Miracle-dz/UniFAR-FTUs huggingface dataset:
Inherent-state melting and the onset of glassy dynamics in two-dimensional supercooled liquids

Significance The dynamics of glass formers exhibit dramatic slowdown below an onset temperature that delineates the high-temperature and supercooled regimes. For two-dimensional (2D) glass formers, we propose that the onset temperature is described by a Kosterlitz–Thouless transition driven by the elastic excitations underlying the relaxation mechanism for glassy dynamics. Analogous to dislocation-mediated melting in 2D solids, the excitations exist as a bound dipole–dipole state in the supercooled regime and as free dipoles above the onset temperature. The Kosterlitz–Thouless scenario explains the elastic behavior of 2D supercooled liquids at intermediate timescales and thus, the Mermin–Wagner fluctuations observed in experiments and simulations of 2D glass formers. The present work reveals the exotic nature of 2D glass formers relevant to systems under extreme confinement.
MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision

Machine programming (MP) is concerned with automating software development. According to studies, software engineers spend upwards of 50% of their development time debugging software. To help accelerate debugging, we present MP-CodeCheck (MPCC). MPCC is an MP system that at-tempts to identify anomalous code patterns within logical program expressions. In designing MPCC, we developed two novel programming language representations, the forma-tions of which are critical in its ability to exhaustively and efficiently process the billions of lines of code that are used in its self-supervised training. To quantify MPCC’s performance, we compare it against ControlFlag, a state-of-the-art self-supervised code anomaly detection system; we find that MPCC is more spatially and temporally efficient. We demonstrate MPCC’s anomalous code detection capabilities by exercising it on a variety of open-source GitHub repositories and one proprietary code base. We also provide a brief qualitative study on some of the different classes of code anomalies that MPCC can detect to provide an abbreviated insight into its capabilities. 1
Curvature-driven instabilities in thin active shells

Spontaneous material shape changes, such as swelling, growth or thermal expansion, can be used to trigger dramatic elastic instabilities in thin shells. These instabilities originate in geometric incompatibility between the preferred extrinsic and intrinsic curvature of the shell, which may be modified by active deformations through the thickness and in plane, respectively. Here, we solve the simplest possible model of such instabilities, which assumes the shells are shallow, thin enough to bend but not stretch, and subject to homogeneous preferred curvatures. We consider separately the cases of zero, positive and negative Gauss curvature. We identify two types of supercritical symmetry-breaking instability, in which the shell’s principal curvature spontaneously breaks discrete up/down symmetry and continuous planar isotropy. These are then augmented by inversion instabilities, in which the shell jumps subcritically between up/down broken symmetry states and rotation instabilities, in which the curvatures rotate by 90° between states of broken isotropy without release of energy. Each instability has a thickness-independent threshold value for the preferred extrinsic curvature proportional to the square root of Gauss curvature. Finally, we show that the threshold for the isotropy-breaking instability is the same for deep spherical caps, in good agreement with recently published data.
Resolvent Analysis of an Under-expanded Planar Supersonic Impinging Jet

This investigation aims to assess the eﬀect of diﬀerent types of actuator forcing on the feedback loop of an under-expanded Mach 1.27 planar impinging jet using a resolvent framework. To this end, we employ a Large Eddy Simulation database as a truth model. The time and spanwise-averaged mean ﬂow is taken as an input to global stability and resolvent analyses with the purpose of examining both the intrinsic instability and input-output characteristics. The results show that the inherent instability and primary energy ampliﬁcation are attributed to the Kelvin-Helmholtz (K-H) instability. Moreover, the K-H response modes obtained from the resolvent analysis are in reasonable agreement with Spectral Proper Orthogonal Decomposition (SPOD) modes from the unsteady LES data. Insights into noise control are obtained by localizing the actuator forcing to the nozzle lip and the ground plate by imposing component-wise forcing to mimic diﬀerent notional actuators. It is observed that energy ampliﬁcation obtained for the localized component-wise forcing is diﬀerent from the global resolvent analysis and dependent on the type of actuator. This provides insights into the type, wavenumber and frequency of actuators for active ﬂow control.
Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography

Myocardial infarction (MI) is the leading cause of mortaZity and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a frame-work for early detection of MI over multi-view echocardio-graphy that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.
Case for Microservices Orchestration Using Workflow Engines

Microservices have become the de-facto software architecture for cloud-native applications. A contentious architectural decision in microservices is to compose them using choreography or orchestration. In choreography, every service works independently, whereas, in orchestration, there is a controller that coordinates service interactions. This paper makes a case for orchestration. The promise of microservices is that each microservice can be independently developed, deployed, tested, upgraded, and scaled. This makes them suitable for systems running on cloud infrastructures. However, microservice-based systems become complicated due to the complex interactions of various services, concurrent events, failing components, developers’ lack of global view, and configurations of the environment. This makes maintaining and debugging such systems very challenging. We hypothesize that orchestrated services are easier to debug and to test this we ported the largest publicly available microservices’ benchmark TrainTicket [24], which is implemented using choreography, to a fault-oblivious stateful workflow framework Temporal [19]. We report our experience in porting the code from traditional choreographed microservice architecture to one orchestrated by Temporal and present our initial findings of time to debug the 22 bugs present in the benchmark. Our findings suggest that an effort towards making a transition to orchestrated approach is worthwhile, making the ported code easier to debug.
TreeStep: Tree Search for Vector Perturbation Precoding under per-Antenna Power Constraint

—Vector Perturbation Precoding (VPP) can speed up downlink data transmissions in Large and Massive Multi-User MIMO systems but is known to be NP-hard. While there are several algorithms in the literature for VPP under total power constraint, they are not applicable for VPP under per-antenna power constraint. This paper proposes a novel, parallel tree search algorithm for VPP under per-antenna power constraint, called TreeStep , to ﬁnd good quality solutions to the VPP problem with practical computational complexity. We show that our method can provide huge performance gain over simple linear precoding like Regularised Zero Forcing. We evaluate TreeStep for several large MIMO ( 16 × 16 and 24 × 24 ) and massive MIMO ( 16 × 32 and 24 × 48 ) and demonstrate that TreeStep outperforms the popular polynomial-time VPP algorithm, the Fixed Complexity Sphere Encoder, by achieving the extremely low BER of 10 − 6 at a much lower SNR.
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy

Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion, and negotiation. Current corpus-based learning manner limits its practical application in real-world scenarios. To this end, we contribute to advancing the study of the proactive dialogue policy to a more natural and challenging setting, i.e., interacting dynamically with users. Further, we call attention to the non-cooperative user behavior - the user talks about off-path topics when he/she is not satisfied with the previous topics introduced by the agent. We argue that the targets of reaching the goal topic quickly and maintaining a high user satisfaction are not always converged, because the topics close to the goal and the topics user preferred may not be the same. Towards this issue, we propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors (dialogue turn, goal completion difficulty, user satisfaction estimation, and cooperative degree). The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.
One-sided weak dominance drawing. (English)

This paper studies the One-Sided Weak Dominance Drawing (OSWDD) problem from the perspectives of computational complexity and approximability. The authors formalize the problem, show that it is NP-hard and describe a half-approximation algorithm for a special case of the general problem. The Weak Dominance Drawing problem (WDD) is deﬁned as follows: Given a DAG G , ﬁnd two topological sorts such that the intersection between them is minimized. WDD is a well-studied problem and there exist several papers in the literature that have studied aspects of this problem. The authors, though, are concerned with the variant called OSWDD.
Polygenic risk, population structure and ongoing difficulties with race in human genetics

‘The Apportionment of Human Diversity’ stands as a noteworthy intervention, both for the field of human population genetics as well as in the annals of public communication of science. Despite the widespread uptake of Lewontin's conclusion that racial classification is of ‘virtually no genetic or taxonomic significance’, the biomedical research community continues to grapple with whether and how best to account for race in its work. Nowhere is this struggle more apparent than in the latest attempts to translate genetic associations with complex disease risk to clinical use in the form of polygenic risk scores, or PRS. In this perspective piece, we trace current challenges surrounding the appropriate development and clinical application of PRS in diverse patient cohorts to ongoing difficulties deciding which facets of population structure matter, and for what reasons, to human health. Despite numerous analytical innovations, there are reasons that emerge from Lewontin's work to remain sceptical that accounting for population structure in the context of polygenic risk estimation will allow us to more effectively identify and intervene on the significant health disparities which plague marginalized populations around the world. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.
Optimal Detection of Latent Mycobacterium tuberculosis Infection by Combined Heparin-Binding Hemagglutinin (HBHA) and Early Secreted Antigenic Target 6 (ESAT-6) Whole-Blood Interferon Gamma Release Assays

Optimal detection of latent tuberculosis (TB) infection (LTBI) remains a challenge, although it is essential to reach the goal of TB elimination. Our objective was to develop and clinically evaluate a user-friendly, 24-h, whole-blood (WB) interferon gamma (IFN-γ) release assay (IGRA) improving the detection of LTBI, compared to available tests. ABSTRACT Optimal detection of latent tuberculosis (TB) infection (LTBI) remains a challenge, although it is essential to reach the goal of TB elimination. Our objective was to develop and clinically evaluate a user-friendly, 24-h, whole-blood (WB) interferon gamma (IFN-γ) release assay (IGRA) improving the detection of LTBI, compared to available tests. One milliliter of blood was divided into four aliquots and in vitro stimulated for 24 h with two different stage-specific mycobacterial antigens, i.e., heparin-binding hemagglutinin (HBHA) and early secreted antigenic target 6 (ESAT-6), a latency-associated antigen and a bacterial replication-related antigen, respectively, in addition to positive and negative controls. Clinical evaluation was performed on two independent cohorts of carefully selected subjects, i.e., a training cohort of 83 individuals and a validation cohort of 69 individuals. Both cohorts comprised LTBI subjects (asymptomatic people with a positive tuberculin skin test result and potential exposure to TB index cases), patients with active TB (aTB), and noninfected controls. The sensitivity and specificity of the WB-HBHA-IGRA to identify LTBI subjects among asymptomatic individuals were 93%. Combining the results in response to HBHA and ESAT-6 allowed us to identify LTBI subgroups. One group, with IFN-γ responses to HBHA only, was easily differentiated from patients with aTB. The other group, responding to both antigens like the aTB group, is likely at risk to reactivate the infection and should be prioritized for prophylactic anti-TB treatment. The combined WB-IGRA may be offered to clinicians for the selection of LTBI subjects to benefit from prophylactic treatment.
Innate immune sensors for detecting nucleic acids during infection

Abstract Innate immune receptors detect nucleic acids, such as viruses, and initiate an immune response by secreting interferon (IFN) and regulating IFN-stimulated genes (ISG). in autoimmune conditions, expression of ISGs funded, show the activation of nucleic acid sensory pathways. However, the nucleus-localized innate sensors are recently found to detect pathogenic nucleic acids for initiating innate response, demonstrating a complicated crosstalk with cytoplasmic sensors and signaling molecules to form an elaborate tiered innate signaling network between nucleus and cytoplasm. to sustain immune hemostasis, these innate immune sensors develop different strategies for discriminating between self or non-self-nucleic acid. We reviewed all the sensors involved in the innate immune system in the present study. A better understanding of these sensors can lead to new treatments for infections, cancer, and autoimmune and inflammatory disorders.
Can Vitamins Slow Down the Body’s Aging Process?

Some people look younger than their age, others older. Have you ever wondered why? Can we help our bodies age more slowly? Although there seems to be no way to reverse the process of aging, we may be able to slow it down. Improving our diets may help! Humans are born with an internal biological clock within our cells, which reflects the aging state of the body. This is called the epigenetic clock, and it can be changed by what we eat. In this study, we found that women who took supplements of folic acid and vitamin B12 had a slower biological aging. More studies on the effects of our diets on the epigenetic clock will help people to live longer and to stay in good health.
Olive Fruit Fly Symbiont Population: Impact of Metamorphosis

The current symbiotic view of the organisms also calls for new approaches in the way we perceive and manage our pest species. The olive fruit fly, the most important olive tree pest, is dependent on an obligate bacterial symbiont to its larvae development in the immature fruit. This symbiont, Candidatus (Ca.) Erwinia dacicola, is prevalent throughout the host life stages, and we have shown significant changes in its numbers due to olive fruit fly metamorphosis. The olive fruit fly microbiota was analyzed through 16S metabarcoding, at three development stages: last instar larvae, pupae, and adult. Besides Ca. E. dacicola, the olive fruit flies harbor a diverse bacterial flora of which 13 operational taxonomic units (grouped in 9 genera/species) were now determined to persist excluding at metamorphosis (Corynebacterium sp., Delftia sp., Enhydrobacter sp., Kocuria sp., Micrococcus sp., Propionibacterium sp., Pseudomonas sp., Raoultella sp., and Staphylococcus sp.). These findings open a new window of opportunities in symbiosis-based pest management.
Control of Behavioral Arousal and Defense by a Glutamatergic Midbrain-Amygdala Pathway in Mice

In response to external threatening signals, animals evolve a series of defensive behaviors that depend on heightened arousal. It is believed that arousal and defensive behaviors are coordinately regulated by specific neurocircuits in the central nervous system. The ventral tegmental area (VTA) is a key structure located in the ventral midbrain of mice. The activity of VTA glutamatergic neurons has recently been shown to be closely related to sleep–wake behavior. However, the specific role of VTA glutamatergic neurons in sleep–wake regulation, associated physiological functions, and underlying neural circuits remain unclear. In the current study, using an optogenetic approach and synchronous polysomnographic recording, we demonstrated that selective activation of VTA glutamatergic neurons induced immediate transition from sleep to wakefulness and obviously increased the amount of wakefulness in mice. Furthermore, optogenetic activation of VTA glutamatergic neurons induced multiple defensive behaviors, including burrowing, fleeing, avoidance and hiding. Finally, viral-mediated anterograde activation revealed that projections from the VTA to the central nucleus of the amygdala (CeA) mediated the wake- and defense-promoting effects of VTA glutamatergic neurons. Collectively, our results illustrate that the glutamatergic VTA is a key neural substrate regulating wakefulness and defensive behaviors that controls these behaviors through its projection into the CeA. We further discuss the possibility that the glutamatergic VTA-CeA pathway may be involved in psychiatric diseases featuring with excessive defense.
Characterization of the Core Determinants of Social Influence From a Computational and Cognitive Perspective

Most human decisions are made among social others, and in what social context the choices are made is known to influence individuals' decisions. Social influence has been noted as an important factor that may nudge individuals to take more risks (e.g., initiation of substance use), but ironically also help individuals to take safer actions (e.g., successful abstinence). Such bi-directional impacts of social influence hint at the complexity of social information processing. Here, we first review the recent computational approaches that shed light on neural and behavioral mechanisms underlying social influence following basic computations involved in decision-making: valuation, action selection, and learning. We next review the studies on social influence from various fields including neuroeconomics, developmental psychology, social psychology, and cognitive neuroscience, and highlight three dimensions of determinants—who are the recipients, how the social contexts are presented, and to what domains and processes of decisions the influence is applied—that modulate the extent to which individuals are influenced by others. Throughout the review, we also introduce the brain regions that were suggested as neural instantiations of social influence from a large body of functional neuroimaging studies. Finally, we outline the remaining questions to be addressed in the translational application of computational and cognitive theories of social influence to psychopathology and health.
Application of 18F-FDG PET-CT Images Based Radiomics in Identifying Vertebral Multiple Myeloma and Bone Metastases

Purpose The purpose of this study was to explore the application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) image radiomics in the identification of spine multiple myeloma (MM) and bone metastasis (BM), and whether this method could improve the classification diagnosis performance compared with traditional methods. Methods This retrospective study collected a total of 184 lesions from 131 patients between January 2017 and January 2021. All images were visually evaluated independently by two physicians with 20 years of experience through the double-blind method, while the maximum standardized uptake value (SUVmax) of each lesion was recorded. A total of 279 radiomics features were extracted from the region of interest (ROI) of CT and PET images of each lesion separately by manual method. After the reliability test, the least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation were used to perform dimensionality reduction and screening of features. Two classification models of CT and PET were derived from CT images and PET images, respectively and constructed using the multivariate logistic regression algorithm. In addition, the ComModel was constructed by combining the PET model and the conventional parameter SUVmax. The performance of the three classification diagnostic models, as well as the human experts and SUVmax, were evaluated and compared, respectively. Results A total of 8 and 10 features were selected from CT and PET images for the construction of radiomics models, respectively. Satisfactory performance of the three radiomics models was achieved in both the training and the validation groups (Training: AUC: CT: 0.909, PET: 0.949, ComModel: 0.973; Validation: AUC: CT: 0.897, PET: 0.929, ComModel: 0.948). Moreover, the PET model and ComModel showed significant improvement in diagnostic performance between the two groups compared to the human expert (Training: P = 0.01 and P = 0.001; Validation: P = 0.018 and P = 0.033), and no statistical difference was observed between the CT model and human experts (P = 0.187 and P = 0.229, respectively). Conclusion The radiomics model constructed based on 18F-FDG PET/CT images achieved satisfactory diagnostic performance for the classification of MM and bone metastases. In addition, the radiomics model showed significant improvement in diagnostic performance compared to human experts and PET conventional parameter SUVmax.