Default Mapping Research Articles

Processability Analysis of the Acquisition of English L2 Syntax by Japanese and Chinese Speakers

Processability Theory (PT; Pienemann, 1998) argues that there is a universal hierarchy in the acquisition of second language (L2) grammar. As for L2 syntax, PT proposes the Lexical Mapping Hypothesis (LMH; Pienemann, et al. 2005) in which learners are predicted to progress from ‘default mapping’, where the argument hierarchy maps onto the grammatical function hierarchy in a default way, to ‘non-default mapping’, where the strictly hierarchical mapping is disrupted under semantic and/or pragmatic pressure (Pienemann, et al., 2015). More recently, an additional intermediate stage between default and non-default mapping stages has been proposed by Bettoni and Di Biase (2015) accounting for locative, ditransitive, and other constructions containing ‘default mapping plus additional argument.’ Although this intermediate stage has been tested to some extent (e.g., Di Biase, et al., 2015; Kawaguchi, 2015; Liu, et al., 2023; Yamaguchi & Kawaguchi, 2022), more detailed analyses of how L2 learners proceed from the default mapping stage to the non-default mapping stage are needed. The participants in the present study are 139 English L2 learners, that is 88 learners of English as a foreign language (EFL) in Japan and 21 Japanese and 30 Chinese speakers in English as a second language (ESL) environments in Australia. Their speech production in English is examined based on the updated LMH (Bettoni & Di Biase, 2015). Results show that, in terms of additional arguments in the intermediate stage, the oblique (OBL) construction emerges before the double object (OBJ) construction. This finding contributes to the further development of PT hypotheses for L2 syntactic development.

Incidence of Cochlear Implant Electrode Contacts in the Functional Acoustic Hearing Region and the Influence on Speech Recognition with Electric-Acoustic Stimulation.

To investigate the incidence of electrode contacts within the functional acoustic hearing region in cochlear implant (CI) recipients and to assess its influence on speech recognition for electric-acoustic stimulation (EAS) users. Retrospective review. Tertiary referral center. One hundred five CI recipients with functional acoustic hearing preservation (≤80 dB HL at 250 Hz). Cochlear implantation with a 24-, 28-, or 31.5-mm lateral wall electrode array. Angular insertion depth (AID) of individual contacts was determined from imaging. Unaided acoustic thresholds and AID were used to calculate the proximity of contacts to the functional acoustic hearing region. The association between proximity values and speech recognition in quiet and noise for EAS users at 6 months postactivation was reviewed. Sixty percent of cases had one or more contacts within the functional acoustic hearing region. Proximity was not significantly associated with speech recognition in quiet. Better performance in noise was observed for cases with close correspondence between the most apical contact and the upper edge of residual hearing, with poorer results for increasing proximity values in either the basal or apical direction ( r14 = 0.48, p = 0.043; r18 = -0.41, p = 0.045, respectively). There was a high incidence of electrode contacts within the functional acoustic hearing region, which is not accounted for with default mapping procedures. The variability in outcomes across EAS users with default maps may be due in part to electric-on-acoustic interference, electric frequency-to-place mismatch, and/or failure to stimulate regions intermediate between the most apical electrode contact and the functional acoustic hearing region.

Influence of the Frequency-to-Place Function on Recognition with Place-Based Cochlear Implant Maps.

Comparison of acute speech recognition for cochlear implant (CI) alone and electric-acoustic stimulation (EAS) users listening with default maps or place-based maps using either a spiral ganglion (SG) or a new Synchrotron Radiation-Artificial Intelligence (SR-AI) frequency-to-place function. Thirteen adult CI-alone or EAS users completed a task of speech recognition at initial device activation with maps that differed in the electric filter frequency assignments. The three map conditions were: (1) maps with the default filter settings (default map), (2) place-based maps with filters aligned to cochlear SG tonotopicity using the SG function (SG place-based map), and (3) place-based maps with filters aligned to cochlear Organ of Corti (OC) tonotopicity using the SR-AI function (SR-AI place-based map). Speech recognition was evaluated using a vowel recognition task. Performance was scored as the percent correct for formant 1 recognition due to the rationale that the maps would deviate the most in the estimated cochlear place frequency for low frequencies. On average, participants had better performance with the OC SR-AI place-based map as compared to the SG place-based map and the default map. A larger performance benefit was observed for EAS users than for CI-alone users. These pilot data suggest that EAS and CI-alone users may experience better performance with a patient-centered mapping approach that accounts for the variability in cochlear morphology (OC SR-AI frequency-to-place function) in the individualization of the electric filter frequencies (place-based mapping procedure). 3 Laryngoscope, 133:3540-3547, 2023.

Unstressed versus stressed German additive auch – what determines a speaker’s choice?

Abstract The German additive particle auch associates with a constituent (the associated constituent, AC) which is related to contextually relevant discourse alternative(s). There are two versions of auch in German: a stressed and an unstressed version. Although in most of the cases, speakers have the choice of using the unstressed or stressed version, there are clear preferences for using one version over the other. It is the aim of the present paper to contribute to a clearer picture concerning factors constraining speakers’ preferences. By integrating another focus particle (nur, ‘only’) in the context, we ask whether syntactic (nur precedes its AC) and information structural properties (the AC being a focus) of a context sentence influence the choice of a speaker, or whether speakers apply default mappings (subjects as prototypical topics). The results of a sentence fragment arrangement task indicate that speakers strongly rely on default mechanisms, but that they are also influenced by syntactic and information structural properties of the context to some extent.

The Impact of Grammar on the Construal of Discourse Alternatives in German and English

This paper investigates additive links to discourse alternatives in picture comparison dialogues produced by adult native speakers of English and German. Additive relations are established across turns when participants are confirming the presence of matching objects on both pictures (A: “I have X”. B: “I also have X”). Speakers thereby describe their own picture and construe the interlocutor (or rather: the interlocutor’s picture) as a discourse alternative. Whereas the vast majority of the confirming descriptions in German contain an additive particle (auch), less than half of the corresponding confirmations in the English data do (“also”, “too”, etc.). Numbers differ even more drastically in polarity questions (“Do you (also) have X?”) that are equally typical for the dialogue task. Such frequency differences are at odds with recent accounts treating additive particles as being quasi obligatory when their presupposition is satisfied. An in-depth contrastive analysis of lexical, syntactic and information structural properties reveals that the default mapping of information units on syntactic functions (subject) in conjunction with the SVO word order of English leads to a structure in which subject, initial (topic) position and the particle’s associated constituent coincide. This would make the relation to its discourse alternative more prominent than warranted by the dialogue task and speakers of English leave this relation unmarked or resort to alternative constructions instead. The V2 syntax of German, on the other hand, allows for a dissociation of discourse topic and associated constituent. It allows the speaker to topicalize reference to the matching object, to highlight the confirmation of its presence on the speaker’s picture, and to relate the changing information to its discourse alternative in a non-contrastive way.

Evaluating the efficacy of sonification for signal detection in univariate, evenly sampled light curves using astronify

ABSTRACT Sonification is the technique of representing data with sound, with potential applications in astronomy research for aiding discovery and accessibility. Several astronomy-focused sonification tools have been developed; however, efficacy testing is extremely limited. We performed testing of astronify, a prototype tool for sonification functionality within the Barbara A. Mikulski Archive for Space Telescopes. We created synthetic light curves containing zero, one, or two transit-like signals with a range of signal-to-noise ratios (SNRs = 3–100) and applied the default mapping of brightness to pitch. We performed remote testing, asking participants to count signals when presented with light curves as a sonification, visual plot, or combination of both. We obtained 192 responses, of which 118 self-classified as experts in astronomy and data analysis. For high SNRs (=30 and 100), experts and non-experts performed well with sonified data (85–100 per cent successful signal counting). At low SNRs (=3 and 5), both groups were consistent with guessing with sonifications. At medium SNRs (=7 and 10), experts performed no better than non-experts with sonifications but significantly better (factor of ∼2–3) with visuals. We infer that sonification training, like that experienced by experts for visual data inspection, will be important if this sonification method is to be useful for moderate SNR signal detection within astronomical archives and broader research. None the less, we show that even a very simple, and non-optimized, sonification approach allows users to identify high SNR signals. A more optimized approach, for which we present ideas, would likely yield higher success for lower SNR signals.

Effect of Place-Based Versus Default Mapping Procedures on Masked Speech Recognition: Simulations of Cochlear Implant Alone and Electric-Acoustic Stimulation.

Cochlear implant (CI) recipients demonstrate variable speech recognition when listening with a CI-alone or electric-acoustic stimulation (EAS) device, which may be due in part to electric frequency-to-place mismatches created by the default mapping procedures. Performance may be improved if the filter frequencies are aligned with the cochlear place frequencies, known as place-based mapping. Performance with default maps versus an experimental place-based map was compared for participants with normal hearing when listening to CI-alone or EAS simulations to observe potential outcomes prior to initiating an investigation with CI recipients. A noise vocoder simulated CI-alone and EAS devices, mapped with default or place-based procedures. The simulations were based on an actual 24-mm electrode array recipient, whose insertion angles for each electrode contact were used to estimate the respective cochlear place frequency. The default maps used the filter frequencies assigned by the clinical software. The filter frequencies for the place-based maps aligned with the cochlear place frequencies for individual contacts in the low- to mid-frequency cochlear region. For the EAS simulations, low-frequency acoustic information was filtered to simulate aided low-frequency audibility. Performance was evaluated for the AzBio sentences presented in a 10-talker masker at +5 dB signal-to-noise ratio (SNR), +10 dB SNR, and asymptote. Performance was better with the place-based maps as compared with the default maps for both CI-alone and EAS simulations. For instance, median performance at +10 dB SNR for the CI-alone simulation was 57% correct for the place-based map and 20% for the default map. For the EAS simulation, those values were 59% and 37% correct. Adding acoustic low-frequency information resulted in a similar benefit for both maps. Reducing frequency-to-place mismatches, such as with the experimental place-based mapping procedure, produces a greater benefit in speech recognition than maximizing bandwidth for CI-alone and EAS simulations. Ongoing work is evaluating the initial and long-term performance benefits in CI-alone and EAS users. https://doi.org/10.23641/asha.19529053.

A bioinformatics pipeline for Mycobacterium tuberculosis sequencing that cleans contaminant reads from sputum samples.

Next-Generation Sequencing (NGS) is widely used to investigate genomic variation. In several studies, the genetic variation of Mycobacterium tuberculosis has been analyzed in sputum samples without previous culture, using target enrichment methodologies for NGS. Alignments obtained by different programs generally map the sequences under default parameters, and from these results, it is assumed that only Mycobacterium reads will be obtained. However, variants of interest microorganism in clinical samples can be confused with a vast collection of reads from other bacteria, viruses, and human DNA. Currently, there are no standardized pipelines, and the cleaning success is never verified since there is a lack of rigorous controls to identify and remove reads from other sputum-microorganisms genetically similar to M. tuberculosis. Therefore, we designed a bioinformatic pipeline to process NGS data from sputum samples, including several filters and quality control points to identify and eliminate non-M. tuberculosis reads to obtain a reliable genetic variant report. Our proposal uses the SURPI software as a taxonomic classifier to filter input sequences and perform a mapping that provides the highest percentage of Mycobacterium reads, minimizing the reads from other microorganisms. We then use the filtered sequences to perform variant calling with the GATK software, ensuring the mapping quality, realignment, recalibration, hard-filtering, and post-filter to increase the reliability of the reported variants. Using default mapping parameters, we identified reads of contaminant bacteria, such as Streptococcus, Rhotia, Actinomyces, and Veillonella. Our final mapping strategy allowed a sequence identity of 97.8% between the input reads and the whole M. tuberculosis reference genome H37Rv using a genomic edit distance of three, thus removing 98.8% of the off-target sequences with a Mycobacterium reads loss of 1.7%. Finally, more than 200 unreliable genetic variants were removed during the variant calling, increasing the report’s reliability.

Effect of boost pressure on combustion, performance and emission characteristics of a multicylinder CRDI engine fueled with argemone biodiesel/diesel blends

The problem of higher NOx emissions with the utilization of biodiesel is a grim prospect indeed. Engine boost pressure has a significant impact on the performance, emission, and combustion characteristics of the engine, therefore, its effect has been studied on 1.9 L multi-cylinder CRDI fueled with AB20 blend. The experiments were performed in two stages with two different modes, Mode-I (part load) and Mode-II (high load). In Stage-I testing, the ECU maps designed and calibrated for diesel fuel were used for testing argemone biodiesel/diesel blends (AB10, AB20, AB30, and AB50) in the engine and an optimized blend (AB20) among these tested fuels was selected for testing in Stage-II. Stage-II testing involves variation of boost pressure from 110 to 140 kPa at Mode- I, and 150 to 180 kPa at Mode-II. It was observed that at part load the default map boost pressure (120 kPa) seems beneficial for AB20 blend with improved performance and emission characteristics of the engine. However, at higher load lower boost pressure improves both the NOx emissions (6%↓) and brake thermal efficiency (4.04%↑) in comparison to diesel fuel. For AB20 blend, lowering the boost increases the ignition delay. Consequently, cylinder pressure peak and heat release rate peak shifts away from TDC. On the other hand, increasing the boost lowers the ignition delay and premixed heat release phase, thereby promoting the diffusion phase of combustion.

Scenario Analysis with the DD-PD Mapping Approach: Stock Market Shocks and U.S. Corporate Default Risk

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative and narrative scenarios informed by expert judgment. At end-2020, risks from stock market corrections in the U.S. are concentrated in the energy, financial and technology sectors, and additional bank capital needs could be large. The paper concludes discussing uses of the mapping beyond PD valuation suitable for capital structure analysis, credit portfolio management, and long-term scenario planning analysis.

Validation nightmare: the slotting approach under International Financial Reporting Standard 9

This paper makes an important contribution to the practice of validation by focusing on an under-researched area of the slotting approach to real estate specialized lending under the International Financial Reporting Standard 9 (IFRS 9) framework. The paper introduces the concept of mapping the probability of default estimates to the slotting scores. A sequential process for deriving the correspondence between the slotting scores and probabilities of default of a particular obligor is proposed as a solution to adapting the slotting approach to the IFRS 9 rules. This solution is especially useful for capturing the increase in credit risk under the IFRS 9 rules using denotching and staging processes. All in all, this paper addresses the research questions of whether and how a slotting model can be used for IFRS 9 compliance purposes. In addition to the core aim of the paper, which is the provision of a probability of default mapping solution under the IFRS 9 framework, we also explain the methodology of a slotting model, discussing specific modeling choices for the real estate slotting approach aligned to the relevant regulatory framework. In doing so, we provide an example of a slotting model that can be used by practitioners as a challenger model during the validation exercise.

Effectiveness of Place-based Mapping in Electric-Acoustic Stimulation Devices.

The default mapping procedure for electric-acoustic stimulation (EAS) devices uses the cochlear implant recipient's unaided detection thresholds in the implanted ear to derive the acoustic settings and assign the lowest frequency filter of electric stimulation. Individual differences for speech recognition with EAS may be due to discrepancies between the electric frequency filters of individual electrode contacts and the cochlear place of stimulation, known as a frequency-to-place mismatch. Frequency-to-place mismatch of greater than 1/2 octave has been demonstrated in up to 60% of EAS users. Aligning the electric frequency filters via a place-based mapping procedure using postoperative imaging may improve speech recognition with EAS. Masked sentence recognition was evaluated for normal-hearing subjects (n = 17) listening with vocoder simulations of EAS, using a place-based map and a default map. Simulation parameters were based on audiometric and imaging data from a representative 24-mm electrode array recipient and EAS user. The place-based map aligned electric frequency filters with the cochlear place frequency, which introduced a gap between the simulated acoustic and electric output. The default map settings were derived from the clinical programming software and provided the full speech frequency range. Masked sentence recognition was significantly better for simulated EAS with the place-based map as compared with the default map. The simulated EAS place-based map supported better performance than the simulated EAS default map. This indicates that individualizing maps may improve performance in EAS users by helping them achieve better asymptotic performance earlier and mitigate the need for acclimatization.

The Acquisition of the Tagalog Symmetrical Voice System: Evidence from Structural Priming

ABSTRACT We report on two experiments that investigated the acquisition of the Tagalog symmetrical voice system, a typologically rare feature of Western Austronesian languages in which there are more than one basic transitive construction and no preference for agents to be syntactic subjects. In the experiments, 3-, 5-, and 7-year-old Tagalog-speaking children and adults completed a structural priming task that manipulated voice and word order, with the uniqueness of Tagalog allowing us to tease apart priming of thematic role order from that of syntactic roles. Participants heard a description of a picture showing a transitive action, and were then asked to complete a sentence of an unrelated picture using a voice-marked verb provided by the experimenter. Our results show that children gradually acquire an agent-before-patient preference, instead of having a default mapping of the agent to the first noun position. We also found an earlier mastery of the patient voice verbal and nominal marker configuration (patient is the subject), suggesting that children do not initially map the agent to the subject. Children were primed by thematic role but not syntactic role order, suggesting that they prioritize mapping of the thematic roles to sentence positions.

RALB‐HC: A resource‐aware load balancer for heterogeneous cluster

SummaryIn the heterogeneous computing environment, programmers map the applications either on CPUs or GPUs. However, this default mapping process does not produce improved results, particularly on the heterogeneous clusters. If one resource of the cluster is more compute capable, then most of the scheduling schemes favor that powerful device. In this scenario, the scheduling schemes overload the powerful resources while making all other compute resources remain under utilized. This load imbalance problem results in higher energy consumption and increased execution time. In this research, a novel Resource‐Aware Load Balancer for the Heterogeneous Cluster (RALB‐HC) is proposed that distributes workload based on resources computing capabilities and applications computing needs. The RALB‐HC uses supervised machine learning approach to classify applications using the static code‐features. The RALB‐HC framework comprises of two phases: (1) job mapping based on the availability of the resources and (2) the resource‐aware load balancing to achieve the higher resource utilization ratio. The experimental results on a large set of real‐world and synthetic workloads show that the RALB‐HC reduces execution time by 31.61%, increased resource utilization ratio by 67.8% and improved throughout 147.35% as compared to baseline scheduling schemes.

Geometric Mapping of Tasks to Processors on Parallel Computers with Mesh or Torus Networks

We present a new method for reducing parallel applications’ communication time by mapping their MPI tasks to processors in a way that lowers the distance messages travel and the amount of congestion in the network. Assuming geometric proximity among the tasks is a good approximation of their communication interdependence, we use a geometric partitioning algorithm to order both the tasks and the processors, assigning task parts to the corresponding processor parts. In this way, interdependent tasks are assigned to “nearby” cores in the network. We also present a number of algorithmic optimizations that exploit specific features of the network or application to further improve the quality of the mapping. We specifically address the case of sparse node allocation, where the nodes assigned to a job are not necessarily located in a contiguous block nor within close proximity to each other in the network. However, our methods generalize to contiguous allocations as well, and results are shown for both contiguous and non-contiguous allocations. We show that, for the structured finite difference mini-application MiniGhost, our mapping methods reduced communication time up to 75 percent relative to MiniGhost's default mapping on 128K cores of a Cray XK7 with sparse allocation. For the atmospheric modeling code E3SM/HOMME, our methods reduced communication time up to 31% on 16K cores of an IBM BlueGene/Q with contiguous allocation.

ZAKI+: A Machine Learning Based Process Mapping Tool for SpMV Computations on Distributed Memory Architectures

Smart cities and other cyber-physical systems (CPSs) rely on various scientific, engineering, business, and social applications that provide timely intelligence for their design, operations, and management. Many of these scientific and analytics applications require the solution of sparse linear equation systems, where sparse matrix-vector (SpMV) product is a key computing operation. Several factors determine the performance of parallel SpMV computations, including matrix characteristics, storage formats, and the rising complexity and heterogeneity of computer systems. There is a pressing need for new ways of exploiting parallelism, and mapping data and applications to the computing resources. We propose here ZAKI+, a data-driven machine-learning approach, allowing users to automatically, effortlessly, and speedily obtain the best configuration (the data distribution, the optimal number of processes, and mapping strategy) and performance for the execution of the parallel SpMV computations on distributed memory machines. We train and test the tool using three machine learning methods-decision trees, random forest, and Xtreme boosting-and nearly 2000 real-world matrices obtained from 45 application domains, including computer vision and robotics. ZAKI+ provides optimal process mapping and outperforms the MPI default mapping policy by a factor of 4.24. This is the first work where the sparsity structure of matrices has been exploited to predict the optimal mapping of processes and data in distributed-memory environments by using different base and ensemble machine learning methods. Various CPSs comprise compute-intensive machine learning applications, such as the SpMV, and hence, the process and data mapping contributions of this paper would be of paramount impact for the CPSs.

Private Banking Advisers at Bcb Edmonton (a)

A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model—a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data they are. The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times—a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it.The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs. Excerpt UVA-QA-0905 Dec. 10, 2018 Private Banking Advisers at BCB Edmonton (A) “I think we need six,” thought Wallace, looking at the data of the Edmonton area branches of the Big Canadian Bank (BCB) (Exhibit 1). “But will the six work? And could five work?” she wondered. “Five would certainly be better than six. Or maybe even four?” Wallace was referring to an important pilot that BCB was planning to run first in Edmonton before rolling out nationally and internationally: Private Banking Advisers (PBA) practice. She needed to decide how many PBAs the Edmonton area would need. Canadian Banking, “Big Five,” and BCB . . .

Energy Efficient Resource Allocation During Initial Mapping of Virtual Machines to Servers in Cloud Datacenters

Energy consumption has been identified as one of the key research challenges during recent time in Cloud computing. Proper placement of Virtual Machines (VMs) on servers may address the issue. The process of placing VMs on servers can be divided into two phases viz. (a) Mapping of VMs on servers during the phase and (b) subsequent VM selection, migration and placement during consolidation phase. If the initial mapping is not efficient, subsequent operations may lead to unnecessary VM migrations, which in turn, may result into increase in migration cost and increase in SLA violations. In this research, the authors aim to improve the resource utilization to address these issues by keeping (i) the number of live server as minimal as possible for achieving energy efficiency, and (ii) the live server, as busy as possible by efficiently utilizing them. The authors conducted series of experiments with existing default technique and various other approaches. The results of our experiments make us conclude that there is a scope of improvement in the default mapping technique currently being used in CloudSim.

A haplotype-based normalization technique for the analysis and detection of allele specific expression

BackgroundAllele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects.ResultsHere, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking.ConclusionsThe correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1238-8) contains supplementary material, which is available to authorized users.

An Evacuation Route Planning for Safety Route Guidance System after Natural Disaster Using Multi-objective Genetic Algorithm

When a natural disaster occurred, some roads cannot be used anymore and sometimes blocked. Also, survivors and refugees cannot follow the evacuation procedures by just using default maps after disaster. A previous study proposed a safety route guidance system that can be used after natural disasters by using participatory sensing. The system estimates safe routes and generates an evacuation map by collecting GPS data and accelerometer data from pedestrians’ smartphone. However, the system does not base on default map data. After that, the system evaluates the safety of each route. However, the previous study did not propose a method of finding evacuation routes from the users’ current location to their destination. Therefore, in this study, we proposed a method of evacuation route planning. We have implemented Multi-Objective Genetic Algorithm (MOGA) into the route planning methodology. The proposed system has three objective functions, which are: evacuation distance, evacuation time and safety of evacuation route. Also, we proposed a new safety evaluation method. As a result, this study gives a better reflection of the change of road conditions. Also, the safety evaluation values are more useful than the previous study's evaluation method of the route. Moreover, the system can provide evacuation routes with different characteristics to users. As a result, the users can select a route which is suitable for their situation.