Reorganizing the Music Library

A few years ago, the choral music library was destroyed. After this, there have been a series of IQPs set up in order to restore and improve upon the sorting system. Our IQP is a conclusion to the restoration and improvements of the system. We set out to sort the remaining pieces into the system and give an analysis of the system that was implemented along with recommendations for improving the system. After gaining an understanding of the library and sorting the remaining pieces, we turned to the analysis of the system. We compared WPI’s system to other systems to determine its efficiency and how to make possible future improvements

Extracting unconventional spin texture in two dimensional topological crystalline insulators via tuning bulk-edge interactions

Tuning the interaction between the bulk and edge states of topological materials is a powerful tool for manipulating edge transport behavior, opening up exciting opportunities for novel electronic and spintronic applications. This approach is particularly suited to topological crystalline insulators (TCI), a class of topologically nontrivial compounds that are endowed with multiple degrees of topological protection. In this study, we investigate how bulk-edge interactions can influence the edge transport in planar bismuthene, a TCI with metallic edge states protected by in-plane mirror symmetry, using first principles calculations and symmetrized Wannier tight-binding models. By exploring the impact of various perturbation effects, such as device size, substrate potentials, and applied transverse electric field, we examine the evolution of the electronic structure and edge transport in planar bismuthene. Our findings demonstrate that the TCI states of planar bismuthene can be engineered to exhibit either a gapped or conducting unconventional helical spin texture via a combination of substrate and electric field effects. Furthermore, under strong electric fields, the edge states can be stabilized through a delicate control of the bulk-edge interactions. These results open up new directions for discovering novel spin transport patterns in topological materials and provide critical insights for the fabrication of topological spintronic devices.Comment: 23 pages, 8 figure

Neuromorphic Imaging with Joint Image Deblurring and Event Denoising

Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic scene with high temporal precision and responds with asynchronous streaming events as a result. It also often supports a simultaneous output of an intensity image. Nevertheless, the raw events typically involve a great amount of noise due to the high sensitivity of the sensor, while capturing fast-moving objects at low frame rates results in blurry images. These deficiencies significantly degrade human observation and machine processing. Fortunately, the two information sources are inherently complementary -- events with microsecond temporal resolution, which are triggered by the edges of objects that are recorded in latent sharp images, can supply rich motion details missing from the blurry images. In this work, we bring the two types of data together and propose a simple yet effective unifying algorithm to jointly reconstruct blur-free images and noise-robust events, where an event-regularized prior offers auxiliary motion features for blind deblurring, and image gradients serve as a reference to regulate neuromorphic noise removal. Extensive evaluations on real and synthetic samples present our superiority over other competing methods in restoration quality and greater robustness to some challenging realistic scenarios. Our solution gives impetus to the improvement of both sensing data and paves the way for highly accurate neuromorphic reasoning and analysis.Comment: Submitted to TI

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.Comment: 82 pages, 32 figure

ChallengeDetect : Investigating the Potential of Detecting In-Game Challenge Experience from Physiological Measures

Challenge is the core element of digital games. The wide spectrum of physical, cognitive, and emotional challenge experiences provided by modern digital games can be evaluated subjectively using a questionnaire, the CORGIS, which allows for a post hoc evaluation of the overall experience that occurred during game play. Measuring this experience dynamically and objectively, however, would allow for a more holistic view of the moment-to-moment experiences of players. This study, therefore, explored the potential of detecting perceived challenge from physiological signals. For this, we collected physiological responses from 32 players who engaged in three typical game scenarios. Using perceived challenge ratings from players and extracted physiological features, we applied multiple machine learning methods and metrics to detect challenge experiences. Results show that most methods achieved a detection accuracy of around 80%. We discuss in-game challenge perception, challenge-related physiological indicators and AI-supported challenge detection to inform future work on challenge evaluation

Electronic bandstructure of in-plane ferroelectric van der Waals β′−In2Se3\beta '-In_{2}Se_{3}

Layered indium selenides ($In_{2}Se_{3}$) have recently been discovered to host robust out-of-plane and in-plane ferroelectricity in the $\alpha$ and $\beta$' phases, respectively. In this work, we utilise angle-resolved photoelectron spectroscopy to directly measure the electronic bandstructure of $\beta '-In_{2}Se_{3}$, and compare to hybrid density functional theory (DFT) calculations. In agreement with DFT, we find the band structure is highly two-dimensional, with negligible dispersion along the c-axis. Due to n-type doping we are able to observe the conduction band minima, and directly measure the minimum indirect (0.97 eV) and direct (1.46 eV) bandgaps. We find the Fermi surface in the conduction band is characterized by anisotropic electron pockets with sharp in-plane dispersion about the $\overline{M}$ points, yielding effective masses of 0.21 $m_{0}$ along $\overline{KM}$ and 0.33 $m_{0}$ along $\overline{\Gamma M}$. The measured band structure is well supported by hybrid density functional theory calculations. The highly two-dimensional (2D) bandstructure with moderate bandgap and small effective mass suggest that $\beta'-In_{2}Se_{3}$ is a potentially useful new van der Waals semiconductor. This together with its ferroelectricity makes it a viable material for high-mobility ferroelectric-photovoltaic devices, with applications in non-volatile memory switching and renewable energy technologies.Comment: 19 pages, 4 + 1 figures; typos corrected;added references; updated figures & discussion to reflect changes in mode

Essays on Digital Content Provision and Consumption

Consumption of digital content has become an inseparable part of consumers' lives today. As providers of digital content, media platforms continuously seek to pursue pricing and product design strategies that increase their profits. This dissertation studies media platforms' digital content provision and consumers' consumption decisions. In the first essay, we focus on the pricing of digital content and analyze the impact of consumers' endogenous content consumption on platforms' paywall strategies. Paywalls increase subscription revenues for platforms, but they also impact content consumption and thus advertising revenues. We build an analytical model that endogenizes consumers' content consumption decisions. We find that under moderate ad rates, a metered paywall under which a limited amount of content is provided for free is optimal when consumers display sufficient heterogeneity in their costs of consuming content. We also study how the amount of free content and the subscription price vary with changes in the advertising rate and consumer preference. In the second essay, we analyze the accuracy of news reported by the news media. When consumers are seeking the truth and accurate reporting is costly, determining the optimal level of accuracy in reporting is a strategic decision for a profit-maximizing media firm. We build an analytical model to study this media firm decision. When consumers and the media firm are both initially uncertain about the true state of the world, we show that the media firm always chooses full accuracy if investigation and reporting are of low cost. However, if achieving accuracy is sufficiently costly, the media firm provides news only when consumers' priors regarding the truth are not too extreme, so that they see enough value in news consumption. Interestingly, consumers' truth-seeking and the firm's profit maximization can lead to reporting inaccuracy and exaggeration of the more likely state a priori. We also discuss the implications of polarization in consumers’ prior beliefs and the media firm’s different objectives on the accuracy of news

Explainability analysis of neural network-based turbulence modeling for transonic axial compressor rotor flows

Model-consistent training has become trending for data-driven turbulence modeling since it can improve model generalizability and reduce data requirements by involving the Reynolds-averaged Navier-Stokes (RANS) equation during model learning. Neural networks are often used for the Reynolds stress representation due to their great expressive power, while they lack interpretability for the causal relationship between model inputs and outputs. Some post-hoc methods have been used to explain the neural network by indicating input feature importance. However, for the model-consistent training, the model explainability involves the analysis of both the neural network inputs and outputs. That is, the effects of model output on the RANS predictions should also be explained in addition to the input feature analysis. In this work, we investigate the explainability of the model-consistent learned model for the internal flow prediction of NASA Rotor 37 at its peak efficiency operating condition. The neural-network-based corrections for the Spalart-Allmaras turbulence model are learned from various experimental data based on the ensemble Kalman method. The learned model can noticeably improve the velocity prediction near the shroud. The explainability of the trained neural network is analyzed in terms of the model correction and the input feature importance. Specifically, the learned model correction increases the local turbulence production in the vortex breakdown region due to non-equilibrium effects, which capture the blockage effects near the shroud. Besides, the ratio of production to destruction and the helicity are shown to have relatively high importance for accurately predicting the compressor rotor flows based on the Shapley additive explanations method.& COPY; 2023 Elsevier Masson SAS. All rights reserved

Effects of biogas slurry fertilization on fruit economic traits and soil nutrients of Camellia oleifera Abel.

Camellia oleifera Abel (C. oleifera) absorb nutrients from surrounding soils and its yield is highly influenced by these nutrients and by fertilizer application. Thus, the soil nutrients play a central role in C. oleifera production. This study investigated the effects of biogas slurry applications on soil nutrients and economic traits of C. oleifera fruits. Five different amounts of biogas slurry (0, 10, 20, 30, or 40 kg/plant/year, three applications per year) were used as fertilizer for C. oleifera plants in 2015 and 2016. The nutrients of rhizosphere soil and the economic traits, including fruit yield, seed rate, and oil yield of C. oleifera fruit, were measured each year. The results showed that fertilization with biogas slurry significantly increased soil organic matter, available nitrogen (N), phosphorus (P), and potassium (K) both in 2015 and 2016. Increases in soil available N, P, and K were maximal in the highest slurry application group followed by the second highest application group. The oil yield correlated with the content of soil available P in both 2015 and 2016, and with soil organic matter in 2015. Fertilization with biogas slurry decreased the saturated fatty acid content in fruit but had no effect on the unsaturated fatty acid content. In conclusion, fertilization with biogas slurry increased rhizosphere soil nutrients and fruit economic traits of C. oleifera and rates of at least30 kg/plant/year had the most positive effects. This study expands the knowledge of fertilization with biogas slurry in C. oleifera production

Continental crust growth induced by slab breakoff in collisional orogens: Evidence from the Eocene Gangdese granitoids and their mafic enclaves, South Tibet

Collisional orogens are traditionally regarded as main regions related to crustal reworking with limited or without crust growth. However, our study on the granitoids and their mafic enclaves of the Ringqenze plutonic complex, a representative syn-collisional intrusive complex of Gangdese batholith, shows huge mantle contributions and considerable continental crust growth. Zircon U-Pb dating yields Eocene ages of 50.2 +/- 0.5 Ma, 46.4 +/- 0.5 Ma, 50.6 +/- 0.6 Ma and 50.4 +/- 0.5 Ma for the quartz diorite, hornblende granodiorite, host pyroxene-bearing granodiorite and its dioritic enclave, respectively. Reverse zoning of plagioclase (spike zone), pyroxene and plagioclase relicts in both of the dioritic enclaves and the granitoids, as well as the mixing trends on plots of major and trace elements, indicate a petrogenesis of magmatic mixing. The relatively low SiO2 (53.43-56.23 wt%) contents, high Mg-# values (48-56) and mineral compositions further suggest that precursor magmas of the enclaves are mantle-derived. The dioritic enclaves and granitoids have indistinguishable (Sr-87/Sr-86); ratios (0.70460-0.70480) but slightly different epsilon(Nd)(t) values (+1.8 to +3.0 for the dioritic enclaves and +0.2 to +0.6 for the granitoids). The (Sr-87/Sr-86); ratios and epsilon(Nd)(t) values plot on the mixing line defined by depleted mantle (DM) and lower crust xenolith from the Southern Lhasa subterrane and further simulation reveals a contribution of >55% from mantle. In the zircon epsilon(Hf)(t) versus delta O-18 diagram, the dioritic enclaves (epsilon(Hf)(t) = +3.7-+8.5, delta O-18 = 4.56-7.00 parts per thousand) and the granitoids (epsilon(Hf)(t) = +1.2-+8.5, delta O-18 = 5.59-7.21 parts per thousand) plot around the mixing line defined by zircons from granulite xenolith and a relatively depleted zircon from the dioritic enclaves. Meanwhile, the simple binary mixing calculation based on Hf-0 isotopes suggests that the mantle contributions are not 1000 degrees C), whereas other samples show lower Ti-in-zircon temperatures mostly below 750 degrees C. Because the Gangdese granitoids with unusual high temperatures had been produced dominantly at a very short time (similar to 50 Ma) and the high temperature was related to underplating of mantle materials, we suggest that a slab breakoff was most likely responsible for the formation of the high-temperature magmas and the continental crust growth at the syn-collisional period in collisional orogens. (C) 2018 Published by Elsevier B.V. on behalf of International Association for Gondwana Research