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Human health can be critically affected by cardiovascular diseases, such as hypertension, arrhythmias, and stroke. Heart rate and blood pressure are important biometric information for the monitoring of cardiovascular system and early diagnosis of cardiovascular diseases. Existing methods for estimating the heart rate are based on electrocardiography and photoplethyomography, which require contacting the sensor to the skin surface. Moreover, catheter and cuff-based methods for measuring blood pressure cause inconvenience and have limited applicability. Therefore, in this thesis, we propose a vision-based method for estimating the heart rate and blood pressure. This thesis proposes a 2-stage deep learning framework consisting of a dual remote photoplethysmography network (DRP-Net) and bounded blood pressure network (BBP-Net). In the first stage, DRP-Net infers remote photoplethysmography (rPPG) signals for the acral and facial regions, and these phase-shifted rPPG signals are utilized to estimate the heart rate. In the second stage, BBP-Net integrates temporal features and analyzes phase discrepancy between the acral and facial rPPG signals to estimate SBP and DBP values. To improve the accuracy of estimating the heart rate, we employed a data augmentation method based on a frame interpolation model. Moreover, we designed BBP-Net to infer blood pressure within a predefined range by incorporating a scaled sigmoid function. Our method resulted in estimating the heart rate with the mean absolute error (MAE) of 1.78 BPM, reducing the MAE by 34.31 % compared to the recent method, on the MMSE-HR dataset. The MAE for estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 10.19 mmHg and 7.09 mmHg. On the V4V dataset, the MAE for the heart rate, SBP, and DBP were 3.83 BPM, 13.64 mmHg, and 9.4 mmHg, respectively., Comment: 33 pages, 7 figures

Magnetic semiconductors may soon improve the energy efficiency of computers, but materials exhibiting these dual properties remain underexplored. Here, we report the computational prediction and realization of a new magnetic and semiconducting material, MnSnN$_2$, via combinatorial sputtering of thin films. Grazing incidence wide angle X-ray scattering and laboratory X-ray diffraction studies show a wide composition tolerance for this wurtzite-like MnSnN$_2$, ranging from $20\% <$ Mn/(Mn+Sn) $< 65$\% with cation disorder across this composition space. Magnetic susceptibility measurements reveal a low-temperature transition ($T^{\mathrm{*}} \approx 10$ K) for MnSnN$_2$ and strong antiferromagnetic correlations, although the ordering below this transition may be complex. This finding contrasts with bulk MnSiN$_2$ and MnGeN$_2$, which exhibited antiferromagnetic ordering above 400 K in previous studies. Spectroscopic ellipsometry identifies an optical absorption onset of 1 eV for the experimentally-synthesized phase exhibiting cation disorder, consistent with the computationally-predicted 1.2 eV bandgap for the cation-ordered structure. Electronic conductivity measurements confirm the semiconducting nature of this new phase by showing increasing conductivity with increasing temperature. This work adds to the set of known semiconductors that are paramagnetic at room temperature and will help guide future work targeted at controlling the structure and properties of semiconducting materials that exhibit magnetic behavior.

Low-dimensional materials with chain-like (one-dimensional) or layered (twodimensional) structures are of significant interest due to their anisotropic electrical, optical, thermal properties. One material with chain-like structure, BaTiS3 (BTS), was recently shown to possess giant in-plane optical anisotropy and glass-like thermal conductivity. To understand the origin of these effects, it is necessary to fully characterize the optical, thermal, and electronic anisotropy of BTS. To this end, BTS crystals with different orientations (aand c-axis orientations) were grown by chemical vapor transport. X-ray absorption spectroscopy (XAS) was used to characterize the local structure and electronic anisotropy of BTS. Fourier transform infrared (FTIR) reflection/transmission spectra show a large inplane optical anisotropy in the a-oriented crystals, while the c-axis oriented crystals were nearly isotropic in-plane. BTS platelet crystals are promising uniaxial materials for IR optics with their optic axis parallel to the c-axis. The thermal conductivity measurements revealed a thermal anisotropy of ~4.5 between the c- and a-axis. Time-domain Brillouin scattering showed that the longitudinal sound speed along the two axes is nearly the same suggesting that the thermal anisotropy is a result of different phonon scattering rates.

A defining signature of strongly correlated electronic systems is the existence of competing phases with similar ground state energies, resulting in a rich phase diagram. While in the recently discovered nickelate superconductors, a high antiferromagnetic exchange energy has been reported, which implies the existence of strong electronic correlations, signatures of competing phases have not yet been observed. Here, we uncover a charge order (CO) in infinite-layer nickelates La1-xSrxNiO2 using resonant x-ray scattering across the Ni L-edge. In the parent compound, the CO arranges along the Ni-O bond direction with an incommensurate wave vector (0.344+/-0.002, 0) r.l.u., distinct from the stripe order in other nickelates which propagates along a direction 45 degree to the Ni-O bond. The CO resonance profile indicates that CO originates from the Ni 3d states and induces a parasitic charge modulation of La electrons. Upon doping, the CO diminishes and the ordering wave vector shifts toward a commensurate value of 1/3 r.l.u., indicating that the CO likely arises from strong correlation effects and not from Fermi surface nesting., Comment: 24 pages, 8 figures

Superconducting detectors have become an important tool in experimental astroparticle physics, which seeks to provide a fundamental understanding of the Universe. In particular, such detectors have demonstrated excellent potential in two challenging research areas involving rare event search experiments, namely, the direct detection of dark matter and the search for neutrinoless double beta decay. Here, we review the superconducting detectors that have been and are planned to be used in these two categories of experiments. We first provide brief histories of the two research areas and outline their significance and challenges in astroparticle physics. Then, we present an extensive overview of various types of superconducting detectors with a focus on sensor technologies and detector physics, which are based on calorimetric measurements and heat flow in the detector components. Finally, we introduce leading experiments and discuss their future prospects for the detection of dark matter and the search for neutrinoless double beta decay employing superconducting detectors.

Understanding the nature of charge density waves (CDW) in cuprate superconductors has been complicated by material specific differences. A striking example is the opposite doping dependence of the CDW ordering wavevector in La-based and Y-based compounds, the two families where charge ordering is strongest and best characterized. Here we report a combined resonant soft X-ray scattering (RSXS) and neutron scattering study of charge and spin density waves in isotopically enriched La$_{1.8-x}$ Eu$_{0.2}$ Sr$_{x}$ CuO$_{4}$ over a range of doping $0.07 \leq x \leq 0.20$. For all dopings studied by RSXS, we find that the CDW amplitude is approximately temperature-independent and develops well above experimentally accessible temperatures. Surprisingly, the CDW ordering wavevector shows a non-monotonic temperature dependence, with a sudden change occurring at temperatures near the SDW onset temperature. We describe this behavior with a Landau-Ginzburg theory for an incommensurate CDW in a metallic system with a finite charge compressibility and CDW-SDW coupling. Our Landau-Ginzburg analysis suggests that the ordering wavevector at high temperatures decreases with increased doping. This behavior is opposite to the trend at low temperatures and highly reminiscent of the doping dependence seen in YBa$_2$ Cu$_3$ O$_{6+\delta}$ , suggesting a common origin of the CDW in hole-doped cuprate superconductors., Comment: Manuscript submitted to PNAS; ArXiv version formatted in Revtex4-2; Supplementary Material included as appendices; 20 pages, 14 figures (6 in the main text), and 41 references

We have investigated photoinduced intramolecular electron transfer dynamics following metal-to-ligand charge-transfer (MLCT) excitation of [Fe(CN)$_4$(2,2'-bipyridine)]$^{2-}$ (1), [Fe(CN)$_4$(2,3-bis(2-pyridyl)pyrazine)]$^{2-}$ (2) and [Fe(CN)$_4$(2,2'-bipyrimidine)]$^{2-}$ (3) complexes in various solvents with static and time-resolved UV-visible absorption spectroscopy and Fe 2p3d resonant inelastic X-ray scattering. We observe $^3$MLCT lifetimes from 180 fs to 67 ps over a wide range of MLCT energies in different solvents by utilizing the strong solvatochromism of the complexes. Intramolecular electron transfer lifetimes governing $^3$MLCT relaxation increase monotonically and (super)exponentially as the $^3$MLCT energy is decreased in 1 and 2 by changing the solvent. This behavior can be described with non-adiabatic classical Marcus electron transfer dynamics along the indirect $^3$MLCT->$^3$MC pathway, where the $^3$MC is the lowest energy metal-centered (MC) excited state. In contrast, the $^3$MLCT lifetime in 3 changes non-monotonically and exhibits a maximum. This qualitatively different behaviour results from direct electron transfer from the $^3$MLCT to the electronic ground state (GS). This pathway involves nuclear tunnelling for the high-frequency polypyridyl skeleton mode ($\hbar\omega$ = 1530 cm$^{-1}$), which is more displaced for 3 than for either 1 or 2, therefore making the direct pathway significantly more efficient in 3. To our knowledge, this is the first observation of an efficient $^3$MLCT->GS relaxation pathway in an Fe polypyridyl complex. Our study suggests that further extending the MLCT state lifetime requires (1) lowering the $^3$MLCT state energy with respect to the $^3$MC state and (2) suppressing the intramolecular distortion of the electron-accepting ligand in the $^3$MLCT excited state to suppress the rate of direct $^3$MLCT->GS electron transfer.

This paper proposes an algorithm for recognizing slab identification numbers in factory scenes. In the development of a deep-learning based system, manual labeling to make ground truth data (GTD) is an important but expensive task. Furthermore, the quality of GTD is closely related to the performance of a supervised learning algorithm. To reduce manual work in the labeling process, we generated weakly annotated GTD by marking only character centroids. Whereas bounding-boxes for characters require at least a drag-and-drop operation or two clicks to annotate a character location, the weakly annotated GTD requires a single click to record a character location. The main contribution of this paper is on selective distillation to improve the quality of the weakly annotated GTD. Because manual GTD are usually generated by many people, it may contain personal bias or human error. To address this problem, the information in manual GTD is integrated and refined by selective distillation. In the process of selective distillation, a fully convolutional network is trained using the weakly annotated GTD, and its prediction maps are selectively used to revise locations and boundaries of semantic regions of characters in the initial GTD. The modified GTD are used in the main training stage, and a post-processing is conducted to retrieve text information. Experiments were thoroughly conducted on actual industry data collected at a steelmaking factory to demonstrate the effectiveness of the proposed method., Comment: 10 pages, 12 figures, submitted to a journal