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2022 ◽  
Vol 16 (4) ◽  
pp. 1-25
Author(s):  
Hanrui Wu ◽  
Michael K. Ng

Multi-source domain adaptation is a challenging topic in transfer learning, especially when the data of each domain are represented by different kinds of features, i.e., Multi-source Heterogeneous Domain Adaptation (MHDA). It is important to take advantage of the knowledge extracted from multiple sources as well as bridge the heterogeneous spaces for handling the MHDA paradigm. This article proposes a novel method named Multiple Graphs and Low-rank Embedding (MGLE), which models the local structure information of multiple domains using multiple graphs and learns the low-rank embedding of the target domain. Then, MGLE augments the learned embedding with the original target data. Specifically, we introduce the modules of both domain discrepancy and domain relevance into the multiple graphs and low-rank embedding learning procedure. Subsequently, we develop an iterative optimization algorithm to solve the resulting problem. We evaluate the effectiveness of the proposed method on several real-world datasets. Promising results show that the performance of MGLE is better than that of the baseline methods in terms of several metrics, such as AUC, MAE, accuracy, precision, F1 score, and MCC, demonstrating the effectiveness of the proposed method.


Author(s):  
Terence J. McElvaney ◽  
Magda Osman ◽  
Isabelle Mareschal

AbstractTo date, it is still unclear whether there is a systematic pattern in the errors made in eyewitness recall and whether certain features of a person are more likely to lead to false identification. Moreover, we also do not know the extent of systematic errors impacting identification of a person from their body rather than solely their face. To address this, based on the contextual model of eyewitness identification (CMEI; Osborne & Davies, 2014, Applied Cognitive Psychology, 28[3], 392–402), we hypothesized that having framed a target as a perpetrator of a violent crime, participants would recall that target person as appearing more like a stereotypical criminal (i.e., more threatening). In three separate experiments, participants were first presented with either no frame, a neutral frame, or a criminal frame (perpetrators of a violent crime) accompanying a target (either a face or body). Participants were then asked to identify the original target from a selection of people that varied in facial threat or body musculature. Contrary to our hypotheses, we found no evidence of bias. However, identification accuracy was highest for the most threatening target bodies high in musculature, as well as bodies paired with detailed neutral contextual information. Overall, these findings suggest that while no systematic bias exists in the recall of criminal bodies, the nature of the body itself and the context in which it is presented can significantly impact identification accuracy.


2021 ◽  
Vol 2 (3) ◽  
pp. 78-81
Author(s):  
Relizha Yeerlanbieke ◽  
Huazhang Wang

Aiming at the current stage of the twin network target tracking algorithm, the tracking target is occluded, the tracking is affected by illumination, and the target's scale change from far to near or from near to far causes tracking failure. This article will optimize and improve from two directions. The twin neural network first uses an adaptive detailed feature extraction, adds a residual network to the twin network, and embeds a detailed feature retention module in each layer, amplifies the changes in the target feature, and retains the important structure of the original target feature Details: Secondly, the introduction of a spatial attention mechanism allows the main branch to pay more attention to the area to be matched, improves the ability to distinguish features, and makes the tracking effect better. In order to verify the effectiveness of this experiment, this experiment was tested on the data set OTB2015. The experiment proved that the proposed algorithm performs better in accuracy and success rate.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xi Du ◽  
Qi Ao ◽  
Lu Qi

The original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and then combines the updated weight values of each filter to obtain a weighted sum. Therefore, the interactive multimodel algorithm can achieve better performance. This paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching. First, this paper proposes an improved Kalman filtering (IKF) algorithm. This method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation. Secondly, using the parallel processing mode of the IMM algorithm to efficiently solve the data association between multiple filters, the IMM-IKF model is proposed. Finally, in order to solve the problem of low computational efficiency and high mismatch rate in image feature point matching, a method of introducing a minimum spanning tree in two-view matching is proposed. Experimental results show that the improved IMM-IKF algorithm can quickly respond to changes in the target state and can find the matching path with the lowest matching cost. In the case of ensuring the matching accuracy, the real-time performance of image matching is ensured.


Author(s):  
Frederick Appoh ◽  
Akilu Yunusa-Kaltungo ◽  
Jyoti Kumar Sinha ◽  
Moray Kidd

AbstractRailway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements.


2021 ◽  
Vol 2 (1) ◽  
pp. 108-112
Author(s):  
Hanan Nugroho

Indonesia has developed a plan for its energy sector far into the future, however, the plan might be challenged by several international agreements that the country ratifies.  The UN Report suggests several pathways for Indonesia to achieve the SDGs’ goal number 7 (affordable and clean energy).  It challenges the current plan for expanding city gas networks, instead, it offers extensive uses of the electric cooking stove. It recommends that Indonesia accelerates its energy conservation efforts and reduce its energy sector’s greenhouse gasses emission by a figure which is higher than the original target.   Besides, Indonesia should develop no more new coal-fired power plants and should continue to remove fossil fuel subsidies and encourage the issuance of green financing.  This paper supports but also challenges the report by several arguments based on the country’s energy-economy-environment problems.


2021 ◽  
Author(s):  
Taylor Levon ◽  
Kit Clemons ◽  
Ben Zapp ◽  
Tim Foltz

Abstract With a recent trend in increased infill well development in the Midland basin and other unconventional plays, it has been shown that depletion has a significant impact on hydraulic fracture propagation. This is largely because production drawdown causes in-situ stress changes, resulting in asymmetric fracture growth toward the depleted regions. In turn, this can have a negative impact on production capacity. For the initial part of this study, an infill child well was drilled and completed adjacent to a parent well that had been producing for two years. Due to drilling difficulties, the child well was steered to a new target zone located 125 feet above the original target. However, relative to the original target, treatment data from the new zone indicated abnormal treatment responses leading to a study to evaluate the source of these variations and subsequent mitigation. The initial study was conducted using a pore pressure estimation derived from drill bit geomechanics data to investigate depletion effects on the infill child well. The pore pressure results were compared to the child well treatment responses and bottom hole pressure measurements in the parent well. Following the initial study, additional hydraulic fracture modeling studies were conducted on a separate pad to investigate depletion around the infill wells, determine optimal well spacing for future wells given the level of depletion, and optimize treatment designs for future wells in similar depletion scenarios. A depletion model workflow was implemented based on integrating hydraulic fracture modeling and reservoir analytics for future infill pad development. The geomechanical properties were calibrated by DFIT results and pressure matching of the parent well treatments for the in-situ virgin conditions. Parent well fracture geometries were used in an RTA for an analytical approach of estimating drainage area of the parent wells. These were then applied to a depletion profile in the hydraulic fracture model for well spacing analysis and treatment design sensitivities. Results of the initial study indicated that stages in the new, higher interval had higher breakdown pressures than the lower interval. Additionally, the child well drilled in the lower interval had normal breakdown pressures in line with the parent well treatments. This suggests that treatment differences in the wells were ultimately due to depletion of the offset parent well. Based on the modeling efforts, optimal infill well spacing was determined based on the on-production time of the parent wells. The optimal treatment designs were also determined under the same conditions to minimize offset frac hits and unnecessary completion costs. This case study presents the use of a multi-disciplinary approach for well spacing and treatment optimization. The integration of a novel method of estimating pore pressure and depletion modeling workflows were used in an inventive way to understand depletion effects on future development.


Author(s):  
Carsten Homburg ◽  
André Hoppe ◽  
Roman Schick ◽  
Amelie Braul

AbstractTarget costing is a well-established strategic cost management tool in theory and practice. The original target costing model implies independence of customer preferences resulting in additive utility functions for the customer-oriented optimization of cost structures. We argue that this independence of preferences is not given until a minimum variant of a product is reached that provides its inherent functionality. This is reasonable since one cannot assign customer utility to a product that does not function in its most basic way. Our modified model accounts for the dependency of customer preferences and differentiates between the costs necessary to produce a minimum variant and those related to product features beyond this minimum variant. The customer-oriented optimization of the cost structure is then conducted only for those costs that exceed the costs of the minimum variant. This modification justifies the preference independence assumption in target costing and allows for a more reasonable assignment of required adjustments in costs per product component.


Molecules ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 389
Author(s):  
Chung-Yin Lin ◽  
Subrata Chakraborty ◽  
Chia-Wei Wong ◽  
Dar-Fu Tai

The present investigation reports an attempt to synthesize naturally occurring α-cyclic tripeptide cyclo(Gly-l-Pro-l-Glu) 1, [cyclo(GPE)], previously isolated from the Ruegeria strain of bacteria with marine sponge Suberites domuncula. Three linear precursors, Boc-GPE(OBn)2, Boc-PE(OBn)G and Boc-E(OBn)GP, were synthesized using a solution phase peptide coupling protocol. Although cyclo(GPE) 1 was our original target, all precursors were dimerized and cyclized at 0 °C with high dilution to form corresponding α-cyclic hexapeptide, cyclo(GPE(OBn))27, which was then converted to cyclic hexapeptide cyclo(GPE)22. Cyclization at higher temperature induced racemization and gave cyclic tripeptide cyclo(GPDE(OBn)) 9. Structure characteristics of the newly synthesized cyclopeptides were determined using 1H-NMR, 13C-NMR and high-resolution mass spectrometry. The chemical shift values of carbonyls of 2 and 7 are larger than 170 ppm, indicating the formation of a cyclic hexapeptide.


Trials ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Julia M. Edwards ◽  
Stephen J. Walters ◽  
Cornelia Kunz ◽  
Steven A. Julious

Abstract Introduction Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy. Objectives This research aims to synthesise current approaches and practical implementation of the promising zone design. Methods This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented. Results The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%). Conclusions Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.


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