COMPLETELY POSITIVE AND CO-POSITIVE PROGRAMMING: APPLICATIONS IN DISRUPTION RISK MANAGEMENT, POST DISASTER HUMANITARIAN LOGISTICS AND HOMELAND SECURITY GAMES

Ph.DDOCTOR OF PHILOSOPH

On the design of sparse but efficient structures in operations

It is widely believed that a little flexibility added at the right place can reap significant benefits for operations. Unfortunately, despite the extensive literature on this topic, we are not aware of any general methodology that can be used to guide managers in designing sparse (i.e., slightly flexible) and yet efficient operations. We address this issue using a distributionally robust approach to model the performance of a stochastic system under different process structures. We use the dual prices obtained from a related conic program to guide managers in the design process. This leads to a general solution methodology for the construction of efficient sparse structures for several classes of operational problems. Our approach can be used to design simple yet efficient structures for workforce deployment and for any level of sparsity requirement, to respond to deviations and disruptions in the operational environment. Furthermore, in the case of the classical process flexibility problem, our methodology can recover the k-chain structures that are known to be extremely efficient for this type of problem when the system is balanced and symmetric. We can also obtain the analog of 2-chain for nonsymmetrical system using this methodology. This paper was accepted by Yinyu Ye, optimization. </jats:p

Entry of copycats of luxury brands

We develop a game-theoretic model to examine the entry of copycats and its implications by incorporating two salient features; these features are two product attributes, i.e., physical resemblance and product quality, and two consumer utilities, i.e., consumption utility and status utility. Our equilibrium analysis suggests that copycats with a high physical resemblance but low product quality are more likely to successfully enter the market by defying the deterrence of the incumbent. Furthermore, we show that higher quality can prevent the copycat from successfully entering the market. Finally, we show that the entry of copycats does not always improve consumer surplus and social welfare. In particular, when the quality of the copycat is sufficiently low, the loss in status utility from consumers of the incumbent product overshadows the small gain in consumption utility from buyers of the copycat, leading to an overall decrease in consumer surplus and social welfare. </jats:p

SOLUBLE ST2 AND CD163 AS POTENTIALBIOMARKERS TO DIFFERENTIATE PRIMARY HEMOPHAGOCYTIC LYMPHOHISTIOCYTOSIS FROM MACROPHAGE ACTIVATION SYNDROME

Abstract Background and Objective: The differentiation of primary haemophagocytic lymphohistiocytosis (pHLH) and macrophage activation syndrome (MAS) poses a challenge to hematologists. The aim of this study was (1) to compare the levels of soluble ST2 (sST2), sCD163, IL-10, IFN-γ, TNF-α and IL-18 in patients with pHLH and MAS and (2) to investigate whether they can help differentiate the two diseases. Methods: A total of 54 participants were recruited in this study, including 12 pHLH patients, 22 MAS patients and 20 healthy subjects. We measured the levels of sST2 and sCD163 in serum by ELISA. The serum levels of IL-10, IFN-γ, TNF-α and IL-18 were detected using a Luminex 200 instrument. Results: The serum levels of sST2 and sCD163 in MAS patients were markedly higher than that in pHLH patients (363.13 ± 307.24 ng/ml vs 80.75 ± 87.04 ng/ml, P = 0.004; 3532.72 ± 2479.68 ng/ml vs 1731.96 ± 1262.07 ng/ml, P = 0.046). There was no significant difference in the expression of IFN-γ (306.89 ± 281.60 pg/ml vs 562.43 ± 399.86 pg/ml), IL-10 (20.40 ± 30.49 pg/ml vs 8.3 ± 13.14 pg/ml), IL-18 (463.33 ± 597.04 pg/ml vs 1247.82 ± 1318.58 pg/ml) and TNF-α (61.48 ± 84.69 pg/ml vs 106.10 ±77.21 pg/ml) between pHLH and MAS. Conclusion: Patients with pHLH and MAS show some differences in cytokine profiles. The elevated levels of IFN-γ, IL-10, IL-18 and TNF-α can contribute to the diagnosis of HLH, but may not discriminate pHLH from MAS. Levels of sST2 and sCD163 may serve as markers to distinguish pHLH from MAS

Disruption risk mitigation in supply chains: The risk exposure index revisited

A novel approach has been proposed in the literature using the time-to-recover (TTR) parameters to analyze the risk-exposure index (REI) of supply chains under disruption. This approach is able to capture the cascading effects of disruptions in the supply chains, albeit in simplified environments; TTRs are deterministic, and at most, one node in the supply chain can be disrupted. In this paper, we propose a new method to integrate probabilistic assessment of disruption risks into the REI approach and measure supply chain resiliency by analyzing the worst-case conditional value at risk of total lost sales under disruptions. We show that the optimal strategic inventory positioning strategy in this model can be fully characterized by a conic program. We identify appropriate cuts that can be added to the formulation to ensure zero duality gap in the conic program. In this way, the optimal primal and dual solutions to the conic program can be used to shed light on comparative statics in the supply chain risk mitigation problem. This information can help supply chain risk managers focus their mitigation efforts on critical suppliers and/or installations that will have a greater impact on the performance of the supply chain when disrupted.Accepted versio

A One-stage Detector for Extremely-small Objects Based on Feature Pyramid Network

Thanks to the recent development in Graphics Processing Unit (GPU) and deep neural network, outstanding enhancement has been made in real-time and multi-scale object detection. However, most of these detectors ignore the situations where the target needs to be identified is extremely-small corresponding to the size of the image or video. The spatial resolution of feature maps is decreasing and detailed information about extremely-small objects is missing during the process of extracting features with stride and pooling. So how to keep the higher spatial resolution when we extract the richer semantic information and enlarge receptive field becomes the crucial core of this project. With the purpose of detecting targets with 30 to 1000 pixels in 1080p videos, we design a one-stage detector that uses DetNet as the backbone and construct the head of detector based on the idea of Feature Pyramid Network (FPN). Taking advantage of the dilated convolutional layer in DetNet, the size of the last three feature maps are not decreasing. By contrast, the receptive field and semantic information are augmented by traversing the backbone of the detector. Besides, with the technique of FPN, feature maps from different stages are combined and assigned to the prediction, making the model more robust and accurate. Additionally, in order to reduce the input size of the image to decrease computational complexity without missing any information of extremely-small objects, we crop the full image based on the distribution of the target’s location in existing data instead of directly resizing the full image. We compare the performance of this proposed detector with YOLOv3 on the custom dataset, and it turns out to obtain remarkably good results on extremely small objects, improving mean average precision by 18%.Tack vare den senaste utvecklingen inom grafikbearbetningsenhet (GPU) och djupa neurala nätverk har enastående förbättring gjorts i realtid och detektering av flera objekt. De flesta av dessa detektorer ignorerar emellertid situationerna där det identifierade målet som identifieras är extremt litet motsvarande storleken på bilden eller videon. Den rumsliga upplösningen för funktionskartor minskar och detaljerad information om extremt små objekt saknas under processen för att extrahera funktioner med steg och poolning. Så hur man kan hålla den högre rumsliga upplösningen när vi extraherar den rikare semantiska informationen och förstorar mottagningsfältet blir den avgörande kärnan i detta projekt. Med syftet att upptäcka 30 till 1000 pixelmål i 1080p-videor designar jag en enstegsdetektor som använder DetNet som ryggraden och konstruerar detektorhuvudet baserat på idén om Feature Pyramid Network (FPN) ). Med utnyttjande av det dilaterade sammanslagningsskiktet i DetNet minskar inte storleken på de tre sista funktionskartorna. Däremot har det mottagande fältet och semantisk information förstärkts genom att korsa detektorens ryggrad. Dessutom, med tekniken för FPN, kombineras funktionskartor från olika stadier och tilldelas förutsägelsen, vilket gör modellen mer robust och korrekt. För att minska bildens inmatningsstorlek för att minska beräkningskomplexiteten utan att sakna information om extremt små objekt, beskär jag dessutom hela bilden baserat på fördelningen av målets plats i befintlig data istället för att direkt ändra storleken på hela bilden. Jag jämförde prestandan för den här föreslagna detektorn med YOLOv3 på det anpassade datasettet, och det visar sig att uppnå anmärkningsvärt bra resultat på extremt små föremål, med 18 poäng avkastning på genomsnittlig genomsnittlig precision jämfört med YOLOv3-motsvarigheten

A One-stage Detector for Extremely-small Objects Based on Feature Pyramid Network

Thanks to the recent development in Graphics Processing Unit (GPU) and deep neural network, outstanding enhancement has been made in real-time and multi-scale object detection. However, most of these detectors ignore the situations where the target needs to be identified is extremely-small corresponding to the size of the image or video. The spatial resolution of feature maps is decreasing and detailed information about extremely-small objects is missing during the process of extracting features with stride and pooling. So how to keep the higher spatial resolution when we extract the richer semantic information and enlarge receptive field becomes the crucial core of this project. With the purpose of detecting targets with 30 to 1000 pixels in 1080p videos, we design a one-stage detector that uses DetNet as the backbone and construct the head of detector based on the idea of Feature Pyramid Network (FPN). Taking advantage of the dilated convolutional layer in DetNet, the size of the last three feature maps are not decreasing. By contrast, the receptive field and semantic information are augmented by traversing the backbone of the detector. Besides, with the technique of FPN, feature maps from different stages are combined and assigned to the prediction, making the model more robust and accurate. Additionally, in order to reduce the input size of the image to decrease computational complexity without missing any information of extremely-small objects, we crop the full image based on the distribution of the target’s location in existing data instead of directly resizing the full image. We compare the performance of this proposed detector with YOLOv3 on the custom dataset, and it turns out to obtain remarkably good results on extremely small objects, improving mean average precision by 18%.Tack vare den senaste utvecklingen inom grafikbearbetningsenhet (GPU) och djupa neurala nätverk har enastående förbättring gjorts i realtid och detektering av flera objekt. De flesta av dessa detektorer ignorerar emellertid situationerna där det identifierade målet som identifieras är extremt litet motsvarande storleken på bilden eller videon. Den rumsliga upplösningen för funktionskartor minskar och detaljerad information om extremt små objekt saknas under processen för att extrahera funktioner med steg och poolning. Så hur man kan hålla den högre rumsliga upplösningen när vi extraherar den rikare semantiska informationen och förstorar mottagningsfältet blir den avgörande kärnan i detta projekt. Med syftet att upptäcka 30 till 1000 pixelmål i 1080p-videor designar jag en enstegsdetektor som använder DetNet som ryggraden och konstruerar detektorhuvudet baserat på idén om Feature Pyramid Network (FPN) ). Med utnyttjande av det dilaterade sammanslagningsskiktet i DetNet minskar inte storleken på de tre sista funktionskartorna. Däremot har det mottagande fältet och semantisk information förstärkts genom att korsa detektorens ryggrad. Dessutom, med tekniken för FPN, kombineras funktionskartor från olika stadier och tilldelas förutsägelsen, vilket gör modellen mer robust och korrekt. För att minska bildens inmatningsstorlek för att minska beräkningskomplexiteten utan att sakna information om extremt små objekt, beskär jag dessutom hela bilden baserat på fördelningen av målets plats i befintlig data istället för att direkt ändra storleken på hela bilden. Jag jämförde prestandan för den här föreslagna detektorn med YOLOv3 på det anpassade datasettet, och det visar sig att uppnå anmärkningsvärt bra resultat på extremt små föremål, med 18 poäng avkastning på genomsnittlig genomsnittlig precision jämfört med YOLOv3-motsvarigheten

Inventory-responsive donor-management policy: A tandem queueing network model

Ministry of Education, Singapore under its Academic Research Funding Tier