WHAT DETERMINES THE PROFITABILITY OF BANKS? EVIDENCE FROM THE US

This paper examines the factors affecting bank profitability. We use a sample of US banks over the period 2002-2014, and measure profitability using both return on assets (ROA) and return on equity (ROE). We find that banks have higher profitability when they have: (1) a lower loans to total assets ratio, (2) a lower customer deposits to total liabilities ratio, (3) a lower nonperforming loans to gross loans ratio, (4) higher efficiency, and (5) higher revenue diversification.   We also find that better-capitalized banks have higher profitability, but only when we measure profitability using ROA. Finally, we find that the relationship between several variables and bank profitability differs across banks of different size and over different sample periods

REVIS: An Error Visualization Tool for Rust

Rust is a programming language that uses a concept of ownership to guarantee memory safety without the use of a garbage collector. However, some error messages related to ownership can be difficult to understand and fix, particularly those that depend on value lifetimes. To help developers fix lifetime-related errors, we developed REVIS, a VSCode extension that visualizes lifetime-related Rust compiler errors. We describe the design and implementation of the VSCode extension, along with a preliminary evaluation of its efficacy for student learners of Rust. Although the number of participants was too low to enable evaluation of the efficacy of REVIS, we gathered data regarding the prevalence and time to fix the compiler errors that the participants encountered.Comment: Presented at HATRA 202

Edge physics at the deconfined transition between a quantum spin Hall insulator and a superconductor

We study the edge physics of the deconfined quantum phase transition (DQCP) between a spontaneous quantum spin Hall (QSH) insulator and a spin-singlet superconductor (SC). Although the bulk of this transition is in the same universality class as the paradigmatic deconfined Neel to valence-bond-solid transition, the boundary physics has a richer structure due to proximity to a quantum spin Hall state. We use the parton trick to write down an effective field theory for the QSH-SC transition in the presence of a boundary. We calculate various edge properties in an $N\to\infty$ limit. We show that the boundary Luttinger liquid in the QSH state survives at the phase transition, but only as "fractional" degrees of freedom that carry charge but not spin. The physical fermion remains gapless on the edge at the critical point, with a universal jump in the fermion scaling dimension as the system approaches the transition from the QSH side. The critical point could be viewed as a gapless analogue of the quantum spin Hall state but with the full $SU(2)$ spin rotation symmetry, which cannot be realized if the bulk is gapped.Comment: 9 pages + reference

S4Net: Single Stage Salient-Instance Segmentation

We consider an interesting problem-salient instance segmentation in this paper. Other than producing bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also its surrounding context, enabling us to distinguish the instances in the same scope even with obstruction. Our network is end-to-end trainable and runs at a fast speed (40 fps when processing an image with resolution 320x320). We evaluate our approach on a publicly available benchmark and show that it outperforms other alternative solutions. We also provide a thorough analysis of the design choices to help readers better understand the functions of each part of our network. The source code can be found at \url{https://github.com/RuochenFan/S4Net}

Estimating Signal Timing of Actuated Signal Control Using Pattern Recognition under Connected Vehicle Environment

The Signal Phase and Timing (SPaT) message is an important input for research and applications of Connected Vehicles (CVs). However, the actuated signal controllers are not able to directly give the SPaT information since the SPaT is influenced by both signal control logic and real-time traffic demand. This study elaborates an estimation method which is proposed according to the idea that an actuated signal controller would provide similar signal timing for similar traffic states. Thus, the quantitative description of traffic states is important. The traffic flow at each approaching lane has been compared to fluids. The state of fluids can be indicated by state parameters, e.g. speed or height, and its energy, which includes kinetic energy and potential energy. Similar to the fluids, this paper has proposed an energy model for traffic flow, and it has also added the queue length as an additional state parameter. Based on that, the traffic state of intersections can be descripted. Then, a pattern recognition algorithm was developed to identify the most similar historical states and also their corresponding SPaTs, whose average is the estimated SPaT of this second. The result shows that the average error is 3.1 seconds

Dopamine Surface Modification of Trititanate Nanotubes: Proposed In‐Situ Structure Models

Two models for self‐assembled dopamine on the surface of trititanate nanotubes are proposed: individual monomer units linked by π–π stacking of the aromatic regions and mono‐attached units interacting through hydrogen bonds. This was investigated with solid state NMR spectroscopy studies and powder X‐ray diffraction.Double bind: Two models for self‐assembled dopamine on the surface of trititanate nanotubes are proposed: individual trimer units linked by π–π stacking of the aromatic regions and mono‐attached units interacting through hydrogen bonds. This was investigated by solid state NMR spectroscopy studies and powder X‐ray diffraction.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/1/chem201600075.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/2/chem201600075_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/3/chem201600075-sup-0001-misc_information.pd

Temporal transformer-spatial graph convolutional network: an intelligent classification model for anti N-methyl-D-aspartate receptor encephalitis based on electroencephalogram signal

Encephalitis is a disease typically caused by viral infections or autoimmunity. The most common type of autoimmune encephalitis is anti-N-methyl-D-aspartate receptor (NMDAR) antibody-mediated, known as anti-NMDA receptor encephalitis, which is a rare disease. Specific EEG patterns, including “extreme delta brush” (EDB), have been reported in patients with anti-NMDA receptor encephalitis. The aim of this study was to develop an intelligent diagnostic model for encephalitis based on EEG signals. A total of 131 Participants were selected based on reasonable inclusion criteria and divided into three groups: health control (35 participants), viral encephalitis (58 participants), and anti NMDAR receptor encephalitis (55 participants). Due to the low prevalence of anti-NMDAR receptor encephalitis, it took several years to collect participants’ EEG signals while they were in an awake state. EEG signals were collected and analyzed following the international 10–20 system layout. We proposed a model called Temporal Transformer-Spatial Graph Convolutional Network (TT-SGCN), which consists of a Preprocess Module, a Temporal Transformer Module (TTM), and a Spatial Graph Convolutional Module (SGCM). The raw EEG signal was preprocessed according to traditional procedures, including filtering, averaging, and Independent Component Analysis (ICA) method. The EEG signal was then segmented and transformed using short-time Fourier transform (STFT) to produce concatenated power density (CPD) maps, which served as inputs for the proposed model. TTM extracted the time-frequency features of each channel, and SGCM fused these features using graph convolutional methods based on the location of electrodes. The model was evaluated in two experiments: classification of the three groups and pairwise classification among the three groups. The model was trained using two stages and achieved the performance, with an accuracy of 82.23%, recall of 80.75%, precision of 82.51%, and F1 score of 81.23% in the classification of the three groups. The proposed model has the potential to become an intelligent auxiliary diagnostic tool for encephalitis