Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing the diagnostic gap in hypertension through possible interventions and scale-up: A microsimulation study

Background Cardiovascular diseases (CVDs) are the leading cause of mortality globally with almost a third of all annual deaths worldwide. Low- and middle-income countries (LMICs) are disproportionately highly affected covering 80% of these deaths. For CVD, hypertension (HTN) is the leading modifiable risk factor. The comparative impact of diagnostic interventions that improve either the accuracy, the reach, or the completion of HTN screening in comparison to the current standard of care has not been estimated. Methods and findings This microsimulation study estimated the impact on HTN-induced morbidity and mortality in LMICs for four different scenarios: (S1) lower HTN diagnostic accuracy; (S2) improved HTN diagnostic accuracy; (S3) better implementation strategies to reach more persons with existing tools; and, lastly, (S4) the wider use of easy-to-use tools, such as validated, automated digital blood pressure measurement devices to enhance screening completion, in comparison to the current standard of care (S0). Our hypothetical population was parametrized using nationally representative, individual-level HPACC data and the global burden of disease data. The prevalence of HTN in the population was 31% out of which 60% remained undiagnosed. We investigated how the alteration of a yearly blood pressure screening event impacts morbidity and mortality in the population over a period of 10 years. The study showed that while improving test accuracy avoids 0.6% of HTN-induced deaths over 10 years (13,856,507 [9,382,742; 17,395,833]), almost 40 million (39,650,363 [31,34,233, 49,298,921], i.e., 12.7% [9.9, 15.8]) of the HTN-induced deaths could be prevented by increasing coverage and completion of a screening event in the same time frame. Doubling the coverage only would still prevent 3,304,212 million ([2,274,664; 4,164,180], 12.1% [8.3, 15.2]) CVD events 10 years after the rollout of the program. Our study is limited by the scarce data available on HTN and CVD from LMICs. We had to pool some parameters across stratification groups, and additional information, such as dietary habits, lifestyle choice, or the blood pressure evolution, could not be considered. Nevertheless, the microsimulation enabled us to include substantial heterogeneity and stochasticity toward the different income groups and personal CVD risk scores in the model. Conclusions While it is important to consider investing in newer diagnostics for blood pressure testing to continuously improve ease of use and accuracy, more emphasis should be placed on screening completion.
Incomplete response to ursodeoxycholic acid in primary biliary cholangitis: criteria, epidemiology, and possible mechanisms

ABSTRACT Introduction As a common autoimmune disease with the characteristic of early complication, primary biliary cholangitis (PBC) leads to an increasing number of mortalities among people with end-stage liver disease (ESLD) waiting for liver transplantation. Ursodeoxycholic acid (UDCA) is the only approved first-line medicine for PBC, and a good response to treatment could acquire an ideal prognosis. Patients with poor UDCA response usually have more adverse outcomes and worse survival, therefore, the management of this group become a major consideration. Areas covered Due to the complexity of race and environment for PBC, different criteria for UDCA response exhibit various predictive performances. Factors affecting UDCA response conditions include gender, age, ethnicity, serum indicators, auto-antibodies, and autoimmune comorbidities, while no agreement has been reached. In this review, we mainly focus on cellular senescence, immune-mediated damage, and vitamin D deficiency as possible mechanisms for UDCA non-responders. Expert opinion The pathogenesis of PBC has yet to be clarified. Immunology-related mechanisms and therapy targets ought to be the main effort made for further study. Irrespective of the response condition, UDCA is recommended for routine administration in all PBC patients without contraindication. Ongoing clinical trials of second-line and additional therapy exhibit promising prospects.
A normal force distance regulation scheme for near‐field optical microscopy

A near‐field optical microscope (NFOM) has been developed that combines the features of a near‐field optical microscope and an atomic force microscope. Improved control over tip‐sample separation has led to improved optical imaging and independent surface topography information. The tip oscillation is normal to the sample plane thereby reducing lateral forces—important for nonperturbative imaging of soft samples. Both topographic images and reflection near‐field optical images are presented which demonstrate the capability of the system.
Wigner distribution for angle coordinates in quantum mechanics

The method of Wigner distribution functions, and the Weyl correspondence between quantum and classical variables, are extended from the usual kind of canonically conjugate position and momentum operators to the case of an angle and angular momentum operator pair. The sense in which one has a description of quantum mechanics using classical phase‐space language is much clarified by this extension.
Aerosol Oxidative Potential in the Greater Los Angeles Area: Source Apportionment and Associations with Socioeconomic Position

Oxidative potential (OP) has been proposed as a possible integrated metric for particles smaller than 2.5 μm in diameter (PM2.5) to evaluate adverse health outcomes associated with particulate air pollution exposure. Here, we investigate how OP depends on sources and chemical composition and how OP varies by land use type and neighborhood socioeconomic position in the Los Angeles area. We measured OH formation (OPOH), dithiothreitol loss (OPDTT), black carbon, and 52 metals and elements for 54 total PM2.5 samples collected in September 2019 and February 2020. The Positive Matrix Factorization source apportionment model identified four sources contributing to volume-normalized OPOH: vehicular exhaust, brake and tire wear, soil and road dust, and mixed secondary and marine. Exhaust emissions contributed 42% of OPOH, followed by 21% from brake and tire wear. Similar results were observed for the OPDTT source apportionment. Furthermore, by linking measured PM2.5 and OP with census tract level socioeconomic and health outcome data provided by CalEnviroScreen, we found that the most disadvantaged neighborhoods were exposed to both the most toxic particles and the highest particle concentrations. OPOH exhibited the largest inverse social gradients, followed by OPDTT and PM2.5 mass. Finally, OPOH was the metric most strongly correlated with adverse health outcome indicators.
Toward regulated photon generations from semiconductor quantum dots and their applications

Quantum information processing and quantum cryptography are expected to realize highly secure future information networks. How to control photon qubits will be one of the major issues for this direction, but the technologies related to generations and detections of individual photons are still being developed. Semiconductor quantum dots (QDs) have been expected to play major role for on-demand generations of single photons as well as entangled photon pairs. In this talk, photon generation processes from a single QD will be studied for the control of the quantum states of generated photons. Generation of entangled photon pairs from QDs based on biexciton-exciton cascade recombination processes is presently in a difficult situation due to exciton states energy splitting related to growth-related issues. An approach to open new paradigm will be discussed with preliminary results.
Cold dark matter cosmogony with hydrodynamics and galaxy formation : galaxy properties at redshift zero

We have supplemented our code, which computes the evolution of the physical state of a representative piece of the universe, to include not only the dynamics of dark matter (with a standard PM code) and the hydrodynamics of the gaseous component (including detailed collisional and radiative processes), but also galaxy formation on a heuristic but plausible basis. If, within a cell, the gas is Jeans-unstable, collapsing and cooling rapidly, it is transformed to galaxy subunits, which are then followed with a collisionless code. We study two representative boxes with sizes L=(80, 8) h −1 Mpc, in both cases utilizing a mesh of 200 3 cells containing 200 3 dark matter particles and having nominal resolutions of (400, 40) h −1 kpc, respectively, with true resolution approximately 2.5 times worse
Enhancing Quantum Adversarial Robustness by Randomized Encodings

The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving certain challenging problems. However, quantum learning systems are vulnerable to adversarial attacks: adding tiny carefully-crafted perturbations on legitimate input samples can cause misclassiﬁcations. To address this issue, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders. In particular, we rigorously prove that both global and local random unitary encoders lead to exponentially vanishing gradients (i.e. barren plateaus) for any variational quantum circuits that aim to add adversarial perturbations, independent of the input data and the inner structures of adversarial circuits and quantum classiﬁers. In addition, we prove a rigorous bound on the vulnerability of quantum classiﬁers under local unitary adversarial attacks. We show that random black-box quantum error correction encoders can protect quantum classiﬁers against local adversarial noises and their robustness increases as we concatenate error correction codes. To quantify the robustness enhancement, we adapt quantum differential privacy as a measure of the prediction stability for quantum classiﬁers. Our results establish versatile defense strategies for quantum classiﬁers against adversarial perturbations, which provide valuable guidance to enhance the reliability and security for both near-term and future quantum learning technologies.
G-MSM: Unsupervised Multi-Shape Matching with Graph-Based Affinity Priors

We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as an unordered set of samples, we explicitly model the underlying shape data manifold. To this end, we propose an adaptive multi-shape matching architecture that constructs an affinity graph on a given set of training shapes in a self-supervised manner. The key idea is to combine putative, pairwise correspondences by propagating maps along shortest paths in the underlying shape graph. During training, we enforce cycle-consistency between such optimal paths and the pairwise matches which enables our model to learn topology-aware shape priors. We explore different classes of shape graphs and recover specific settings, like template-based matching (star graph) or learnable ranking/sorting (TSP graph), as special cases in our framework. Finally, we demonstrate state-of-the-art performance on several recent shape correspondence benchmarks, including realworld 3D scan meshes with topological noise and challenging inter-class pairs.111Our implementation is available under the following link: https://github.com/marvin-eisenberger/gmsm-matching
AbHE: All Attention-Based Homography Estimation

Homography estimation is a fundamental task in computer vision that involves obtaining the transformation between multiview images for image alignment. Although convolutional neural network (CNN) has shown state-of-the-art performance in this task, few works have explored the use of transformer-based models that have demonstrated superiority in high-level vision tasks. In this article, we propose a strong baseline model for homography estimation that combines a Swin transformer feature representation for global features and a CNN feature representation for local features. Additionally, we introduce a cross-nonlocal layer to coarsely search for matched features within the feature maps. In the homography regression stage, we adopt an attention layer to drop weak correlation feature points from the channels of the correlation volume. Our experiments show that our method outperforms the state-of-the-art methods in eight-degree-of-freedom (DOF) homography estimation. The code is available at https://github.com/mingxiaohuo/ABHE.
Searches for mass-asymmetric compact binary coalescence events using neural networks in the LIGO/Virgo third observation period

We present the results on the search for the coalescence of compact binary mergers with very asymmetric mass configurations using convolutional neural networks and the LIGO/Virgo data for the O3 observation period. Two-dimensional images in time and frequency are used as input. Masses in the range between 0.01 Msun and 20 Msun are considered. We explore neural networks trained with input information from a single interferometer, pairs of interferometers, or all three interferometers together, indicating that the use of the maximum information available leads to an improved performance. A scan over the O3 data set using the convolutional neural networks for detection results into no significant excess from an only-noise hypothesis. The results are translated into 90% confidence level upper limits on the merger rate as a function of the mass parameters of the binary system.
Collision-tolerant Aerial Robots: A Survey

As aerial robots are tasked to navigate environments of increased complexity, embedding collision tolerance in their design becomes important. In this survey we review the current state-of-the-art within the niche field of collision-tolerant micro aerial vehicles and present different design approaches identified in the literature, as well as methods that have focused on autonomy functionalities that exploit collision resilience. Subsequently, we discuss the relevance to biological systems and provide our view on key directions of future fruitful research.
PRISM: Probabilistic Real-Time Inference in Spatial World Models

We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. Previous approaches either lack uncertainty estimates for the map and agent state, do not run in real-time, do not have a dense scene representation or do not model agent dynamics. Our solution reconciles all of these aspects. We start from a predefined state-space model which combines differentiable rendering and 6-DoF dynamics. Probabilistic inference in this model amounts to simultaneous localisation and mapping (SLAM) and is intractable. We use a series of approximations to Bayesian inference to arrive at probabilistic map and state estimates. We take advantage of well-established methods and closed-form updates, preserving accuracy and enabling real-time capability. The proposed solution runs at 10Hz real-time and is similarly accurate to state-of-the-art SLAM in small to medium-sized indoor environments, with high-speed UAV and handheld camera agents (Blackbird, EuRoC and TUM-RGBD).
A Generic Methodology for the Modular Verification of Security Protocol Implementations

Security protocols are essential building blocks of modern IT systems. Subtle flaws in their design or implementation may compromise the security of entire systems. It is, thus, important to prove the absence of such flaws through formal verification. Much existing work focuses on the verification of protocol models, which is not sufficient to show that their implementations are actually secure. Verification techniques for protocol implementations (e.g., via code generation or model extraction) typically impose severe restrictions on the used programming language and code design, which may lead to sub-optimal implementations. In this paper, we present a methodology for the modular verification of strong security properties directly on the level of the protocol implementations. Our methodology leverages state-of-the-art verification logics and tools to support a wide range of implementations and programming languages. We demonstrate its effectiveness by verifying memory safety and security of Go implementations of the Needham-Schroeder-Lowe, Diffie-Hellman key exchange, and WireGuard protocols, including forward secrecy and injective agreement for WireGuard. We also show that our methodology is agnostic to a particular language or program verifier with a prototype implementation for C.
Temperature-dependent Multi-well Free-energy Landscape for Phase Transitions: PbTiO3 as a Prototype

Yi Wang, Tiannan Yang, Shun-Li Shang, Long-Qing Chen*, and Zi-Kui Liu Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA. It has been a long challenge to analytically construct the quantitative temperaturedependent multi-well free-energy landscape over the space of order parameters describing phase transitions and associated critical phenomena. Here we propose a simple analytical model for the free energy landscape based on a priori concept of Boltzmann thermal mixing among multiple parabolic potentials representing different energetically degenerated ground states in contrast to the popular Landau theory using the high-temperature disordered state as a reference. The model recovers both the Weiss molecular field theory and the temperature dependent behaviors of the second order Landau coefficient. It is rather remarkable that such a simple analytical expression can describe a wide variety of properties across the ferroelectric phase transition in PbTiO3, including the temperature dependences of the spontaneous polarization, the heat capacities, the permittivity tensor, and the lattice parameters. The approach also allows parametrization of the free energy function across phase transitions based directly on the 0 K thermodynamics data that can be obtained from DFT calculations. We anticipate that the approach can be equally applied to other types of critical phenomena, e.g., the superconducting phase transition, the metal-insulator transition, and the magnetic transitions.
Temperature-dependent behaviors of single spin defects in solids determined with Hz-level precision

Revealing the properties of single spin defects in solids is essential for quantum applications based on solid-state systems. However, it is intractable to investigate the temperature-dependent properties of single defects, due to the low precision for single-defect measurements in contrast to defect ensembles. Here we report that the temperature dependence of the Hamiltonian parameters for single negatively charged nitrogen-vacancy (NV − ) centers in diamond is precisely measured, and the results ﬁnd a reasonable agreement with ﬁrst-principles calculations. Particularly, the hyperﬁne interactions with randomly distributed 13 C nuclear spins are clearly observed to vary with temperature, and the relevant coeﬃcients are measured with Hz-level precision. The temperature-dependent behaviors are attributed to both thermal expansion and lattice vibrations by ﬁrst-principles calculations. Our results pave the way for taking nuclear spins as more stable thermometers at nanoscale. The methods developed here for high-precision measurements and ﬁrst-principles calculations can be further extended to other solid-state spin defects
Using Decoupled Features for Photorealistic Style Transfer

In this work we propose a photorealistic style transfer method for image and video that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of sample statistics. The novel aspects of our approach include matching decoupled moments of higher order than in common style transfer approaches, and matching a descriptor of the power spectrum so as to characterize and transfer diffusion effects between source and target, which is something that has not been considered before in the literature. The results are of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that our method compares very well with the state-of-the-art. The computational complexity of the algorithm is low, and we propose a numerical implementation that is amenable for real-time video application. Finally, another contribution of our work is to point out that current deep learning approaches for photorealistic style transfer don't really achieve photorealistic quality outside of limited examples, because the results too often show unacceptable visual artifacts.
Cohomology and the controlling Algebra of Crossed Homomorphisms on 3-Lie Algebras

In this paper, first we give the notion of a crossed homomorphism on a 3-Lie algebra with respect to an action on another 3-Lie algebra, and characterize it using a homomorphism from a Lie algebra to the semidirect product Lie algebra. We also establish the relationship between crossed homomorphisms and relative Rota-Baxter operators of weight 1 on 3-Lie algebras. Next we construct a cohomology theory for a crossed homomorphism on 3-Lie algebras and classify infinitesimal deformations of crossed homomorphisms using the second cohomology group. Finally, using the higher derived brackets, we construct an $L_\infty$-algebra whose Maurer-Cartan elements are crossed homomorphisms. Consequently, we obtain the twisted $L_\infty$-algebra that controls deformations of a given crossed homomorphism on 3-Lie algebras.