Strain and grain connectivity in Bi2223/Ag superconducting tapes

The critical current reduction in silver-sheathed (Bi,Pb)2 Sr2Ca2Cu3O10 superconducting tapes (Bi2223/Ag) is investigated when loaded with uni-axial strains in combination with a magnetic field perpendicular to the tape surface. The number and quality of the grain-to-grain connections and the alignment of the superconducting cores mainly determine the critical current in Bi2223/Ag tapes. It is assumed that the transport current flows simultaneously through two current carrying paths in the tape: one through the network of Josephson junctions and the other is through the well-connected grains. The model describes well the magnetic field dependence of the critical current at various strains. A detailed analysis has shown that strain deteriorates grain connectivity, induces cracking and hence changes the current carrying path. Furthermore, strain may introduce new defects inside the grains along the strong-link current path and increase intra-granular pinning strengt

Concept-wise Fine-tuning Matters in Preventing Negative Transfer

A multitude of prevalent pre-trained models mark a major milestone in the development of artificial intelligence, while fine-tuning has been a common practice that enables pretrained models to figure prominently in a wide array of target datasets. Our empirical results reveal that off-the-shelf finetuning techniques are far from adequate to mitigate negative transfer caused by two types of underperforming features in a pre-trained model, including rare features and spuriously correlated features. Rooted in structural causal models of predictions after fine-tuning, we propose a Concept-wise fine-tuning (Concept-Tuning) approach which refines feature representations in the level of patches with each patch encoding a concept. Concept-Tuning minimizes the negative impacts of rare features and spuriously correlated features by (1) maximizing the mutual information between examples in the same category with regard to a slice of rare features (a patch) and (2) applying front-door adjustment via attention neural networks in channels and feature slices (patches). The proposed Concept-Tuning consistently and significantly (by up to 4.76%) improves prior state-of-the-art fine-tuning methods on eleven datasets, diverse pre-training strategies (supervised and self-supervised ones), various network architectures, and sample sizes in a target dataset

Essays in Applied Economics and Machine Learning

This dissertation consists of three chapters in applied behavioral economics and machine learning applications in economics. The first chapter studies how reference-dependent utilities influence people's behaviors on crowd-sourced review websites and cause attribution bias. Using data from Yelp, I tested how potential disappointments may affect customers' reviews by applying a regression discontinuity design to control for unobserved factors that may also simultaneously influence ratings. This chapter links to an emerging literature of attribution bias in economics and provides empirical evidence and implications of attribution bias on online reputation systems. The second chapter extends the work of first study and explores attribution bias when both reference dependence and state dependence are possible to appear. I specifically use the scenario of special occasions to test two leading theories of attribution bias empirically. The empirical results can be explained by one theory of attribution bias where people have higher expectations about restaurants on special occasions and then misattribute their disappointments to the qualities of the restaurants. From the connection between our empirical analyses and theories of attribution bias, this chapter provides another piece of evidence of how attribution bias influences people's perceptions and behaviors. The third chapter connects machine learning with financial forecasting. I construct a model with recurrent neural networks and focus on the point forecasting of the yield curve to explore the possibility of having better forecasts for the term structure. While allowing similar interpretation as previous econometric methods, the neural network model in this paper shows better forecasting accuracy

Frustratingly Easy Transferability Estimation

Transferability estimation has been an essential tool in selecting a pre-trained model and the layers of it to transfer, so as to maximize the performance on a target task and prevent negative transfer. Existing estimation algorithms either require intensive training on target tasks or have difficulties in evaluating the transferability between layers. We propose a simple, efficient, and effective transferability measure named TransRate. With single pass through the target data, TransRate measures the transferability as the mutual information between the features of target examples extracted by a pre-trained model and labels of them. We overcome the challenge of efficient mutual information estimation by resorting to coding rate that serves as an effective alternative to entropy. TransRate is theoretically analyzed to be closely related to the performance after transfer learning. Despite its extraordinary simplicity in 10 lines of codes, TransRate performs remarkably well in extensive evaluations on 22 pre-trained models and 16 downstream tasks

A Novel Cultural Quantum-behaved Particle Swarm Optimization Algorithm

kai.zenger @ aalto.fi A novel cultural quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to improve the performance of the quantum-behaved PSO (QPSO). The cultural framework is embedded in the QPSO, and the knowledge stored in the belief space can guide the evolution of the QPSO. 15 high-dimensional and multi-modal functions are employed to investigate the proposed algorithm. Numerical simulation results demonstrate that the CQPSO can indeed outperform the QPSO

Did the nHZ Gravitational Waves Signatures Observed By NANOGrav Indicate Multiple Sector SUSY Breaking?

Discrete R symmetries always play an important role in low energy SUSY. The spontaneously broken of such discrete R symmetries, for example, by gaugino condensation, can lead to domain walls, which need to be either inflated away or collapse to avoid cosmic difficulties. We propose that explicitly R symmetry violation needed for collapse of domain walls can be the consequence of multiple sector SUSY breaking. The consistency constraints for the generation of non-problematic domain walls from gaugino condensation are discussed. We also study the emitted gravitational waves related to the collapse of domain walls. We find that, for SUSY breaking scale of order ${\cal O}(1)$ ${\rm GeV}$ in one of the sequestered sector (and also a low reheating temperature of order ${\rm MeV}$ if the reheating is not completed when the domain walls collapse), the peak frequency of gravitational waves emitted can lie at nHz. Such a low SUSY breaking scale can be consistency and natural in multiple sector SUSY breaking scenario. The GWs signal by NANOGrav could be a signal of such multiple sector SUSY breaking scenario and it may also indicate the existences of light goldstini at ${\rm eV}$ mass scale.Comment: 13 page