Query: Borg, Omega, and Kubernetes

Relevant passages:
Large-scale cluster management at Google with Borg <=SEP=> Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines.
 It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior.
 We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.

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.

The tail at scale <=SEP=> Software techniques that tolerate latency variability are vital to building responsive large-scale Web services.

Characterizing Machines and Workloads on a Google Cluster <=SEP=> Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, job- and task-level workload behavior, and how the overall cluster resources are utilized.

Characterizing Cloud Applications on a Google Data Center <=SEP=> In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.

The Autism Diagnostic Observation Schedule, Toddler Module: Standardized Severity Scores. <=SEP=> Standardized calibrated severity scores (CSS) have been created for Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) Modules 1-4 as a metric of the relative severity of autism-specific behaviors. Total and domain CSS were created for the Toddler Module to facilitate comparison to other modules. Analyses included 388 children with ASD age 12-30 months and were replicated on 435 repeated assessments from 127 children with ASD. Compared to raw scores, associations between total and domain CSS and participant characteristics were reduced in the original sample. Verbal IQ effects on Social Affect-CSS were not reduced in the replication sample. Toddler Module CSS increases comparability of ADOS-2 scores across modules and allows studies of symptom trajectories to extend to earlier ages.

Arx : A DBMS with Semantically Secure Encryption Rishabh <=SEP=> In recent years, encrypted databases have emerged as a promising direction that provides data confidentiality without sacrificing functionality: queries are executed on encrypted data. However, existing practical proposals rely on a set of weak encryption schemes that have been shown to leak sensitive data. In this paper, we propose Arx, a practical and functionally rich database system that encrypts the data only with semantically secure encryption schemes. We show that Arx supports real applications such as ShareLaTeX and a health data cloud provider with a modest performance overhead.

Design and Vlsi Implementation of Anti- Collision Enabled Robot Processor Using Rfid Technology <=SEP=> RFID is a low power wireless emerging technology which has given rise to highly promising applications in real life. It can be employed for robot navigation. In multi-robot environment, when many robots are moving in the same workspace, there is a possibility of their physical collision with themselves as well as with physical objects. In the present work, we have proposed and developed a processor incorporating smart algorithm for avoiding such collisions with the help of RFID technology and implemented it by using VHDL. The design procedure and the simulated results are very useful in designing and implementing a practical RFID system. The RTL schematic view of the processor is achieved by successfully synthesizing the proposed design.

The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM <=SEP=> We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence — among synchronous or asynchronous methods — on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.

Text Steganography in SMS <=SEP=> One of the services used in mobile phone is the short message service (SMS) which is widely used by the public in all parts of the world especially in Asia and Europe. This service enables people to write and exchange short messages via mobile phone. Due to the limited size of SMS, lack of a proper keyboard on the mobile phone and to improve the speed of typing, new abbreviations have been invented for different words and phrases which has lead to the invention of a new language called SMS-texting. One of the main issues in communication is information security and privacy. There are many methods for secret communication and many researchers are working on steganography. In steganography the data is hidden in a cover media such as picture or text. The present paper offers a new method for secret exchange of information through SMS by using and developing abbreviation text steganography with the use of the invented language of SMS-texting. This project has been implemented by J2ME (Java 2 Micro Edition) programming language and tested on a Nokia N71 mobile phone.

Synchronization and cell search algorithms in 3GPP long term evolution systems (FDD mode) <=SEP=> In this paper initial downlink synchronization (sync) and cell identification algorithms for the 3rd generation partnership project (3GPP) long term evolution (LTE) systems are presented. The frequency division duplex (FDD) mode is used in the downlink radio frame structure. A user equipment digital receiver architecture is proposed. The orthogonality of signals in orthogonal frequency division multiplexing (OFDM) may be lost due to some impairments such as frequency, time, and phase offsets. Therefore, major parts of this research are involved in restoring the orthogonality at the user equipment (UE) by using some techniques to estimate the sync parameters, and to detect cell identity among candidate cells based on sync signals. A Farrow structure interpolator is used to compensate the fractional timing offset. Both inter site synchronous and inter site asynchronous networks are presented. Computer simulations are used to demonstrate the performance of the proposed schemes with multipath Rayleigh fading channel, frequency offsets, timing offsets, and additive white Gaussian noise (AWGN). Results show high probability of cell identification in a very short time 20 ms in both multi cell model scenarios, especially when multiple input multiple output (MIMO) technique, oversampling at the UE, and high order Farrow structure interpolator are used. Key-Words: 3GPP LTE, OFDM, MIMO, Frequency and Time Synchronization, Cell Search

A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces <=SEP=> In this paper we introduce the TTCS system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. TTCS takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.

Social norms and human cooperation <=SEP=> The existence of social norms is one of the big unsolved problems in social cognitive science. Although no other concept is invoked more frequently in the social sciences, we still know little about how social norms are formed, the forces determining their content, and the cognitive and emotional requirements that enable a species to establish and enforce social norms. In recent years, there has been substantial progress, however, on how cooperation norms are enforced. Here we review evidence showing that sanctions are decisive for norm enforcement, and that they are largely driven by non-selfish motives. Moreover, the explicit study of sanctioning behavior provides instruments for measuring social norms and has also led to deeper insights into the proximate and ultimate forces behind human cooperation.

Combined Effects of Attention and Motivation on Visual Task Performance: Transient and Sustained Motivational Effects <=SEP=> We investigated how the brain integrates motivational and attentional signals by using a neuroimaging paradigm that provided separate estimates for transient cue- and target-related signals, in addition to sustained block-related responses. Participants performed a Posner-type task in which an endogenous cue predicted target location on 70% of trials, while motivation was manipulated by varying magnitude and valence of a cash incentive linked to task performance. Our findings revealed increased detection performance (d') as a function of incentive value. In parallel, brain signals revealed that increases in absolute incentive magnitude led to cue- and target-specific response modulations that were independent of sustained state effects across visual cortex, fronto-parietal regions, and subcortical regions. Interestingly, state-like effects of incentive were observed in several of these brain regions, too, suggesting that both transient and sustained fMRI signals may contribute to task performance. For both cue and block periods, the effects of administering incentives were correlated with individual trait measures of reward sensitivity. Taken together, our findings support the notion that motivation improves behavioral performance in a demanding attention task by enhancing evoked responses across a distributed set of anatomical sites, many of which have been previously implicated in attentional processing. However, the effect of motivation was not simply additive as the impact of absolute incentive was greater during invalid than valid trials in several brain regions, possibly because motivation had a larger effect on reorienting than orienting attentional mechanisms at these sites.

Circular Pulley Versus Variable Radius Pulley: Optimal Design Methodologies and Dynamic Characteristics Analysis <=SEP=> Human-centered robotics has received growing interest in low-impedance actuations. In particular, pneumatic artificial muscles (PAMs) provide compliance and high force-to-weight ratio, which allow for safe actuation. However, several performance drawbacks prevent PAMs from being more pervasive. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs, such as restricted workspace and torque capacity, remain to be addressed. This paper analyzes the characteristics and limitations of PAMs-driven joints and subsequently provides an optimization strategy for circular pulleys (CPs) in order to improve joint torque capacity over a large workspace. In addition to CPs, this paper proposes a design methodology to synthesize a pair of variable radius pulleys (VRPs) for further improvement. Simulation and experimental results show that newly synthesized VRPs significantly improve torque capacity in the enlarged workspace without loss of dynamic performance. Finally, the characteristics of CPs and VRPs are discussed in terms of physical human-robot interaction.

An IT Balance Scorecard Design under Service Management Philosophy <=SEP=> The present study proposes a design for an Information Technology Balanced Scorecard (IT BSC) that integrates with business and environment and balances and optimizes deployment and control of the Information Technology strategy. This Strategic and Tactical IT Planning model enhances IT's role in obtaining and measuring its contribution to business value: optimizing IT operational efficiency or proposing new business ideas with a high IT component. The proposed model stimulates innovation in each of the parts of the IT Services lifecycle, is based on IT Service Management best practices and uses ideas from the ITIL v3 standard.

Privacy-Preserving Profile Matching for Proximity-Based Mobile Social Networking <=SEP=> Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users. The first step toward effective PMSN is for mobile users to choose whom to interact with. Profile matching refers to two users comparing their personal profiles and is promising for user selection in PMSN. It, however, conflicts with users' growing privacy concerns about disclosing their personal profiles to complete strangers. This paper tackles this open challenge by designing novel fine-grained private matching protocols. Our protocols enable two users to perform profile matching without disclosing any information about their profiles beyond the comparison result. In contrast to existing coarse-grained private matching schemes for PMSN, our protocols allow finer differentiation between PMSN users and can support a wide range of matching metrics at different privacy levels. The performance of our protocols is thoroughly analyzed and evaluated via real smartphone experiments.

A Survey of Machine Learning Techniques for Spam Filtering <=SEP=> Email spam or junk e-mail (unwanted e-mail “usually of a commercial nature sent out in bulk”) is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters include organizing incoming email and removal of spam and computer viruses. A less common use is to inspect outgoing email at some companies to ensure that employees comply with appropriate laws. Users might also employ a mail filter to prioritize messages, and to sort them into folders based on subject matter or other criteria. Mail filters can be installed by the user, either as separate programs, or as part of their email program (email client). In email programs, users can make personal, "manual" filters that then automatically filter mail according to the chosen criteria. In this paper, we present a survey of the performance of five commonly used machine learning methods in spam filtering. Most email programs now also have an automatic spam filtering function.

Advances in High-Voltage Modulators for Applications in Pulsed Power and Plasma-Based Ion Implantation <=SEP=> Modern pulsed power technology has its roots in the late 1950s and early 1960s, and it was driven overwhelmingly by applications in national defense carried out by several countries, especially the U.S., U.K., Russia, and China. The following decades, particularly the early 1990s, witnessed an increased interest in compact systems with pulse repetition rate that could be used in nondefense applications such as treatment of material surfaces by plasma and beam interactions, treatment of pollutants, food sterilization, medical applications, etc. This spawned a new generation of pulsed power components (solid-state switches) that led to completely solid-state modulators. This paper describes how the pulsed power technology used originally in beam sources and cathodic arcs has converged to produce power sources for plasma-based ion implantation (PBII) and related technologies. The present state of the art is reviewed, and prospects for future advances are described, especially for PBII.

Chaotic bat algorithm <=SEP=> Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are vailable online xxx eywords: at algorithm haos etaheuristic lobal optimization utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks. © 2013 Elsevier B.V. All rights reserved. . Introduction Many design optimization problems are often highly nonlinar, which can typically have multiple modal optima, and it is thus ery challenging to solve such multimodal problems. To cope with his issue, global optimization algorithms are widely attempted, owever, traditional algorithms may not produce good results, and atest trends are to use new metaheuristic algorithms [1]. Metaeuristic techniques are well-known global optimization methods hat have been successfully applied in many real-world and comlex optimization problems [2,3]. These techniques attempt to imic natural phenomena or social behavior so as to generate etter solutions for optimization problem by using iterations and tochasticity [4]. They also try to use both intensification and iversification to achieve better search performance. Intensificaion typically searches around the current best solutions and selects he best candidate designs, while the diversification process allows he optimizer to explore the search space more efficiently, mostly y randomization [1]. In recent years, several novel metaheuristic algorithms have een proposed for global search. Such algorithms can increase he computational efficiency, solve larger problems, and implePlease cite this article in press as: A.H. Gandomi, X.-S. http://dx.doi.org/10.1016/j.jocs.2013.10.002 ent robust optimization codes [5]. For example, Xin-She Yang [6] ecently developed a promising metaheuristic algorithm, called bat lgorithm (BA). Preliminary studies suggest that the BA can have ∗ Corresponding author. Tel.: +44 2084112351. E-mail addresses: a.h.gandomi@gmail.com, ag72@uakron.edu (A.H. Gandomi), .yang@mdx.ac.uk (X.-S. Yang). 877-7503/$ – see front matter © 2013 Elsevier B.V. All rights reserved. ttp://dx.doi.org/10.1016/j.jocs.2013.10.002 superior performance over genetic algorithms and particle swarm optimization [6], and it can solve real world and engineering optimization problems [7–10]. On the other hand, recent advances in theories and applications of nonlinear dynamics, especially chaos, have drawn more attention in many fields [10]. One of these fields is the applications of chaos in optimization algorithms to replace certain algorithm-dependent parameters [11]. Previously, chaotic sequences have been used to tune parameters in metaheuristic optimization algorithms such as genetic algorithms [12], particle swarm optimization [13], harmony search [14], ant and bee colony optimization [15,16], imperialist competitive algorithm [17], firefly algorithm [18], and simulated annealing [19]. Such a combination of chaos with metaheuristics has shown some promise once the right set of chaotic maps are used. It is still not clear why the use of chaos in an algorithm to replace certain parameters may change the performance, however, empirical studies indeed indicate that chaos can have high-level of mixing capability, and thus it can be expected that when a fixed parameter is replaced by a chaotic map, the solutions generated may have higher mobility and diversity. For this reason, it may be useful to carry out more studies by introducing chaos to other, especially newer, metaheuristic algorithms. Therefore, one of the aims of this paper is to introduce chaos into the standard bat algorithm, and as a result, we propose a chaosbased bat algorithm (CBA). As different chaotic maps may lead to different behavior of the algorithm, we then have a set of chaosYang, Chaotic bat algorithm, J. Comput. Sci. (2013), based bat algorithms. In these algorithms, we use different chaotic systems to replace the parameters in BA. Thus different methods that use chaotic maps as potentially efficient alternatives to pseudorandom sequences have been proposed. In order to evaluate the

Splitting Compounds by Semantic Analogy <=SEP=> Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. In this paper, we investigate whether distributional semantics in the form of word embeddings can enable a deeper, i.e., more knowledge-rich, processing of compounds than the standard string-based methods. We present an unsupervised approach that exploits regularities in the semantic vector space (based on analogies such as “bookshop is to shop as bookshelf is to shelf”) to produce compound analyses of high quality. A subsequent compound splitting algorithm based on these analyses is highly effective, particularly for ambiguous compounds. German to English machine translation experiments show that this semantic analogy-based compound splitter leads to better translations than a commonly used frequency-based method.

Polymorphic Encryption and Pseudonymisation for Personalised Healthcare <=SEP=> Polymorphic encryption and Pseudonymisation, abbreviated as PEP, form a novel approach for the management of sensitive personal data, especially in health care. Traditional encryption is rather rigid: once encrypted, only one key can be used to decrypt the data. This rigidity is becoming an every greater problem in the context of big data analytics, where different parties who wish to investigate part of an encrypted data set all need the one key for decryption. Polymorphic encryption is a new cryptographic technique that solves these problems. Together with the associated technique of polymorphic pseudonymi-sation new security and privacy guarantees can be given which are essential in areas such as (personalised) healthcare, medical data collection via self-measurement apps, and more generally in privacy-friendly identity management and data analytics. The key ideas of polymorphic encryption are: 1. Directly after generation, data can be encrypted in a 'polymorphic' manner and stored at a (cloud) storage facility in such a way that the storage provider cannot get access. Crucially, there is no need to a priori fix who gets to see the data, so that the data can immediately be protected. For instance a PEP-enabled self-measurement device will store all its measurement data in polymorphically encrypted form in a back-end data base. 2. Later on it can be decided who can decrypt the data. This decision will be made on the basis of a policy, in which the data subject should play a key role. The user of the PEP-enabled device can, for instance, decide that doctors X, Y, Z may at some stage decrypt to use the data in their diagnosis, or medical researcher groups A, B, C may use it for their investigations, or third parties U, V, W may use it for additional services, etc. 3. This 'tweaking' of the encrypted data to make it decryptable by a specific party can be done in a blind manner. It will have to be done by a trusted party who knows how to tweak the ciphertext for whom. This PEP technology can provide the necessary security and privacy infrastructure for big data analytics. People can entrust their data in polymorphically encrypted form, and each time decide later to make (parts of) it available (de-cryptable) for specific parties, for specific analysis purposes. In this way users remain in control, and can monitor which of their data is used where by whom for which purposes. The …

Video Captioning by Adversarial LSTM <=SEP=> In this paper, we propose a novel approach to video captioning based on adversarial learning and long short-term memory (LSTM). With this solution concept, we aim at compensating for the deficiencies of LSTM-based video captioning methods that generally show potential to effectively handle temporal nature of video data when generating captions but also typically suffer from exponential error accumulation. Specifically, we adopt a standard generative adversarial network (GAN) architecture, characterized by an interplay of two competing processes: a “generator” that generates textual sentences given the visual content of a video and a “discriminator” that controls the accuracy of the generated sentences. The discriminator acts as an “adversary” toward the generator, and with its controlling mechanism, it helps the generator to become more accurate. For the generator module, we take an existing video captioning concept using LSTM network. For the discriminator, we propose a novel realization specifically tuned for the video captioning problem and taking both the sentences and video features as input. This leads to our proposed LSTM–GAN system architecture, for which we show experimentally to significantly outperform the existing methods on standard public datasets.

Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset <=SEP=> We investigate the problem of readeraware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset. The annotated dataset for RA-MDS is available online1.

Bee Colony Optimization (BCO) <=SEP=> Swarm Intelligence is the part of Artificial Intel ligence based on study of actions of individuals in various decentralized sys tems. The Bee Colony Optimization (BCO) metaheuristic has been introduce fairly recently as a new direction in the field of Swarm Intelligence. Artif icial bees represent agents, which collaboratively solve complex combinatorial optimiz ation problem. The chapter presents a classification and analysis of the resul ts achieved using Bee Colony Optimization (BCO) to model complex engineering and management processes. The primary goal of this chapter is to acquaint readers with the basic principles of Bee Colony Optimization, as well as to indicate potenti al BCO applications in engineering and management.

SparkClouds: Visualizing Trends in Tag Clouds <=SEP=> Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.

Big data needs approximate computing: technical perspective <=SEP=> 以容忍。还有一点让人印象深刻: 对于不同的程序,这些提升和能 耗降低并不需要使用不同的加速 器实现方式——它们通过相同的 近似加速器得到,那些加速器可 用于所有程序中所有类型的可近 似代码区。 除了文中描述的之外,通过让 近似计算引擎在明显低得多的电压 下运行,还能节约更多的能源。业 界增长的真正引擎——登纳德缩放 (Dennard scaling)——允许晶体 管减少尺寸,同时消耗更少的能源, 得到更高的性能。但是登纳德缩放 正趋近极限。设备的尺寸已经缩减 到了原子级别。而且,在不牺牲设 备可靠性的情况下,无法继续显著 降低电压。新技术会带来不错的能 耗性能,但由于需要利用冗余来补 偿它们的不可靠性,这一优势被抵 消了。因此,对于传统的交易系统 而言,未来的技术不大可能大幅提 升其能耗性能。然而,对于那些生 成近似结果的系统(比如作者描述 的系统),需要的冗余可能有所不 同且要简单得多。对于这些系统类 型,低电压运行是可行的。 未 来, 当 我 们 使 用 缩 小 的 CMOS 和后硅技术,利用海量计算 处理世上产生的海量数据时,近似 计算似乎是我们最有可能取得成功 的希望。下面的文章就是为实现此 目标所需开展的工作的一个很好的 例子。

Torus data center network with smart flow control enabled by hybrid optoelectronic routers [Invited] <=SEP=> A torus-topology photonic data center network with smart flow control is presented, where optical packet switching (OPS), optical circuit switching (OCS), and virtual OCS (VOCS) schemes are all enabled on a unified hardware platform via the combination of hybrid optoelectronic routers (HOPR) and a centralized network controller. An upgraded HOPR is being developed with the goal of handling 100 Gbps optical packets with a high energy efficiency of 90 mW/Gbps and low latency of less than 100 ns. The architecture of HOPR and its enabling technologies are reviewed, including a label processor, optical switch, and optoelectronic shared buffer. We also explain the concept and operation mechanisms of both the data and control planes in the hybrid OPS/OCS/VOCS torus network. The performance of these three transmission modes is evaluated by numerical simulations for a network composed of 4096 HOPRs.

High Performance Rogowski Current Transducers <=SEP=> A Rogowski current transducer is an invaluable tool for semiconductor and power electronic circuit development since it is non-intrusive and does not saturate at high currents. This paper reviews the operating principles, performance limitations and development of this measurement technology and outlines improvements to the integrator design that enables bandwidths of 10MHz to be achieved.

ANNarchy: a code generation approach to neural simulations on parallel hardware <=SEP=> Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.