scholarly journals Reshaping Diverse Planning

2020 ◽  
Vol 34 (06) ◽  
pp. 9892-9899
Author(s):  
Michael Katz ◽  
Shirin Sohrabi
Keyword(s):  
Recent Work ◽  
Plan Quality ◽  
Post Processing ◽  
Predominant Role ◽  
Solution Quality ◽  
Optimization Criteria ◽  
Novel Approach ◽  
Key Factor ◽  
Planning Task ◽  
Diversity Metrics

The need for multiple plans has been established by various planning applications. In some, solution quality has the predominant role, while in others diversity is the key factor. Most recent work takes both plan quality and solution diversity into account under the generic umbrella of diverse planning. There is no common agreement, however, on a collection of computational problems that fall under that generic umbrella. This in particular might lead to a comparison between planners that have different solution guarantees or optimization criteria in mind. In this work we revisit diverse planning literature in search of such a collection of computational problems, classifying the existing planners to these problems. We formally define a taxonomy of computational problems with respect to both plan quality and solution diversity, extending the existing work. We propose a novel approach to diverse planning, exploiting existing classical planners via planning task reformulation and choosing a subset of plans of required size in post-processing. Based on that, we present planners for two computational problems, that most existing planners solve. Our experiments show that the proposed approach significantly improves over the best performing existing planners in terms of coverage, the overall solution quality, and the overall diversity according to various diversity metrics.

scholarly journals Top-Quality Planning: Finding Practically Useful Sets of Best Plans

2020 ◽  
Vol 34 (06) ◽  
pp. 9900-9907 ◽  
Author(s):  
Michael Katz ◽  
Shirin Sohrabi ◽  
Octavian Udrea
Keyword(s):  
Recent Work ◽  
Superior Performance ◽  
Plan Quality ◽  
Solution Quality ◽  
New Approach ◽  
Quality Planning ◽  
New Family ◽  
Complete Procedure ◽  
The Difference

The need for finding a set of plans rather than one has been motivated by a variety of planning applications. The problem is studied in the context of both diverse and top-k planning: while diverse planning focuses on the difference between pairs of plans, the focus of top-k planning is on the quality of each individual plan. Recent work in diverse planning introduced additionally restrictions on solution quality. Naturally, there are application domains where diversity plays the major role and domains where quality is the predominant feature. In both cases, however, the amount of produced plans is often an artificial constraint, and therefore the actual number has little meaning. Inspired by the recent work in diverse planning, we propose a new family of computational problems called top-quality planning, where solution validity is defined through plan quality bound rather than an arbitrary number of plans. Switching to bounding plan quality allows us to implicitly represent sets of plans. In particular, it makes it possible to represent sets of plans that correspond to valid plan reorderings with a single plan. We formally define the unordered top-quality planning computational problem and present the first planner for that problem. We empirically demonstrate the superior performance of our approach compared to a top-k planner-based baseline, ranging from 41% increase in coverage for finding all optimal plans to 69% increase in coverage for finding all plans of quality up to 120% of optimal plan cost. Finally, complementing the new approach by a complete procedure for generating all valid reorderings of a given plan, we derive a top-quality planner. We show the planner to be competitive with a top-k planner based baseline.

Visualizing Emergency Department Admission through Data Mining

2019 ◽  
Vol 1 (2) ◽  
pp. 19-31
Author(s):  
Kalaivani S ◽  
Shalini Dhiman ◽  
Rajagopal T.K.P.
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Prediction Models ◽  
Personal Data ◽  
Mobility Data ◽  
Novel Approach ◽  
Key Factor ◽  
Ed Overcrowding ◽  
Historic Data ◽  
Short Time

Emergency Department (ED) boarding –the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. Based on these  factors, a  real-time prediction  model is  developed which  is able  to correctly predict  the  admission  result  of  four  out  of  every  five  ED  patients.  The  proposed admission  model  can  be  used  by inpatient  units  to  estimate  the  likelihood  of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using  similar prediction models,  hospitals can evaluate their short-time needs for inpatient care more accurately Emergency Department (ED) boarding – the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. The proposed admission model can be used by inpatient units to estimate the likelihood of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using similar prediction models, hospitals can evaluate their short-time needs for inpatient care more accurately. We use three algorithms to build the predictive models: (1) logistic regression, (2) decision trees, and Analytic tools (accuracy=80.31%, AUC-ROC=0.859) than the decision tree accuracy=80.06%, AUC-ROC=0.824) and the logistic regression model (accuracy=79.94%, AUC-ROC=0.849). Drawing on logistic regression, we identify several factors related to hospital admissions including hospital site, age, arrival mode, triage category, care group, previous admission in the past month, and previous admission in the past year. From a different perspective, the research focuses on mobility data instead of personal data in general using Structural Equation Modelling analysis method. Based on this research finding, we identified an unexplored factor that can be used to predict the intention to disclose mobility data, and the result also confirmed that context aspects such as demographics and different personal data categories.

Graph-Based Modelling of Concurrent Sequential Patterns

2010 ◽  
Vol 6 (2) ◽  
pp. 41-58 ◽  
Author(s):  
Jing Lu ◽  
Weiru Chen ◽  
Malcolm Keech
Keyword(s):  
Data Mining ◽  
Graph Representation ◽  
Sequential Patterns ◽  
Mining Method ◽  
Post Processing ◽  
Synthetic Sample ◽  
Novel Approach ◽  
Sample Data ◽  
Modelling Methods ◽  
Graph Based Model

Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a corresponding data mining method was proposed for Concurrent Sequential Patterns (ConSP). This article refines the approach in the context of ConSP modelling, where a companion graph-based model is devised as an extension of previous work. Two new modelling methods are presented here together with a construction algorithm, to complete the transformation of concurrent sequential patterns to a ConSP-Graph representation. Customer orders data is used to demonstrate the effectiveness of ConSP mining while synthetic sample data highlights the strength of the modelling technique, illuminating the theories developed.

scholarly journals Enhanced Resorption of Liposomal Packed Vitamin C Monitored by Ultrasound

2020 ◽  
Vol 9 (6) ◽  
pp. 1616 ◽  
Author(s):  
Lukas Prantl ◽  
Andreas Eigenberger ◽  
Sebastian Gehmert ◽  
Silke Haerteis ◽  
Thiha Aung ◽  
...  
Keyword(s):  
Vitamin C ◽  
Oral Intake ◽  
The Body ◽  
Post Processing ◽  
Novel Approach ◽  
Health Related ◽  
Essential Nutrient ◽  
Freeze Etching ◽  
New Formulation ◽  
First Time

Vitamin C is an essential nutrient for humans and is involved in a plethora of health-related functions. Several studies have shown a connection between vitamin C intake and an improved resistance to infections that involves the immune system. However, the body cannot store vitamin C and both the elevated oral intake, and the intravenous application have certain disadvantages. In this study, we wanted to show a new formulation for the liposomal packaging of vitamin C. Using freeze etching electron microscopy, we show the formed liposomes. With a novel approach of post-processing procedures of real-time sonography that combines enhancement effects by contrast-like ultrasound with a transducer, we wanted to demonstrate the elevated intestinal vitamin C resorption on four participants. With the method presented in this study, it is possible to make use of the liposomal packaging of vitamin C with simple household materials and equipment for intake elevation. For the first time, we show the enhanced resorption of ingested liposomes using microbubble enhanced ultrasound imaging.

scholarly journals Key factor for the anti-Arrhenius low-temperature heterogeneous catalysis induced by H+ migration: H+ coverage over support

2020 ◽  
Vol 56 (23) ◽  
pp. 3365-3368 ◽  
Author(s):  
Kota Murakami ◽  
Yuta Tanaka ◽  
Ryuya Sakai ◽  
Yudai Hisai ◽  
Sasuga Hayashi ◽  
...  
Keyword(s):  
Heterogeneous Catalysis ◽  
Low Temperature ◽  
Electric Field ◽  
Catalytic Reaction ◽  
Small Scale ◽  
Heterogeneous Catalytic Reaction ◽  
Heterogeneous Catalytic ◽  
On Demand ◽  
Novel Approach ◽  
Key Factor

Low-temperature heterogeneous catalytic reaction in an electric field is anticipated as a novel approach for on-demand and small-scale catalytic processes.

Improvement of Interstellar Material and Energy Flows

2021 ◽  
Vol 9 (4) ◽  
pp. 57-66
Author(s):  
Jan Stenis
Keyword(s):  
Outer Space ◽  
Life Forms ◽  
Optimal Production ◽  
Stellar Objects ◽  
Use Of Resources ◽  
Energy Flows ◽  
Novel Approach ◽  
Key Factor ◽  
Interstellar Material ◽  
The Universe

Objectives A novel approach is suggested to utilize the inherent forces of the universe for the benefit of mankind, by applying the model for Efficient Use of Resources for Optimal Production Economy (EUROPE) to improve the major flows of gas, dust, material and energy between stars and galaxies. This endeavour is regarded as promoting the altruism and benevolence by increasing life-forms’ chances to survive. Methods It is shown how the torus-shaped flows of gas, dust material and energy between stellar bodies in cosmos can be improved with the EUROPE model in order to preserve universe and secure its existence by allocating shadow costs to waste-like flows and unwanted radiation in this torus. The fewer shadow costs being allocated to the stellar residuals, the more efficient the cosmic torus flows, expressed in equivalents of antimatter released, by inducing incentives to improve the conditions for all inhabitants. Results The methodology promotes the economy when travelling in space, advances the technology used and improves the environment, when outer space in the future is explored and exploited. A single key factor enables monitoring, managing and evaluating the development of universe and the flows of gas, dust, material and energy between various stellar objects, such as the distant stars and whole galaxies. Conclusions I recommend using the EUROPE model to monitor, manage and evaluate the wastes and spillages of all material and energy phenomena throughout the known universe, to uphold its very existence.

Neural Arabic Text Diacritization: State-of-the-Art Results and a Novel Approach for Arabic NLP Downstream Tasks

2022 ◽  
Vol 21 (1) ◽  
pp. 1-25
Author(s):  
Ali Fadel ◽  
Ibraheem Tuffaha ◽  
Mahmoud Al-Ayyoub
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Arabic Text ◽  
Learning Models ◽  
Post Processing ◽  
Feed Forward Neural Network ◽  
Novel Approach ◽  
Normalized Gradient ◽  
Processing Steps

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF), and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models even those requiring human-crafted language-dependent post-processing steps, unlike ours. Moreover, we show how diacritics in Arabic can be used to enhance the models of downstream NLP tasks such as Machine Translation (MT) and Sentiment Analysis (SA) by proposing novel Translation over Diacritization (ToD) and Sentiment over Diacritization (SoD) approaches.

An Approach to Calculate Leak Channels and Leak Rates Between Metallic Sealing Surfaces

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Feikai Zhang ◽  
Jianhua Liu ◽  
Xiaoyu Ding ◽  
Zhimeng Yang
Keyword(s):  
Finite Element ◽  
Specific Surface ◽  
Surface Topography ◽  
Three Dimensional ◽  
Contact Model ◽  
3D Finite Element ◽  
Novel Approach ◽  
Interfacial Pressure ◽  
Key Factor ◽  
Cfd Method

Surface topography of sealing interface is a key factor affecting sealing performance. In industry, it has always been desirable to optimize the performance of static seals by optimizing the surface topography. The evolution of leak channels and the quantitative effects of surface topography on leak rates are expected to be clarified. This paper proposes a novel approach to calculate leak channels and leak rates between sealing surfaces for specific surface topographies, based on three-dimensional (3D) finite-element contact analysis. First, a macromechanical analysis of the entire sealing structure is conducted to calculate the interfacial pressure. Second, the surface topography data are measured and processed. Third, the interfacial pressure is used as the boundary condition applied on the microscale 3D finite-element contact model, which is built based on the specific surface topography. Fourth, the geometrical models of leak channels are extracted from the finite-element contact model, and the leak rates are calculated using the computational fluid dynamics (CFD) method. The proposed approach was applied to a hollow bolt-sealing structure. Finally, experimental results verified the accuracy and effectiveness of the proposed approach, which can provide valuable information for optimizing surface processing techniques, surface topography, and static seal performance.

scholarly journals Why Austerity? The Mass Politics of a Contested Policy

2021 ◽  
pp. 1-20
Author(s):  
KIRK BANSAK ◽  
MICHAEL M. BECHTEL ◽  
YOTAM MARGALIT
Keyword(s):  
Financial Crises ◽  
Public Acceptance ◽  
Economic Interests ◽  
Novel Approach ◽  
Highly Sensitive ◽  
Mass Politics ◽  
Economic Downturns ◽  
Key Factor ◽  
Original Survey ◽  
Spending Cuts

The effects of austerity in response to financial crises are widely contested and assumed to cause significant electoral backlash. Nonetheless, governments routinely adopt austerity when confronting economic downturns and swelling deficits. We explore this puzzle by distinguishing public acceptance of austerity as a general approach and support for specific austerity packages. Using original survey data from five European countries, we show that austerity is in fact the preferred response among most voters. We develop potential explanations for this surprising preference and demonstrate the empirical limitations of accounts centered on economic interests or an intuitive framing advantage. Instead, we show that the preference for austerity is highly sensitive to its political backers and precise composition of spending cuts and tax hikes. Using a novel approach to estimate support for historical austerity programs, we contend that governments’ strategic crafting of policy packages is a key factor underlying the support for austerity.

scholarly journals LSGAN-AT: enhancing malware detector robustness against adversarial examples

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jianhua Wang ◽  
Xiaolin Chang ◽  
Yixiang Wang ◽  
Ricardo J. Rodríguez ◽  
Jianan Zhang
Keyword(s):  
Recognition Rate ◽  
Black Box ◽  
Least Square ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Novel Approach ◽  
Key Factor ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
And Training

AbstractAdversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN module and AT module. LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples. AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD). Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack. The results also verify the performance of the generated RMD in the recognition rate of AME.

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