Miracle-dz/UniFAR-FTUs huggingface dataset:
Average speed and its powers 
vn
 of a heavy quark in quarkonia

The typical velocity of a heavy quark in a quarkonium is widely used quantity, in this paper, based on the relativistic Bethe-Salpeter equation method, we calculate the average values $\overline{\boldsymbol{q}^n}$ and $\overline{\boldsymbol{v}^n}\equiv v^n$ of a heavy quark in a $S$ wave or $P$ wave quarkonium rest frame, where $\boldsymbol{q}$ and $\boldsymbol{v}$ are the three dimensional momentum and velocity, $n=1,2,3,4$. For a charm quark in $J/\psi$, we obtained $v_{J/\psi}=0.46$, $v^2_{J/\psi}=0.26$, $v^3_{J/\psi}=0.18$ and $v^4_{J/\psi}=0.14$, for a bottom quark in $\Upsilon(1S)$, $v_{\Upsilon(1S)}=0.24$, $v^2_{\Upsilon(1S)}=0.072$, $v^3_{\Upsilon(1S)}=0.025$ and $v^4_{\Upsilon(1S)}=0.010$. The values indicate that ${v^n} >{v^{n_1}}\cdot{v^{n_2}}$, where $n_1+n_2=n$, which is correct for all the charmonia and bottomonia. Our results also show the poor convergence if we make the velocity expansion in charmonium system, but good for bottomonium. Based on the $v^n$ values and the following obtained relations $v^n_{4S} > v^n_{3S}> v^n_{2S}>v^n_{1S}$, $v^n_{4P} > v^n_{3P}> v^n_{2P}>v^n_{1P}$ and $v^n_{mP}>v^n_{mS}$ ($n,m=1,2,3,4$), we conclude that highly excited quarkonia have larger relativistic corrections than those of the corresponding low excited and ground states, and there are large relativistic corrections in charmonium system.
Majorana representation of adiabatic and superadiabatic processes in three-level systems

We show that stimulated Raman adiabatic passage (STIRAP) and its superadiabatic version (saSTIRAP) have a natural geometric two-star representation on the Majorana sphere. In the case of STIRAP, we find that the evolution is confined to a vertical plane. A faster evolution can be achieved in the saSTIRAP protocol, which employs a counterdiabatic Hamiltonian to nullify the non-adiabatic excitations. We observe how, under realistic experimental parameters, the counterdiabatic term corrects the trajectory of the Majorana stars toward the dark state. We also introduce a spin-1 average vector and present its evolution during the two processes. We show that the Majorana representation can be used as a sensitive tool for the detection of process errors due to ac Stark shifts and non-adiabatic transitions.
Fault-Tolerant Coherent $H^\infty$ Control for Linear Quantum Systems

Robustness and reliability are two key requirements for developing practical quantum control systems. The purpose of this article is to design a coherent feedback controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop quantum system has both fault tolerance and <inline-formula><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> disturbance attenuation performance. This article first extends the physical realization conditions from the time-invariant case to the time-varying case for linear stochastic quantum systems. By relating the fault-tolerant <inline-formula><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> control problem to the dissipation properties and the solutions of Riccati differential equations, an <inline-formula><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> controller for the quantum system is then designed by solving a set of linear matrix inequalities. In particular, an algorithm is employed to introduce additional quantum inputs and to construct the corresponding input matrices to ensure the physical realizability of the quantum controller. Also, we propose a real application of the developed fault-tolerant control strategy to quantum optical systems. A linear quantum system example from quantum optics, where the amplitude of the pumping field randomly jumps among different values due to the fault processes, can be modeled as a linear Markovian jumping system. It is demonstrated that a quantum <inline-formula><tex-math notation="LaTeX">$H^\infty$</tex-math></inline-formula> controller can be designed and implemented using some basic optical components to achieve the desired control goal for this class of systems.
A Stackelberg Game Theoretic Model of Lane-Merging

Merging in the form of a mandatory lane-change is an important issue in transportation research. Even when safely completed, merging may disturb the mainline traffic and reduce the efficiency or capacity of the roadway. In this paper, we consider a Stackelberg game-theoretic driver behavior model where the so-called utilities or payoffs reflect the merging vehicle's aggressiveness as it pertains the decision to merge as the situation stands or to accelerate, decelerate prior to the actual lane-change maneuver. The interaction of the merging vehicle with the mainline traffic is also considered whereby the combination of aggressiveness of the respective vehicles leads to both longitudinal and lateral disturbances to the mainline flow as well as subsequent reduction in the roadway throughput. The present study shows in semi-quantitative form that this impact depends on the level of aggressiveness of the merging and mainline vehicles, leading to the potential use of this model in traffic flow analysis and autonomous driving.
Near‐infrared
 photometry of superthin edge‐on galaxies

We perform near-infrared photometry of a large sample of 49 superthin edge-on galaxies. These galaxies are selected based on optical photometry because of high radial-to-vertical scale ratio in their stellar disks. The Near Infrared (NIR) H and K observations were conducted with the cryogenic-cooled camera ASTRONIRCAM on the 2.5m telescope at the Caucasus Mountain Observatory of Lomonosov Moscow State University. A majority of galaxies in our sample show comparable or better photometric depth than the Sloan Digital Sky Survey (SDSS) optical images. We estimate the structural parameters of stellar disks in the galaxies and find that the NIR scale height of stellar disks is comparable to that estimated from the optical, SDSS g, r and i, whereas the H and K scale length of the stellar disks is significantly shorter than in the g, r and i. We investigate if a realistic distribution of dust alone can explain the difference in the scale length and find that in the majority of the galaxies the radial variation of the stellar population is actually responsible for the color distribution. The latter suggests a younger age of the disks periphery, and the inside out building up of stellar disks in the superthin galaxies.
Simulating Hybrid Aerial- and Ground-based Vehicular Networks with ns-3 and LIMoSim

Integrating Unmanned Aerial Vehicles (UAVs) into future Intelligent Transportation Systems (ITSs) allows to exploit their unique mobility potentials for improving the performance of services such as near-field parcel delivery, dynamic network provisioning, and aerial sensing. In order to provide a controllable environment for the methodological performance analysis, simulation frameworks need to support ground- and aerial-based mobility as well as the involved communication technologies. In this paper, we present the open source Lightweight ICT-centric Mobility Simulation (LIMoSim) framework for simulating hybrid vehicular networks within Network Simulator 3 (ns-3). LIMoSim implements a shared codebase coupling approach which integrates all required components in a single simulation process. The low-level mobility behaviors rely on well-known analytical models. Different case studies discussing cutting-edge communication technologies such as Cellular Vehicle-to-Everything (C-V2X) and millimeter Wave (mmWave) are presented in order to illustrate how the proposed framework can be integrated into ns-3-based network simulation setups.
Koopman–von Neumann approach to quantum simulation of nonlinear classical dynamics

Quantum computers can be used to simulate nonlinear non-Hamiltonian classical dynamics on phase space by using the generalized Koopman-von Neumann formulation of classical mechanics. The Koopman-von Neumann formulation implies that the conservation of the probability distribution function on phase space, as expressed by the Liouville equation, can be recast as an equivalent Schrodinger equation on Hilbert space with a Hermitian Hamiltonian operator and a unitary propagator. This Schrodinger equation is linear in the momenta because it derives from a constrained Hamiltonian system with twice the classical phase space dimension. A quantum computer with finite resources can be used to simulate a finite-dimensional approximation of this unitary evolution operator. Quantum simulation of classical dynamics is exponentially more efficient than a deterministic Eulerian discretization of the Liouville equation if the Koopman-von Neumann Hamiltonian is sparse. Utilizing quantum walk techniques for state preparation and amplitude estimation for the calculation of observables leads to a quadratic improvement over classical probabilistic Monte Carlo algorithms.
New Security Challenges on Machine Learning Inference Engine: Chip Cloning and Model Reverse Engineering

Machine learning inference engine is of great interest to smart edge computing. Compute-in-memory (CIM) architecture has shown significant improvements in throughput and energy efficiency for hardware acceleration. Emerging non-volatile memory technologies offer great potential for instant on and off by dynamic power gating. Inference engine is typically pre-trained by the cloud and then being deployed to the filed. There are new attack models on chip cloning and neural network model reverse engineering. In this paper, we propose countermeasures to the weight cloning and input-output pair attacks. The first strategy is the weight fine-tune to compensate the analog-to-digital converter (ADC) offset for a specific chip instance while inducing significant accuracy drop for cloned chip instances. The second strategy is the weight shuffle and fake rows insertion to allow accurate propagation of the activations of the neural network only with a key.
Quantum boomerang effect for interacting particles

When a quantum particle is launched with a finite velocity in a disordered potential, it may surprisingly come back to its initial position at long times and remain there forever. This phenomenon, dubbed ``quantum boomerang effect'', was introduced in [Phys. Rev. A 99, 023629 (2019)]. Interactions between particles, treated within the mean-field approximation, are shown to partially destroy the boomerang effect: the center of mass of the wave packet makes a U-turn, but does not completely come back to its initial position. We show that this phenomenon can be quantitatively interpreted using a single parameter, the average interaction energy.
Robust Out-of-distribution Detection for Neural Networks

Detecting anomalous inputs is critical for safely deploying deep learning models in the real world. Existing approaches for detecting out-of-distribution (OOD) examples work well when evaluated on natural samples drawn from a sufficiently different distribution than the training data distribution. However, in this paper, we show that existing detection mechanisms can be extremely brittle when evaluating on inputs with minimal adversarial perturbations which don't change their semantics. Formally, we introduce a novel and challenging problem, Robust Out-of-Distribution Detection, and propose an algorithm that can fool existing OOD detectors by adding small perturbations to the inputs while preserving their semantics and thus the distributional membership. We take a first step to solve this challenge, and propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples. Our method can be flexibly combined with, and render existing methods robust. On common benchmark datasets, we show that ALOE substantially improves the robustness of state-of-the-art OOD detection, with 58.4% AUROC improvement on CIFAR-10 and 46.59% improvement on CIFAR-100. Finally, we provide theoretical analysis for our method, underpinning the empirical results above.
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization

Knowledge graphs (KGs) store highly heterogeneous information about the world in the structure of a graph, and are useful for tasks such as question answering and reasoning. However, they often contain errors and are missing information. Vibrant research in KG refinement has worked to resolve these issues, tailoring techniques to either detect specific types of errors or complete a KG. In this work, we introduce a unified solution to KG characterization by formulating the problem as unsupervised KG summarization with a set of inductive, soft rules, which describe what is normal in a KG, and thus can be used to identify what is abnormal, whether it be strange or missing. Unlike first-order logic rules, our rules are labeled, rooted graphs, i.e., patterns that describe the expected neighborhood around a (seen or unseen) node, based on its type, and information in the KG. Stepping away from the traditional support/confidence-based rule mining techniques, we propose KGist, Knowledge Graph Inductive SummarizaTion, which learns a summary of inductive rules that best compress the KG according to the Minimum Description Length principle—a formulation that we are the first to use in the context of KG rule mining. We apply our rules to three large KGs (NELL, DBpedia, and Yago), and tasks such as compression, various types of error detection, and identification of incomplete information. We show that KGist outperforms task-specific, supervised and unsupervised baselines in error detection and incompleteness identification, (identifying the location of up to 93% of missing entities—over 10% more than baselines), while also being efficient for large knowledge graphs.
The loss of anterior cruciate ligament integrity and the development of radiographic knee osteoarthritis

Osteoarthritis (OA) is the most common joint disorder, and a leading cause of disability. Due to an aging population and increasing obesity the incidence of OA is rising. The etiology of OA is multifactorial and complex thus prevention of OA remains challenging. Risk factors can be divided into person-level factors, such as age, sex, obesity, genetics, race/ethnicity and diet, and joint-level factors including injury, malalignment and abnormal loading of the joints. This narrative review provides a brief overview on the person level risk factors and a more in depth analysis of those at the joint level. It is only through an improved understanding of risk factors for disease that we may be able to meaningfully intervene to prevent its occurrence. Corresponding Author: Victoria L Johnson
The New Role of Health Educators in Reducing Health Disparities: From Research to Action Plan

Concern about health disparities has long been an ongoing dilemma and has received increased attention both by academic study and public policy. The increased awareness and interest in health disparities is clearly reflected in the evolution of the federal government publication Healthy People. Addressing health disparities was not included as one of the overarching goals in the first 1979 Surgeon General’s Report, Healthy People: The Surgeon General’s Report on Health Promotion and Disease Prevention. Nor was it an overarching goal in Healthy People 1990: Promoting Health/Preventing Disease: Objectives for the Nation. However, since then, it has been explicitly included in subsequent publications of Healthy People and given increased prominence as an overarching goal. For example, in Healthy People 2000: National Health Promotion and Disease Prevention Objectives, it first appeared as “to reduce health disparities among Americans. ” In Healthy People 2010: Understanding and Improving Health, it was changed to eliminate, not just reduce, health disparities. That goal was further expanded in Healthy People 2020 to achieve health equity, eliminate disparities, and improve the health of all groups. Healthy People 2020 defines a health disparity as “a particular type of health difference that is closely linked with social or economic disadvantage. ” Health disparities have been well documented ad nauseam in terms of morbidity, disability, and mortality. Countless studies have shown that vulnerable populations exhibit higher rates of adverse health conditions and experience worse health outcomes. Reducing or eliminating health disparities can enhance efforts designed to prevent or delay the onset of major chronic diseases and illnesses such as cardiovascular disease, cancer, diabetes, chronic obstructive pulmonary disorder, cirrhosis, Alzheimer’s/dementia, etc., and enhance efforts in chronic disease management. Most apparent has been classifying disparities by racial or ethnic group, gender, income, education, geographic location, or in various combinations of these and other characteristics historically linked to discrimination or exclusion. Based on published research, there has been no shortage of strategies and suggestions to reduce health disparities in our country. Yet, health disparities remain ubiquitous. Although disparities have been documented for decades and there have been overall improvements in our nation’s health, many disparities have persisted and, in some cases, widened over time.
The state of network neutrality regulation

The Network Neutrality (NN) debate refers to the battle over the design of a regulatory framework for preserving the Internet as a public network and open innovation platform. Fueled by concerns that broadband access service providers might abuse network management to discriminate against third party providers (e.g., content or application providers), policymakers have struggled with designing rules that would protect the Internet from unreasonable network management practices. In this article, we provide an overview of the history of the debate in the U.S. and the EU and highlight the challenges that will confront network engineers designing and operating networks as the debate continues to evolve.
Facial Temperature Changes Following Intranasal Sphenopalatine Ganglion Nerve Block

Objectives: To determine whether a facial temperature change occurs following lidocaine delivery to the sphenopalatine foramen via an intranasal catheter. Background: Intranasal SPG blockade has received renewed interest with the advent of intranasal catheters that direct medication to the sphenopalatine foramen to achieve neural blockade. However, it is unclear whether such procedures have a measurable effect on cranial autonomic function. To date there have been no published reports of cranial autonomic nervous system effect via temperature change from intranasal SPG block procedures. Methods: This was a retrospective analysis of 47 patients with a diagnosis of chronic head or facial pain who underwent intranasal sphenopalatine ganglion (SPG) blockade. Facial temperatures were monitored using skin temperature probes over the ipsilateral zygoma and recorded pre-procedure and 15 minutes post-procedure. Results: 47 patients were included. The average temperature change was an increase of +1.4 °C which was statistically significant (p<0.001) and 50% of patients had a 1 °C or higher temperature change. Conclusion: This study is the first to show a statistically significant increase in temperature following intranasal SPG block. This suggests that delivery of anesthetic to the sphenopalatine foramen results in blockade of the sympathetic fiber activity possibly via the maxillary artery plexus branches surrounding the sphenopalatine foramen prior to parasympathetic activity in the SPG. Journal of Nature and Science (JNSCI), 3(5):e354, 2017
Proactive HIV testing strategies in primary care

After the introduction of therapy, HIV changed from a deadly disease to a chronic treatable infection, with those infected – if treated in time – having almost the same life expectancy as those not infected. Also, other studies have reported that early initiation of therapy greatly reduces the risk of transmission of HIV to sexual partners. Thus early detection is essential, but to date HIV testing of high-risk groups has not halted the progress of the epidemic in the Netherlands. The studies in this thesis were initiated with the goal of increasing our understanding of additional proactive HIV testing strategies in primary care and other healthcare settings. The results of this thesis contribute in particular to improve HIV testing programmes and assist in the implementation of these strategies in daily practice. General practitioners are key players in a proactive HIV testing policy. One of the main findings of this thesis is that HIV-infected patients frequently visit their general practitioner and a large group of these individuals present with HIV indicator conditions in the period prior to HIV diagnosis. HIV indicator condition-guided testing by general practitioners could be the starting point for more proactive provider-initiated HIV testing.
Estrogen Reverses HDAC Inhibitor-Mediated Repression of Aicda and Class-Switching in Antibody and Autoantibody Responses by Downregulation of miR-26a

Estrogen contributes to females' strong antibody response to microbial vaccines and proneness to autoimmunity, particularly antibody-mediated systemic autoimmunity, in females. We have hypothesized that this is due to estrogen-mediated potentiation of class switch DNA recombination (CSR) and somatic hypermutation (SHM). As we have shown, estrogen boosts AID expression, which is critical for both CSR and SHM, through upregulation of HoxC4, which together with NF-κB critically mediates Aicda (AID gene) promoter activation. We contend here that additional regulation of Aicda expression by estrogen occurs through epigenetic mechanisms. As we have shown, histone deacetylase inhibitors (HDIs) short-chain fatty acid (SCFA) butyrate and propionate as well as the pharmacologic HDI valproic acid upregulate miRNAs that silence AID expression, thereby modulating specific antibody responses in C57BL/6 mice and autoantibody responses in lupus-prone MRL/Faslpr/lpr mice. Here, using constitutive knockout Esr1−/− mice and B cells as well as conditional knockout Aicdacre/creEsr1flox/flox mice and B cells, we showed that the HDI-mediated downregulation of Aicda expression as well as the maturation of antibody and autoantibody responses is reversed by estrogen and enhanced by deletion of ERα or E2 inhibition. Estrogen's reversion of HDI-mediated inhibition of Aicda and CSR in antibody and autoantibody responses occurred through downregulation of B cell miR-26a, which, as we showed, targets Aicda mRNA 3′UTR. miR-26a was significantly upregulated by HDIs. Accordingly, enforced expression of miR-26a reduced Aicda expression and CSR, while miR-26a-sponges (competitive inhibitors of miR-26a) increased Aicda expression and CSR. Thus, our findings show that estrogen reverses the HDI-mediated downregulation of AID expression and CSR through selective modulation of miR-26a. They also provide mechanistic insights into the immunomodulatory activity of this hormone and a proof-of-principle for using combined ER inhibitor-HDI as a potential therapeutic approach.