Query: Euclidean and Hamming Embedding for Image Patch Description with Convolutional Networks

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Dimensionality Reduction by Learning an Invariant Mapping <=SEP=> Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE.

A Performance Evaluation of Local Descriptors <=SEP=> 

Vlfeat: an open and portable library of computer vision algorithms <=SEP=> VLFeat is an open and portable library of computer vision algorithms. It aims at facilitating fast prototyping and reproducible research for computer vision scientists and students. It includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization. The source code and interfaces are fully documented. The library integrates directly with MATLAB, a popular language for computer vision research.

BRIEF: Computing a Local Binary Descriptor Very Fast <=SEP=> Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor, which we call BRIEF, on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.

Learning Local Feature Descriptors Using Convex Optimisation <=SEP=> The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal. First, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity. Second, it is shown that descriptor dimensionality reduction can also be formulated as a convex optimisation problem, using Mahalanobis matrix nuclear norm regularisation. Both formulations are based on discriminative large margin learning constraints. As the third contribution, we evaluate the performance of the compressed descriptors, obtained from the learnt real-valued descriptors by binarisation. Finally, we propose an extension of our learning formulations to a weakly supervised case, which allows us to learn the descriptors from unannotated image collections. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning on the annotated local patches data set of Brown et al. and unannotated photo collections of Philbin et al. .

Quantitative Evaluation of DC Microgrids Availability: Effects of System Architecture and Converter Topology Design Choices <=SEP=> This paper presents a quantitative method to evaluate dc microgrids availability by identifying and calculating minimum cut sets occurrence probability for different microgrid architectures and converter topologies. Hence, it provides planners with an essential tool to evaluate downtime costs and decide technology deployments based on quantitative risk assessments by allowing to compare the effect that converter topologies and microgrid architecture choices have on availability. Conventional architectures with single-input converters and alternative configurations with multiple-input converters (MICs) are considered. Calculations yield that all microgrid configurations except those utilizing center converters achieve similar availability of 6-nines. Three converter topologies are used as representatives of many other circuits. These three benchmark circuits are the boost, the isolated SEPIC (ISEPIC), and the current-source half-bridge. Marginal availability differences are observed for different circuit topology choices, although architectures with MICs are more sensitive to this choice. MICs and, in particular, the ISEPIC, are identified as good compromise options for dc microgrids source interfaces. The analysis also models availability influence of local energy storage, both in batteries and generators' fuel. These models provide a quantitative way of comparing dc microgrids with conventional backup energy systems. Calculations based on widely accepted data in industry supports the analysis.

VM Placement Strategies for Cloud Scenarios <=SEP=> The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP) that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSP technique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.

Cachexia: a new definition. <=SEP=> On December 13th and 14th a group of scientists and clinicians met in Washington, DC, for the cachexia consensus conference. At the present time, there is no widely agreed upon operational definition of cachexia. The lack of a definition accepted by clinician and researchers has limited identification and treatment of cachectic patient as well as the development and approval of potential therapeutic agents. The definition that emerged is: "cachexia, is a complex metabolic syndrome associated with underlying illness and characterized by loss of muscle with or without loss of fat mass. The prominent clinical feature of cachexia is weight loss in adults (corrected for fluid retention) or growth failure in children (excluding endocrine disorders). Anorexia, inflammation, insulin resistance and increased muscle protein breakdown are frequently associated with cachexia. Cachexia is distinct from starvation, age-related loss of muscle mass, primary depression, malabsorption and hyperthyroidism and is associated with increased morbidity. While this definition has not been tested in epidemiological or intervention studies, a consensus operational definition provides an opportunity for increased research.

Deep Neural Network Capacity <=SEP=> In recent years, deep neural network exhibits its powerful superiority on information discrimination in many computer vision applications. However, the capacity of deep neural network architecture is still a mystery to the researchers. Intuitively, larger capacity of neural network can always deposit more information to improve the discrimination ability of the model. But, the learnable parameter scale is not feasible to estimate the capacity of deep neural network. Due to the overfitting, directly increasing hidden nodes number and hidden layer number are already demonstrated not necessary to effectively increase the network discrimination ability. In this paper, we propose a novel measurement, named “total valid bits”, to evaluate the capacity of deep neural networks for exploring how to quantitatively understand the deep learning and the insights behind its super performance. Specifically, our scheme to retrieve the total valid bits incorporates the skilled techniques in both training phase and inference phase. In the network training, we design decimal weight regularization and 8-bit forward quantization to obtain the integer-oriented network representations. Moreover, we develop adaptive-bitwidth and non-uniform quantization strategy in the inference phase to find the neural network capacity, total valid bits. By allowing zero bitwidth, our adaptive-bitwidth quantization can execute the model reduction and valid bits finding simultaneously. In our extensive experiments, we first demonstrate that our total valid bits is a good indicator of neural network capacity. We also analyze the impact on network capacity from the network architecture and advanced training

The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500 <=SEP=> The aim of this research is to analyse the effectiveness of the Chicago Board Options Exchange Market Volatility Index (VIX) when used with Support Vector Machines (SVMs) in order to forecast the weekly change in the S&P 500 index. The data provided cover the period between 3 January 2000 and 30 December 2011. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as Relative Strength Index, Moving Average Convergence Divergence, VIX and the daily return of the S&P 500. The SVM identifies the best situations in which to buy or sell in the market. The two outputs of the SVM are the movement of the market and the degree of set membership. The obtained results show that SVM using VIX produce better results than the Buy and Hold strategy or SVM without VIX. The influence of VIX in the trading system is particularly significant when bearish periods appear. Moreover, the SVM allows the reduction in the Maximum Drawdown and the annualised standard deviation.

A tourism destination recommender system using users’ sentiment and temporal dynamics <=SEP=> With the development and popularity of social networks, an increasing number of consumers prefer to order tourism products online, and like to share their experiences on social networks. Searching for tourism destinations online is a difficult task on account of its more restrictive factors. Recommender system can help these users to dispose information overload. However, such a system is affected by the issue of low recommendation accuracy and the cold-start problem. In this paper, we propose a tourism destination recommender system that employs opinion-mining technology to refine user sentiment, and make use of temporal dynamics to represent user preference and destination popularity drifting over time. These elements are then fused with the SVD+ + method by combining user sentiment and temporal influence. Compared with several well-known recommendation approaches, our method achieves improved recommendation accuracy and quality. A series of experimental evaluations, using a publicly available dataset, demonstrates that the proposed recommender system outperforms the existing recommender systems.

Dynamic MRI reconstruction using low rank plus sparse tensor decomposition <=SEP=> In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low-rank plus sparse tensor decomposition problem, we propose an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the state-of-the-art reconstruction methods.

Strategy and architecture of a safety concept for fully automatic and autonomous driving assistance systems <=SEP=> As drivers back out of the driving task, when transported automatically by an intelligent car for a longer time, they are not always able to react properly, if a driver take over request occurs. This paper presents two ways, how to deal with this problem within the scope of a functional safety concept. Thereto, the difference between fully automatic and autonomous driving assistance systems is explained. Afterwards two different strategies to reach a safe state in consequence of a system boundary crossing are proposed. In the first case the fall back state is reached by a driver take over, in the second case by an automatic, active fail-safe mechanism. Subsequently the necessary components for monitoring and reaching a safe state and their embedment in a basic, functional architecture of a driving assistance system are described. In this context, special regard is paid to aspects of redundancy as well. In the end it is concluded, that the safety concept proposed here is crucial for guaranteeing enduring safety in an automatically driving car and in consequence for making automatic driving functions commercially ready for serial production.

Open innovation : State of the art and future perspectives <=SEP=> Open innovation has become one of the hottest topics in innovation management. This article intends to explore the limits in our understanding of the open innovation concept. In doing so, I address the questions of what (the content of open innovation), when (the context dependency) and how (the process). Open innovation is a rich concept, that can be implemented in many different ways. The context dependency of open innovation is one of the least understood topics; more research is needed on the internal and external environment characteristics affecting performance. The open innovation process relates to both the transition towards open innovation, and the various open innovation practices. As with any new concept, initial studies focus on successful and early adopters, are based on case studies, and descriptive. However, not all lessons learned from the early adopters may be applicable to following firms. Case study research increases our understanding of how things work and enables us to identify important phenomena. They should be followed by quantitative studies involving large samples to determine the relative importance of factors, to build path models to understand chains of effects, and to formally test for context dependencies. However, the evidence shows that open innovation has been a valuable concept for so many firms and in so many contexts, that it is on its way to find its final place in innovation management. & 2010 Elsevier Ltd. All rights reserved.

LINE-mediated retrotransposition of marked Alu sequences <=SEP=> Alu elements are the most successful transposons in humans. They are 300-bp non-coding sequences transcribed by RNA polymerase III (Pol III) and are expected to retrotranspose with the aid of reverse transcriptases of cellular origin. We previously showed that human LINEs can generate cDNA copies of any mRNA transcript by means of a retroposition process involving reverse transcription and integration by the LINE-encoded endonuclease and reverse transcriptase. Here we show mobility of marked Alu sequences in human HeLa cells with the canonical features of a retrotransposition process, including splicing out of an autocatalytic intron introduced into the marked sequence, target site duplications of varying lengths and integrations into consensus A-rich sequences. We further show that the poly-A stretch at the Alu 3′ end is essential for mobility, that LINEs are required for transposition and that the rate of retroposition is 100–1,000 times higher for Alu transcripts than for control mRNAs, thus accounting for the high mutational activity of these elements observed in humans.

Adaptive Image Registration via Hierarchical Voronoi Subdivision <=SEP=> Advances in image acquisition systems have made it possible to capture high-resolution images of a scene, recording considerable scene details. With increased resolution comes increased image size and geometric difference between multiview images, complicating image registration. Through Voronoi subdivision, we subdivide large images into small corresponding regions, and by registering small regions, we register the images in a piecewise manner. Image subdivision reduces the geometric difference between regions that are registered and simplifies the correspondence process. The proposed method is a hierarchical one. While previous methods use the same block size and shape at a hierarchy, the proposed method adapts the block size and shape to the local image details and geometric difference between the images. This adaptation makes it possible to keep geometric difference between corresponding regions small and simplifies the correspondence process. Implementational details of the proposed image registration method are provided, and experimental results on various types of images are presented and analyzed.

Security Strategies for SCADA Networks <=SEP=> SCADA systems have historically been isolated from other computing resources. However, the use of TCP/IP as a carrier protocol and the trend to interconnect SCADA systems with enterprise networks introduce serious security threats. This paper describes two strategies for securing SCADA networks, both of which have been implemented in a laboratory-scale Modbus network. The first utilizes a security services suite that minimizes the impact on time-critical industrial process systems while adhering to industry standards. The second engages a sophisticated forensic system for SCADA network traffic collection and analysis. The forensic system supports the post mortem analysis of security breaches and the monitoring of process behavior to optimize plant

BSSync: Processing Near Memory for Machine Learning Workloads with Bounded Staleness Consistency Models <=SEP=> Parallel machine learning workloads have become prevalent in numerous application domains. Many of these workloads are iterative convergent, allowing different threads to compute in an asynchronous manner, relaxing certain read-after-write data dependencies to use stale values. While considerable effort has been devoted to reducing the communication latency between nodes by utilizing asynchronous parallelism, inefficient utilization of relaxed consistency models within a single node have caused parallel implementations to have low execution efficiency. The long latency and serialization caused by atomic operations have a significant impact on performance. The data communication is not overlapped with the main computation, which reduces execution efficiency. The inefficiency comes from the data movement between where they are stored and where they are processed. In this work, we propose Bounded Staled Sync (BSSync), a hardware support for the bounded staleness consistency model, which accompanies simple logic layers in the memory hierarchy. BSSync overlaps the long latency atomic operation with the main computation, targeting iterative convergent machine learning workloads. Compared to previous work that allows staleness for read operations, BSSync utilizes staleness for write operations, allowing stale-writes. We demonstrate the benefit of the proposed scheme for representative machine learning workloads. On average, our approach outperforms the baseline asynchronous parallel implementation by 1.33x times.

Potential Bacillus probiotics enhance bacterial numbers, water quality and growth during early development of white shrimp (Litopenaeus vannamei). <=SEP=> Epidemics of epizootics and occurrence of multiresistant antibiotics of pathogenic bacteria in aquaculture have put forward a development of effective probiotics for the sustainable culture. This study examined the effectiveness of forms of mixed Bacillus probiotics (probiotic A and probiotic B) and mode of probiotic administration on growth, bacterial numbers and water quality during rearing of white shrimp (Litopenaeus vannamei) in two separated experiments: (1) larval stages and (2) postlarval (PL) stages. Forms of Bacillus probiotics and modes of probiotic administration did not affect growth and survival of larval to PL shrimp. The compositions of Bacillus species in probiotic A and probiotic B did not affect growth and survival of larvae. However, postlarvae treated with probiotic B exhibited higher (P<0.05) growth than probiotic A and controls, indicating Bacillus probiotic composition affects the growth of PL shrimp. Total heterotrophic bacteria and Bacillus numbers in larval and PL shrimp or culture water of the treated groups were higher (P<0.05) than in controls. Levels of pH, ammonia and nitrite of the treated shrimp were significantly decreased, compared to the controls. Microencapsulated Bacillus probiotic was effective for rearing of PL L. vannamei. This investigation showed that administration of mixed Bacillus probiotics significantly improved growth and survival of PL shrimp, increased beneficial bacteria in shrimp and culture water and enhanced water quality for the levels of pH, ammonia and nitrite of culture water.

A Piezoelectric Energy Harvester for Rotary Motion Applications: Design and Experiments <=SEP=> This paper investigates the analysis and design of a vibration-based energy harvester for rotary motion applications. The energy harvester consists of a cantilever beam with a tip mass and a piezoelectric ceramic attached along the beam that is mounted on a rotating shaft. Using this system, mechanical vibration energy is induced in the flexible beam due to the gravitational force applied to the tip mass while the hub is rotating. The piezoelectric transducer is used to convert the induced mechanical vibration energy into electricity. The equations of motion of the flexible structure are utilized along with the physical characteristics of the piezoelectric transducer to derive expressions for the electrical power. Furthermore, expressions for the optimum load resistance and maximum output power are obtained and validated experimentally using PVDF and PZT transducers. The results indicate that a maximum power of 6.4 mW at a shaft speed of 138 rad/s can be extracted by using a PZT transducer with dimensions 50.8 mm × 38.1 mm × 0.13 mm. This amount of power is sufficient to provide power for typical wireless sensors such as accelerometers and strain gauges.

Corrosion Study and Intermetallics Formation in Gold and Copper Wire Bonding in Microelectronics Packaging <=SEP=> A comparison study on the reliability of gold (Au) and copper (Cu) wire bonding is conducted to determine their corrosion and oxidation behavior in different environmental conditions. The corrosion and oxidation behaviors of Au and Cu wire bonding are determined through soaking in sodium chloride (NaCl) solution and high temperature storage (HTS) at 175 °C, 200 °C and 225 °C. Galvanic corrosion is more intense in Cu wire bonding as compared to Au wire bonding in NaCl solution due to the minimal formation of intermetallics in the former. At all three HTS annealing temperatures, the rate of Cu-Al intermetallic formation is found to be three to five times slower than Au-Al intermetallics. The faster intermetallic growth rate and lower activation energy found in this work for both Au/Al and Cu/Al as compared to literature could be due to the thicker Al pad metallization which removed the rate-determining step in previous studies due to deficit in Al material.

POMDP-based dialogue manager adaptation to extended domains <=SEP=> Existing spoken dialogue systems are typically designed to operate in a static and well-defined domain, and are not well suited to tasks in which the concepts and values change dynamically. To handle dynamically changing domains, techniques will be needed to transfer and reuse existing dialogue policies and rapidly adapt them using a small number of dialogues in the new domain. As a first step in this direction, this paper addresses the problem of automatically extending a dialogue system to include a new previously unseen concept (or slot) which can be then used as a search constraint in an information query. The paper shows that in the context of Gaussian process POMDP optimisation, a domain can be extended through a simple expansion of the kernel and then rapidly adapted. As well as being much quicker, adaptation rather than retraining from scratch is shown to avoid subjecting users to unacceptably poor performance during the learning stage.

Towards Automated Cost-Efficient Data Management for Federated Cloud Services <=SEP=> Cloud computing has transformed the accessibility and usage of information technology resources, by offering them as services via the Internet. Cloud service provisioning spans across infrastructure, platform and software levels. The management of the services at these levels is based on monitoring. The analysis of monitoring data provides insight into Cloud operations in order to make informed decisions. Due to the emergence of numerous heterogenous Cloud platforms with proprietary APIs, service and monitoring data are being formatted using diverse and mostly incompatible data interchange formats. This results to interoperability issues and makes the analysis of monitoring data from multi-Cloud service deployments difficult to handle. The existing research efforts on data interchange formats have been mainly focused on general performance analyses. Little or no effort has been channelled towards a combination of multiple data interchange formats based on data type to achieve efficient serialisation that can facilitate interoperability in federated Clouds, and also reduce the size of data and bandwidth utilisation cost. This paper addresses these issues by presenting automated framework that is capable of automatically selecting the most suitable data interchange formats for achieving an efficient formatting and serialisation outcome. The goal of the framework is to enable robust and transparent communication within and between multiple Cloud deployments. Based on three use case scenarios, we evaluate the proposed framework to demonstrate its efficacy in formatting and serialising data.

Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates <=SEP=> Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying participants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.

Psychophysical magic: rendering the visible ‘invisible’ <=SEP=> What are the neural correlates of conscious visual awareness? Tackling this question requires contrasting neural correlates of stimulus processing culminating in visual awareness with neural correlates of stimulus processing unaccompanied by awareness. To produce these two neural states, one must be able to erase an otherwise visible stimulus from awareness. This article describes and assesses visual phenomena involving dissociation of physical stimulation and conscious awareness: degraded stimulation, visual masking, visual crowding, bistable figures, binocular rivalry, motion-induced blindness, inattentional blindness, change blindness and attentional blink. No single approach stands above the others, but those producing changing visual awareness despite invariant physical stimulation are clearly preferable. Such phenomena can help lead us ultimately to a comprehensive account of the neural correlates of conscious awareness.

A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs <=SEP=> Sorting is at the core of many database operations, such as index creation, sort-merge joins, and user-requested output sorting. As GPUs are emerging as a promising platform to accelerate various operations, sorting on GPUs becomes a viable endeavour. Over the past few years, several improvements have been proposed for sorting on GPUs, leading to the first radix sort implementations that achieve a sorting rate of over one billion 32-bit keys per second. Yet, state-of-the-art approaches are heavily memory bandwidth-bound, as they require substantially more memory transfers than their CPU-based counterparts. Our work proposes a novel approach that almost halves the amount of memory transfers and, therefore, considerably lifts the memory bandwidth limitation. Being able to sort two gigabytes of eight-byte records in as little as 50 milliseconds, our approach achieves a 2.32-fold improvement over the state-of-the-art GPU-based radix sort for uniform distributions, sustaining a minimum speed-up of no less than a factor of 1.66 for skewed distributions. To address inputs that either do not reside on the GPU or exceed the available device memory, we build on our efficient GPU sorting approach with a pipelined heterogeneous sorting algorithm that mitigates the overhead associated with PCIe data transfers. Comparing the end-to-end sorting performance to the state-of-the-art CPU-based radix sort running 16 threads, our heterogeneous approach achieves a 2.06-fold and a 1.53-fold improvement for sorting 64 GB key-value pairs with a skewed and a uniform distribution, respectively.

Context-Aware Filtering and Visualization of Web Service Clusters <=SEP=> Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches do not consider domain-specific contexts in measuring similarity because they have failed to capture the semantic similarity of Web services in a given domain and this has affected their filtering performance. In this paper, we propose a context-aware similarity method that uses a support vector machine and a domain dataset from a context-specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three-dimensional sphere to a two-dimensional (2D) spherical surface for 2D visualization. Experimental results show that our filtering approach works efficiently.

Long-Span Program Behavior Modeling and Attack Detection <=SEP=> Intertwined developments between program attacks and defenses witness the evolution of program anomaly detection methods. Emerging categories of program attacks, e.g., non-control data attacks and data-oriented programming, are able to comply with normal trace patterns at local views. This article points out the deficiency of existing program anomaly detection models against new attacks and presents long-span behavior anomaly detection (LAD), a model based on mildly context-sensitive grammar verification. The key feature of LAD is its reasoning of correlations among arbitrary events that occurred in long program traces. It extends existing correlation analysis between events at a stack snapshot, e.g., paired call and ret, to correlation analysis among events that historically occurred during the execution. The proposed method leverages specialized machine learning techniques to probe normal program behavior boundaries in vast high-dimensional detection space. Its two-stage modeling/detection design analyzes event correlation at both binary and quantitative levels. Our prototype successfully detects all reproduced real-world attacks against sshd, libpcre, and sendmail. The detection procedure incurs 0.1 ms to 1.3 ms overhead to profile and analyze a single behavior instance that consists of tens of thousands of function call or system call events.

Electrically programmable fuse (eFUSE) using electromigration in silicides <=SEP=> For the first time we describe a positive application of electromigration, as an electrically programmable fuse device (eFUSE). Upon programming, eFUSE's show a large increase in resistance that enable easy sensing. The transient device characteristics show that the eFUSE stays in a low resistance state during programming due to the local heating of the fuse link. The programming is enhanced by a device design that uses a large cathode which increases the temperature gradient and minimizes the effect of microstructural variations.

Deformation Constraints in a Mass-Spring Model to Describe Rigid Cloth Behavior <=SEP=> This paper describes a physically-based model for animating cloth objects, derived from elastically deformable models, and improved in order to take into account the non-elastic properties of woven fabrics . A cloth object is first approximated to a deformable surface composed of a network of masses and springs, the movement of which is evaluated using the numerical integration of the fundamental law of dynamics. We show that when a concentration of high stresses occurs in a small region of the surface, the local deformation becomes unrealistic compared to real deformations of textiles . With such an elastic model , the only solution to decrease these deformations has been so far to increase the stiffness of the deformed springs, but we show that it dramatically increases the cost of the algorithm. We present therefore a new method to adapt our model to the particularly stiff properties of textiles, inspired from dynamic inverse procedures. R esume Cet article decrit un modele physique d'animation des tissus, variante des modeles elastiques deformables, et ameliore de fa<;on a prendre en compte les proprietes non elastiques des textiles . Nous modelisons tout d 'abord une piece de tissu par une surface deformable, constituee d 'un reseau de masses et de ressorts. Son mouvement est evalue grace a I'integration numerique de la loi fondamentale de la dynamique. Nous montrons que lorsqu'une forte concentration de contraintes apparait a certains endroits de la surface, la deformation locale y devient irrealiste comparee aux deformations rencontrees dans les tissus reels . A vec un tel modele elastique, la seule solution permettant d'attenuer cette deformation etait jusqu'a present d 'augmenter la raideur des ressorts deformes, mais nous montrons que ceci faisait croitre dramatiquement le cout de I'algorithme. Nous presentons donc ici une nouvelle methode permettant d 'adapter notre mode le aux proprietes particulierement rigides des textiles, inspiree des procedures de dynamique inverse.