bluuebunny/crossref_metadata_2025 huggingface dataset:
Scanning for Digital Content
Authors: Guerreiro João , Gonçalves Daniel
Published: February 2016

The widespread availability of digital media has changed the way that people consume information and has impacted the consumption of auditory information. Despite this recent popularity among sighted people, the use of auditory feedback to access digital information is not new for visually impaired users. However, its sequential nature undermines both blind and sighted people’s ability to efficiently find relevant information in the midst of several potentially useful items. We propose taking advantage of the
Cocktail Party Effect
, which states that people are able to focus on a single speech source among several conversations, but still identify relevant content in the background. Therefore, in contrast to one sequential speech channel, we hypothesize that people can leverage concurrent speech channels to quickly get the gist of digital information. In this article, we present an experiment with 46 (23 blind, 23 sighted) participants, which aims to understand people’s ability to search for relevant content listening to two, three, or four concurrent speech channels. Our results suggest that both blind and sighted people are able to process concurrent speech in scanning scenarios. In particular, the use of two concurrent sources may be used both to identify and understand the content of the relevant sentence. Moreover, three sources may be used for most people depending on the task intelligibility demands and user characteristics. Contrasting with related work, the use of different voices did not affect the perception of concurrent speech but was highly preferred by participants. To complement the analysis, we propose a set of scenarios that may benefit from the use of concurrent speech sources, for both blind and sighted people, toward a
Design for All
paradigm.
Adaptive Model Rules From High-Speed Data Streams
Authors: Duarte João , Gama João , Bifet Albert
Published: February 2016

Decision rules are one of the most expressive and interpretable models for machine learning. In this article, we present Adaptive Model Rules (AMRules), the first stream rule learning algorithm for regression problems. In AMRules, the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of the attributes. In order to maintain a regression model compatible with the most recent state of the process generating data, each rule uses a Page-Hinkley test to detect changes in this process and react to changes by pruning the rule set. Online learning might be strongly affected by outliers. AMRules is also equipped with outliers detection mechanisms to avoid model adaption using anomalous examples. In the experimental section, we report the results of AMRules on benchmark regression problems, and compare the performance of our system with other streaming regression algorithms.
Yield and Speedup Improvements in Extensible Processors by Allocating Extra Cycles to Some Custom Instructions
Authors: Kamal Mehdi , Afzali-Kusha Ali , Safari Saeed , Pedram Massoud
Published: February 2016

In this article, we investigate the application of different techniques for mitigating the impact of process variations on the custom functional unit (CFU) of extensible processors. The techniques include using extra cycles for the CFU and extending the clock period for the extensible processor. The former technique is based on providing an extra clock cycle to those custom instructions (CIs) that have timing yields smaller than one. For this purpose, we make use of a lookup table (LUT) for each fabricated processor. Based on a post-fabrication analysis, the need for an extra clock cycle for some CIs is determined. Consequently, the CI timing violations are prevented, and all manufactured extensible processors will work with a predefined clock cycle time. To study the effect of the objective function (used during the CI selection phase) on the efficacy of the suggested architectural technique, we investigate three different objective functions. In the second technique, the clock period extension is used to guarantee a design yield of one. Our results demonstrate that combining both techniques helps increase the speedup achieved by the extensible processor. To assess the efficacies of the proposed methods, several benchmarks from different application domains are used. Results of the study reveal that the suggested techniques provide considerable improvements in the speedups of the extensible processors when compared to those of approaches that do not consider the impact of process variations.
Adaptive and Hierarchical Runtime Manager for Energy-Aware Thermal Management of Embedded Systems
Authors: Das Anup , Al-Hashimi Bashir M. , Merrett Geoff V.
Published: February 2016

Modern embedded systems execute applications, which interact with the operating system and hardware differently depending on the type of workload. These cross-layer interactions result in wide variations of the chip-wide thermal profile. In this article, a reinforcement learning-based runtime manager is proposed that guarantees application-specific performance requirements and controls the POSIX thread allocation and voltage/frequency scaling for energy-efficient thermal management. This controls three thermal aspects: peak temperature, average temperature, and thermal cycling. Contrary to existing learning-based runtime approaches that optimize energy and temperature individually, the proposed runtime manager is the first approach to combine the two objectives, simultaneously addressing all three thermal aspects. However, determining thread allocation and core frequencies to optimize energy and temperature is an NP-hard problem. This leads to exponential growth in the learning table (significant memory overhead) and a corresponding increase in the exploration time to learn the most appropriate thread allocation and core frequency for a particular application workload. To confine the learning space and to minimize the learning cost, the proposed runtime manager is implemented in a two-stage hierarchy: a heuristic-based thread allocation at a longer time interval to improve thermal cycling, followed by a learning-based hardware frequency selection at a much finer interval to improve average temperature, peak temperature, and energy consumption. This enables finer control on temperature in an energy-efficient manner while simultaneously addressing scalability, which is a crucial aspect for multi-/many-core embedded systems. The proposed hierarchical runtime manager is implemented for Linux running on nVidia’s Tegra SoC, featuring four ARM Cortex-A15 cores. Experiments conducted with a range of embedded and cpu-intensive applications demonstrate that the proposed runtime manager not only reduces energy consumption by an average 15% with respect to Linux but also improves all the thermal aspects—average temperature by 14°C, peak temperature by 16°C, and thermal cycling by 54%.
Adapting to Varying Distribution of Unknown Response Bits
Authors: Suresh Chandra K. H. , Sinanoglu Ozgur , Ozev Sule
Published: February 2016

Traditionally, test patterns that are generated for a given circuit are applied in an identical manner to all manufactured devices until each device under test either fails or passes each test. With increasing process variations, the statistical diversity of manufactured devices is increasing, making such one-size-fits-all approaches increasingly inefficient. Adaptive test techniques address this problem by tailoring the test decisions for the statistical characteristics of the device under test. In this article, we present several adaptive strategies to enable adaptive unknown bit masking for faster-than-at-speed testing so as to ensure no yield loss while attaining the maximum test quality based on tester memory constraints. We also develop a tester-enabled compression scheme that helps alleviate memory constraints further, shifting the tradeoff space favorably to improve test quality.
Learning Hardware-Friendly Classifiers Through Algorithmic Stability
Authors: Oneto Luca , Ridella Sandro , Anguita Davide
Published: February 2016

Most state-of-the-art machine-learning (ML) algorithms do not consider the computational constraints of implementing the learned model on embedded devices. These constraints are, for example, the limited depth of the arithmetic unit, the memory availability, or the battery capacity. We propose a new learning framework, the Algorithmic Risk Minimization (ARM), which relies on Algorithmic-Stability, and includes these constraints inside the learning process itself. ARM allows one to train advanced resource-sparing ML models and to efficiently deploy them on smart embedded systems. Finally, we show the advantages of our proposal on a smartphone-based Human Activity Recognition application by comparing it to a conventional ML approach.
Reliability-Aware Resource Allocation and Binding in High-Level Synthesis
Authors: Chen Liang , Ebrahimi Mojtaba , Tahoori Mehdi B.
Published: February 2016

Soft error is nowadays a major reliability issue for nanoscale VLSI, and addressing it during high-level synthesis is essential to improve the efficiency of error mitigation. Motivated by the observation that for behavioral designs, especially control-flow intensive ones, variables and operations have non-uniform soft error vulnerabilities, we propose a novel reliability-aware allocation and binding technique to explore more effective soft error mitigation during high level synthesis. We first perform a comprehensive vulnerability analysis at the behavioral level by considering error propagation and masking in both control and data flows. Then the optimizations based on integer linear programming, as well as heuristic algorithm, are employed to incorporate the behavioral vulnerabilities into the register and functional unit binding phases to achieve cost-efficient error mitigation. The experimental results reveal that compared with the previous techniques which ignored behavioral vulnerabilities, the proposed approach can achieve up to 85% reliability improvement with the same amount of area budget in the RTL design.
Biometric Recognition via Eye Movements
Authors: Rigas Ioannis , Komogortsev Oleg , Shadmehr Reza
Published: February 2016

Previous research shows that human eye movements can serve as a valuable source of information about the structural elements of the oculomotor system and they also can open a window to the neural functions and cognitive mechanisms related to visual attention and perception. The research field of eye movement-driven biometrics explores the extraction of individual-specific characteristics from eye movements and their employment for recognition purposes. In this work, we present a study for the incorporation of dynamic saccadic features into a model of eye movement-driven biometrics. We show that when these features are added to our previous biometric framework and tested on a large database of 322 subjects, the biometric accuracy presents a relative improvement in the range of 31.6--33.5% for the verification scenario, and in range of 22.3--53.1% for the identification scenario. More importantly, this improvement is demonstrated for different types of visual stimulus (random dot, text, video), indicating the enhanced robustness offered by the incorporation of saccadic vigor and acceleration cues.
Understanding Mass Interactions in Online Sports Viewing
Authors: Ko Minsam , Choi Seungwoo , Lee Joonwon , Lee Uichin , Segev Aviv
Published: February 2016

This article aims to deepen understanding of these mass interactions in online sports viewing through studying Naver Sports, the largest online sports viewing service in Korea. We examined the diverse aspects of mass interactions, including interactive experiences, usage motives, and relationships between usage patterns and motives, through analysis of almost 6 million chats from Naver Sports and from self-reporting survey data from 1,123 users. First, we found that online sports viewing provides unique interactive experiences when compared to other settings such as offline sports viewing and social TV viewing with friends. Second, we found the key motives inspiring online sports viewing include the following: sharing feelings/thoughts, wanting to be entertained, sharing information, and wanting to feel membership in a group. Third, these motives were significantly related to specific usage patterns. Finally, we explored how the study’s key findings can offer practical design implications to enhance online sports viewing services, and to show system designers how to support particular usage patterns to better accommodate specific user motives.
Software-Based Selective Validation Techniques for Robust CGRAs Against Soft Errors
Authors: Ko Yohan , Kang Jihoon , Lee Jongwon , Kim Yongjoo , Kim Joonhyun , So Hwisoo , Lee Kyoungwoo , Paek Yunheung
Published: February 2016

Coarse-Grained Reconfigurable Architectures (CGRAs) are drawing significant attention since they promise both performances with parallelism and flexibility with reconfiguration. Soft errors (or transient faults) are becoming a serious design concern in embedded systems including CGRAs since the soft error rate is increasing exponentially as technology is scaling. A recently proposed software-based technique with TMR (Triple Modular Redundancy) implemented on CGRAs incurs extreme overheads in terms of runtime and energy consumption mainly due to expensive voting mechanisms for the outputs from the triplication of every operation. In this article, we propose selective validation mechanisms for efficient modular redundancy techniques in the datapaths on CGRAs. Our techniques selectively validate the results at synchronous operations rather than every operation in order to reduce the expensive performance overhead from the validation mechanism. We also present an optimization technique to further improve the runtime and the energy consumption by minimizing synchronous operations where a validating mechanism needs to be applied. Our experimental results demonstrate that our selective validation-based TMR technique with our optimization on CGRAs can improve the runtime by 41.0% and the energy consumption by 26.2% on average over benchmarks as compared to the recently proposed software-based TMR technique with the full validation.
Processing Top-k Dominating Queries in Metric Spaces
Authors: Tiakas Eleftherios , Valkanas George , Papadopoulos Apostolos N. , Manolopoulos Yannis , Gunopulos Dimitrios
Published: February 2016

Top
-
k
dominating queries
combine the natural idea of selecting the
k
best items with a comprehensive “goodness” criterion based on dominance. A point
p
1
dominates
p
2
if
p
1
is as good as
p
2
in all attributes and is strictly better in at least one. Existing works address the problem in settings where data objects are multidimensional points. However, there are domains where we only have access to the distance between two objects. In cases like these, attributes reflect distances from a set of input objects and are dynamically generated as the input objects change. Consequently, prior works from the literature cannot be applied, despite the fact that the dominance relation is still meaningful and valid. For this reason, in this work, we present the first study for processing

top-
k
dominating queries

over distance-based dynamic attribute vectors, defined over a
metric space
. We propose four progressive algorithms that utilize the properties of the underlying metric space to efficiently solve the problem and present an extensive, comparative evaluation on both synthetic and real-world datasets.
Sign Transition Modeling and a Scalable Solution to Continuous Sign Language Recognition for Real-World Applications
Authors: Li Kehuang , Zhou Zhengyu , Lee Chin-Hui
Published: February 2016

We propose a new approach to modeling transition information between signs in continuous Sign Language Recognition (SLR) and address some scalability issues in designing SLR systems. In contrast to Automatic Speech Recognition (ASR) in which the transition between speech sounds is often brief and mainly addressed by the coarticulation effect, the sign transition in continuous SLR is far from being clear and usually not easily and exactly characterized. Leveraging upon hidden Markov modeling techniques from ASR, we proposed a modeling framework for continuous SLR having the following major advantages, namely: (i) the system is easy to scale up to large-vocabulary SLR; (ii) modeling of signs as well as the transitions between signs is robust even for noisy data collected in real-world SLR; and (iii) extensions to training, decoding, and adaptation are directly applicable even with new deep learning algorithms. A pair of low-cost digital gloves affordable for the deaf and hard of hearing community is used to collect a collection of training and testing data for real-world SLR interaction applications. Evaluated on 1,024 testing sentences from five signers, a word accuracy rate of 87.4% is achieved using a vocabulary of 510 words. The SLR speed is in real time, requiring an average of 0.69s per sentence. The encouraging results indicate that it is feasible to develop real-world SLR applications based on the proposed SLR framework.
Faster Compressed Suffix Trees for Repetitive Collections
Authors: Navarro Gonzalo , Pereira Alberto Ordóñez
Published: February 2016

Recent compressed suffix trees targeted to highly repetitive sequence collections reach excellent compression performance, but operation times are very high. We design a new suffix tree representation for this scenario that still achieves very low space usage, only slightly larger than the best previous one, but supports the operations orders of magnitude faster. Our suffix tree is still orders of magnitude slower than general-purpose compressed suffix trees, but these use several times more space when the collection is repetitive. Our main novelty is a practical grammar-compressed tree representation with full navigation functionality, which is useful in all applications where large trees with repetitive topology must be represented.
Tree-Based Coarsening and Partitioning of Complex Networks
Authors: Glantz Roland , Meyerhenke Henning , Schulz Christian
Published: February 2016

A hierarchy of increasingly coarse versions of a network allows one to represent the network on multiple scales at the same time. Often, the elementary operation for generating a hierarchy on a network is merging adjacent vertices, an operation that can be realized through contracting the edge between the two vertices. Such a hierarchy is defined by the selection of the edges to be contracted between a level and the next coarser level. The selection may involve (i) rating the edges, (ii) constraining the selection (e.g., that the selected edges form a matching), as well as (iii) maximizing the total rate of the selected edges under the constraints. Hierarchies of this kind are, among others, involved in multilevel methods for partitioning networks—a prerequisite for processing in parallel with distributed memory.

In this article, we propose a new edge rating by (i) defining weights for the edges of a network that express the edges’ importance for connectivity via shortest paths, (ii) computing a minimum weight spanning tree with respect to these weights, and (iii) rating the network edges based on the conductance values of the tree’s fundamental cuts.

To make the computation of our new edge rating efficient, we develop the first optimal linear-time algorithm to compute the conductance values of
all
fundamental cuts of a given spanning tree. We integrate the new edge rating into a leading multilevel graph partitioner and equip the latter also with a new greedy postprocessing for optimizing the Maximum Communication Volume (MCV) of a partition.

Our experiments, in which we bipartition frequently used benchmark networks, show that the postprocessing reduces MCV by 11.3%. Our new edge rating, here used for matching-based coarsening, further reduces MCV by 10.3% compared to the previously best rating with MCV postprocessing in place for both ratings. In total, with a modest increase in running time, our new approach reduces the MCV of complex network partitions by 20.4%.
Finding Social Points of Interest from Georeferenced and Oriented Online Photographs
Authors: Thomee Bart , Arapakis Ioannis , Shamma David A.
Published: February 2016

Points of interest are an important requirement for location-based services, yet they are editorially curated and maintained, either professionally or through community. Beyond the laborious manual annotation task, further complications arise as points of interest may appear, relocate, or disappear over time, and may be relevant only to specific communities. To assist, complement, or even replace manual annotation, we propose a novel method for the automatic localization of points of interest depicted in photos taken by people across the world. Our technique exploits the geographic coordinates and the compass direction supplied by modern cameras, while accounting for possible measurement errors due to the variability in accuracy of the sensors that produced them. We statistically demonstrate that our method significantly outperforms techniques from the research literature on the task of estimating the geographic coordinates and geographic footprints of points of interest in various cities, even when photos are involved in the estimation process that do not show the point of interest at all.
Morphological Coevolution for Fluid Dynamical-Related Risk Mitigation
Authors: Filippone Giuseppe , D’ambrosio Donato , Marocco Davide , Spataro William
Published: February 2016

In the lava flow mitigation context, the determination of areas exposed to volcanic risk is crucial for diminishing consequences in terms of human causalities and damages of material properties. In order to mitigate the destructive effects of lava flows along volcanic slopes, the building and positioning of artificial barriers is fundamental for controlling and slowing down the lava flow advance. In this article, an evolutionary computation-based decision support system for defining and optimizing volcanic hazard mitigation interventions is proposed. In particular, the SCIARA-fv2 Cellular Automata numerical model has been applied for simulating lava flows at Mt. Etna (Italy) volcano and Parallel Genetic Algorithms (PGA) adopted for optimizing protective measures construction by morphological evolution. The PGA application regarded the optimization of the position, orientation, and extension of earth barriers built to protect Rifugio Sapienza, a touristic facility located near the summit of the volcano. A preliminary release of the algorithm, called single barrier (SBA) approach, was initially considered. Subsequently, a second GA strategy, called Evolutionary Greedy Strategy (EGS), was implemented by introducing multibarrier protection measures in order to improve the efficiency of the final solution. Finally, a Coevolutionary Cooperative Strategy (CCS), has been introduced where all barriers are encoded in the genotype and, because all the constituents parts of the solution interact with the GA environment, a mechanism of cooperation between individuals has been favored. The study has produced extremely positive results and represents, to our knowledge, the first application of morphological evolution for lava flow mitigation.
GCTrees
Authors: Dragga Chris , Santry Douglas J.
Published: February 2016

File-system snapshots have been a key component of enterprise storage management since their inception. Creating and managing them efficiently, while maintaining flexibility and low overhead, has been a constant struggle. Although the current state-of-the-art mechanism—hierarchical reference counting—performs reasonably well for traditional small-file workloads, these workloads are increasingly vanishing from the enterprise data center, replaced instead with virtual machine and database workloads. These workloads center around a few very large files, violating the assumptions that allow hierarchical reference counting to operate efficiently. To better cope with these workloads, we introduce Generational Chain Trees (GCTrees), a novel method of space management that uses concepts of block lineage across snapshots rather than explicit reference counting. As a proof of concept, we create a prototype file system—gcext4, a modified version of ext4 that uses GCTrees as a basis for snapshots and copy-on-write. In evaluating this prototype empirically, we find that although they have a somewhat higher overhead for traditional workloads, GCTrees have dramatically lower overhead than hierarchical reference counting for large-file workloads, improving by a factor of 34 or more in some cases. Furthermore, gcext4 performs comparably to ext4 across all workloads, showing that GCTrees impose minor cost for their benefits.
SuperMalloc: a super fast multithreaded malloc for 64-bit machines
Authors: Kuszmaul Bradley C.
Published: February 2016

SuperMalloc is an implementation of malloc(3) originally designed for X86 Hardware Transactional Memory (HTM)@. It turns out that the same design decisions also make it fast even without HTM@. For the malloc-test benchmark, which is one of the most difficult workloads for an allocator, with one thread SuperMalloc is about 2.1 times faster than the best of DLmalloc, JEmalloc, Hoard, and TBBmalloc; with 8 threads and HTM, SuperMalloc is 2.75 times faster; and on 32 threads without HTM SuperMalloc is 3.4 times faster. SuperMalloc generally compares favorably with the other allocators on speed, scalability, speed variance, memory footprint, and code size. SuperMalloc achieves these performance advantages using less than half as much code as the alternatives. SuperMalloc exploits the fact that although physical memory is always precious, virtual address space on a 64-bit machine is relatively cheap. It allocates 2 chunks which contain objects all the same size. To translate chunk numbers to chunk metadata, SuperMalloc uses a simple array (most of which is uncommitted to physical memory). SuperMalloc takes care to avoid associativity conflicts in the cache: most of the size classes are a prime number of cache lines, and nonaligned huge accesses are randomly aligned within a page. Objects are allocated from the fullest non-full page in the appropriate size class. For each size class, SuperMalloc employs a 10-object per-thread cache, a per-CPU cache that holds about a level-2-cache worth of objects per size class, and a global cache that is organized to allow the movement of many objects between a per-CPU cache and the global cache using $O(1)$ instructions. SuperMalloc prefetches everything it can before starting a critical section, which makes the critical sections run fast, and for HTM improves the odds that the transaction will commit.
Don't race the memory bus: taming the GC leadfoot
Authors: Hussein Ahmed , Hosking Antony L. , Payer Mathias , Vick Christopher A.
Published: February 2016

Dynamic voltage and frequency scaling (DVFS) is ubiquitous on mobile devices as a mechanism for saving energy. Reducing the clock frequency of a processor allows a corresponding reduction in power consumption, as does turning off idle cores. Garbage collection is a canonical example of the sort of memory-bound workload that best responds to such scaling. Here, we explore the impact of frequency scaling for garbage collection in a real mobile device running Android's Dalvik virtual machine, which uses a concurrent collector. By controlling the frequency of the core on which the concurrent collector thread runs we can reduce power significantly. Running established multi-threaded benchmarks shows that total processor energy can be reduced up to 30%, with end-to-end performance loss of at most 10%.
Safe and efficient hybrid memory management for Java
Authors: Stancu Codruţ , Wimmer Christian , Brunthaler Stefan , Larsen Per , Franz Michael
Published: February 2016

Java uses automatic memory management, usually implemented as a garbage-collected heap. That lifts the burden of manually allocating and deallocating memory, but it can incur significant runtime overhead and increase the memory footprint of applications. We propose a hybrid memory management scheme that utilizes region-based memory management to deallocate objects automatically on region exits. Static program analysis detects allocation sites that are safe for region allocation, i.e., the static analysis proves that the objects allocated at such a site are not reachable after the region exit. A regular garbage-collected heap is used for objects that are not region allocatable. The region allocation exploits the temporal locality of object allocation. Our analysis uses coarse-grain source code annotations to disambiguate objects with non-overlapping lifetimes, and maps them to different memory scopes. Region-allocated memory does not require garbage collection as the regions are simply deallocated when they go out of scope. The region allocation technique is backed by a garbage collector that manages memory that is not region allocated. We provide a detailed description of the analysis, provide experimental results showing that as much as 78% of the memory is region allocatable and discuss how our hybrid memory management system can be implemented efficiently with respect to both space and time.
A partial read barrier for efficient support of live object-oriented programming
Authors: Miranda Eliot , Béra Clément
Published: February 2016

Live programming, originally introduced by Smalltalk and Lisp, and now gaining popularity in contemporary systems such as Swift, requires on-the-fly support for object schema migration, such that the layout of objects may be changed while the program is at one and the same time being run and developed. In Smalltalk schema migration is supported by two primitives, one that answers a collection of all instances of a class, and one that exchanges the identities of pairs of objects, called the become primitive. Existing instances are collected, copies using the new schema created, state copied from old to new, and the two exchanged with become, effecting the schema migration. Historically the implementation of become has either required an extra level of indirection between an object's address and its body, slowing down slot access, or has required a sweep of all objects, a very slow operation on large heaps. Spur, a new object representation and memory manager for Smalltalk-like languages, has neither of these deficiencies. It uses direct pointers but still provides a fast become operation in large heaps, thanks to forwarding objects that when read conceptually answer another object and a partial read barrier that avoids the cost of explicitly checking for forwarding objects on the vast majority of object accesses.
Stop and go: understanding yieldpoint behavior
Authors: Lin Yi , Wang Kunshan , Blackburn Stephen M. , Hosking Antony L. , Norrish Michael
Published: February 2016

Yieldpoints are critical to the implementation of high performance garbage collected languages, yet the design space is not well understood. Yieldpoints allow a running program to be interrupted at well-defined points in its execution, facilitating exact garbage collection, biased locking, on-stack replacement, profiling, and other important virtual machine behaviors. In this paper we identify and evaluate yieldpoint design choices, including previously undocumented designs and optimizations. One of the designs we identify opens new opportunities for very low overhead profiling. We measure the frequency with which yieldpoints are executed and establish a methodology for evaluating the common case execution time overhead. We also measure the median and worst case time-to-yield. We find that Java benchmarks execute about 100M yieldpoints per second, of which about 1/20000 are taken. The average execution time overhead for untaken yieldpoints on the VM we use ranges from 2.5% to close to zero on modern hardware, depending on the design, and we find that the designs trade off total overhead with worst case time-to-yield. This analysis gives new insight into a critical but overlooked aspect of garbage collector implementation, and identifies a new optimization and new opportunities for very low overhead profiling.
Type families with class, type classes with family
Authors: Serrano Alejandro , Hage Jurriaan , Bahr Patrick
Published: February 2016

Type classes and type families are key ingredients in Haskell programming. Type classes were introduced to deal with ad-hoc polymorphism, although with the introduction of functional dependencies, their use expanded to type-level programming. Type families also allow encoding type-level functions, but more directly in the form of rewrite rules. In this paper we show that type families are powerful enough to simulate type classes (without overlapping instances), and we provide a formal proof of the soundness and completeness of this simulation. Encoding instance constraints as type families eases the path to proposed extensions to type classes, like closed sets of instances, instance chains, and control over the search procedure. The only feature which type families cannot simulate is elaboration, that is, generating code from the derivation of a rewriting. We look at ways to solve this problem in current Haskell, and propose an extension to allow elaboration during the rewriting phase.
Modular reifiable matching: a list-of-functors approach to two-level types
Authors: Oliveira Bruno C. d. S. , Mu Shin-Cheng , You Shu-Hung
Published: February 2016

This paper presents Modular Reifiable Matching (MRM): a new approach to two level types using a
fixpoint of list-of-functors
representation. MRM allows the modular definition of datatypes and functions by pattern matching, using a style similar to the widely popular Datatypes a la Carte (DTC) approach. However, unlike DTC, MRM uses a fixpoint of list-of-functors approach to two-level types. This approach has advantages that help with various aspects of extensibility, modularity and reuse. Firstly, modular pattern matching definitions are collected using a list of matches that is fully reifiable. This allows for extensible pattern matching definitions to be easily reused/inherited, and particular matches to be overridden. Such flexibility is used, among other things, to implement
extensible generic traversals
. Secondly, the subtyping relation between lists of functors is quite simple, does not require backtracking, and is easy to model in languages like Haskell. MRM is implemented as a Haskell library, and its use and applicability are illustrated through various examples in the paper.
Bridging the GUI gap with reactive values and relations
Authors: Perez Ivan , Nilsson Henrik
Published: February 2016

There are at present two ways to write GUIs for functional code. One is to use standard GUI toolkits, with all the benefits they bring in terms of feature completeness, choice of platform, conformance to platform-specific look-and-feel, long-term viability, etc. How- ever, such GUI APIs mandate an imperative programming style for the GUI and related parts of the application. Alternatively, we can use a functional GUI toolkit. The GUI can then be written in a func- tional style, but at the cost of foregoing many advantages of stan- dard toolkits that often will be of critical importance. This paper introduces a light-weight framework structured around the notions of reactive values and reactive relations. It allows standard toolk- its to be used from functional code written in a functional style. We thus bridge the gap between the two worlds, bringing the ad- vantages of both to the developer. Our framework is available on Hackage and has been been validated through the development of non-trivial applications in a commercial context, and with different standard GUI toolkits.
10th European Summer School in Information Retrieval (ESSIR 2015)
Authors: Tsikrika Theodora , Papadopoulos Symeon , Vrochidis Stefanos , Kompatsiaris Yiannis
Published: February 2016

The 10th European Summer School in Information Retrieval (ESSIR 2015) was held in Thessaloniki, Greece between August 31 and September 4, 2015. The summer school offered high quality lectures on 13 topics in Information Retrieval and related areas, a new edition of the Symposium on Future Directions in Information Access (FDIA), group activities, and a rich social programme. This report provides an overview of a successful international summer school that attracted a total of 51 participants from a wide range of countries in Europe and North America.
2nd Asian Summer School in Information Access (ASSIA 2015)
Authors: Ke Hao-Ren , Joho Hideo
Published: February 2016

The second Asian Summer School in Information Access (ASSIA 2015) was held between 24th and 27th August, 2015 in Taipei City, Taiwan, R.O.C. The summer school offered 10 lectures in Information Retrieval, Social Media Search, and related topics, along with one panel discussion, a poster session, and a session from commercial sectors and young scholars. This reports a successful international summer school in Asia attracting a total of 45 participants from the range of countries in Asia and Europe.
Report on the First SIGIR Workshop on Graph Search and Beyond (GSB'15)
Authors: Alonso Omar , Hearst Marti A. , Kamps Jaap
Published: February 2016

Modern Web data is highly structured in terms of entities and relations from large knowledge resources, geo-temporal references and social network structure, resulting in a massivemultidimensional graph. This graph essentially unifies both the searcher and the information resources that played a fundamentally different role in traditional IR, and "Graph Search" offers major new ways to access relevant information. Graph search affects both query formulation (complex queries about entities and relations building on the searcher's context) as well as result exploration and discovery (slicing and dicing the information using the graph structure) in a completely personalized way. This new graph based approach introduces great opportunities, but also great challenges, in terms of data quality and data integration, user interface design, and privacy.

We view the notion of "graph search" as searching information from your personal point of view (you are the query) over a highly structured and curated information space. This goes beyond the traditional two-term queries and ten blue links results that users are familiar with, requiring a highly interactive session covering both query formulation and result exploration. The workshop brought together researchers from a range of areas in information access, who worked together on searching information from your personal point of view over a highly structured and curated information space.
Report on the SIGIR 2015 Workshop on Social Personalization and Search
Authors: Trattner Christoph , Parra Denis , Brusilovsky Peter , Marinho Leandro B.
Published: February 2016

The use of contexts --side information associated to information tasks-- has been one of the most important dimensions for the improvement of Information Retrieval tasks, helping to clarify the information needs of the users which usually start from a few keywords in a text box. Particularly, the social context has been leveraged in search and personalization since the inception of the Social Web, but even today we find new scenarios of information filtering, search, recommendation and personalization where the use of social signals can produce a steep improvement. In addition, the action of searching has become a social process on the Web, making traditional assumptions of relevance obsolete and requiring new paradigms for matching the most useful resources that solve information needs. This escenario has motivated us for organizing the Social Personalization and Search (SPS) workshop, a forum aimed at sharing and discussing research that leverage social data for improving classic personalization models for information access and to revisiting search from individual phenomena to a collaborative process.
Workshop on Social and Collaborative Information Seeking (SCIS)
Authors: Shah Chirag , Capra Rob , Hansen Preben
Published: February 2016

This is a report from the Workshop on Social and Collaborative Information Seeking (SCIS), held on May 14-15, 2015 at Rutgers University. Twenty-eight participants contributed to the workshop in the form of short talks and work sessions. Outcomes from the workshop include a "research roadmap" of important future research needed in the area of social and collaborative search. The report presented here summarizes the short talks and the discussions that took place in six breakout groups. The report ends with pointers for future directions (research roadmap) and a few take-away points.
Report on the Fifth International Workshop on Location and the Web (LocWeb 2015)
Authors: Ahlers Dirk , Wilde Erik , Martins Bruno
Published: February 2016

Location is a major feature for describing the real world, appearing in many Web applications and search systems as a cross-cutting issue. Envisioning the establishment of an adequate venue for the location topic, LocWeb 2015 continued a workshop series at the intersection of geospatial search, information management, and Web architecture, with a main focus on location-aware information access. Contributions to the workshop highlighted various fields that benefit from a tailored approach towards location features. LocWeb 2015 showed an interdisciplinary mix of contributions, featuring one keynote and three full papers. In this report, we provide a brief overview of the workshop and its contributions.
Secretome of Olfactory Mucosa Mesenchymal Stem Cell, a Multiple Potential Stem Cell
Authors: Ge Lite , Jiang Miao , Duan Da , Wang Zijun , Qi Linyu , Teng Xiaohua , Zhao Zhenyu , Wang Lei , Zhuo Yi , Chen Ping , He Xijing , Lu Ming
Published: February 2016

Nasal olfactory mucosa mesenchymal stem cells (OM‐MSCs) have the ability to promote regeneration in the nervous system in vivo. Moreover, with view to the potential for clinical application, OM‐MSCs have the advantage of being easily accessible from patients and transplantable in an autologous manner, thus eliminating immune rejection and contentious ethical issues. So far, most studies have been focused on the role of OM‐MSCs in central nervous system replacement. However, the secreted proteomics of OM‐MSCs have not been reported yet. Here, proteins secreted by OM‐MSCs cultured in serum‐free conditions were separated on SDS‐PAGE and identified by LC‐MS/MS. As a result, a total of 274 secreted proteins were identified. These molecules are known to be important in neurotrophy, angiogenesis, cell growth, differentiation, and apoptosis, and inflammation which were highly correlated with the repair of central nervous system. The proteomic profiling of the OM‐MSCs secretome might provide new insights into their nature in the neural recovery. However, proteomic analysis for clinical biomarkers of OM‐MSCs needs to be further studied.
The Effect of Task-Irrelevant Fearful-Face Distractor on Working Memory Processing in Mild Cognitive Impairment versus Healthy Controls: An Exploratory fMRI Study in Female Participants
Authors: Burhan Amer M. , Anazodo Udunna C. , Chung Jun Ku , Arena Amanda , Graff-Guerrero Ariel , Mitchell Derek G. V.
Published: February 2016

In mild cognitive impairment (MCI), a risk state for Alzheimer’s disease, patients have objective cognitive deficits with relatively preserved functioning. fMRI studies have identified anomalies during working memory (WM) processing in individuals with MCI. The effect of task-irrelevant emotional face distractor on WM processing in MCI remains unclear. We aim to explore the impact of fearful-face task-irrelevant distractor on WM processing in MCI using fMRI.Hypothesis. Compared to healthy controls (HC), MCI patients will show significantly higher BOLD signal ina prioriidentified regions of interest (ROIs) during a WM task with a task-irrelevant emotional face distractor.Methods. 9 right-handed female participants with MCI and 12 matched HC performed a WM task with standardized task-irrelevant fearful versus neutral face distractors randomized and counterbalanced across WM trials. MRI images were acquired during the WM task and BOLD signal was analyzed using statistical parametric mapping (SPM) to identify signal patterns during the task response phase.Results. Task-irrelevant fearful-face distractor resulted in higher activation in the amygdala, anterior cingulate, and frontal areas, in MCI participants compared to HC.Conclusions. This exploratory study suggests altered WM processing as a result of fearful-face distractor in MCI.
Determining the Optimal Placement of Sensors on a Concrete Arch Dam Using a Quantum Genetic Algorithm
Authors: Zhu Kai , Gu Chongshi , Qiu Jianchun , Liu Wanxin , Fang Chunhui , Li Bo
Published: February 2016

Structural modal identification has become increasingly important in health monitoring, fault diagnosis, vibration control, and dynamic analysis of engineering structures in recent years. Based on an analysis of traditional optimization algorithms, this paper proposes a novel sensor optimization criterion that combines the effective independence (EFI) method with the modal strain energy (MSE) method. Considering the complex structure and enormous degrees of freedom (DOFs) of modern concrete arch dam, a quantum genetic algorithm (QGA) is used to optimize the corresponding sensor network on the upstream surface of a dam. Finally, this study uses a specific concrete arch dam as an example and determines the optimal sensor placement using the proposed method. By comparing the results with the traditional optimization methods, the proposed method is shown to maximize the spatial intersection angle among the modal vectors of sensor network and can effectively resist ambient perturbations, which will make the identified modal parameters more precise.
Improving Patient Safety through Simulation Training in Anesthesiology: Where Are We?
Authors: Green Michael , Tariq Rayhan , Green Parmis
Published: February 2016

There have been colossal technological advances in the use of simulation in anesthesiology in the past 2 decades. Over the years, the use of simulation has gone from low fidelity to high fidelity models that mimic human responses in a startlingly realistic manner, extremely life-like mannequin that breathes, generates E.K.G, and has pulses, heart sounds, and an airway that can be programmed for different degrees of obstruction. Simulation in anesthesiology is no longer a research fascination but an integral part of resident education and one of ACGME requirements for resident graduation. Simulation training has been objectively shown to increase the skill-set of anesthesiologists. Anesthesiology is leading the movement in patient safety. It is rational to assume a relationship between simulation training and patient safety. Nevertheless there has not been a demonstrable improvement in patient outcomes with simulation training. Larger prospective studies that evaluate the improvement in patient outcomes are needed to justify the integration of simulation training in resident education but ample number of studies in the past 5 years do show a definite benefit of using simulation in anesthesiology training. This paper gives a brief overview of the history and evolution of use of simulation in anesthesiology and highlights some of the more recent studies that have advanced simulation-based training.