Miracle-dz/UniFAR-FTUs huggingface dataset:
Lean body mass and the cardiovascular system constitute a female-specific relationship

Recent evidence points toward a link between lean body mass (LBM) and cardiovascular capacity in women. This study aimed at determining the sex-specific relationship of LBM with central and peripheral circulatory variables in healthy women and men (n=70) matched by age (60±12 years versus 58±15 years), physical activity, and cardiovascular risk factors. Regional (legs, arms, and trunk) and whole-body (total) body composition were assessed via dual-energy x-ray absorptiometry. Cardiac structure, function, and central/peripheral hemodynamics were measured via transthoracic echocardiography and the volume-clamp method at rest and peak incremental exercise. Regression analyses determined sex-specific relationships between LBM and cardiovascular variables. Regional and total LBM were lower in women than men (P<0.001), with little overlap between sexes. Leg and arm LBM positively associated with left ventricular (LV) internal resting dimensions in women (r≥0.53, P≤0.002) but not men (P≥0.156). Leg, arm, and total LBM only associated with LV relaxation in women (r≥0.43, P≤0.013). All LBM variables strongly associated with LV volumes at peak exercise in women (r≥0.54, P≤0.001) but not men and negatively associated with total peripheral resistance at peak exercise in women (r≥0.43, P≤0.023). Adjustment by adiposity-related or cardiovascular risk factors did not alter results. In conclusion, leg and arm LBM independently associate with internal cardiac dimensions, ventricular relaxation, and systemic vascular resistance in a sex-specific manner, with these relationships exclusively present in women. Description Lean body mass in women but not men is independently associated with major determinants of the functional capacity of the circulatory system. Differences of the heart Physiological dimorphisms between men and women have the potential to reshape our understanding of both health and disease. Examining a cohort of healthy lean adults, Diaz-Canestro et al. found that systemic and peripheral cardiovascular parameters including left ventricular size, diastolic function, and peripheral resistance are associated with lean body mass in women but not in men. Although these relationships await investigation in other patient populations, the sex-specific relationship of lean body mass to cardiovascular capacity may have relevance for cardiovascular interventions and risk of heart failure.
A Unified Protocol for CRISPR/Cas9-Mediated Gene Knockout in Tephritid Fruit Flies Led to the Recreation of White Eye and White Puparium Phenotypes in the Melon Fly

Abstract Tephritid fruit flies are among the most invasive and destructive agricultural pests worldwide. Over recent years, many studies have implemented the CRISPR/Cas9 genome-editing technology to dissect gene functions in tephritids and create new strains to facilitate their genetics, management, and control. This growing literature allows us to compare diverse strategies for delivering CRISPR/Cas9 components into tephritid embryos, optimize procedures, and advance the technology to systems outside the most thoroughly studied species within the family. Here, we revisit five years of CRISPR research in Tephritidae and propose a unified protocol for candidate gene knockout in fruit flies using CRISPR/Cas9. We demonstrated the efficiency of our protocol by disrupting the eye pigmentation gene white eye (we) in the melon fly, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae). High rates of somatic and germline mutagenesis were induced by microinjecting pre-assembled Cas9-sgRNA complexes through the chorion of embryos at early embryogenesis, leading to the rapid development of new mutant lines. We achieved comparable results when targeting the we orthologue in the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), illustrating the reliability of our methods when transferred to other related species. Finally, we functionally validated the recently discovered white pupae (wp) loci in the melon fly, successfully recreating the white puparium phenotype used in suppression programs of this and other major economically important tephritids. This is the first demonstration of CRISPR-based genome-editing in the genus Zeugodacus, and we anticipate that the procedures described here will contribute to advancing genome-editing in other non-model tephritid fruit flies.
Sedentary time and the risk of metabolic syndrome: A systematic review and dose–response meta‐analysis

The dose–response association between sedentary time and the risk of metabolic syndrome is unclear, which indicates an important knowledge gap in public health. The objective of this study was to determine the categorical and continuous dose–response associations between sedentary time and the risk of metabolic syndrome. A systematic literature search of English articles published in PubMed, CINHAL, Embase, and Web of Science Core Collection prior to June 2022 was conducted. All cohort and cross‐sectional studies that examined the association between sedentary time and the risk of metabolic syndrome were considered, and duplicate and non‐related studies were excluded. Data extraction using a standardized chart and quality assessment using two appraisal tools were also performed. Two independent reviewers were involved in these processes. In categorical meta‐analyses, the pooled effect sizes for metabolic syndrome associated with different categories of sedentary time were calculated by comparing the highest and intermediate with the lowest categories. In continuous meta‐analyses, the linear and nonlinear dose–response associations were estimated using generalized least squares and restricted cubic spline models, respectively. Data were collected and analyzed from March to June 2022. Four prospective cohort studies and 22 cross‐sectional studies with 105,239 participants and 16,185 MetS cases were included in this study. In categorical analyses, both intermediate (median duration: 4.11 h/day; pooled OR: 1.17, 95% CI: 1.08–1.26, P < 0.001) and high levels (median duration: 7.26 h/day; pooled OR: 1.71, 95% CI: 1.43–2.04, P < 0.001) of total sedentary time were significantly associated with an increased risk of metabolic syndrome. Similarly, a significant association between screen time and the risk of metabolic syndrome was also found in intermediate (median duration: 2.22 h/day; pooled OR: 1.20, 95% CI: 1.10–1.32, P < 0.001) and high levels (median duration: 3.40 h/day; pooled OR: 1.63, 95% CI: 1.44–1.86, P < 0.001) of exposure. Of note, these associations were significantly stronger in women. Different patterns of the behavior‐disease association were not observed in children, adolescents, and adults. The findings of continuous meta‐analyses could not provide solid evidence for the linearity and nonlinearity of the behavior‐disease association. This study demonstrated that long‐time sedentary behavior was associated with a higher risk of MetS independent of physical activity and the patterns of association varied by gender instead of age. These findings have implications for future guideline recommendations on physical activity, sedentary behavior, and prevention of metabolic syndrome.
Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods

Part I: Introduction to Stochastic Recursive Algorithms. - Introduction. - Deterministic Algorithms for Local Search. - Stochastic Approximation Algorithms. - Part II: Gradient Estimation Schemes. - Kiefer-Wolfowitz Algorithm. - Gradient Schemes with Simultaneous Perturbation Stochastic Approximation. - Smoothed Functional Gradient Schemes. - Part III: Hessian Estimation Schemes. - Hessian Estimation with Simultaneous Perturbation Stochasti Approximation. - Smoothed Functional Hessian Schemes. - Part IV: Variations to the Basic Scheme. - Discrete Optimization. - Algorithms for Contrained Optimization. - Reinforcement Learning. - Part V: Applications. - Service Systems. - Road Traffic Control. - Communication Networks.
Fractional Laplacian, Levy stable distribution, and time-space models for linear and nonlinear frequency-dependent lossy media

The frequency-dependent attenuation typically obeys an empirical power law with an exponent ranging from 0 to 2. The standard time-domain partial differential equation models can describe merely two extreme cases of frequency independent and frequency-squared dependent attenuations. The otherwise non-zeor and non-square frequency dependency occuring in many cases of practical interest is thus often called the anomalous attenuation. In this study, we developed a linear integro-differential equation wave model for the anomalous attenuation by using the space fractional Laplacian operation, and the strategy is then extended to the nonlinear Burgers, KZK, and Westervelt equations. A new definition of the fractional Laplacian is also introduced which naturally includes the boundary conditions and has inherent regularization to ease the hyper-singularity in the conventional fractional Laplacian. Under the Szabo's smallness approximation where attenuation is assumed to be much smaller than the wave number, our linear model is found consistent with arbitrary frequency dependencies. According to the fact that the physical attenuation can be understood a statistic process, the empirical range [0,2] of the power law exponent is explained via the Levy stable distribution theory. It is noted that the power law attentuation underlies fractal microstructures of anomalously attenuative media.
Electrical characterization and physical modeling of unipolar/bipolar resistive switching materials

≪Nam Sibyllam quidem Cumis ego ipse oculis meis vidi in ampulla pendere, et cum illi pueri dicerent: Σιβυλλα τι Θελεις; respondebat illa: απoΘαυειν Θελω. ≫ For Ezra Pound, il miglior fabbro. Thomas Stearn Eliot 1922, THE WASTE LAND In recent years the technology has marked the deepest changes in our lives and culture. The impressive development of new electronic devices is by far the fastest progress that human kind has ever experienced. The continuous demand of new portable, low-cost, low-power devices has forced a huge effort in R&D, pushing the limits of the current technology. The demand of memory supports represents an excellent example of such rush toward the limits of our technology. Flash memory, representing the main stream memory technology, experienced an impressive development being, at the time of writing, at the 25 nm node. The foresee of the intrinsic limit of Flash scalability, is also driving a tremendous amount of research in completely new memory technologies. This research is yielding to an exciting activity directed to a deeper physical understanding and, at the same time, to a fast technology development. This Doctoral Dissertation will be focused on Nickel Oxide-based and Hafnium Oxide-based Resistive-switching RAM (RRAM). The work describes working principles, physical/numerical modeling, reliability issues and innovative memory structures of RRAM technology. Experimental characterization has been a crucial step in this task, being the starting point of physical understanding and thus modeling. The first Chapter briefly describes the non-volatile memory market scenario, explaining the Flash technology basics showing its limits and presenting a few solutions that have been proposed. A first introduction
Simulation of printed-on-fabric assemblies

Printing-on-fabric is an affordable and practical method for creating self-actuated deployable surfaces: thin strips of plastic are deposited on top of a pre-stretched piece of fabric using a commodity 3D printer; the structure, once released, morphs to a programmed 3D shape. Several physics-aware modeling tools have recently been proposed to help designing such surfaces. However, existing simulators do not capture well all the deformations these structures can exhibit. In this work, we propose a new model for simulating printed-on-fabric composites based on a tailored bilayer formulation for modeling plastic-on-top-of-fabric strips, and an extended Saint-Venant–Kirchhoff material law for modeling the surrounding stretchy fabric. We show how to calibrate our model through a series of standard experiments. Finally, we demonstrate the improved accuracy of our simulator by conducting various tests.
The $A^2$ asymmetry and propagators in lattice $SU(2)$ gluodynamics at $T>T_c$

We study numerically the chromoelectric-chromomagnetic asymmetry of the dimension two $A^2$ gluon condensate as well as the transverse and longitudinal gluon propagators at $T>T_c$ in the Landau-gauge $SU(2)$ lattice gauge theory with a particular emphasis on finite-volume effects. We show that previously found so called symmetric point at which asymmetry changes sign is an artifact of the finite volume effects. We find that with increasing temperature the asymmetry decreases approaching zero value from above in agreement with perturbative result. Instead of the asymmetry we suggest the ratio of the transverse to longitudinal propagator taken at zero momentum as an indicator of the boundary of the postconfinement domain and find it at $T \simeq 1.7 T_c$.
Fake news in India

80 COMMUNICATIONS OF THE ACM | NOVEMBER 2022 | VOL. 65 | NO. 11 India is a nation that realizes unity in diversity. Indians follow different religions, practice different customs and traditions, and speak diverse languages.e These factors and others make it difficult to detect fake news in India. More specifically, we observe the following: ˲ Multilinguality. Mother tongue of Indians is diverse. There are 22 official languages and only 10.67% of the population converse in English.f The current fake news detection solutions are most effective for English and might fail to identify and process information in other languages. ˲ Instant messaging platform. We cannot undermine WhatsApp’s role in forming and mobilizing the online public. Since WhatsApp is end-to-end encrypted, identifying and quashing false stories is possible only with support of the users. ˲ Digital illiteracy. In India, the surge in the Internet pen-
A study on CSD-based storage engine scheduling for high-speed processing of large-scale data

As we enter the era of the 4th industrial revolution, artificial intelligence and big data technologies are being introduced in various industrial fields, and the computing architecture paradigm is shifting towards data. Accordingly, the importance of a database management system (DBMS) that manages a large amount of data is increasing, and a computing environment for processing memory-and storage-oriented data rather than process-oriented arithmetic processing is required. A computational storage device (CSD)-based DBMS performs query offloading in an environment in which the existing DBMS engine and computational storage are separated, enabling parallel computation and shortening query execution time. A snippet scheduling process considering the current situation is necessary. This study focuses on distributed parallel processing and high-speed query request processing by proposing the architecture of the CSD-based DBMS computational storage engine and the snippet scheduling technique that minimizes the movement of large-capacity data.
Hierarchical facial expression recognition

Learning facial expressions constitutes a challenging job due to the uncertainties caused by the ambiguity of facial expressions. To address this issue, we propose a simple yet efficient Coarse-to-Fine Network (CFNet) inspired by human being's cognitive mode for suppressing such uncertainties. A child learns quickly whether his behavior is allowed by reading adults' coarse facial expressions like positive or negative, and then adjusts accordingly via further interpreting its fine meaning like happy or angry. Similarly, CFNet first aggregates basic facial expressions into 3 coarse categories based on their distributions in the Valence-Arousal (VA) emotion space. Then, CFNet leverages fine labels with the coarse classification results for fine-grained facial expression recognition with discriminative loss handling high intra-class variations within coarse categories. Experiments on benchmark datasets demonstrate the superiority of our method over the state-of-art rivals.
Lore: a learning-based approach for workflow scheduling in clouds

The workflow scheduling problem is a critical challenge in clouds. Meticulously designed heuristics have attempted to address the intricate decision problem at a high cost. A more general approach is expected to handle different types of workflows and resource configurations. In this paper, a deep reinforcement Learning based apprOach for woRkflow schEduling (Lore) in clouds has been proposed to minimize the completion time of workflows. Moreover, Monte Carlo Tree Search and graph convolutional network are applied to improve performance further. Experimental results show that Lore outperforms the baselines, reducing average makespan by 2--10%, and enabling resource utilization increase by up to 20%.
Influence in Social Networks Through Visual Analysis of Image Memes

Memes evolve and mutate through their diffusion in social media. They have the potential to propagate ideas and, by extension, products. Many studies have focused on memes, but none so far, to our knowledge, on the users that post them, their relationships, and the reach of their influence. In this article, we define a meme influence graph together with suitable metrics to visualize and quantify influence between users who post memes, and we also describe a process to im-plement our definitions using a new approach to meme detection based on text-to- image area ratio and contrast. After applying our method to a set of users of the social media platform Instagram, we conclude that our metrics add information to already existing user characteristics.
Application of neural networks for the prediction of railway bearing failures

Bearing overheating and anomalous accelerations are two principal failure modes for this safety component. The supervision of bearing’s behaviour is essential to ensure a safe and reliable operation. A safety component’s failure may cause a speed limitation or even a non-available train for operating, so a predictive maintenance for bearings and other critical components is mandatory for the manufacturers, operators and maintainers in the railway sector. Bearing temperature, exterior temperature, train speed and other variables are measured every second in real time. From all the data collected and stored in the last years some algorithms and models are designed and trained in this paper to detect bearing anomalies 2 days before a real failure is detected and the safety alarm is enabled. The methodology for obtaining the optimal algorithm is exposed. Different artificial neural networks based on different optimization models such as the Mini-batch Gradient Descent (MGD) or Adam optimizer are compared. A final neural network with 10 hidden layers to detect bearing failure is proposed reaching 99% of accuracy, 95% of precision and 90% of sensitivity. The objective of predicting a bearing anomaly with some days in advanced is reached with high precision level, which will lead also to cost savings and a contribution for the sustainability because many inspections could be reduced and the energy cost associated to them.
Ethno-religious neighbourhood infrastructures and the life satisfaction of immigrants and their descendants in Germany

Urban research assigns immigrant enclaves an ambiguous role. While such areas are seen as rich in beneficial ethno-religious infrastructures and networks, they also tend to be located in deprived and stigmatised inner-city neighbourhoods. Research on neighbourhood attainment provides evidence for both, a desire to attain mainstream middle-class neighbourhoods, which grows the more immigrants and their descendants establish themselves in society, but also a continuing attraction of residing close to co-ethnics. To tease apart this ambiguity, we study how the life satisfaction of immigrants and their descendants depends on the characteristics of the neighbourhood they live in, and pay special attention to heterogeneity along generation, country of origin orientation and income. We use classic measures of neighbourhood quality vis-à-vis newly collected data on the spatial density of ethno-religious minority associations, places of worship and grocers. We link these data to the geocoded German Socio-Economic Panel to predict life satisfaction among immigrants and their descendants. To strengthen a causal interpretation of our results, we employ specifications that address self-selection into neighbourhoods and unobserved confounding. Contra the assumptions of standard assimilation models, we document that ethno-religious infrastructures contribute to increased life satisfaction primarily among the second generation, and there especially among sending-country oriented individuals. This suggests a continuing importance of origin-culture infrastructures for some groups. Furthermore, we find little evidence that overall neighbourhood quality, or the mere share of co-ethnics in a neighbourhood, increases life satisfaction either among immigrants or their descendants.
Cybersecurity in India

SECURING CYBERSPACE TODAY involves in-depth defense, which in turn requires taking a multilayered view of the systems in play. All layers must be secured, from the hardware processor and platform to the peripherals, firmware, operating system, network, protocols, crypto-algorithms, applications, services and microservices, APIs, virtualization, and cloud distribution. Cybersecurity also involves threat modeling; vulnerability assessment; risk analysis; and risk-driven, best-effort security controls. Further, based on the use of computing and networking, we have information-technology (IT) and operationaltechnology (OT) systems at various industrial-control and critical-infrastructure systems. In India, several academic institutes have chartered course work in cryptography and security. The earliest steps trace back to the launch of the Cryptology Research Society of India (CRSI)14 by multiple academic institutes, such as the Indian Statistical Institute (ISI), Indian Institute of Science (IISc), Institute of Mathematical Sciences (IMSc) Chennai, and the Indian Institute of Technology (IIT) Madras/Kanpur/Kharagpur. To extend the theoretical foundations of cryptology to information security and cybersecurity, special initiatives were taken by various academic partners. ISI Kolkata has been one of the forerunners in paving the path of academic research in the world of cryptography, focusing on varied areas, including symmetric key cryptography and various research topics in the domain of cryptology and security.32 IIT Kharagpur developed expertise in a state-of-the-art research laboratory called Secured Embedded Architecture Laboratory (SEAL), focusing on hardware security.38 IIT Kanpur, along with partners at IIT Kharagpur, IIIT Allahabad (IIITA), and IISc, runs a technology innovation hub (TIH)6 for cyber-physical systems (CPS) security, where IIT Kanpur mainly focuses on industrial control system (ICS)/supervisory control and data acquisition (SCADA) security while IIT Kharagpur focuses on hardware security, Trojan detection, and automotive security.39 IIT Kanpur is also involved in the security of unmanned aerial vehicles (UAVs). IIITA focuses on IoT security, while IISc focuses on multiparty computation. IIT Madras has a leading research group in foundational aspects of cybersecurity,13 and IIT Bombay, which fosters a strong group in theoretical cryptography, has also made pioneering contributions in secure computation, specifically in the domain of secure multiparty computations. This article looks at some of the major research accomplishments resulting from these efforts. Further, we discuss significant government initiatives that have helped to develop India’s discourse around cryptology and research.