Automatic Resolution Research Articles

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography-Mass Spectrometry.

Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC-MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data. Furthermore, orthogonal projection resolution (OPR) was integrated into GCMSFormer to resolve minor components. The GCMSFormer model was trained, validated, and tested using 100,000 augmented data. It achieves 99.88% of the bilingual evaluation understudy (BLEU) value on the test set, significantly higher than the 97.68% BLEU value of the baseline sequence-to-sequence model long short-term memory (LSTM). GCMSFormer was also compared with two nondeep learning resolution tools (MZmine and AMDIS) and two deep learning resolution tools (PARAFAC2 with DL and MSHub/GNPS) on a real plant essential oil GC-MS data set. Their resolution results were compared on evaluation metrics, including the number of compounds resolved, mass spectral match score, correlation coefficient, explained variance, and resolution speed. The results demonstrate that GCMSFormer has better resolution performance, higher automation, and faster resolution speed. In summary, GCMSFormer is an end-to-end, fast, fully automatic, and accurate method for analyzing GC-MS data of complex samples.

Accelerated 2D visualization using adaptive resolution scaling and temporal reconstruction

Data visualization relies on efficient rendering to allow users to interactively explore and understand their data. However, achieving interactive frame rates is often challenging, especially for high-resolution displays or large datasets. In computer graphics, several methods temporally reconstruct full-resolution images from multiple consecutive lower-resolution frames. Besides providing temporal image stability, they amortize the rendering costs over multiple frames and thus improve the minimum frame rate. We present a method that adopts this idea to accelerate 2D information visualization, without requiring any changes to the rendering itself. By exploiting properties of orthographic projection, our method significantly improves rendering performance while minimizing the loss of image quality during camera manipulation. For static scenes, it quickly converges to the full-resolution image. We discuss the characteristics and different modes of our method concerning rendering performance and image quality and the corresponding trade-offs. To improve ease of use, we provide automatic resolution scaling in our method to adapt to user-defined target frame rate. Finally, we present extensive rendering benchmarks to examine real-world performance for examples of parallel coordinates and scatterplot matrix visualizations, and discuss appropriate application scenarios and contraindications for usage.Graphical

Lagrangian and Eulerian algorithms for water droplets in in-flight ice accretion

A particle tracking framework for the computation of the collection efficiency for in-flight ice accretion is presented. Algorithms for the computation of the collection efficiency are presented for both Lagrangian and Eulerian descriptions of the droplets equations. Strengths and weaknesses of each method are analyzed and possible solutions are described and implemented. For the Lagrangian solver, a method for automatic resolution adaptation is introduced, and a strategy for exploiting parallelism is described. In the Eulerian frame a scheme is presented for the solution of the equations on unstructured 3D grids using a relaxation approach. An hybrid Lagrangian Eulerian approach for the computation of the collection efficiency is also introduced which is relevant for Super-cooled Large Droplet condition. All presented algorithms are thoroughly verified, and a comprehensive validation is performed.

Navigation Data Exchange for Traffic Control

An increase in the number of cars is higher than rates of transport infrastructure development, resulting in a reduction of cargo and passenger transportation efficiency in city conditions. Simulation of flow irregularity in time (peak hour) shows the key role of a car motion interval as a factor of overcoming accumulation at average speed reduction in conditions of highly loaded roads. To reduce the effective time of driver reaction, defining the least distance between cars, it is necessary to minimize the influence of human factors. Automation of the process (unmanned control) requires an effective exchange of navigation and route data between traffic participants. A summary of requirements for such an information exchange system defines the priority of the suggested communication and navigation system (CNS) on the base of radio broadcast communication. Its application gives an opportunity to rise simultaneously traffic safety and efficiency. An increase in neighbor driver action predictability leads to traffic safety ensuring. The exchange of data with traffic control centers (TCC) enables the centralization of motion regulation. A distributed network of transceiver stations forms a local positioning system based on trilateration principles. Algorithms of onboard positioning result verification and automatic resolution of communication conflicts ensure high reliability of CNS functioning. Refusal from point-to-point communication principles allows it to operate even in conditions of high car density up to several thousand per square kilometer. In cooperation with advanced technologies of traffic organization (formation of city highway grid and “total green wave” mode), CNS and TCC are capable of rising the average speed in city conditions higher than 45 km/hour. The aggregate economy of expense on last mile transportation because of the suggested innovations is to be at the level of several GDP percent due to a decrease in accidents and congestion even without accounting for social and ecological effects.

МЕТОД АВТОМАТИЧЕСКОГО ГРУППИРОВАНИЯ НАБЛЮДЕНИЙ ИСТОЧНИКОВ РАДИОИЗЛУЧЕНИЯ, ОСНОВАННЫЙ НА ИХ ПРОСТРАНСТВЕННО-ВРЕМЕННОЙ СВЯЗИ

Radio emission sources surveys automatic resolution method based on their spatiotemporal connection and algorithm of its implementation have been introduced. Radioelectronic surveillance model has been used to show that surveys resolution by this method has high probability of right decisions, provides visual validity to results and clear data visualization. Using this method in complex systems of surveys processing has been considered.

Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples

Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to distill chemical information from raw GC-MS data efficiently. In this study, we proposed an Automatic Resolution method (AutoRes) based on pseudo-Siamese convolutional neural networks (pSCNN) to extract the meaningful features swamped by the noises, baseline drifts, retention time shifts, and overlapped peaks. Two pSCNN models were trained with 400,000 augmented spectral pairs, respectively. They can predict the selective region (pSCNN1) and elution region (pSCNN2) of compounds in an untargeted manner. The accuracies of the pSCNN1 model and the pSCNN2 model on their test sets are 99.9% and 92.6%, respectively. Then, the chromatographic profile of each component was automatically resolved by full rank resolution (FRR) based on the predicted regions by these models. The performance of AutoRes was evaluated on the simulated and plant essential oil datasets. Compared to AMDIS and MZmine, AutoRes resolves more reasonable mass spectra, chromatograms, and peak areas to identify and quantify compounds. The average match scores of AutoRes (925 and 936) outperformed AMDIS (909 and 925) and MZmine (888 and 916) when resolving mass spectra from overlapped peaks on the Set Ⅰ and Set Ⅱ of plant essential oil dataset and matching them against the NIST17 library. It extracted peak areas and mass spectra automatically from 10 GC-MS files of plant essential oils, and the entire process was completed in 8 min without any prior information or manual intervention. It is implemented in Python and is available as an open-source package at https://github.com/dyjfan/AutoRes.

Automatic resolution of model merging conflicts using quality-based reinforcement learning

Modeling is an activity in the software development life cycle in which different experts and stakeholders collaborate as a team. In collaborative modeling, adhering to the optimistic versioning paradigm allows users to apply concurrent changes to the same model. In such a situation, conflicts may arise. To have an integrated yet consistent merged model, conflicts have to be resolved. To this end, automation is currently at its limit or is not supported at all, and user interaction is often required. To alleviate this flaw, there is an opportunity to apply Artificial Intelligence techniques in a collaborative modeling environment to empower the provisioning of automated and intelligent decision-making. In this paper, we propose the use of reinforcement learning algorithms to achieve merging conflict resolution with a high degree of automation. This enables the personalized and quality-based integration of model versions. To evaluate our idea, we demonstrate the resolution of UML class diagram conflicts using a learning process in an illustrative modeling scenario. We also show the applicability of our approach through a proof of concept implementation and assess its accuracy compared to the greedy and search-based algorithms. Moreover, we conducted an experience with five experts to evaluate the satisfaction of actual users with the selection of resolution actions for different conflicts. The result of the assessment validates our proposal with various syntactic and semantic conflicts.

Fully automatic resolution of untargeted GC-MS data with deep learning assistance

DeepResolution (Deep learning-assisted multivariate curve Resolution) has been proposed to solve the co-eluting problem for GC-MS data. However, DeepResolution models must be retrained when encountering unknown components, which is undoubtedly time-consuming and burdensome. In this study, a new pipeline named DeepResoution2 was proposed to overcome these limitations. DeepResolution2 utilizes deep neural networks to divide the profile into segments, estimate the number of components in each segment, and predict the elution region of each component. Subsequently, the information obtained by these deep learning models is used to assist the multivariate curve resolution procedure. Only seven models (1 + 1 + 5) are required to automate the whole analysis procedure of untargeted GC-MS data, which is an important improvement over DeepResolution. These seven models are stable and universal. Once established, they can be used to resolve most GC-MS data. Compared with MS-DIAL, ADAP-GC, and AMDIS, DeepResolution2 can obtain more reasonable mass spectra, chromatograms and peak areas to identify and quantify compounds. DeepResoution2 (0.955) outperformed AMDIS (0.939), MS-DIAL (0.948) and ADAP-GC (0.860) in terms of the linear correlation between concentrations and peak areas on overlapped peaks in fatty acid dataset. In real biological samples of human male infertility plasma, the peak areas and mass spectra of 136 untargeted GC-MS files were automatically extracted by DeepResolution2 without any prior information and manual intervention. DeepResolution2 includes all the functions for analyzing untargeted GC-MS datasets from the feature extraction of raw data files to the establishment of discriminant models.

Application of Artificial Intelligence in the automatic identification and classification repetitive demand resolution incident in the Brazilian Court of Justice

One of the areas of knowledge with several possibilities for applying artificial intelligence is Law. Recent changes in Brazilian legislation have facilitated the use of information technology resources to streamline the progress and judgment of cases, such as repetitive demand resolution incident (IRDRs). The aim of this paper is to develop and apply an AI method that can identify and relate new lawsuits with consolidated repetitive judgments (IRDRs). The datasets used in this research are judges' repetitive judgment documents, and consolidated in IRDRs. Court documents are transformed into weighted vectors. The construction of the weights in the vector is based on the co-occurrence of the terms, calculated from the combination of the term frequency-inverse document frequency and their similarity in the corpus of the same IRDR. Artificial neural networks are trained with these vectors to recognize whether new lawsuits are related to an IRDR. As the methodology obtained 93% accuracy, 97% precision, and 93% in recall in the simulations, the method can streamline the work of the Court of Justice, seeking to solve society’s conflicts as quickly as possible. Although the method can be used in several scenarios, the simulations were carried out in judicial documents.

Automatic Symbol Resolution on Embedded Platforms by the Example of Smart TV Device

Smart TV devices are gaining increasingly more popularity. Due to their nature, Smart TVs can access a lot of sensitive data. This is one of the reasons why the Smart TV has become a popular target of hacking recently. Manufacturers try to make such attacks more difficult, and one of the methods they use is the removal of symbols from the firmware. In principle, this would prevent or significantly hinder the preparation of malwares or homebrew that could run on different firmware versions. This article is focused on developing algorithms for automatic symbol resolution. We proposed two automatic symbol resolution methods designed for Smart TVs. Presented methods were tested on the firmwares of the most popular Smart TV manufacturers’, Samsung and LG, devices. Furthermore, an original framework is presented, which automatically locates the desired function in the binaries based on characteristic strings used in or near searched function. The developed framework is commonly used by homebrew developers (e.g., SamyGO) and releases developers from hardcoding function’s addresses for different firmwares.

Scrum metaprocess: a process line approach for customizing Scrum

Scrum is currently the most widely used agile methodology. However, it is regarded as a framework rather than a concrete process. Unfortunately, the resources available on Scrum do not explicitly define its variable parts and do not offer proper guidance on how to resolve those variabilities. Process (re)configuration is thus left to Scrum Retrospective sessions; this can delay the vital decisions that can significantly improve the process before problems arise. This paper aims to address the problems associated with configuring/reconfiguring Scrum by identifying all the variabilities (variation points) in the Scrum framework, along with the situations where a variation point can be resolved by one or more specific variants. We propose a Software Process Line (SPrL) approach for achieving this: we have represented the process variabilities of Scrum as a Scrum metaprocess, which acts as the core process of a generic SPrL for Scrum. The situations in which each variation point of the metaprocess can be resolved by a specific variant have been identified. The metaprocess has been implemented in the Medini-QVT tool, along with transformation rules that provide the means for automatic resolution of the variabilities. The validity of the metaprocess has been evaluated through an industrial case study, the results of which show that the metaprocess is applicable in real situations. Furthermore, the results indicate that the processes instantiated from the metaprocess can improve the existing processes by proposing specific practices for addressing their shortcomings.

A Sub-mW On-Chip Period-Jitter Measurement Circuit Using Automatic Hidden Runtime Resolution Calibration

It is challenging to design an on-chip jitter measurement circuit (OCJM) for complex systems-on-chip (SoCs) with high resolution at low power that is resilient to PVT variations. In this work, an approximate period-to-digital converter (APDC) and a parameter-extraction and resolution-calibration circuit (PERC) are proposed for use in an OCJM to achieve the design goals of high resolution without a reference clock and automatic hidden runtime resolution calibration for high PVT-variation resilience. The measurement results of a 28-nm test chip show that a 1.0-ps resolution specification can definitely be met at different PVT corners during runtime. The measurement results from 15 chips show that the OCJM is resilient to chip-to-chip variations. The proposed OCJM consumes 0.95 mW, which corresponds to 8% of the power consumption in a state-of-the-art stochastic time-to-digital-based OCJM reimplemented with the same CMOS technology.

Towards a Semantic Approach for Candidate Answer Generation in Solving Crossword Puzzles

Abstract Language games represent one of the most fascinating challenges of research in Artificial Intelligence. One of the problems that lies in this set is the development of an automated solution generator for Crossword puzzles. Automatic resolution of crossword puzzles is an open natural language challenge that demands the filling of puzzle grid with candidate answers, while satisfying the grid constraints. Moreover, providing an accurate list of answer candidates has a major impact on the performance of the automatic crossword resolution task. This paper proposes a semantic-based search approach for generating candidate answer lists for definition type clues by taking advantage of the lexical relations encoded in WordNet, the lexical database for English.

An All-Digital On-Chip Peak-to-Peak Jitter Measurement Circuit With Automatic Resolution Calibration for High PVT-Variation Resilience

A new, all-digital on-chip peak-to-peak (p-p) jitter measurement circuit (OCJM) that features automatic resolution calibration for high PVT-variation resilience without a reference clock and off-line calibration is presented in this paper. The OCJM uses a front-end self-referenced circuit (SRC) to eliminate the jitter-free reference signal and a back-end p-p jitter detector (PPD) to perform p-p jitter measurements. The key design of the proposed OCJM is that the SRC and the PPD share a Vernier delay line (VDL) so that the run-time PVT information automatically extracted from the SRC operation can be carried onto the PPD to achieve automatic on-line resolution calibration. Besides this feature, the OCJM uses on-chip direct p-p jitter measurement with only 1-time readouts to eliminate the huge power consumption of the off-chip data communication while avoiding data loss of a possible large jitter caused by PVT variations during off-chip data communication. These techniques make the proposed OCJM suitable for any-time, any-site jitter measurements for SoC applications. The proposed OCJM is fabricated in a 28-nm CMOS. The measurement results show that the timing resolution and minimum measurable jitter range specifications are met under extreme PVT conditions while achieving 98% clock cycle and energy reduction compared to the conventional OCJM designs.

The life-cycle of merge conflicts: processes, barriers, and strategies

Merge conflicts occur when developers make concurrent changes to the same part of the code. They are an inevitable and disruptive aspect of collaborative software development. Thus tool builders and researchers have focused on the prevention and automatic resolution of merge conflicts. However, there is little empirical knowledge about how developers actually monitor for merge conflicts and plan, perform, and evaluate resolutions. Without such knowledge, tool builders might be building on the wrong assumptions and researchers might miss opportunities for improving the state of the art. We conducted semi-structured interviews with 10 software developers across 7 organizations, including both open-source and commercial projects. We identify key processes, techniques, and perceptions from developers, which we extend and validate via two surveys, a Barriers Survey and a Processes Survey, of 162 and 102 developers, respectively. Among others, we find that developers rely on reactive strategies of monitoring for merge conflicts. We find that developers defer responding to conflicts based on their perception of the complexity of the conflicting code and that deferring affects the workflow of the entire team. Developers also rely on this perception to visually evaluate their merge conflict resolutions for correctness. Finally, developers’ perceptions alter the impact of tools and processes designed to preemptively and efficiently resolve merge conflicts. Understanding their processes and perceptions can help design human-oriented tools that better support their individual development processes.

72. Digital breast tomosynthesis: Flat panel and photon counting detector comparison

Purpose In this study three digital breast tomosynthesis (DBT) devices equipped with different flat panel detectors (FPD) and a prototype equipped with a scan-spectral photon counting detector (PCD) are compared. The aim is to evaluate if a PCD device can guarantee a comparable or even better image quality delivering lower radiation doses than FPD devices. Methods Image quality performances were evaluated exposing two image quality DBT phantoms, Tomophan and Agatha and a homemade phantom for the evaluation of low contrast detectability (LCD). Exposures were performed in automatic exposure control (AEC) mode, both with standard (ST) and high resolution (HR) modality when available. Images with the lowest achievable post-processing were analyzed. Geometric distortion, signal-difference-to-noise ratio (SDNR), LCD with the statistical method, in-plane resolution in terms of modulation transfer function (MTF) in scan and sub-scan directions, slice-sensitivity profile (SSP) and artifact spread function (ASF) were evaluated. Results Geometric distortion resulted comparable between PCD and FPD devices. SDNR for PCD was comparable to two of the FDP devices. PCD device showed the best LCD. In-plane resolution was higher for PCD device: FPD devices exhibit a reduction in MTF 10% between 38% and 62%, both in scan and sub-scan direction. SSP evaluation showed comparable results for PCD and one of the FPD devices, while it was up to 60% worse for the others. PCD device showed a slightly worse ASF compared to FPD devices. Delivered dose in AEC with PCD device was 50% lower than doses delivered with FPD devices. Conclusions PCD device ensures a comparable or even better image quality than FPD devices, especially for spatial resolution and detectability of low contrast objects, delivering much lower radiation dose. Photon counting seems to be really an impressive approach to DBT. Download : Download high-res image (129KB) Download : Download full-size image

A parametric morphing method for generating structured meshes for marine free surface flow applications with plane symmetry

Abstract This article introduces a method for morphing a parametric, rectangular and structured 3D template mesh such that it embodies: (1) a given (half) hull surface properly aligned with a pre-defined air-water interface zone, (2) a high-quality boundary layer region near the hull, and (3) a smooth transition of the mesh to the template mesh structure away from the hull. The performance of the proposed method is successfully validated on three widely studied benchmark cases, namely the parabolic Wigley hull, the Series 60 hull and the DTMB 5415 combatant hull. The proposed automated mesh generation method is useful in shape optimization for computational fluid dynamics, among others. Highlights A new method for creating structured meshes for marine CFD applications is presented. The proposed method is parametric and thus fully programmable. The method includes an automatic resolution of CAD geometry errors. The resulting meshes are strictly orthogonal near the inter-phase zone and the hull. The main application of the proposed method is in CFD-based shape optimization.

Conjunctive coding in an evolved spiking model of retrosplenial cortex.

Retrosplenial cortex (RSC) is an association cortex supporting spatial navigation and memory. However, critical issues remain concerning the forms by which its ensemble spiking patterns register spatial relationships that are difficult for experimental techniques to fully address. We therefore applied an evolutionary algorithmic optimization technique to create spiking neural network models that matched electrophysiologically observed spiking dynamics in rat RSC neuronal ensembles. Virtual experiments conducted on the evolved networks revealed a mixed selectivity coding capability that was not built into the optimization method, but instead emerged as a consequence of replicating biological firing patterns. The experiments reveal several important outcomes of mixed selectivity that may subserve flexible navigation and spatial representation: (a) robustness to loss of specific inputs, (b) immediate and stable encoding of novel routes and route locations, (c) automatic resolution of input variable conflicts, and (d) dynamic coding that allows rapid adaptation to changing task demands without retraining. These findings suggest that biological retrosplenial cortex can generate unique, first-trial, conjunctive encodings of spatial positions and actions that can be used by downstream brain regions for navigation and path integration. Moreover, these results are consistent with the proposed role for the RSC in the transformation of representations between reference frames and navigation strategy deployment. Finally, the specific modeling framework used for evolving synthetic retrosplenial networks represents an important advance for computational modeling by which synthetic neural networks can encapsulate, describe, and predict the behavior of neural circuits at multiple levels of function. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Automatic Resolution of Normative Conflicts in Supportive Technology Based on User Values

Social commitments (SCs) provide a flexible, norm-based, governance structure for sharing and receiving data. However, users of data sharing applications can subscribe to multiple SCs, possibly producing opposing sharing and receiving requirements. We propose resolving such conflicts automatically through a conflict resolution model based on relevant user values such as privacy and safety. The model predicts a user’s preferred resolution by choosing the commitment that best supports the user’s values. We show through an empirical user study ( n = 396) that values, as well as recency and norm type, significantly improve a system’s ability to predict user preference in location sharing conflicts.

Система автоматического разрешения омографии на основе семантической связи слов смежных предложений в текстовом отрывке

Система автоматического разрешения омографии на основе семантической связи слов смежных предложений в текстовом отрывке