Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Pro-Inflammatory Cytokines in the Pathogenesis of Cardiovascular Disease

With cardiovascular disease (CVD) being a primary source of global morbidity and mortality, it is crucial that we understand the molecular pathophysiological mechanisms at play. Recently, numerous pro-inflammatory cytokines have been linked to several different CVDs, which are now often considered an adversely pro-inflammatory state. These cytokines most notably include interleukin-6 (IL-6),tumor necrosis factor (TNF)α, and the interleukin-1 (IL-1) family, amongst others. Not only does inflammation have intricate and complex interactions with pathophysiological processes such as oxidative stress and calcium mishandling, but it also plays a role in the balance between tissue repair and destruction. In this regard, pre-clinical and clinical evidence has clearly demonstrated the involvement and dynamic nature of pro-inflammatory cytokines in many heart conditions; however, the clinical utility of the findings so far remains unclear. Whether these cytokines can serve as markers or risk predictors of disease states or act as potential therapeutic targets, further extensive research is needed to fully understand the complex network of interactions that these molecules encompass in the context of heart disease. This review will highlight the significant advances in our understanding of the contributions of pro-inflammatory cytokines in CVDs, including ischemic heart disease (atherosclerosis, thrombosis, acute myocardial infarction, and ischemia-reperfusion injury), cardiac remodeling (hypertension, cardiac hypertrophy, cardiac fibrosis, cardiac apoptosis, and heart failure), different cardiomyopathies as well as ventricular arrhythmias and atrial fibrillation. In addition, this article is focused on discussing the shortcomings in both pathological and therapeutic aspects of pro-inflammatory cytokines in CVD that still need to be addressed by future studies.
Shallow learning enables real-time inference of molecular composition from spectroscopy of brain tissue

Optical imaging modalities such as near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of functional and structural properties of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra in real-time deems the spectroscopy techniques as a unique diagnostic tool. However, due to a lack of paired optical and molecular profiling studies, building a mapping between a spectral signature and a corresponding set of molecular concentrations is still an unsolved problem. Furthermore, no established methods exist to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. In this paper, we analyse a technique for fast and accurate inference of changes in the molecular composition of brain tissue. We base our method on the Beer-Lambert law to analytically connect the spectra with concentrations and use a deep-learning approach to significantly speed up the concentration inference compared to traditional optimization methods. We test our approach on real data obtained from the broadband NIRS- and HSI-based optical monitoring of brain tissue. The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional linear and non-linear optimization solvers.
Extraordinary Optical and Transport Properties of Disordered Stealthy Hyperuniform Two-Phase Media.

Disordered stealthy hyperuniform two-phase media are a special subset of hyperuniform structures with novel physical properties due to their hybrid crystal-liquid nature. We have previously shown that the rapidly converging strong-contrast expansion of a linear fractional form of the effective dynamic dielectric constant εe(k1,ω) [Phys. Rev. X 11, 296 021002 (2021)] leads to accurate approximations for both hyperuniform and nonhyperuniform two-phase composite media when truncated at the two-point level for distinctly different types of microstructural symmetries in three dimensions. In this paper, we further elucidate the extraordinary optical and transport properties of disordered stealthy hyperuniform media. Among other results, we provide detailed proofs that stealthy hyperuniform layered and transversely isotropic media are perfectly transparent (i.e., no Anderson localization, in principle) within finite wavenumber intervals through the third-order terms. Remarkably, these results imply that there can be no Anderson localization within the predicted perfect transparency interval in stealthy hyperuniform layered and transversely isotropic media in practice because the localization length (associated with only possibly negligibly small higher-order contributions) should be very large compared to any practically large sample size. We further contrast and compare the extraordinary physical properties of stealthy hyperuniform two-phase layered, transversely isotropic media, and fully 3D isotropic media to other model nonstealthy microstructures, including their attenuation characteristics, as measured by the imaginary part of εe (k1 , ω), and transport properties, as measured by the time-dependent diffusion spreadability S(t). We demonstrate that there are cross-property relations between them, namely, we quantify how the imaginary parts of εe(k1,ω) and the spreadability at long times are positively correlated as the structures span from nonhyperuniform, nonstealthy hyperuniform, and stealthy hyperuniform media. It will also be useful to establish cross-property relations for stealthy hyperuniform media for other wave phenomena (e.g., elastodynamics) as well as other transport properties. Cross-property relations are generally useful because they enable one to estimate one property, given a measurement of another property.
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy

The inability to acquire clean high-resolution (HR) electron microscopy (EM) images over a large brain tissue volume hampers many neuroscience studies. To address this challenge, we propose a deep-learning-based image super-resolution (SR) approach to computationally reconstruct clean HR 3D-EM with a large field of view (FoV) from noisy low-resolution (LR) acquisition. Our contributions are I) Investigating training with no-clean references for $\ell_2$ and $\ell_1$ loss functions; II) Introducing a novel network architecture, named EMSR, for enhancing the resolution of LR EM images while reducing inherent noise; and, III) Comparing different training strategies including using acquired LR and HR image pairs, i.e., real pairs with no-clean references contaminated with real corruptions, the pairs of synthetic LR and acquired HR, as well as acquired LR and denoised HR pairs. Experiments with nine brain datasets showed that training with real pairs can produce high-quality super-resolved results, demonstrating the feasibility of training with non-clean references for both loss functions. Additionally, comparable results were observed, both visually and numerically, when employing denoised and noisy references for training. Moreover, utilizing the network trained with synthetically generated LR images from HR counterparts proved effective in yielding satisfactory SR results, even in certain cases, outperforming training with real pairs. The proposed SR network was compared quantitatively and qualitatively with several established SR techniques, showcasing either the superiority or competitiveness of the proposed method in mitigating noise while recovering fine details.
Exploring the Diversity of Music Experiences for Deaf and Hard of Hearing People

Sensory substitution or enhancement techniques have been proposed to enable deaf or hard of hearing (DHH) people to listen to and even compose music. However, little is known about how such techniques enhance DHH people's music experience. Since deafness is a spectrum -- as are DHH people's preferences and perceptions of music -- a more situated understanding of their interaction with music is needed. To understand the music experience of this population, we conducted social media analyses, both qualitatively and quantitatively, in the deaf and hard of hearing Reddit communities. Our content analysis revealed that DHH people leveraged sign language and visual/haptic cues to feel the music and preferred familiar, non-lyrical, instrument-heavy, and loud music. In addition, hearing aids were not customized for music, and the visual/haptic techniques developed were not widely adopted by DHH people, leading to their suboptimal music experiences. The DHH community embodied mutual support among music lovers, evidenced by active information sharing and Q&A around music and hearing loss. We reflect on design justice for DHH people's music experience and propose practical design implications to create a more accessible music experience for them.
PIM-STM: Software Transactional Memory for Processing-In-Memory Systems

Processing-In-Memory (PIM) is a novel approach that augments existing DRAM memory chips with lightweight logic. By allowing to offload computations to the PIM system, this architecture allows for circumventing the data-bottleneck problem that affects many modern workloads. This work tackles the problem of how to build efficient software implementations of the Transactional Memory (TM) abstraction by introducing PIM-STM, a library that provides a range of diverse TM implementations for UPMEM, the first commercial PIM system. Via an extensive study we assess the efficiency of alternative choices in the design space of TM algorithms on this emerging architecture. We further quantify the impact of using different memory tiers of the UPMEM system (having different trade-offs for what concerns latency vs capacity) to store the metadata used by different TM implementations. Finally, we assess the gains achievable in terms of performance and memory efficiency when using PIM-STM to accelerate TM applications originally conceived for conventional CPU-based systems.
Study on Soil Organic Carbon Fractions and Their Influential Factors in Rocky Desertification Process in Southwest of China

Through collecting typical soil samples in different phases of rocky desertification and analyzing their basic properties in karst region in Southwest of China,the soil organic carbon fractions and their influential factors were studied.The results were as follows. (1)The sequence of the contents of soil organic carbon in karst region was Humin(HM) Fulvic acid(FA) humic acid(HA),and owing to the value of FA/HA that was currently less than 0.4,karst soils was fulvic acid soils that accord with the same horizontal zonal soils. (2)There had significant difference in the soil organic fractions in the rocky desertification process,and the contents of HM,FA,HA had the same trend:non-degraded potential rocky desertification light rocky desertification moderate rocky desertification. (3)In the space distribution the contents of soil organic fractions had clear difference.The contents of soil HM,FA,HA in topsoil were more than in subsoil,and the value of FA/HA had increasing trend from peak to foot in karst region. (4)There were significant correlations between soil organic carbon,pH value,CaCO3,bulk density,noncrystalline aluminum oxides,soil particle composition,available phosphorus and the soil organic carbon fractions in karst region.Soil organic carbon content and soil texture were the important impact factors to the soil organic carbon fractions.
Phase-free Dynamic Movement Primitives Applied to Kinesthetic Guidance in Robotic Co-manipulation Tasks

When there is a need to define and adapt a robotic task based on a reference motion, Dynamic Movement Primitives (DMP) is a standard and efficient method for encoding it. The nominal trajectory is typically obtained through a Programming by Demonstration (PbD) approach, where the robot is taught a specific task through kinesthetic guidance. Subsequently, the motion is reproduced by the manipulator in terms of both geometric path and timing law. The basic approach for modifying the duration of the execution involves adjusting a time constant characterizing the model. On the contrary, the goal of this paper is to achieve complete decoupling between the geometric information of the task, encoded into the DMP, and the phase law governing the execution, allowing them to be chosen independently. This enables the optimization of the task duration to satisfy constraints such as velocity or acceleration or even to define a phase law dependent on external inputs, such as the force applied by a user in a co-manipulation task. As an example, this mechanism will be exploited to define a rehabilitation activity where the cobot assists humans in performing various pre-planned exercises.
Same Data, Diverging Perspectives: The Power of Visualizations to Elicit Competing Interpretations

People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how the data is visually represented or what the viewer finds salient. We also demonstrate that viewer interpretation of data is similar to that of 'ambiguous figures' such that two people looking at the same data can come to different decisions. In our studies, participants read visualizations depicting competitions between two entities, where one has a historical lead (A) but the other has been gaining momentum (B) and predicted a winner, across two chart types and three annotation approaches. They either saw the historical lead as salient and predicted that A would win, or saw the increasing momentum as salient and predicted B to win. These results suggest that decisions can be influenced by both how data are presented and what patterns people find visually salient.
In the Eyes of the Bystander: Are the Stances on Different Conflicts Correlated?

Public opinion on international conflicts, such as the concurrent Russia-Ukraine and Israel-Palestine crises, often reflects a society's values, beliefs, and history. These simultaneous conflicts have sparked heated global online discussions, offering a unique opportunity to explore the dynamics of public opinion in multiple international crises. This study investigates how public opinions toward one conflict might influence or relate to another, a relatively unexplored area in contemporary research. Focusing on Chinese netizens, who represent a significant online population, this study examines their perspectives, which are increasingly influential in global discourse due to China's unique cultural and political landscape. The research finds a range of opinions, including neutral stances towards both conflicts and a statistical correlation between attitudes towards each, indicating interconnected or mutually influenced viewpoints. The study also highlights the significant role of news media, particularly in China, where state policies and global politics shape conflict portrayal, in impacting public opinion.
Symmetry-Driven Valleytronics in Single-Layer Tin Chalcogenides

The concept of valleytronics has recently gained considerable research attention due to its intriguing physical phenomena and practical applications in optoelectronics and quantum information. In this study, by employing GW-BSE calculations and symmetry analysis, we demonstrate that single-layer orthorhombic SnS and SnSe possess high carrier mobility and exceptional excitonic effects. Especially, these materials display spontaneous linearly polarized optical selectivity, a behavior that differs from the valley-selective circular dichroism observed in the hexagonal lattices. Specifically, when subjected to a zigzag polarization of light, only the A exciton (stemming from the X valley) becomes optically active, while the B exciton (arising from the Y valley) remains dark. The armchair-polarized light triggers the opposite behavior. This selective optical excitation arises from the symmetry of the bands under mirror symmetry. Additionally, the study reveals a strong coupling between valley physics and ferroelectricity in layered tin chalcogenides, enabling the manipulation of electronic transport and exciton polarization. Layered tin chalcogenides thus emerge as promising candidates for both valleytronic and ferroelectric materials.
Prescribing curvatures in the disk via conformal changes of the metric: the case of negative Gaussian curvature

This paper deals with the question of prescribing the Gaussian curvature on a disk and the geodesic curvature of its boundary by means of a conformal deformation of the metric. We restrict ourselves to a symmetric setting in which the Gaussian curvature is negative, and we are able to give general existence results. Our approach is variational, and solutions will be searched as critical points of an associated functional. The proofs use a perturbation argument via the monotonicity trick of Struwe, together with a blow-up analysis and Morse index estimates. We also give a nonexistence result that shows that, to some extent, the assumptions required for existence are necessary.
On the origins of CMB anomalies and testing a new theory of inflationary quantum fluctuations

In this paper, we present compelling evidence suggesting a statistical violation of parity symmetry (a discrete symmetry that is separate from isotropy) in the Cosmic Microwave Background (CMB) map, measured through two-point temperature correlations. This parity asymmetric CMB challenges our understanding of the quantum physics of the early Universe rather than LCDM ($\Lambda$ Cold-Dark-Matter). We commence by conducting a comprehensive analysis of the Planck CMB, focusing on the distribution of power in low-multipoles and temperature anticorrelations at parity conjugate points in position space. We find tension with the near scale-invariant power-law power spectrum of Standard Inflation (SI), with p-values of the order $\mathcal{O}\left( 10^{-4}-10^{-3} \right)$. Subsequently, we explore the recently proposed direct-sum inflation (DSI), where a quantum fluctuation arises as a direct-sum of two components evolving forward and backward in time at parity conjugate points in physical space. We found that DSI is consistent with data on parity asymmetry, the absence of power at $\theta>60^{\circ}$, and power suppression at low-even-multipoles which are major data anomalies in the SI. Furthermore, we discover that the parameters characterizing the hemispherical power asymmetry anomaly become statistically insignificant when the large SI quadrupole amplitude is reduced to align with the data. DSI explains this low quadrupole with a p-value of $3.5\%$, 39 times higher than SI. Combining statistics from parameters measuring parity and low-$\ell$ angular power spectrum, we find that DSI is 50-650 times more probable than SI. In summary, our investigation suggests that CMB temperature fluctuations exhibit homogeneity and isotropy but parity-violating consistent with predictions of DSI. This observation provides tantalizing evidence for the quantum mechanical nature of gravity.
ANÁLISIS DEL EFECTO DE LA RESPONSABILIDAD SOCIAL EMPRESARIAL EN LOS RESULTADOS EMPRESARIALES DE LAS MICRO, PEQUEÑAS Y MEDIANAS EMPRESAS (MIPYMES)

Este trabajo pretende analizar la influencia de la Responsabilidad Social Empresarial (RSE) en los Resultados Empresariales (RE) en las micro, pequenas y medianas empresas (Mipymes). Para ello se propone un modelo utilizando datos de los informes de sostenibilidad de 278 Mipymes espanolas confeccionados bajo los estandares de GRI. Para el contraste de las hipotesis y la obtencion de resultados se ha utilizado software SmartPLS 3.2. 3. Los resultados nos permiten afirmar que las Mipymes tambien desarrollan actividades de RSE y que aquellas Mipymes que las llevan a cabo mejoran sus resultados.
Material Informatics through Neural Networks on Ab-Initio Electron Charge Densities: the Role of Transfer Learning

In this work, the dynamic realms of Materials Science and Computer Science advancements meet the critical challenge of identifying efficient descriptors capable of capturing the essential features of physical systems. Such task has remained formidable, with solutions often involving ad-hoc scalar and vectorial sets of materials properties, making optimization and transferability challenging. We extract representations directly from ab-initio differential electron charge density profiles using Neural Networks, highlighting the pivotal role of transfer learning in such task. Firstly, we demonstrate significant improvements in regression of a specific defected-materials property with respect to training a deep network from scratch, both in terms of predictions and their reproducibilities, by considering various pre-trained models and selecting the optimal one after fine-tuning. The remarkable performances obtained confirmed the transferability of the existent pre-trained Convolutional Neural Networks (CNNs) on physics domain data, very different from the original training data. Secondly, we demonstrate a saturation in the regression capabilities of computer vision models towards properties of an extensive variety of undefected systems, and how it can be overcome with the help of large language model (LLM) transformers, with as little text information as composition names. Finally, we prove the insufficiency of open-models, like GPT-4, in achieving the analogous tasks and performances as the proposed domain-specific ones. The work offers a promising avenue for enhancing the effectiveness of descriptor identification in complex physical systems, shedding light over the power of transfer learning to easily adapt and combine available models, with different modalities, to the physics domain, at the same time opening space to a benchmark for LLMs capabilities in such domain.
Mitigating Bias in Machine Learning Models for Phishing Webpage Detection

The widespread accessibility of the Internet has led to a surge in online fraudulent activities, underscoring the necessity of shielding users' sensitive information from cybercriminals. Phishing, a well-known cyberattack, revolves around the creation of phishing webpages and the dissemination of corresponding URLs, aiming to deceive users into sharing their sensitive information, often for identity theft or financial gain. Various techniques are available for preemptively categorizing zero-day phishing URLs by distilling unique attributes and constructing predictive models. However, these existing techniques encounter unresolved issues. This proposal delves into persistent challenges within phishing detection solutions, particularly concentrated on the preliminary phase of assembling comprehensive datasets, and proposes a potential solution in the form of a tool engineered to alleviate bias in ML models. Such a tool can generate phishing webpages for any given set of legitimate URLs, infusing randomly selected content and visual-based phishing features. Furthermore, we contend that the tool holds the potential to assess the efficacy of existing phishing detection solutions, especially those trained on confined datasets.