Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic effects of Mg doping on TiO2 for improved toxic gas sensing performance at room temperature

The gas sensing characteristics of magnesium (Mg)-doped titanium dioxide (TiO2) films were investigated using a spray pyrolysis method. TiO2 Thin films with varying Mg doping concentrations (0, 2.5, and 5 weight percentages) were deposited and tested for their gas detection ability to organic compounds such as ethanol, butanol, toluene, xylene, and formaldehyde at room temperature. Results disclosed that introducing Mg into TiO2 enhanced the gas sensing characteristics, particularly for formaldehyde. Mg-doped TiO2 film improved the change in electrical resistance during gas adsorption, leading to an increased response in formaldehyde detection. Additionally, XRD revealed the crystal structure, while Raman spectroscopy provided insights into molecular vibrational modes of the fabricated films. FESEM allowed for high-resolution imaging of surface morphology, and atomic force microscope assessed surface roughness and other properties of the as deposited samples. UV-Vis spectroscopy was utilized to examine the optical characteristics. The collective results strongly indicated that the introduction of Mg significantly improved the gas-sensing capabilities of TiO2 films, making them highly promising for various gas-sensing applications.
Particle Image Velocimetry and Finite Volume Method Study of Bi-leaflet
 Artificial Heart Valve

The key feature of the bi-leaflet valve is the geometry of the two leaflets, which can be crucial in determining the flow field. In this paper, observations were made on the flow pattern of the blood through the use of bi-leaflet type mechanical prosthetic valve (MHV). Finite volume method (FVM) analysis was conducted using fluidstructure interaction (FSI) method that solved on a dynamic mesh. In terms of the validation, particle image velocimetry (PIV) was used to verify the findings obtained from FVM analysis. The results of velocity and vorticity were the main parameters to be compared. Based on the findings, the results computed for the leaflets motion and the flow field using FVM was found to be in agreement with PIV experimental data. The pressure obtained for the simulation is in the range of 10,666 – 16,000 Pa, which is an ideal and healthy blood pressure level of human. The vorticity was observed to be formed behind the valve with DVI value of 1.275 (simulation) and 1.457 (experiment), lower than the expected range for a normal DVI in mitral valve. The maximum shear stress achieved (22.5481 Pa) is in the range of platelets activation, which could lead to thrombus formation. The maximum Von Mises stress was found to be at the hinge region of the bi-leaflet valve. These results will serve as a basis for valve design to improve the hemodynamic properties of the heart valve.
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation

Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed understanding of 3D geometry, which is often lacking in 2D image features. This work bridges this 2D-to-3D gap for robotic manipulation by leveraging distilled feature fields to combine accurate 3D geometry with rich semantics from 2D foundation models. We present a few-shot learning method for 6-DOF grasping and placing that harnesses these strong spatial and semantic priors to achieve in-the-wild generalization to unseen objects. Using features distilled from a vision-language model, CLIP, we present a way to designate novel objects for manipulation via free-text natural language, and demonstrate its ability to generalize to unseen expressions and novel categories of objects.
Imaging-Assisted Single-Photon Doppler-Free Laser Spectroscopy and the Ionization Energy of Metastable Triplet Helium.

Skimmed supersonic beams provide intense, cold, collision-free samples of atoms and molecules and are one of the most widely used tools in atomic and molecular laser spectroscopy. High-resolution optical spectra are typically recorded in a perpendicular arrangement of laser and supersonic beams to minimize Doppler broadening. Typical Doppler widths are nevertheless limited to tens of MHz by the residual transverse-velocity distribution in the gas-expansion cones. We present an imaging method to overcome this limitation that exploits the correlation between the positions of the atoms and molecules in the supersonic expansion and their transverse velocities, and thus their Doppler shifts. With the example of spectra of (1s)(np) ^{3}P_{0-2}←(1s)(2s) ^{3}S_{1} transitions to high Rydberg states of metastable triplet He, we demonstrate the suppression of the residual Doppler broadening and a reduction of the full linewidths at half maximum to only about 1 MHz in the UV. Using a retroreflection arrangement for the laser beam and a cross-correlation method, we determine Doppler-free spectra without any signal loss from the selection, by imaging, of atoms within ultranarrow transverse-velocity classes. As an illustration, we determine the ionization energy of triplet metastable He and confirm the significant discrepancy between recent experimental [G. Clausen et al., Phys. Rev. Lett. 127, 093001 (2021)PRLTAO0031-900710.1103/PhysRevLett.127. 093001] and high-level theoretical [V. Patkós et al., Phys. Rev. A 103, 042809 (2021)PLRAAN2469-992610.1103/PhysRevA.103. 042809] values of this quantity.
The role of miRNAs carried by extracellular vesicles in type 2 diabetes and its complications

Diabetes poses severe global public health problems and places heavy burdens on the medical and economic systems of society. Type 2 diabetes (T2D) accounts for 90% of these cases. Diabetes also often accompanies serious complications that threaten multiple organs such as the brain, eyes, kidneys, and the cardiovascular system. MicroRNAs (miRNAs) carried by extracellular vesicles (EV‐miRNAs) are considered to mediate cross‐organ and cross‐cellular communication and have a vital role in the pathophysiology of T2D. They also offer promising sources of diabetes‐related biomarkers and serve as effective therapeutic targets. Here, we briefly reviewed studies of EV‐miRNAs in T2D and related complications. Specially, we innovatively explore the targeting nature of miRNA action due to the target specificity of vesicle binding, aiding mechanism understanding as well as the detection and treatment of diseases.
Cervicovaginal and anal self-sampling for HPV testing in a transgender and gender diverse population assigned female at birth: comfort, difficulty, and willingness to use

Background: Transgender and gender diverse (TGD) people assigned female at birth (AFAB) face numerous barriers to preventive care, including for HPV and cervical cancer screening. Self-sampling options may expand access to HPV testing for TGD people AFAB. Methods: We recruited TGD individuals AFAB to collect cervicovaginal and anal specimens at-home using self-sampling for HPV testing, and individuals reported their perceptions of self-sampling. Associations between demographic and health characteristics and each of comfort of use, ease of use, and willingness to use self-sampling were estimated using robust Poisson regression. Results: The majority of the 101 participants who completed the study reported that the cervicovaginal self-swab was not uncomfortable (68.3%) and not difficult to use (86.1%), and nearly all (96.0%) were willing to use the swab in the future. Fewer participants found the anal swab to not be uncomfortable (47.5%), but most participants still found the anal swab to not be difficult to use (70.2%) and were willing to use the swab in the future (89.1%). Participants were more willing to use either swab if they had not seen a medical professional in the past year. About 70% of participants who reported negative experiences with either self-swab were still willing to use that swab in the future. Conclusions: TGD AFAB individuals were willing to use and preferred self-sampling methods for cervicovaginal and anal HPV testing. Developing clinically approved self-sampling options for cancer screening could expand access to HPV screening for TGD AFAB populations.
The usability of neopterin in determining disease course in COVID-19

Objectives: To investigate the usability of neopterin in demonstrating the progression of COVID-19. As a result of uncontrolled activation of COVID-19 monocytes and macrophages, IFN gamma increases and the resulting inflammatory response causes organ damage. IFN released from T cells causes an increase in gamma neopterin levels. Therefore, measurement of neopterin levels can be used to indicate immune system activation and disease progression. Methods: The study was carried out prospectively in two different centers. The patients were divided into two groups (mild-moderate and severe) and clinical, laboratory, imaging findings and neopterin levels at hospitalization were compared. Results: 100 patients were included in our study, 41 of these patients were male. Forty-six patients were identified as severe COVID-19. C-reactive protein, lymphocyte count, fibrinogen, D dimers, lactate dehydrogenase, procalcitonin, troponin and neopterin levels were significant in indicating disease severity. (p<0.05). In ROC analysis, 0.642 for neopterin, 0.698 C-reactive protein, 0.331 lymphocyte count, 0.679 procalcitonin, 0.633 fibrinogen, 0.667 D dimers, 0.655 troponin and 0.706 lactate dehydrogenase were detected and these values were significant. Conclusion: In our study, neopterin was detected as an important indicator in determining the course of COVID-19.
Complete $N_f^2$ contributions to four-loop pure-singlet splitting functions

The scale evolution of parton distributions is determined by universal splitting functions. As a milestone towards the computation of these functions to four-loop order in QCD, we compute all contributions to the pure-singlet quark-quark splitting functions that involve two closed fermion loops. The splitting functions are extracted from the pole terms of off-shell operator matrix elements, and the workflow for their calculation is outlined. We reproduce known results for the non-singlet four-loop splitting functions and validate our new pure-singlet results against fixed Mellin moments.
Microstructure-Empowered Stock Factor Extraction and Utilization

High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level.
PCSK9 genetic variants and risk of vascular and non-vascular diseases in Chinese and UK populations

Background: Lowering low-density lipoprotein cholesterol (LDL-C) through PCSK9 inhibition represents a new therapeutic approach to preventing and treating cardiovascular disease (CVD). Phenome-wide analyses of PCSK9 genetic variants in large prospective biobanks can help to identify unexpected effects of PCSK9 inhibition. Methods: The prospective China Kadoorie Biobank genotyped >100,000 participants with follow-up for fatal and non-fatal disease events. A PCSK9 genetic score (PCSK9-GS) was constructed using three single nucleotide polypmorphisms at the PCSK9 locus associated with directly-measured LDL-C, including a loss-of-function variant (rs151193009). Logistic regression gave estimated odds ratios (ORs) for PCSK9-GS associations with CVD and non-CVD outcomes, scaled to 1-standard deviation (SD) lower LDL-C. Results: PCSK9-GS was associated with lower apolipoprotein B, and with lower risks of carotid plaque (n=8340 cases; OR=0.61 [95%CI: 0.45-0.83]; P=0.0015), major occlusive vascular events (n=15,752; 0.80 [0.67-0.95]; P=0.011), and ischaemic stroke (n=11,467; 0.80 [0.66-0.98]; P=0.029). However, PCSK9-GS was also associated with higher risk of hospitalisation with chronic obstructive pulmonary disease (COPD: n=6836; 1.38 [1.08-1.76]; P=0.0089), and with even higher risk of fatal exacerbations among individuals with pre-existing COPD (n=730; 3.61 [1.71-7.60]; P=7.3x10-4). Risk of acute upper respiratory tract infection (URTI) was also increased (n=1,095; 2.18 [1.34-3.53]; P=0.0016), as previously reported in UK Biobank, with a pooled OR after meta-analysis of 1.87 ([1.38-2.54]; P=5.4x10-5). We also replicated a previously-reported association with self-reported asthma (pooled OR 1.17 ([1.04-1.30]; P=0.0071). There was heterogeneity between the associations of PCSK9-GS and a polygenic LDL-C score for each of COPD hospitalisation (P-het=0.015), fatal COPD exacerbation (P-het=0.0012), and URTI (P-het=0.013). Conclusions: LDL-C-lowering PCSK9 genetic variants are associated with lower risk of subclinical and clinical atherosclerotic vascular disease, but higher risks of respiratory diseases which appear unrelated to LDL-C. Pharmacovigilance studies may be required to monitor patients treated with therapeutic PCSK9 inhibitors for exacerbations of respiratory diseases or respiratory tract infections.
Principle of Terahertz Time-Domain Spectroscopy

In this work, the detection of terahertz (THz) pulse via different schemes of THz time-domain spectroscopy (THz-TDS), including electro-optic sampling (EOS) and photoconductive sampling (PCS), has been reviewed by insisting on its principles and characteristics. Furthermore, four developments of THz-TDS, i.e. THz air-breakdown/biased coherent detection (THz-ABCD), THz-radiation-enhanced emission of fluorescence (THz-REEF), single-shot THz-TDS, and THz asynchronous optical sampling (THz-ASOPS) have also been introduced.
Compactifications of pseudofinite and pseudo-amenable groups

We first give simplified and corrected accounts of some results in \cite{PiRCP} on compactifications of pseudofinite groups. For instance, we use a classical theorem of Turing \cite{Turing} to give a simplified proof that any definable compactification of a pseudofinite group has an abelian connected component. We then discuss the relationship between Turing's work, the Jordan-Schur Theorem, and a (relatively) more recent result of Kazhdan \cite{Kazh} on approximate homomorphisms, and we use this to widen our scope from finite groups to amenable groups. In particular, we develop a suitable continuous logic framework for dealing with definable homomorphisms from pseudo-amenable groups to compact Lie groups. Together with the stabilizer theorems of \cite{HruAG,MOS}, we obtain a uniform (but non-quantitative) analogue of Bogolyubov's Lemma for sets of positive measure in discrete amenable groups. We conclude with brief remarks on the case of amenable topological groups.
Detecting Olives with Synthetic or Real Data? Olive the Above

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.
Availability, storage capacity, and diffusion: Stationary states of an asymmetric exclusion process connected to two reservoirs

We explore how the interplay of finite availability, carrying capacity of particles at different parts of a spatially extended system and particle diffusion between them control the steady state currents and density profiles in a one-dimensional current-carrying channel connecting the different parts of the system. To study this, we construct a minimal model consisting of two particle reservoirs of finite carrying capacities connected by a totally asymmetric simple exclusion process (TASEP). In addition to particle transport via TASEP between the reservoirs, the latter can also directly exchange particles, modeling particle diffusion between them that can maintain a steady current in the system. We investigate the steady state density profiles and the associated particle currents in the TASEP lane. The resulting phases and the phase diagrams are quite different from an open TASEP, and are characterised by the model parameters defining particle exchanges between the TASEP and the reservoirs, direct particle exchanges between the reservoirs, and the filling fraction of the particles that determines the total resources available. These parameters can be tuned to make the density on the TASEP lane globally uniform or piecewise continuous, and can make the two reservoirs preferentially populated or depopulated.
Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review

A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency of the manufacturing process, because they cause critical failures and wafer-yield limitations. As nodes and patterns get smaller, even high-resolution imaging techniques such as Scanning Electron Microscopy (SEM) produce noisy images due to operating close to sensitivity levels and due to varying physical properties of different underlayers or resist materials. This inherent noise is one of the main challenges for defect inspection. One promising approach is the use of machine learning algorithms, which can be trained to accurately classify and locate defects in semiconductor samples. Recently, convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images, including the most recent innovations and developments. 38 publications were selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of these, the application, methodology, dataset, results, limitations and future work were summarized. A comprehensive overview and analysis of their methods is provided. Finally, promising avenues for future work in the field of SEM-based defect inspection are suggested.
Efficient Simulation of Leakage Errors in Quantum Error Correcting Codes Using Tensor Network Methods

Leakage errors, in which a qubit is excited to a level outside the qubit subspace, represent a significant obstacle in the development of robust quantum computers. We present a computationally efficient simulation methodology for studying leakage errors in quantum error correcting codes (QECCs) using tensor network methods, specifically Matrix Product States (MPS). Our approach enables the simulation of various leakage processes, including thermal noise and coherent errors, without approximations (such as the Pauli twirling approximation) that can lead to errors in the estimation of the logical error rate. We apply our method to two QECCs: the one-dimensional (1D) repetition code and a thin $3\times d$ surface code. By leveraging the small amount of entanglement generated during the error correction process, we are able to study large systems, up to a few hundred qudits, over many code cycles. We consider a realistic noise model of leakage relevant to superconducting qubits to evaluate code performance and a variety of leakage removal strategies. Our numerical results suggest that appropriate leakage removal is crucial, especially when the code distance is large.
Large time asymptotics for partially dissipative hyperbolic systems without Fourier analysis: application to the nonlinearly damped p-system

A new framework to obtain time-decay estimates for partially dissipative hyperbolic systems set on the real line is developed. Under the classical Shizuta-Kawashima (SK) stability condition, equivalent to the Kalman rank condition in control theory, the solutions of these systems decay exponentially in time for high frequencies and polynomially for low ones. This allows to derive a sharp description of the space-time decay of solutions for large time. However, such analysis relies heavily on the use of the Fourier transform that we avoid here, developing the"physical space version"of the hyperbolic hypocoercivity approach introduced by Beauchard and Zuazua, to prove new asymptotic results in the linear and nonlinear settings. The new physical space version of the hyperbolic hypocoercivity approach allows to recover the natural heat-like time-decay of solutions under sharp rank conditions, without employing Fourier analysis or $L^1$ assumptions on the initial data. Taking advantage of this Fourier-free framework, we establish new enhanced time-decay estimates for initial data belonging to weighted Sobolev spaces. These results are then applied to the nonlinear compressible Euler equations with linear damping. We also prove the logarithmic stability of the nonlinearly damped $p$-system.