Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

Fair task allocation in transportation

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost

Multi-objective Optimization Methods for Allocation and Prediction

In this thesis we focus on two different aspects of auctions and we employ techniques and methods from both operations research and computer science. _First,_ we study the allocation of tasks to agents at the end of an auction. Usually, tasks are allocated in such a way that minimizes the total cost for the auctioneer. This allocation is optimal in a one-shot auction, but if the auction is repeated, this can have negative consequences for the results in the long run. Therefore, we consider a fair allocation, which costs slightly more in a one-shot auction, but has positive effects on the participation level of agents and the total cost for the auctioneer in repeated auctions. _Second,_ we consider the auction design. How an auction is set up, like which tasks should be auctioned first, or what the starting price should be, impacts the result. Usually there are experts who know what has occurred in previous auctions and how a future auction should be designed in order to obtain the best results. However, historical auctions can obtain so much information that experts overlook things. We use a combination of machine learning and optimization models to extract information from historical auctions and use this information to help design future auctions for better results

Tandem Michael addition/ylide epoxidation for the synthesis of highly functionalized cyclohexadiene epoxide derivatives

A highly efficient diastereoselective synthesis of cyclohexadiene epoxide derivatives with a multi-stereocenter has been developed via a tandem ylide Michael addition/epoxidation. By employing a chiral sulfonium ylide, up to 96% ee can be achieved in good yields

CIP2A Causes Tau/APP Phosphorylation, Synaptopathy, and Memory Deficits in Alzheimer's Disease

Protein phosphatase 2A (PP2A) inhibition causes hyperphosphorylation of tau and APP in Alzheimer's disease (AD). However, the mechanisms underlying the downregulation of PP2A activity in AD brain remain unclear. We demonstrate that Cancerous Inhibitor of PP2A (CIP2A), an endogenous PP2A inhibitor, is overexpressed in AD brain. CIP2A-mediated PP2A inhibition drives tau/APP hyperphosphorylation and increases APP beta-cleavage and A beta production. Increase in CIP2A expression also leads to tau mislocalization to dendrites and spines and synaptic degeneration. In mice, injection of AAV-CIP2A to hippocampus induced AD-like cognitive deficits and impairments in long-term potentiation (LTP) and exacerbated AD pathologies in neurons. Indicative of disease exacerbating the feedback loop, we found that increased CIP2A expression and PP2A inhibition in AD brains result from increased A beta production. In summary, we show that CIP2A overexpression causes PP2A inhibition and AD-related cellular pathology and cognitive deficits, pointing to CIP2A as a potential target for AD therapy

Genomic and oncogenic preference of HBV integration in hepatocellular carcinoma

Hepatitis B virus (HBV) can integrate into the human genome, contributing to genomic instability and hepatocarcinogenesis. Here by conducting high-throughput viral integration detection and RNA sequencing, we identify 4,225 HBV integration events in tumour and adjacent non-tumour samples from 426 patients with HCC. We show that HBV is prone to integrate into rare fragile sites and functional genomic regions including CpG islands. We observe a distinct pattern in the preferential sites of HBV integration between tumour and non-tumour tissues. HBV insertional sites are significantly enriched in the proximity of telomeres in tumours. Recurrent HBV target genes are identified with few that overlap. The overall HBV integration frequency is much higher in tumour genomes of males than in females, with a significant enrichment of integration into chromosome 17. Furthermore, a cirrhosis-dependent HBV integration pattern is observed, affecting distinct targeted genes. Our data suggest that HBV integration has a high potential to drive oncogenic transformation

Identification of MSRA gene on chromosome 8p as a candidate metastasis suppressor for human hepatitis B virus-positive hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>The prognosis of patients with hepatocellular carcinoma (HCC) still remains very dismal, which is mainly due to metastasis. In our previous studies, we found that chromosome 8p deletions might contribute to metastasis of HCC. In this study, we aimed to identify the candidate metastatic suppressor gene on chromosome 8p.</p> <p>Methods</p> <p>Oligo-nucleotide microarrays which included 322 genes on human chromosome 8p were constructed to analyze the difference in gene expression profiles between HCC tissues with and without metastasis. The leading differentially expressed genes were identified and selected for further analysis by real-time PCR and Western blotting. Recombinant expression plasmid vectors for each target gene were constructed and transfected into HCC cells and its <it>in vitro </it>effects on proliferation and invasion of HCC cells were also investigated.</p> <p>Results</p> <p>Sixteen leading differentially expressed genes were identified from the HCC tissues with metastasis compared with those without metastasis (<it>p </it>< 0.01, <it>q </it>< 16 %). Among of the 10 significantly down-regulated genes in HCC with metastasis, methionine sulfoxide reductase A (<it>MSRA</it>) had the lowest <it>p </it>value and false discovery rate (FDR), and was considered as a potential candidate for metastasis suppressor gene. Real-time PCR and Western blotting confirmed that the mRNA and protein expression levels of <it>MSRA </it>were significantly decreased in HCC with metastasis compared with those without metastasis (<it>p </it>< 0.001), and <it>MSRA </it>mRNA level in HCCLM6 cells (with high metastatic potential) was also much lower than that of other HCC cell lines. Transfection of a recombinant expression plasmid vector and overexpression of <it>MSRA </it>gene could obviously inhibit cell colony formation (4.33 ± 2.92 vs. 9.17 ± 3.38, <it>p </it>= 0.008) and invasion (7.40 ± 1.67 vs. 17.20 ± 2.59, <it>p</it>= 0.0001) of HCCLM6 cell line.</p> <p>Conclusion</p> <p><it>MSRA </it>gene on chromosome 8p might possess metastasis suppressor activity in HCC.</p

The gas-phase formation mechanism of iodic acid as an atmospheric aerosol source

Iodine is a reactive trace element in atmospheric chemistry that destroys ozone and nucleates particles. Iodine emissions have tripled since 1950 and are projected to keep increasing with rising O-3 surface concentrations. Although iodic acid (HIO3) is widespread and forms particles more efficiently than sulfuric acid, its gas-phase formation mechanism remains unresolved. Here, in CLOUD atmospheric simulation chamber experiments that generate iodine radicals at atmospherically relevant rates, we show that iodooxy hypoiodite, IOIO, is efficiently converted into HIO3 via reactions (R1) IOIO + O-3 -> IOIO4 and (R2) IOIO4 + H2O -> HIO3 + HOI + O-(1)(2). The laboratory-derived reaction rate coefficients are corroborated by theory and shown to explain field observations of daytime HIO3 in the remote lower free troposphere. The mechanism provides a missing link between iodine sources and particle formation. Because particulate iodate is readily reduced, recycling iodine back into the gas phase, our results suggest a catalytic role of iodine in aerosol formation.Peer reviewe