Relationship Between Noninterest Income and Bank Valuation: Evidence from the U.S. Bank Holding Companies

This paper investigates the impact of noninterest income on bank valuation using 625 U.S. Bank Holding Companies over the period 2003-2015. We use two measures of valuation: Tobin’s q and the market-to-book ratio. Using the whole sample, we find a positive relation between noninterest income and valuation. We then divide banks in our sample into three groups based on size, and the sample period into three sub-periods. We find that noninterest income is positively related to valuation (1) for large banks in each sub-period, (2) for medium-sized banks during and after the financial crisis of 2007-2009, and (3) for small banks after the financial crisis

A Novel PTS Scheme for PAPR Reduction of Filtered-OFDM Signals without Side Information

In this paper, a novel partial transmit sequence (PTS) scheme is proposed for reducing the peak-to-average power ratio (PAPR) of filtered orthogonal frequency division multiplexing (f-OFDM) systems. The PTS method is modified such that no side information (SI) transmission is needed. The data and pilot recovery are accomplished by a simple detector, making use of the correlation property of the Hadamard sequence and the transparency property of the pilot signal and an iterative phase detection is further added in a fading channel. Simulation results show that the modified solution provides a higher correct detection probability without increasing the system complexity nor affecting the PAPR suppression performance

Low-Complexity Non-uniform Constellation Demapping Algorithm for Broadcasting System

This paper presents a novel low-complexity soft demapping algorithm for  two-dimensional non-uniform spaced constellations (2D-NUCs) and massive order one-dimensional NUCs (1D-NUCs). NUCs have been implemented in a wide range of new broadcasting systems to approach the Shannon limit further, such as DVB-NGH, ATSC 3.0 and NGB-W. However, the soft demapping complexity is extreme due to the substantial distance calculations. In the proposed scheme, the demapping process is classified into four cases based on different quadrants. To deal with the complexity problem, four groups of reduced subsets in terms of the quadrant for each bit are separately calculated and stored in advance. Analysis and simulation prove that the proposed demapper only introduces a small penalty under 0.02dB with respect to Max-Log-MAP demapper, whereas a significant complexity reduction ranging from 68.75\% to 88.54\% is obtained

Factory optimization using deep reinforcement learning AI

The long-standing goal of factory optimization is to find optimal machine and conveyor belt placement to maximize the efficiency of the assembly line. We are developing a reinforcement learning agent to play Factorio, a game where you build and maintain factories, without prior domain knowledge. Factorio is the perfect environment for deep reinforcement learning as it supports extensive modification using an in-game debugging mode which allows our agent to interface with the game effortlessly. The reinforcement learning agent implements a policy of actions based on the reward function, continuously optimizing towards incremental goals specified by the user. The ultimate goal of our agent is to learn how to automate production, find creative solutions to maximize production efficiency in the game, and then transfer this learning to the design and management of real-world factories

Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples

Referring video object segmentation (RVOS), as a supervised learning task, relies on sufficient annotated data for a given scene. However, in more realistic scenarios, only minimal annotations are available for a new scene, which poses significant challenges to existing RVOS methods. With this in mind, we propose a simple yet effective model with a newly designed cross-modal affinity (CMA) module based on a Transformer architecture. The CMA module builds multimodal affinity with a few samples, thus quickly learning new semantic information, and enabling the model to adapt to different scenarios. Since the proposed method targets limited samples for new scenes, we generalize the problem as - few-shot referring video object segmentation (FS-RVOS). To foster research in this direction, we build up a new FS-RVOS benchmark based on currently available datasets. The benchmark covers a wide range and includes multiple situations, which can maximally simulate real-world scenarios. Extensive experiments show that our model adapts well to different scenarios with only a few samples, reaching state-of-the-art performance on the benchmark. On Mini-Ref-YouTube-VOS, our model achieves an average performance of 53.1 J and 54.8 F, which are 10% better than the baselines. Furthermore, we show impressive results of 77.7 J and 74.8 F on Mini-Ref-SAIL-VOS, which are significantly better than the baselines. Code is publicly available at https://github.com/hengliusky/Few_shot_RVOS.Comment: Accepted by ICCV202