Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian estimation of covariate assisted principal regression for brain functional connectivity

This paper presents a Bayesian reformulation of covariate-assisted principal (CAP) regression of Zhao et al. (2021), which aims to identify components in the covariance of response signal that are associated with covariates in a regression framework. We introduce a geometric formulation and reparameterization of individual covariance matrices in their tangent space. By mapping the covariance matrices to the tangent space, we leverage Euclidean geometry to perform posterior inference. This approach enables joint estimation of all parameters and uncertainty quantification within a unified framework, fusing dimension reduction for covariance matrices with regression model estimation. We validate the proposed method through simulation studies and apply it to analyze associations between covariates and brain functional connectivity, utilizing data from the Human Connectome Project.
SecOComp: A Fast and Secure Simultaneous Compression and Encryption Scheme

We live in a data-driven era that involves the generation, collection and processing of a massive amount of data. This data often contains valuable intellectual property and sensitive user information that must be safeguarded. There is a need to both encrypt and compress the data at line speed and sometimes with added power constraints. The majority of the currently available simultaneous compression and encryption (SCE) schemes are tailored for a specific type of data such as images for instance. This reduces their generic applicability. In this paper, we tackle this issue and propose a generic, efficient, and secure simultaneous compression and encryption scheme where the data is simultaneously encrypted using chaotic maps and compressed using a fast lossless compression algorithm. We claim that employing multiple chaotic maps and a lossless compression method can help us create not only an efficient encryption scheme but also compress the data efficiently in a hardware-friendly manner. We avoid all the known pitfalls of chaos theory based encryption that have prevented its widespread usage. Our algorithm passes all the NIST tests for nine different types of popular datasets. The proposed implementation uses 1.51x less storage as compared to the nearest computing work.
Neural Replicator Analysis of the genus Flavivirus

The results of applying neural replicator analysis (NRA) to the genomes of viruses belonging to the genus Flavivirus are presented. It is shown that the viral genomes considered in this study can be placed in five different cells of the viral genome table. Some of these cells appear for the first time and are characterized by 9-periodicity of WS-encoded genomic sequences. It is noteworthy that Japanese encephalitis viral strains and Zika viral strains occupy not one, but two common cells of this table. We also present the results of the NRA of Zika viral strains and suggest that the earliest strain in Asia is an Indian strain that spread from Africa (Uganda) to the East. The fine structure of the sets of Japanese encephalitis viral strains is presented and it is shown that their generally accepted genotypes 1 and 3 can be clearly divided into two subgenotypes. It is also shown that probably not Indonesian, but Indian strains of this virus can be considered the earliest known strains that further evolved and spread in Asian countries.
Hsa_circ_0023826 protects against glaucoma by regulating miR-188-3p/MDM4 axis.

OBJECTIVE Circular RNAs (circRNAs) are characterized as a class of covalently closed circRNA transcripts and are associated with various cellular processes and neurological diseases by sponging microRNAs. The most common feature of glaucoma, a form of retinal neuropathy, is the loss of retinal ganglion cells. Although the pathogenesis of glaucoma is not fully understood, elevated intraocular pressure is undoubtedly the only proven modifiable factor in the classic glaucoma model. This study investigated the role of circ_0023826 in glaucoma-induced retinal neurodegeneration by modifying the miR-188-3p/mouse double minute 4 (MDM4) axis. METHODS The expression pattern of circ_0023826 was analyzed during retinal neurodegeneration. The effect of circ_0023826, miR-188-3p, and MDM4 on retinal neurodegeneration in vivo was assessed by visual behavioral testing and HandE staining in glaucoma rats, while that on in vitro retinal ganglion cells (RGCs) was evaluated by MTT assay, flow cytometry, Western blot, and ELISA. Bioinformatics analysis, RNA pull-down assay, luciferase reporter assay were performed to reveal the regulatory mechanism of circ_0023826-mediated retinal neurodegeneration. RESULTS Circ_0023826 expression was downregulated during retinal neurodegeneration. Upregulating circ_0023826 attenuated the visual impairment in rats and promoted the survival of RGCs in vitro. Circ_0023826 acted as a sponge of miR-188-3p sponge, resulting in increased expression of MDM4. MDM4 silencing or miR-188-3p upregulation reversed the protective effect of upregulated circ_0023826 on glaucoma-induced neuroretinal degeneration in vitro and in vivo. CONCLUSION Overall, circ_0023826 protects against glaucoma by regulating the miR-188-3p/MDM4 axis, and targeted intervention of circ_0023826 expression is a promising therapeutic strategy for the treatment of retinal neurodegeneration.
Altered intestinal microbiome and epithelial damage aggravate intestinal graft-versus-host disease

ABSTRACT Despite significant achievements in hematopoietic stem cell transplantation (HSCT), graft-versus-host disease (GVHD), especially intestinal GVHD, remains a major obstacle to this procedure. GVHD has long been regarded as a pathogenic immune response, and the intestine has been simply considered as a target of immune attack. In effect, multiple factors contribute to intestinal damage after transplantation. Impaired intestinal homeostasis including altered intestinal microbiome and epithelial damage results in delayed wound healing, amplified immune response and sustained tissue destruction, and it may not fully recover following immunosuppression. In this review, we summarize factors leading to intestinal damage and discuss the relationship between intestinal damage and GVHD. We also describe the great potential of remodeling intestinal homeostasis in GVHD management.
A novel biodegradable polymer-coated sirolimus-eluting stent: 1-year results of the HELIOS registry

Abstract Background: The HELIOS stent is a sirolimus-eluting stent with a biodegradable polymer and titanium oxide film as the tie-layer. The study aimed to evaluate the safety and efficacy of HELIOS stent in a real-world setting. Methods: The HELIOS registry is a prospective, multicenter, cohort study conducted at 38 centers across China between November 2018 and December 2019. A total of 3060 consecutive patients were enrolled after application of minimal inclusion and exclusion criteria. The primary endpoint was target lesion failure (TLF), defined as a composite of cardiac death, non-fatal target vessel myocardial infarction (MI), and clinically indicated target lesion revascularization (TLR) at 1-year follow-up. Kaplan–Meier methods were used to estimate the cumulative incidence of clinical events and construct survival curves. Results: A total of 2998 (98.0%) patients completed the 1-year follow-up. The 1-year incidence of TLF was 3.10% (94/2998, 95% closed interval: 2.54–3.78%). The rates of cardiac death, non-fatal target vessel MI and clinically indicated TLR were 2.33% (70/2998), 0.20% (6/2998), and 0.70% (21/2998), respectively. The rate of stent thrombosis was 0.33% (10/2998). Age ≥60 years, diabetes mellitus, family history of coronary artery disease, acute myocardial infarction at admission, and device success were independent predictors of TLF at 1 year. Conclusion: The 1-year incidence rates of TLF and stent thrombosis were 3.10% and 0.33%, respectively, in patients treated with HELIOS stents. Our results provide clinical evidence for interventional cardiologists and policymakers to evaluate HELIOS stent. Clinical trial registration: ClinicalTrials.gov, NCT03916432
Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers

Abstract. Purpose Length and width measurements of the kidneys aid in the detection and monitoring of structural abnormalities and organ disease. Manual measurement results in intra- and inter-rater variability, is complex and time-consuming, and is fraught with error. We propose an automated approach based on machine learning for quantifying kidney dimensions from two-dimensional (2D) ultrasound images in both native and transplanted kidneys. Approach An nnU-net machine learning model was trained on 514 images to segment the kidney capsule in standard longitudinal and transverse views. Two expert sonographers and three medical students manually measured the maximal kidney length and width in 132 ultrasound cines. The segmentation algorithm was then applied to the same cines, region fitting was performed, and the maximum kidney length and width were measured. Additionally, single kidney volume for 16 patients was estimated using either manual or automatic measurements. Results The experts resulted in length of 84.8 ± 26.4 mm [95% CI: 80.0, 89.6] and a width of 51.8 ± 10.5 mm [49.9, 53.7]. The algorithm resulted a length of 86.3 ± 24.4 [81.5, 91.1] and a width of 47.1 ± 12.8 [43.6, 50.6]. Experts, novices, and the algorithm did not statistically significant differ from one another (p > 0.05). Bland–Altman analysis showed the algorithm produced a mean difference of 2.6 mm (SD = 1.2) from experts, compared to novices who had a mean difference of 3.7 mm (SD = 2.9 mm). For volumes, mean absolute difference was 47 mL (31%) consistent with ∼1 mm error in all three dimensions. Conclusions This pilot study demonstrates the feasibility of an automatic tool to measure in vivo kidney biometrics of length, width, and volume from standard 2D ultrasound views with comparable accuracy and reproducibility to expert sonographers. Such a tool may enhance workplace efficiency, assist novices, and aid in tracking disease progression.
Expanding the reach of quantum optimization with fermionic embeddings

Quadratic programming over orthogonal matrices encompasses a broad class of hard optimization problems that do not have an efficient quantum representation. Such problems are instances of the little noncommutative Grothendieck problem (LNCG), a generalization of binary quadratic programs to continuous, noncommutative variables. In this work, we establish a natural embedding for this class of LNCG problems onto a fermionic Hamiltonian, thereby enabling the study of this classical problem with the tools of quantum information. This embedding is accomplished by identifying the orthogonal group with its double cover, which can be represented as fermionic quantum states. Correspondingly, the embedded LNCG Hamiltonian is a two-body fermion model. Determining extremal states of this Hamiltonian provides an outer approximation to the original problem, a quantum analogue to classical semidefinite relaxations. In particular, when optimizing over the special orthogonal group our quantum relaxation obeys additional, powerful constraints based on the convex hull of rotation matrices. The classical size of this convex-hull representation is exponential in matrix dimension, whereas our quantum representation requires only a linear number of qubits. Finally, to project the relaxed solution back into the feasible space, we propose rounding procedures which return orthogonal matrices from appropriate measurements of the quantum state. Through numerical experiments we provide evidence that this rounded quantum relaxation can produce high-quality approximations.
LDMRes-Net: Enabling Real-Time Disease Monitoring through Efficient Image Segmentation

—Retinal eye diseases can lead to irreversible vision loss in both eyes if not diagnosed and treated earlier. Owing to the complexities of retinal diseases, the likelihood that retinal images would contain two or more abnormalities is very high. The current deep learning algorithms used for segmenting retinal images with multiple labels and features suffer from inadequate detection accuracy and a lack of generalizability. In this paper, we propose a lightweight and efficient network, featuring dual multi-residual connections to enhance segmentation performance while minimizing computational cost. The proposed network is evaluated on eight publicly available retinal image datasets and achieved promising segmentation results, which demonstrate the effectiveness of the proposed network for retinal image analysis tasks. The proposed network’s lightweight and efficient design makes it a promising candidate for real-time retinal image analysis applications.
New method to revisit the gravitational lensing analysis of the Bullet Cluster using radio waves

Gravitational lensing studies of the Bullet Cluster suggested convincingly in favor of the existence of dark matter. However, it was performed without the knowledge of the original orientation of each galaxy before gravitational lensing. A potential improvement to this issue lies in the measurement of the original orientation from the polarization direction of radio waves emitted from each galaxy. In this context, Francfort et al. derived a formula that can utilize the information about the original orientation of each galaxy to obtain what is called shear. However, we demonstrate that shear in their formula should be replaced by reduced shear when the change in sizes of images of galaxies is taken into account. As the previous gravitational lensing analysis of the Bullet Cluster used reduced shear, we suggest applying our improved formula directly for the reanalysis once we obtain the polarization direction of radio waves. In particular, we show that our new formula can yield a more accurate analysis than the previous one, if the polarization direction can be measured more precisely than 10°. Moreover, the approach discussed in this work is generically applicable to the gravitational lensing analysis of clusters, not only limited to the Bullet Cluster.
Conditional Matrix Flows for Gaussian Graphical Models

Studying conditional independence among many variables with few observations is a challenging task. Gaussian Graphical Models (GGMs) tackle this problem by encouraging sparsity in the precision matrix through $l_q$ regularization with $q\leq1$. However, most GMMs rely on the $l_1$ norm because the objective is highly non-convex for sub-$l_1$ pseudo-norms. In the frequentist formulation, the $l_1$ norm relaxation provides the solution path as a function of the shrinkage parameter $\lambda$. In the Bayesian formulation, sparsity is instead encouraged through a Laplace prior, but posterior inference for different $\lambda$ requires repeated runs of expensive Gibbs samplers. Here we propose a general framework for variational inference with matrix-variate Normalizing Flow in GGMs, which unifies the benefits of frequentist and Bayesian frameworks. As a key improvement on previous work, we train with one flow a continuum of sparse regression models jointly for all regularization parameters $\lambda$ and all $l_q$ norms, including non-convex sub-$l_1$ pseudo-norms. Within one model we thus have access to (i) the evolution of the posterior for any $\lambda$ and any $l_q$ (pseudo-) norm, (ii) the marginal log-likelihood for model selection, and (iii) the frequentist solution paths through simulated annealing in the MAP limit.
Foundations of Causal Discovery on Groups of Variables

Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models. In this work, we focus on widely employed assumptions for causal discovery when objects of interest are (multivariate) groups of random variables rather than individual (univariate) random variables, as is the case in a variety of problems in scientific domains such as climate science or neuroscience. If the group-level causal models are derived from partitioning a micro-level model into groups, we explore the relationship between micro and group-level causal discovery assumptions. We investigate the conditions under which assumptions like Causal Faithfulness hold or fail to hold. Our analysis encompasses graphical causal models that contain cycles and bidirected edges. We also discuss grouped time series causal graphs and variants thereof as special cases of our general theoretical framework. Thereby, we aim to provide researchers with a solid theoretical foundation for the development and application of causal discovery methods for variable groups.
Portfolio reshaping under 1st order stochastic dominance constraints by the exact penalty function methods

The paper addresses general constrained and non-linear optimization problems. For some of these notoriously hard problems, there exists a reformulation as an unconstrained, global optimization problem. We illustrate the transformation, and the performance of the reformulation for a non-linear problem in stochastic optimization. The problem is adapted from portfolio optimization with first order stochastic dominance constraints.
Cybersecurity Training for Users of Remote Computing

End users of remote computing systems are frequently not aware of basic ways in which they could enhance protection against cyber-threats and attacks. In this paper, we discuss specific techniques to help and train users to improve cybersecurity when using such systems. To explain the rationale behind these techniques, we go into some depth explaining possible threats in the context of using remote, shared computing resources. Although some of the details of these prescriptions and recommendations apply to specific use cases when connecting to remote servers, such as a supercomputer, cluster, or Linux workstation, the main concepts and ideas can be applied to a wider spectrum of cases.
D3L: Decomposition of 3D Rotation and Lift from 2D Joint to 3D for Human Mesh Recovery

Existing methods for 3D human mesh recovery always directly estimate SMPL parameters, which involve both joint rotations and shape parameters. However, these methods present rotation semantic ambiguity, rotation error accumulation, and shape estimation overfitting, which also leads to errors in the estimated pose. Additionally, these methods have not efficiently leveraged the advancements in another hot topic, human pose estimation. To address these issues, we propose a novel approach, Decomposition of 3D Rotation and Lift from 2D Joint to 3D mesh (D3L). We disentangle 3D joint rotation into bone direction and bone twist direction so that the human mesh recovery task is broken down into estimation of pose, twist, and shape, which can be handled independently. Then we design a 2D-to-3D lifting network for estimating twist direction and 3D joint position from 2D joint position sequences and introduce a nonlinear optimization method for fitting shape parameters and bone directions. Our approach can leverage human pose estimation methods, and avoid pose errors introduced by shape estimation overfitting. We conduct experiments on the Human3.6M dataset and demonstrate improved performance compared to existing methods by a large margin.
Generalization of Kirchhoff's Law of Thermal Radiation: The Inherent Relations Between Quantum Efficiency and Emissivity

Planck's law of thermal radiation depends on the temperature, $T$, and the emissivity, $\epsilon$, of a body, where emissivity is the coupling of heat to radiation that depends on both phonon-electron nonradiative interactions and electron-photon radiative interactions. Another property of a body is absorptivity, $\alpha$, which only depends on the electron-photon radiative interactions. At thermodynamic equilibrium, nonradiative interactions are balanced, resulting in Kirchhoff's law of thermal radiation that equals these two properties, i.e., $\epsilon = \alpha$. For non-equilibrium, quantum efficiency ($QE$) describes the statistics of photon emission, which like emissivity depends on both radiative and nonradiative interactions. Past generalized Planck's equation extends Kirchhoff's law out of equilibrium by scaling the emissivity with the pump-dependent chemical-potential $\mu$, obscuring the relations between the body properties. Here we theoretically and experimentally demonstrate a prime equation relating these properties in the form of $\epsilon = \alpha(1-QE)$, which is in agreement with a recent universal modal radiation law for all thermal emitters. At equilibrium, these relations are reduced to Kirchhoff's law. Our work lays out the fundamental evolution of non-thermal emission with temperature, which is critical for the development of lighting and energy devices.
Vocoder-Free Non-Parallel Conversion of Whispered Speech With Masked Cycle-Consistent Generative Adversarial Networks

Cycle-consistent generative adversarial networks have been widely used in non-parallel voice conversion (VC). Their ability to learn mappings between source and target features without relying on parallel training data eliminates the need for temporal alignments. However, most methods decouple the conversion of acoustic features from synthesizing the audio signal by using separate models for conversion and waveform synthesis. This work unifies conversion and synthesis into a single model, thereby eliminating the need for a separate vocoder. By leveraging cycle-consistent training and a self-supervised auxiliary training task, our model is able to efficiently generate converted high-quality raw audio waveforms. Subjective listening tests show that our method outperforms the baseline in whispered speech conversion (up to 6.7% relative improvement), and mean opinion score predictions yield competitive results in conventional VC (between 0.5% and 2.4% relative improvement).