COMPARING ASSET PRICING MODELS USING QUANTILE REGRESSIONS FOR DISTANCE-BASED METRICS

This thesis compares the performance of ten well-known asset-pricing models for cross-sectional returns of various portfolios from January 1967 to December 2016. We rely on the distance-based metrics as the primary performance measure and use quantile regressions to compare models at a wide range of quantiles of the asset return distribution. The model performance is examined from both statistical and economic perspectives. We find that the Fama and French (2018) six-factor model reliably outperforms other competing models in pricing the selected portfolios. In particular, both the momentum factor and the value factor are necessary in asset-pricing models to explain the return variations in different quantiles. We also find that the performance of Barilla and Shanken (2018) six-factor model exhibits strong explanatory power in medium to high quantiles, despite some existing findings that their model performs poorly in OLS regressions. Overall, we show that the distance-based metrics coupled with quantile regressions provide a consistent and robust model-comparison methodology that largely enhances the existing OLS-based statistical measures

Alterferroicity with seesaw-type magnetoelectricity

Primary ferroicities like ferroelectricity and ferromagnetism are essential physical properties of matter. Multiferroics, with coexisting multiple ferroic orders in a single phase, provide a convenient route to magnetoelectricity. Even so, the general trade-off between magnetism and polarity remains inevitable, which prevents practicable magnetoelectric cross control in the multiferroic framework. Here an alternative strategy, i.e. the so-called alterferroicity, is proposed to circumvent the magnetoelectric exclusiveness, which exhibits multiple but non-coexisting ferroic orders. The natural exclusion between magnetism and polarity, as an insurmountable weakness of multiferroicity, becomes a distinct advantage in alterferroicity, making it an inborn rich ore for intrinsic strong magnetoelectricity. The general design rules for alterferroic materials rely on the competition between the instabilities of phononic and electronic structures in covalent systems. Based on primary density functional theory calculations, Ti-based trichalcogenides are predicted to be alterferroic candidates, which exhibit unique seesaw type magnetoelectricity. This alterferroicity, as an emerging branch of ferroic family, re-shapes the framework of magnetoelectricity, going beyond the established scenario based on multiferroicity.Comment: 7 pages, 4 figure

Quantum-trajectory analysis for charge transfer in solid materials induced by strong laser fields

We investigate the dependence of charge transfer on the intensity of driving laser field when SiO2 crystal is irradiated by an 800 nm laser. It is surprising that the direction of charge transfer undergoes a sudden reversal when the driving laser intensity exceeds critical values with different carrier envelope phases. By applying quantum-trajectory analysis, we find that the Bloch oscillation plays an important role in charge transfer in solid. Also, we study the interaction of strong laser with gallium nitride (GaN) that is widely used in optoelectronics. A pump-probe scheme is applied to control the quantum trajectories of the electrons in the conduction band. The signal of charge transfer is controlled successfully by means of theoretically proposed approach

First-principles demonstration of Roman surface topological multiferroicity

The concept of topology has been widely applied to condensed matter, going beyond the band crossover in reciprocal spaces. A recent breakthrough suggested unconventional topological physics in a quadruple perovskite TbMn$_3$Cr$_4$O$_{12}$, whose magnetism-induced polarization manifests a unique Roman surface topology [Nat. Commun. \textbf{13}, 2373 (2022)]. However, the available experimental evidence based on tiny polarizations of polycrystalline samples is far from sufficient. Here, this topological multiferroicity is demonstrated by using density functional theory calculations, which ideally confirms the Roman surface trajectory of magnetism-induced polarization. In addition, an alternative material in this category is proposed to systematically enhance the performance, by promoting its magnetism-induced polarization to an easily detectable level.Comment: 6 pages, 4 figure

SEMI: Self-supervised Exploration via Multisensory Incongruity

Efficient exploration is a long-standing problem in reinforcement learning. In this work, we introduce a self-supervised exploration policy by incentivizing the agent to maximize multisensory incongruity, which can be measured in two aspects: perception incongruity and action incongruity. The former represents the uncertainty in multisensory fusion model, while the latter represents the uncertainty in an agent's policy. Specifically, an alignment predictor is trained to detect whether multiple sensory inputs are aligned, the error of which is used to measure perception incongruity. The policy takes the multisensory observations with sensory-wise dropout as input and outputs actions for exploration. The variance of actions is further used to measure action incongruity. Our formulation allows the agent to learn skills by exploring in a self-supervised manner without any external rewards. Besides, our method enables the agent to learn a compact multimodal representation from hard examples, which further improves the sample efficiency of our policy learning. We demonstrate the efficacy of this formulation across a variety of benchmark environments including object manipulation and audio-visual games

Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth Estimation

With the frequent use of self-supervised monocular depth estimation in robotics and autonomous driving, the model's efficiency is becoming increasingly important. Most current approaches apply much larger and more complex networks to improve the precision of depth estimation. Some researchers incorporated Transformer into self-supervised monocular depth estimation to achieve better performance. However, this method leads to high parameters and high computation. We present a fully convolutional depth estimation network using contextual feature fusion. Compared to UNet++ and HRNet, we use high-resolution and low-resolution features to reserve information on small targets and fast-moving objects instead of long-range fusion. We further promote depth estimation results employing lightweight channel attention based on convolution in the decoder stage. Our method reduces the parameters without sacrificing accuracy. Experiments on the KITTI benchmark show that our method can get better results than many large models, such as Monodepth2, with only 30 parameters. The source code is available at https://github.com/boyagesmile/DNA-Depth