Thermodynamics of an Exactly Solvable Model for Superconductivity in a Doped Mott Insulator

Computing superconducting properties starting from an exactly solvable model for a doped Mott insulator stands as a grand challenge. We have recently shown that this can be done starting from the Hatsugai-Kohmoto (HK) model which can be understood generally as the minimal model that breaks the non-local $\mathbb Z_2$ symmetry of a Fermi liquid, thereby constituting a new quartic fixed point for Mott physics [Phillips et al., Nature Physics 16, 1175 (2020); Huang et al., Nature Physics (2022)]. In the current work, we compute the thermodynamics, condensation energy, and electronic properties such as the NMR relaxation rate $1/T_1$ and ultrasonic attenuation rate. Key differences arise with the standard BCS analysis from a Fermi liquid: 1) the free energy exhibits a local minimum at $T_p$ where the pairing gap turns on discontinuously above a critical value of the repulsive HK interaction, thereby indicating a first-order transition, 2) a tri-critical point emerges, thereby demarcating the boundary between the standard second-order superconducting transition and the novel first-order regime, 3) Mottness changes the sign of the quartic coefficient in the Landau-Ginzburg free-energy fuctional relative to that in BCS, 4) as this obtains in the strongly interacting regime, it is Mott physics that underlies the generic first-order transition, 5) the condensation energy exceeds that in BCS theory suggesting that multiple Mott bands might be a way of enhancing superconducting, 6) the heat-capacity jump is non-universal and increases with the Mott scale, 7) Mottness destroys the Hebel-Slichter peak in NMR, and 8) Mottness enhances the fall-off of the ultrasonic attenuation at the pairing temperature $T_p$. As several of these properties are observed in the cuprates, our analysis here points a way forward in computing superconducting properties of strongly correlated electron matter.Comment: accepted in PR

MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation

Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures i.e., Vestibular Schwannoma (VS) and Cochlea on high-resolution T2 images. First, a segmentation-enhanced contrastive unpaired image translation module is designed for image-level domain adaptation from source T1 to target T2. Next, multi-scale deep supervision and consistency regularization are introduced to a mean teacher network for self-ensemble learning to further close the domain gap. Furthermore, self-training and intensity augmentation techniques are utilized to mitigate label scarcity and boost cross-modality segmentation performance. Our method demonstrates promising segmentation performance with a mean Dice score of 83.8% and 81.4% and an average asymmetric surface distance (ASSD) of 0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.Comment: Accepted by BrainLes MICCAI proceedings (5th solution for MICCAI 2022 Cross-Modality Domain Adaptation (crossMoDA) Challenge

Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation. We apply the proposed DLSC-based method to Human Connectome Project (HCP) motor tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar circuits in somatomotor networks show that the DLSC-based denoising framework can significantly improve the prominent connectivity patterns, in comparison to the temporal non-local means (tNLM)-based denoising method as well as the case without denoising, which is consistent and neuroscientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.Comment: 8 pages, 3 figures, MLMI201

Antireflection silicon structures with hydrophobic property fabricated by three-beam laser interference

This paper demonstrates antireflective structures on silicon wafer surfaces with hydrophobic property fabricated by three-beam laser interference. In this work, a three-beam laser interference system was set up to generate periodic micro-nano hole structures with hexagonal distributions. Compared with the existing technologies, the array of hexagonally-distributed hole structures fabricated by three-beam laser interference reveals a design guideline to achieve considerably low solar-weighted reflectance (SWR) in the wavelength range of 300-780 nm. The resulting periodic hexagonally-distributed hole structures have shown extremely low SWR (1.86%) and relatively large contact angle (140°) providing with a self-cleaning capability on the solar cell surface

High Voltage Energy Harvesters

Green energy helps in reducing carbon emission from fossil fuel, harvesting energy from natural resources like wind to power consumer appliances. To date, many researches have been focusing on designing circuits that harvest energy from electromagnetic signals wirelessly. While it could be designed to be efficient, the generated power however is insufficient to drive large loads. Wind energy is highly available environmentally but development of small-scale energy harvesting apparatus aiming to extract significant power from miniature brushless fan has received limited attention. The aim of this chapter is to give audience an insight of different voltage multipliers used in energy harvester and knowledge on various circuit techniques to configure voltage multipliers for use in different high voltage applications. These include AC-DC converter, AC-AC converter and variable AC-DC converter

A Room-Temperature High-Conductivity Metal Printing Paradigm with Visible-Light Projection Lithography

Fabricating electronic devices require integrating metallic conductors and polymeric insulators in complex structures. Current metal-patterning methods such as evaporation and laser sintering require vacuum, multistep processes, and high temperature during sintering or postannealing to achieve desirable electrical conductivity, which damages low-temperature polymer substrates. Here reports a facile ecofriendly room-temperature metal printing paradigm using visible-light projection lithography. With a particle-free reactive silver ink, photoinduced redox reaction occurs to form metallic silver within designed illuminated regions through a digital mask on substrate with insignificant temperature change (<4 °C). The patterns exhibit remarkably high conductivity achievable at room temperature (2.4 × 107 S m−1, ≈40% of bulk silver conductivity) after simple room-temperature chemical annealing for 1–2 s. The finest silver trace produced reaches 15 µm. Neither extra thermal energy input nor physical mask is required for the entire fabrication process. Metal patterns were printed on various substrates, including polyethylene terephthalate, polydimethylsiloxane, polyimide, Scotch tape, print paper, Si wafer, glass coverslip, and polystyrene. By changing inks, this paradigm can be extended to print various metals and metal–polymer hybrid structures. This method greatly simplifies the metal-patterning process and expands printability and substrate materials, showing huge potential in fabricating microelectronics with one system

On Hermite–Hadamard-Type Inequalities for Coordinated h-Convex Interval-Valued Functions

This paper is devoted to establishing some Hermite–Hadamard-type inequalities for interval-valued functions using the coordinated h-convexity, which is more general than classical convex functions. We also discuss the relationship between coordinated h-convexity and h-convexity. Furthermore, we introduce the concepts of minimum expansion and maximum contraction of interval sequences. Based on these two new concepts, we establish some new Hermite–Hadamard-type inequalities, which generalize some known results in the literature. Additionally, some examples are given to illustrate our results

Fetal Intelligent Navigation Echocardiography (FINE) Detects 98% of Congenital Heart Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146329/1/jum14616.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146329/2/jum14616_am.pd

Impact of Human-AI Interaction on User Trust and Reliance in AI-Assisted Qualitative Coding

While AI shows promise for enhancing the efficiency of qualitative analysis, the unique human-AI interaction resulting from varied coding strategies makes it challenging to develop a trustworthy AI-assisted qualitative coding system (AIQCs) that supports coding tasks effectively. We bridge this gap by exploring the impact of varying coding strategies on user trust and reliance on AI. We conducted a mixed-methods split-plot 3x3 study, involving 30 participants, and a follow-up study with 6 participants, exploring varying text selection and code length in the use of our AIQCs system for qualitative analysis. Our results indicate that qualitative open coding should be conceptualized as a series of distinct subtasks, each with differing levels of complexity, and therefore, should be given tailored design considerations. We further observed a discrepancy between perceived and behavioral measures, and emphasized the potential challenges of under- and over-reliance on AIQCs systems. Additional design implications were also proposed for consideration.Comment: 27 pages with references, 9 figures, 5 table

Quantum correlations in spin models

Bell nonlocality, entanglement and nonclassical correlations are different aspects of quantum correlations for a given state. There are many methods to measure nonclassical correlations. In this paper, nonclassical correlations in two-qubit spin models are measured by use of measurement-induced disturbance (MID) [Phys. Rev. A, 77, 022301 (2008)] and geometric measure of quantum discord (GQD) [Phys. Rev. Lett. 105, 190502 (2010)]. Their dependencies on external magnetic field, spin-spin coupling, and Dzyaloshinski-Moriya (DM) interaction are presented in detail. We also compare Bell nonlocality, entanglement measured by concurrence, MID and GQD and illustrate their different characteristics.Comment: 1 text and 5 eps figures, accepted by Annals of Physic