence from Indonesian EFL Classrooms <=SEP=> The present study examined the impact of Information Communication Technology [ICT] on a group of university students' academic performance in an Indonesian English as a Foreign Language (EFL) classroom. As the platform of the ICT usage in this current study, English learning websites was used as the independent variable. Academic performance (students' score on pre and post test) was used the dependent variable. The participants in the study were 60 students of the Department of Public Health at the State University of Gorontalo, Indonesia, i.e an experimental group of 30 students (n=30) and a control group of 30 students (n=30). They took English courses as one of the compulsory subjects in the university curriculum. This study used a mixed method of a quasi-experimental and a qualitative interview approaches. Based on the result of the quantitative method of data collection, ttests of this study indicated that students in the experiment group performed significantly better than the students in the control group. Test results also showed that there was a significant difference between students' score on the preand post-test. The students' score in the post test and post test in the control group, however, were not significantly different. As interview results showed, participants expressed their positive learning experience with technologies, growing motivation in English learning, and positive feeling and confidence on their language performance.

Evaluation of Features Detectors and Descriptors based on 3D Objects <=SEP=> We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. These correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. We test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25–30∘.

Logic-Based Models for the Analysis of Cell Signaling Networks† <=SEP=> Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.

Engineering design review of stance-control knee-ankle-foot orthoses. <=SEP=> Persons with quadriceps muscle weakness are often prescribed a knee-ankle-foot orthosis that locks the knee in full extension during both stance and swing phases of gait. Locking the knee results in abnormal gait patterns characterized by hip hiking and leg circumduction during swing. The stance-control knee-ankle-foot orthosis (SCKAFO), a new type of orthosis, has emerged that permits free knee motion during swing while resisting knee flexion during stance, thereby supporting the limb during weight-bearing. This article examines various SCKAFO designs, discuss the existing design limitations, and identify remaining design challenges. Several commercial SCKAFOs have been released that incorporate different locking mechanisms. Preliminary gait studies have shown some devices to be promising; however, an important functional limitation in some SCKAFOs is dependence on specific joint angles to switch between stance and swing modes. Important design factors such as size, weight, and noise must be considered in new orthosis designs to ensure wide consumer acceptance.

A Novel Cross Layer Intrusion Detection System in MANET <=SEP=> Intrusion detection System forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. In this paper, we have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection. We have used cooperative anomaly intrusion detection with data mining technique to enhance the proposed architecture. We have implemented fixed width clustering algorithm for efficient detection of the anomalies in the MANET traffic and also generated different types of attacks in the network. The simulation of the proposed architecture is performed in OPNET simulator and we got the result as we expected.

Towards online anti-opinion spam: Spotting fake reviews from the review sequence <=SEP=> Detecting review spam is important for current e-commerce applications. However, the posted order of review has been neglected by the former work. In this paper, we explore the issue on fake review detection in review sequence, which is crucial for implementing online anti-opinion spam. We analyze the characteristics of fake reviews firstly. Based on review contents and reviewer behaviors, six time sensitive features are proposed to highlight the fake reviews. And then, we devise supervised solutions and a threshold-based solution to spot the fake reviews as early as possible. The experimental results show that our methods can identify the fake reviews orderly with high precision and recall.

Knowledge management strategy and its link to knowledge creation process <=SEP=> Knowledge has become to be considered as valuable strategic assets that can provide proprietary competitive advantages. It is more important for companies to distinguish themselves through knowledge management strategies. Without a constant creation of knowledge, a business is condemned to poor performance. However, it is still unclear how these strategies affect knowledge creation. Knowledge management strategies can be categorized as being either human or system oriented. This paper proposes a model to illustrate the link between the strategies and its creating process. The model is derived on the basis of samples from 58 Korean firms. The model depicts how companies should align the strategies with four knowledge creation modes such as socialization, externalization, combination, and internalization. It is found that human strategy is more likely to be effective for socialization while system strategy is more likely to be effective for combination. Furthermore, the survey result suggests that managers should adjust knowledge management strategies in view of the characteristics of their departments. q 2002 Elsevier Science Ltd. All rights reserved.

What makes a chair a chair? <=SEP=> Many object classes are primarily defined by their functions. However, this fact has been left largely unexploited by visual object categorization or detection systems. We propose a method to learn an affordance detector. It identifies locations in the 3d space which “support” the particular function. Our novel approach “imagines” an actor performing an action typical for the target object class, instead of relying purely on the visual object appearance. So, function is handled as a cue complementary to appearance, rather than being a consideration after appearance-based detection. Experimental results are given for the functional category “sitting”. Such affordance is tested on a 3d representation of the scene, as can be realistically obtained through SfM or depth cameras. In contrast to appearance-based object detectors, affordance detection requires only very few training examples and generalizes very well to other sittable objects like benches or sofas when trained on a few chairs.

High-Frequency Behavior of Graphene-Based Interconnects—Part II: Impedance Analysis and Implications for Inductor Design <=SEP=> This paper provides the first detailed insights into the ultrahigh-frequency behavior of graphene ribbons (GRs) and analyzes their consequences in designing interconnects and low-loss on-chip inductors. In the companion paper (part I), an accurate impedance modeling methodology has been developed based on the Boltzmann equation with the magnetic vector potential Green's function approach incorporating the dependency of current on the nonlocal electric field. Based on the developed methodology, this paper for the first time embarks on the rigorous investigation of the intricate processes occurring at high frequencies in GRs, such as anomalous skin effect (ASE), high-frequency resistance and inductance saturation, intercoupled relation between edge specularity and ASE, and the influence of the linear dimensions on impedance. A comparative study of the high-frequency response of GRs with that of carbon nanotubes (CNTs) and Cu is made to highlight the potential of GR interconnects for high-frequency applications. Subsequently, the high-frequency performance of GR inductors is analyzed, and it is shown that they can achieve 32% and 50% improvements in maximum Q-factor compared to Cu and single-walled CNT inductors with 1/3 metallic fraction, respectively.

Age estimation via face images: a survey <=SEP=> Facial aging adversely impacts performance of face recognition and face verification and authentication using facial features. This stochastic personalized inevitable process poses dynamic theoretical and practical challenge to the computer vision and pattern recognition community. Age estimation is labeling a face image with exact real age or age group. How do humans recognize faces across ages? Do they learn the pattern or use age-invariant features? What are these age-invariant features that uniquely identify one across ages? These questions and others have attracted significant interest in the computer vision and pattern recognition research community. In this paper, we present a thorough analysis of recent research in aging and age estimation. We discuss popular algorithms used in age estimation, existing models, and how they compare with each other; we compare performance of various systems and how they are evaluated, age estimation challenges, and insights for future research.

Machine Learning Based Missing Value Imputation Method for Clinical Datasets <=SEP=> Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the incomplete cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using decision tree, fuzzy unordered rule induction, KNN and K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy unordered rule induction algorithm is used to predict missing attribute values for K-Mean clustering and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.

Multi-terminal transport measurements of MoS2 using a van der Waals heterostructure device platform. <=SEP=> Atomically thin two-dimensional semiconductors such as MoS2 hold great promise for electrical, optical and mechanical devices and display novel physical phenomena. However, the electron mobility of mono- and few-layer MoS2 has so far been substantially below theoretically predicted limits, which has hampered efforts to observe its intrinsic quantum transport behaviours. Potential sources of disorder and scattering include defects such as sulphur vacancies in the MoS2 itself as well as extrinsic sources such as charged impurities and remote optical phonons from oxide dielectrics. To reduce extrinsic scattering, we have developed here a van der Waals heterostructure device platform where MoS2 layers are fully encapsulated within hexagonal boron nitride and electrically contacted in a multi-terminal geometry using gate-tunable graphene electrodes. Magneto-transport measurements show dramatic improvements in performance, including a record-high Hall mobility reaching 34,000 cm(2) V(-1) s(-1) for six-layer MoS2 at low temperature, confirming that low-temperature performance in previous studies was limited by extrinsic interfacial impurities rather than bulk defects in the MoS2. We also observed Shubnikov-de Haas oscillations in high-mobility monolayer and few-layer MoS2. Modelling of potential scattering sources and quantum lifetime analysis indicate that a combination of short-range and long-range interfacial scattering limits the low-temperature mobility of MoS2.

Test-potentiated learning: three independent replications, a disconfirmed hypothesis, and an unexpected boundary condition. <=SEP=> Arnold and McDermott [(2013). Test-potentiated learning: Distinguishing between direct and indirect effects of testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 940-945] isolated the indirect effects of testing and concluded that encoding is enhanced to a greater extent following more versus fewer practice tests, referred to as test-potentiated learning. The current research provided further evidence for test-potentiated learning and evaluated the covert retrieval hypothesis as an alternative explanation for the observed effect. Learners initially studied foreign language word pairs and then completed either one or five practice tests before restudy occurred. Results of greatest interest concern performance on test trials following restudy for items that were not correctly recalled on the test trials that preceded restudy. Results replicate Arnold and McDermott (2013) by demonstrating that more versus fewer tests potentiate learning when trial time is limited. Results also provide strong evidence against the covert retrieval hypothesis concerning why the effect occurs (i.e., it does not reflect differential covert retrieval during pre-restudy trials). In addition, outcomes indicate that the magnitude of the test-potentiated learning effect decreases as trial length increases, revealing an unexpected boundary condition to test-potentiated learning.

An Improved Dijkstra Algorithm for Firefighting <=SEP=> In this paper, briefly the efforts going toward a mathematical calculation to validate a new-Dijkstra algorithm from the original normal Dijkstra algorithm [1] as an improvised Dijkstra. The result of this improvised Dijkstra was used to find the shortest path for firefighting unit to reach the location of the fire exactly. The idea depends on the influence of turns on the path, while there are two equal paths’, the more number of turns take more time to pass through it and the less number turns takes less time to pass through it. To apply this scenario practically we take a small real area in south Khartoum. The result gives strong justification that proves and verifies our methodology with a clear contribution in Improved Dijkstra algorithm for firefighting like Geo-Dijkstra. Furthermore, an evaluation of the above-mentioned algorithms has been done showing very promising and realistic results.

Computers and Creativity <=SEP=> The Painting Fool is software that we hope will one day be taken seriously as a creative artist in its own right. This aim is being pursued as an Artificial Intelligence (AI) project, with the hope that the technical difficulties overcome along the way will lead to new and improved generic AI techniques. It is also being pursued as a sociological project, where the effect of software which might be deemed as creative is tested in the art world and the wider public. In this chapter, we summarise our progress so far in The Painting Fool project. To do this, we first compare and contrast The Painting Fool with software of a similar nature arising from AI and graphics projects. We follow this with a discussion of the guiding principles from Computational Creativity research that we adhere to in building the software. We then describe five projects with The Painting Fool where our aim has been to produce increasingly interesting and culturally valuable pieces of art. We end by discussing the issues raised in building an automated painter, and describe further work and future prospects for the project. By studying both the technical difficulties and sociological issues involved in engineering software for creative purposes, we hope to help usher in a new era where computers routinely act as our creative collaborators, as well as independent and creative artists, musicians, writers, designers, engineers and scientists, and contribute in meaningful and interesting ways to human culture.

AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification <=SEP=> Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.

300mm low k wafer dicing saw study <=SEP=> With the farther shrink of the IC dimension, the low k material has been widely used to replace the traditional SiO/sub 2/ ILD in order to reduce the interconnect delay. The introduction of low k material into silicon imposed challenges on dicing saw process, ILD and metal layers peeling and its penetration into the sealing ring of the die during dicing saw are the most common defects. In this paper, the low k material structure and its impact on wafer dicing were elaborated. A practical dicing quality inspection matrix was developed to assess the cutting process variation. A 300mm CMOS 90nm dual damascene low k wafer was chosen as a test vehicle to develop robust low k dicing saw process. The critical factors (dicing blade, index speed, spindle speed, cut in depth, test pattern in the saw street...) affecting cutting quality were studied and optimized. The selected C90 dual damascene low k device passed package reliability test with the optimized low k dicing saw recipe and process. The further improvement and solutions in eliminating the low k dicing saw peeling from both wafer fab and packaging assembly were also explored.

Evidence for perceptual deficits in associative visual (prosop)agnosia: a single-case study <=SEP=> Associative visual agnosia is classically defined as normal visual perception stripped of its meaning [Archiv für Psychiatrie und Nervenkrankheiten 21 (1890) 22/English translation: Cognitive Neuropsychol. 5 (1988) 155]: these patients cannot access to their stored visual memories to categorize the objects nonetheless perceived correctly. However, according to an influential theory of visual agnosia [Farah, Visual Agnosia: Disorders of Object Recognition and What They Tell Us about Normal Vision, MIT Press, Cambridge, MA, 1990], visual associative agnosics necessarily present perceptual deficits that are the cause of their impairment at object recognition Here we report a detailed investigation of a patient with bilateral occipito-temporal lesions strongly impaired at object and face recognition. NS presents normal drawing copy, and normal performance at object and face matching tasks as used in classical neuropsychological tests. However, when tested with several computer tasks using carefully controlled visual stimuli and taking both his accuracy rate and response times into account, NS was found to have abnormal performances at high-level visual processing of objects and faces. Albeit presenting a different pattern of deficits than previously described in integrative agnosic patients such as HJA and LH, his deficits were characterized by an inability to integrate individual parts into a whole percept, as suggested by his failure at processing structurally impossible three-dimensional (3D) objects, an absence of face inversion effects and an advantage at detecting and matching single parts. Taken together, these observations question the idea of separate visual representations for object/face perception and object/face knowledge derived from investigations of visual associative (prosop)agnosia, and they raise some methodological issues in the analysis of single-case studies of (prosop)agnosic patients.

An Infrastructure Approach to Context-Aware Computing <=SEP=> The Context Toolkit (Dey, Salber, and Abowd 2001 [this special issue]) is only one of many possible architectures for supporting context-aware applications. In this essay, we look at the trade-offs involved with a service infrastructure approach to context-aware computing. We describe the advantages that a service infrastructure for contextawareness has over other approaches, outline some of the core technical challenges that must be addressed before such an infrastructure can be built, and point out promising research directions for overcoming these challenges.

Intrusion Detection in Computer Networks based on Machine Learning Algorithms <=SEP=> Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements; they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, machine learning-based intrusion detection systems have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. This work aims to compare efficiency of machine learning methods in intrusion detection system, including artificial neural networks and support vector machine, with the hope of providing reference for establishing intrusion detection system in future. Compared with other related works in machine learning-based intrusion detectors, we propose to calculate the mean value via sampling different ratios of normal data for each measurement, which lead us to reach a better accuracy rate for observation data in real world. We compare the accuracy, detection rate, false alarm rate for 4 attack types. The extensive experimental results on the KDD-cup intrusion detection benchmark dataset demonstrate that the proposed approach produces higher performance than KDD Winner, especially for U2R and U2L type attacks.

BA-NET: DENSE BUNDLE ADJUSTMENT NETWORKS <=SEP=> This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable, so that the network can learn suitable features that make the BA problem more tractable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates several basis depth maps according to the input image, and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA. The basis depth maps generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem. Experiments on large scale real data prove the success of the proposed method.

Elastic textures for additive fabrication <=SEP=> We introduce elastic textures: a set of parametric, tileable, printable, cubic patterns achieving a broad range of isotropic elastic material properties: the softest pattern is over a thousand times softer than the stiffest, and the Poisson's ratios range from below zero to nearly 0.5. Using a combinatorial search over topologies followed by shape optimization, we explore a wide space of truss-like, symmetric 3D patterns to obtain a small family. This pattern family can be printed without internal support structure on a single-material 3D printer and can be used to fabricate objects with prescribed mechanical behavior. The family can be extended easily to create anisotropic patterns with target orthotropic properties. We demonstrate that our elastic textures are able to achieve a user-supplied varying material property distribution. We also present a material optimization algorithm to choose material properties at each point within an object to best fit a target deformation under a prescribed scenario. We show that, by fabricating these spatially varying materials with elastic textures, the desired behavior is achieved.