Miracle-dz/UniFAR-FTUs huggingface dataset:
$\mathcal{N}=1$ Supersymmetric SU(3) Gauge Theory with a Twist

We investigate the pure gauge sector of Super-QCD, i.e. Super-Yang-Mills (SYM) theory, with focus on the bound states. To improve chiral symmetry as well as supersymmetry at finite lattice spacing, we use a deformed SYM lattice action. It contains a twist term, similar to the lattice formulation of twisted mass QCD. We present the status of our theoretical and numerical investigation.
A model predictive approach for online mobile manipulation of non-holonomic objects using learned dynamics

Assistive robots designed for physical interaction with objects will play an important role in assisting with mobility and fall prevention in healthcare facilities. Autonomous mobile manipulation presents a hurdle prior to safely using robots in real-life applications. In this article, we introduce a mobile manipulation framework based on model predictive control using learned dynamics models of objects. We focus on the specific problem of manipulating legged objects such as those commonly found in healthcare environments and personal dwellings (e.g., walkers, tables, chairs). We describe a probabilistic method for autonomous learning of an approximate dynamics model for these objects. In this method, we learn dynamic parameters using a small dataset consisting of force and motion data from interactions between the robot and object. Moreover, we account for multiple manipulation strategies by formulating manipulation planning as a mixed-integer convex optimization. The proposed framework considers the hybrid control system composed of (i) choosing which leg to grasp and (ii) control of continuous applied forces for manipulation. We formalize our algorithm based on model predictive control to compensate for modeling errors and find an optimal path to manipulate the object from one configuration to another. We present results for several objects with various wheel configurations. Simulation and physical experiments show that the obtained dynamics models are sufficiently accurate for safe and collision-free manipulation. When combined with the proposed manipulation planning algorithm, the robot successfully moves the object to the desired pose while avoiding any collision.
Quantum Memristors in Frequency-Entangled Optical Fields

A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.
Solution to Electric Field Screening in Diamond Quantum Electrometers

There are diverse interdisciplinary applications for nanoscale resolution electrometry of elementary charges under ambient conditions. These include characterization of 2D electronics, charge transfer in biological systems, and measurement of fundamental physical phenomena. The nitrogen-vacancy center in diamond is uniquely capable of such measurements, however electrometry thus far has been limited to charges within the same diamond lattice. It has been hypothesized that the failure to detect charges external to diamond is due to quenching and surface screening, but no proof, model, or design to overcome this has yet been proposed. In this work we affirm this hypothesis through a comprehensive theoretical model of screening and quenching within a diamond electrometer and propose a solution using controlled nitrogen doping and a fluorine-terminated surface. We conclude that successful implementation requires further work to engineer diamond surfaces with lower surface defect concentrations.
A fast and efficient SIMD track reconstruction algorithm for the LHCb upgrade 1 VELO-PIX detector

The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance. We further compare the algorithm to current state-of-the-art scalar pattern recognition algorithms. We show a factor 2 speedup while achieving similar or better levels of physics performance.
Perturbative approach to tunneling and quantum interferences in spin clusters

Collective tunneling is a ubiquitous phenomenon in finite-size spin clusters that shows up in systems as diverse as molecular magnets or spin clusters adsorbed at surfaces. The problem we explore is to understand how small flipping terms can cooperate to flip a large spin to the opposite direction or a cluster of interacting elementary Ising spins into the time-reversed state. These high order processes will involve at least two channels, a single spin-flip channel due to a transverse field and a two-spin flip channel due to exchange or due to single-ion anisotropies. In the present paper, we show that high-order perturbation theory can be formulated and evaluated with the help of simple recurrence relations, leading to a compact theory of tunnelling in macroscopic spins, in one-dimensional clusters, as well as in small higher-dimensional clusters. This is demonstrated explicitly in the case of the Ising model with a transverse field and transverse exchange, and in the case of macroscopic spins with uniaxial anisotropy. Our approach provides a transparent theory of level crossings, where the tunneling between time reversed configurations vanishes as a function of the external field. The crossings result from destructive quantum interferences between competing flipping channels. Our theory consistently predicts N crossings in chains of N Ising spins, 2S crossings in single spins of magnitude S, and yields explicit analytical formulae for the level crossings of open chains and macroscopic spins. Disorder can be easily implemented in this perturbative formalism. Leading disorder effects can be treated analytically for spin rings. At the smallest transverse field crossing, the suppression of tunneling is most robust with respect to disorder. We briefly discuss the implications of our findings for the use of realistic spin clusters on surfaces to store information.
Practical Solutions for Machine Learning Safety in Autonomous Vehicles

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we review and organize practical machine learning safety techniques that can complement engineering safety for machine learning based software in autonomous vehicles. Our organization maps safety strategies to state-of-the-art machine learning techniques in order to enhance dependability and safety of machine learning algorithms. We also discuss security limitations and user experience aspects of machine learning components in autonomous vehicles.
High Resolution Millimeter Wave Imaging For Self-Driving Cars

Recent years have witnessed much interest in expanding the use of networking signals beyond communication to sensing, localization, robotics, and autonomous systems. This paper explores how we can leverage recent advances in 5G millimeter wave (mmWave) technology for imaging in self-driving cars. Specifically, the use of mmWave in 5G has led to the creation of compact phased arrays with hundreds of antenna elements that can be electronically steered. Such phased arrays can expand the use of mmWave beyond vehicular communications and simple ranging sensors to a full-fledged imaging system that enables self-driving cars to see through fog, smog, snow, etc. Unfortunately, using mmWave signals for imaging in self-driving cars is challenging due to the very low resolution, the presence of fake artifacts resulting from multipath reflections and the absence of portions of the car due to specularity. This paper presents HawkEye, a system that can enable high resolution mmWave imaging in self driving cars. HawkEye addresses the above challenges by leveraging recent advances in deep learning known as Generative Adversarial Networks (GANs). HawkEye introduces a GAN architecture that is customized to mmWave imaging and builds a system that can significantly enhance the quality of mmWave images for self-driving cars.
Markov blankets, information geometry and stochastic thermodynamics

This paper considers the relationship between thermodynamics, information and inference. In particular, it explores the thermodynamic concomitants of belief updating, under a variational (free energy) principle for self-organization. In brief, any (weakly mixing) random dynamical system that possesses a Markov blanket—i.e. a separation of internal and external states—is equipped with an information geometry. This means that internal states parametrize a probability density over external states. Furthermore, at non-equilibrium steady-state, the flow of internal states can be construed as a gradient flow on a quantity known in statistics as Bayesian model evidence. In short, there is a natural Bayesian mechanics for any system that possesses a Markov blanket. Crucially, this means that there is an explicit link between the inference performed by internal states and their energetics—as characterized by their stochastic thermodynamics. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
Population Differentiation and Demographic History of the Cycas taiwaniana Complex (Cycadaceae) Endemic to South China as Indicated by DNA Sequences and Microsatellite Markers

Historical geology, climatic oscillations, and seed dispersal capabilities are thought to influence the population dynamics and genetics of plants, especially for distribution-restricted and threatened species. Investigating the genetic resources within and among taxa is a prerequisite for conservation management. The Cycas taiwaniana complex consists of six endangered species that are endemic to South China. In this study, we investigated the relationship between phylogeographic history and the genetic structure of the C. taiwaniana complex. To estimate the phylogeographic history of the complex, we assessed the genetic structure and divergence time, and performed phylogenetic and demographic historical analyses. Two chloroplast DNA intergenic regions (cpDNA), two single-copy nuclear genes (SCNGs), and six microsatellite loci (SSR) were sequenced for 18 populations. The SCNG data indicated a high genetic diversity within populations, a low genetic diversity among populations, and significant genetic differentiation among populations. Significant phylogeographical structure was detected. Structure and phylogenetic analyses both revealed that the 18 populations of the C. taiwaniana complex have two main lineages, which were estimated to diverge in the Middle Pleistocene. We propose that Cycas fairylakea was incorporated into Cycas szechuanensis and that the other populations, which are mainly located on Hainan Island, merged into one lineage. Bayesian skyline plot analyses revealed that the C. taiwaniana complex experienced a recent decline, suggesting that the complex probably experienced a bottleneck event. We infer that the genetic structure of the C. taiwaniana complex has been affected by Pleistocene climate shifts, sea-level oscillations, and human activities. In addition to providing new insights into the evolutionary legacy of the genus, the genetic characterizations will be useful for the conservation of Cycas species.
Combination of STING Pathway Agonist With Saponin Is an Effective Adjuvant in Immunosenescent Mice

There is an urgent need to improve protective responses to influenza vaccination in the elderly population, which is at especially high risk for adverse outcomes from influenza infection. Currently available inactivated vaccines provide limited protection, even when a 4-fold higher dose of the vaccine is administered. Adjuvants are often added to vaccines to boost protective efficacy. Here we describe a novel combination of an activator of the STING pathway, 2′,3′-cyclic guanosine monophosphate–adenosine monophosphate (cGAMP) with a saponin adjuvant, that we found to be highly effective in boosting protective immunity from vaccination in an aged mouse model. Using this combination with a subunit influenza vaccine, we observed that survival of vaccinated 20 month-old mice after lethal challenge increased from 0 to 20% with unadjuvanted vaccine to 80–100%, depending on the vaccination route. Compared to unadjuvanted vaccine, the levels of vaccine-specific IgG and IgG2a increased by almost two orders of magnitude as early as 2 weeks after a single immunization with the adjuvanted formulation. By analyzing phosphorylation of interferon regulatory factor 3 (IRF3) in cell culture, we provide evidence that the saponin component increases access of exogenous cGAMP to the intracellular STING pathway. Our findings suggest that combining a STING activator with a saponin-based adjuvant increases the effectiveness of influenza vaccine in aged hosts, without having to increase dose or perform additional vaccinations. This study reports a novel adjuvant combination that (a) is more effective than current methods of boosting vaccine efficacy, (b) can be used to enhance efficacy of licensed influenza vaccines, and (c) results in effective protection using a single vaccine dose.
The role of FNAC in diagnostically challenging malignant lesions of breast

Background: Fine needle aspiration cytology is a simple investigation to evaluate the malignant tumors of the breast when compared to tru-cut biopsy. It is invaluable in the diagnosis of the common invasive carcinoma of the breast including its subtypes. The morphology of the uncommon subtypes and other rare tumors are the source of diagnostic confusion. Aims: The objective of this study is to assess the role of FNAC in diagnostically challenging malignant lesions of the breast. Methods and Material: The study included 526 female patients with palpable breast lumps subjected to FNAC for one year from November 2015 to October 2016 in the department of pathology. The cytology features of these patients were studied and correlated with histopathology. Results: Diagnostic difficulty was encountered in certain tumors having either bland cytology or a predominant discrete pattern or a combination of extracellular mucin and papillary pattern. These were reanalyzed in correlation with histopathology, clinical findings, radiology and immunohistochemistry. Conclusions: Awareness of the morphology of these rare lesions improves the diagnostic accuracy making cytology a more efficient tool for the preoperative assessment of malignant breast tumors.
A Clinical and Radiographic Evaluation of the Efficacy of Nanohydroxyapatite (Sybograf™) versus Bioactive Calcium Phosphosilicate Putty (Novabone®) in the Treatment of Human Periodontal Infrabony Defects: A Randomized Clinical Trial

Aim: The aim of this study is to compare and to evaluate clinically and radiographically the bone regeneration and the amount of bone fill (BL) between nanocrystalline hydroxyapatite (Nc-HA) (Sybograf™) and bioactive synthetic NovaBone Putty in the treatment of intrabony component of periodontal osseous defects. Materials and Methods: Twenty sites in 20 patients, within the age range of 25–55 years, showing intrabony defects were selected and divided into Group I (Nc-HA) and Group II (Bioactive synthetic NovaBone Putty). All the selected sites were assessed with the clinical and radiographic parameters such as plaque index, gingival index, sulcus bleeding index, probing pocket depth, clinical attachment level, gingival recession, and radiographic BL. All the clinical and radiographic parameter values obtained at different intervals (baseline, 3, and 6 and 9 months) were subjected to statistical analysis. Results: A statistically significant reduction in pocket depth of 4.400 ± 0.843 mm (Group I), 3.800 ± 0.789 mm (Group II) and gain in clinical attachment level of 6.2 mm (Group I), 5.9 mm (Group II) were recorded at the end of the study. A slight increase in gingival recession was observed. The mean percentage changes in the amount of radiographic BL of Group II and Group I were significant, However, when compared between the groups, there is no significant difference in BL observed. Conclusion: Both the graft materials appear to have nearly comparable effects, with nanocrystalline hydroxyapatite (Sybograf™), displaying slightly superior effect over bioactive glass especially in relation to clinical parameters. However, long-term, controlled clinical trials are required to confirm these findings.
Feasibility and Pilot Efficacy Testing of Integrated Yoga and Shirodhara (Ayurvedic Oil-Dripping) Intervention on Clinical Symptoms, Cognitive Functions and Sleep Quality of Adults with Anxiety Disorder

Background: Beneficial effects of yoga therapy in anxiety disorders (ADs) are known. Traditional texts describe usefulness of Ayurvedic oil-dripping, Shirodhara technique, in relieving anxiety. Thus, present study was planned to assess the feasibility and synergistic value of Shirodhara as an add-on to yoga therapy in adults with AD. Materials and Methods: Thirty adults (males = 14, females = 16) admitted in a residential holistic health care center with an age range of 29.66 ± 6.63 years and diagnosis of one of the ADs (generalized AD, n = 18; social phobia, n = 8; and panic AD, n = 4) as per mini-international neuropsychiatric interview (M. I. N. I. English version 5.0) by a psychiatrist were randomly divided into two groups: (1) integrated yoga-based lifestyle program (YT; n = 15) and (2) YT + Ayurveda (YA; n = 15). Both groups continued to receive conventional treatment and were on stable medications throughout the study period except in cases of emergency. Assessments were done by an independent assessor at baseline and after 2 weeks of intervention for clinical symptoms (HAM-A, State and Trait Anxiety Inventory, and Brief Psychiatric Rating Scale), sleep quality (sleep rating questionnaire), and cognition (Stroop test and digit letter substitution test) using standard validated tools. Parametric tests were applied using SPSS 10.0 to analyze the data. Results: Twelve subjects in yoga group and thirteen subjects in YA group completed the trial. No side effects were reported in any of the groups. Within-group comparisons showed a significant improvement in clinical symptoms, cognition and sleep quality in both the groups. Between-group comparisons showed significantly better scores in Stroop word task for YA group as compared to YT group. Furthermore, there was a trend toward better improvement in sleep quality for YA group. Conclusion: Adding of Shirodhara technique to YT was feasible and may be useful in improving executive memory and sleep quality in adults with ADs.
Risk factors associated with recurrent stroke: A retrospective hospital-based study

Objective: To determine the prevalence of the recurrent stroke and its risk factors in the city of Ahvaz, Iran. Methods: The present study investigated the medical records of 389 ischemic stroke patients admitted to the Department of Neurology, Ahvaz Hospitals from March 2015 to January 2016. Patients’ information was collected. Patients who suffered recurrent stroke but survived were followed up for at least 12 months after the first attack, and their medical records were collected. Results: The incidence rate of recurrent stroke was 14.91% (n=58). There was a significant relationship between the incidence rate of recurrent ischemic stroke with atrial fibrillation (OR=2.012, P<0.05) and ischemic heart disease (OR=1.695, P<0.05). However, there was no significant relationship between recurrent stroke and age, sex, dyslipidemia, diabetes, hypertension, and cigarette smoking (P>0.05). Conclusions: Patients with a history of atrial fibrillation and ischemic heart disease were at a higher risk of recurrent ischemic stroke. The incidence can be reduced by better treatment of atrial fibrillation and active follow-up.
Oil pulling as an adjunct to improve oral health in orthodontic patients: A clinicomicrobial study

Introduction: Oil pulling is an Indian folk remedy with both systemic and dental benefits ranging from the strengthening of teeth, gums and jaws, preventing decay, oral malodor, and bleeding gums. However, there are limited scientific data illustrating the role of oil pulling as an adjunct to improve the oral health in orthodontic patients. Aim: The aim of this study was to evaluate the effect of oil pulling with sesame oil to improve oral health in orthodontic patients as compared to chlorhexidine (CHX) mouth wash. Settings and Design: Twenty patients undergoing fixed orthodontic treatment were randomly selected. They were divided randomly into the study or oil pulling group and the control or CHX group with ten subjects in each group. Methods and Material: Colony-forming unit counts of bacteria were assessed at baseline and after 14 days. Furthermore, patient acceptance from either group was evaluated using a questionnaire. Conclusion: The oil pulling therapy showed a reduction in total colony counts of aerobic microorganisms in the patients undergoing fixed orthodontic treatment.
Technology, Healthcare, and Big Data Analytics

In the late 20th century, with the rise of information technology and availability of computer systems, the transition from written medical records to digital records started. Doctors have been treating patients for centuries and thus, in the process, have generated an endless repository of data. We can digitize handwritten records, and in the past decade, a lot of records are digital as more hospitals and clinics now prefer Electronic Medical Records. Electronic Medical Records are devoid of any spelling errors, legibility issues, inconsistency of terminology, incompleteness, inaccuracy, and unnecessary information. We have accumulated enormous data, and this dynamic process enriches the repository every moment. We have data from hospitals and clinics; outpatient and inpatient departments; pharmacies; pathology, biochemistry, and microbiology laboratories; radiology department; superspecialties such as pulmonology, infectious diseases, neurology, nephrology, rheumatology, oncology, endocrinology, and gastroenterology; nursing records, physician notes, orders, and consultations; patient measurements and patient care procedures; medical devices data and patient monitors; and others.