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

Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The~experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100\% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296; Sensors 2018, 18(4), 129

Molecular Lines of 13 Galactic Infrared Bubble Regions

We investigated the physical properties of molecular clouds and star formation processes around infrared bubbles which are essentially expanding HII regions. We performed observations of 13 galactic infrared bubble fields containing 18 bubbles. Five molecular lines, 12CO (J=1-0), 13CO (J=1-0), C18O(J=1-0), HCN (J=1-0), and HCO+ (J=1-0), were observed, and several publicly available surveys, GLIMPSE, MIPSGAL, ATLASGAL, BGPS, VGPS, MAGPIS, and NVSS, were used for comparison. We find that these bubbles are generally connected with molecular clouds, most of which are giant. Several bubble regions display velocity gradients and broad shifted profiles, which could be due to the expansion of bubbles. The masses of molecular clouds within bubbles range from 100 to 19,000 solar mass, and their dynamic ages are about 0.3-3.7 Myr, which takes into account the internal turbulence pressure of surrounding molecular clouds. Clumps are found in the vicinity of all 18 bubbles, and molecular clouds near four of these bubbles with larger angular sizes show shell-like morphologies, indicating that either collect-and-collapse or radiation-driven implosion processes may have occurred. Due to the contamination of adjacent molecular clouds, only six bubble regions are appropriate to search for outflows, and we find that four of them have outflow activities. Three bubbles display ultra-compact HII regions at their borders, and one of them is probably responsible for its outflow. In total, only six bubbles show star formation activities in the vicinity, and we suggest that star formation processes might have been triggered.Comment: 55 Pages, 32 figures. Accepted for publication in A

Continuum field theory of 3D topological orders with emergent fermions and braiding statistics

Universal topological data of topologically ordered phases can be captured by topological quantum field theory in continuous space time by taking the limit of low energies and long wavelengths. While previous continuum field-theoretical studies of topological orders in $3$D real space focus on either self-statistics, braiding statistics, shrinking rules, fusion rules or quantum dimensions, it is yet to systematically put all topological data together in a unified continuum field-theoretical framework. Here, we construct the topological $BF$ field theory with twisted terms (e.g., $AAdA$ and $AAB$) as well as a $K$-matrix $BB$ term, in order to simultaneously explore all such topological data and reach anomaly-free topological orders. Following the spirit of the famous $K$-matrix Chern-Simons theory of $2$D topological orders, we present general formulas and systematically show how the $K$-matrix $BB$ term confines topological excitations, and how self-statistics of particles is transmuted between bosonic one and fermionic one. In order to reach anomaly-free topological orders, we explore, within the present continuum field-theoretical framework, how the principle of gauge invariance fundamentally influences possible realizations of topological data. More concretely, we present the topological actions of (i) particle-loop braidings with emergent fermions, (ii) multiloop braidings with emergent fermions, and (iii) Borromean-Rings braidings with emergent fermions, and calculate their universal topological data. Together with the previous efforts, our work paves the way toward a more systematic and complete continuum field-theoretical analysis of exotic topological properties of $3$D topological orders. Several interesting future directions are also discussed

Non-Abelian Fusion, Shrinking and Quantum Dimensions of Abelian Gauge Fluxes

Braiding and fusion rules of topological excitations are indispensable topological invariants in topological quantum computation and topological orders. While excitations in 2D are always particle-like anyons, those in 3D incorporate not only particles but also loops -- spatially nonlocal objects -- making it novel and challenging to study topological invariants in higher dimensions. While 2D fusion rules have been well understood from bulk Chern-Simons field theory and edge conformal field theory, it is yet to be thoroughly explored for 3D fusion rules from higher dimensional bulk topological field theory. Here, we perform a field-theoretical study on (i) how loops that carry Abelian gauge fluxes fuse and (ii) how loops are shrunk into particles in the path integral, which generates fusion rules, loop-shrinking rules, and descendent invariants, e.g., quantum dimensions. We first assign a gauge-invariant Wilson operator to each excitation and determine the number of distinct excitations through equivalence classes of Wilson operators. Then, we adiabatically shift two Wilson operators together to observe how they fuse and are split in the path integral; despite the Abelian nature of the gauge fluxes carried by loops, their fusions may be of non-Abelian nature. Meanwhile, we adiabatically deform world-sheets of unknotted loops into world-lines and examine the shrinking outcomes; we find that the resulting loop-shrinking rules are algebraically consistent to fusion rules. Interestingly, fusing a pair of loop and anti-loop may generate multiple vacua, but fusing a pair of anyon and anti-anyon in 2D has one vacuum only. By establishing a field-theoretical ground for fusion and shrinking in 3D, this work leaves intriguing directions, e.g., symmetry enrichment, quantum gates, and physics of braided monoidal 2-category of 2-group.Comment: Title adjusted. Abstract, Intro and Discussions revised. about 30 pages, 5 figures. 9 table

Reconstruction of the Hirnantian (Late Ordovician) Palaeotopography in the Upper Yangtze Region

Reconstruction of the Hirnantian (Late Ordovician) palaeotopography in South China is important for understanding the distribution pattern of the Hirnantian marine depositional environment. In this study, we reconstructed the Hirnantian palaeotopography in the Upper Yangtze region based on the rankings of the palaeo-water depths, which were inferred according to the lithofacies and biofacies characteristics of the sections. Data from 374 Hirnantian sections were collected and standardized through the online Geobiodiversity Database. The Ordinary Kriging interpolation method in the ArcGIS software was applied to create the continuous surface of the palaeo-water depths, i.e. the Hirnantian palaeotopography. Meanwhile, the line transect analysis was used to further observe the terrain changes along two given directions. The reconstructed palaeotopographic map shows a relatively flat and shallow epicontinental sea with three local depressions and a submarine high on the Upper Yangtze region during the Hirnantian. The water depth is mostly less than 60 m and the Yangtze Sea gradually deepens towards the north

Surprising complexity of the ancestral apoptosis network

A comparative genomics approach revealed that the genes for several components of the apoptosis network with single copies in vertebrates have multiple paralogs in cnidarian-bilaterian ancestors, suggesting a complex evolutionary history for this network