Enhancing Generic Segmentation with Learned Region Representations. (arXiv:1911.08564v1 [cs.CV])

Current successful approaches for generic (non-semantic) segmentation rely mostly on edge detection and have leveraged the strengths of deep learning mainly by improving the edge detection stage in the algorithmic pipeline. This is in contrast to semantic and instance segmentation, where deep learning has made a dramatic affect and DNNs are applied directly to generate…

Strategy-Proof and Non-Wasteful Multi-Unit Auction via Social Network. (arXiv:1911.08809v1 [cs.GT])

Auctions via social network, pioneered by Li et al. (2017), have been attracting considerable attention in the literature of mechanism design for auctions. However, no known mechanism has satisfied strategy-proofness, non-deficit, non-wastefulness, and individual rationality for the multi-unit unit-demand auction, except for some naive ones. In this paper, we first propose a mechanism that satisfies…

Statistical Inference on Partially Linear Panel Model under Unobserved Linearity. (arXiv:1911.08830v1 [econ.EM])

A new statistical procedure, based on a modified spline basis, is proposed to identify the linear components in the panel data model with fixed effects. Under some mild assumptions, the proposed procedure is shown to consistently estimate the underlying regression function, correctly select the linear components, and effectively conduct the statistical inference. When compared to…

Competition of noise and collectivity in global cryptocurrency trading: route to a self-contained market. (arXiv:1911.08944v1 [q-fin.ST])

Cross-correlations in fluctuations of the daily exchange rates within the basket of the 100 highest-capitalization cryptocurrencies over the period October 1, 2015, through March 31, 2019, are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others largely coincide with those of Wishart random matrices. However, the magnitude of…

Combining Outcome-Based and Preference-Based Matching: The g-Constrained Priority Mechanism. (arXiv:1902.07355v2 [econ.GN] UPDATED)

We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold…

Allowance prices in the EU ETS — fundamental price drivers and the recent upward trend. (arXiv:1906.10572v4 [econ.EM] UPDATED)

In 2017 allowance prices in the EU Emissions Trading Scheme (ETS) have started to rally from persistently low levels in previous years. Market observers attribute this development to the ETS reform, naming anticipation of the tightening of allowance supply through the Market Stability Reserve (MSR) and speculative buying as main price drivers. The former suggests…

A simulation of the insurance industry: The problem of risk model homogeneity. (arXiv:1907.05954v2 [econ.GN] UPDATED)

We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their…

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing. (arXiv:1905.03929v3 [cs.LG] UPDATED)

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a…

Norm-based generalisation bounds for multi-class convolutional neural networks. (arXiv:1905.12430v2 [cs.LG] UPDATED)

Using proof techniques involving $L^\infty$ covering numbers, we show generalisation error bounds for deep learning with two main improvements over the state of the art. First, our bounds have no explicit dependence on the number of classes except for logarithmic factors. This holds even when formulating the bounds in terms of the $L^2$-norm of the…