bluuebunny/crossref_metadata_2025 huggingface dataset:
Asthma in pesticide users: an update from the Great Britain Prospective Investigation of Pesticide Applicators’ Health (PIPAH) cohort study
Authors: Fishwick David , Harding Anne-Helen , Chen Y , Pearce Neil , Frost Gillian
Published: February 2022

Objectives

To define the prevalence and incidence of asthma in a large working population of pesticide workers and to assess which exposures are potentially of relevance to causing or aggravating this condition.

Methods

A baseline cross-sectional study at recruitment (2013–2017, n=5817), with follow-up in 2018 (n=2578), was carried out in predominantly Great Britain based pesticide workers. At baseline, participants completed a health and work questionnaire which included questions on demographic, lifestyle, socioeconomic and work-related factors, pesticide use and doctor diagnosed health conditions. In January 2018, a follow-up questionnaire focused on respiratory ill health, with questions covering self-reported respiratory symptoms and doctor diagnosed respiratory conditions. The associations of various exposures with asthma were estimated using logistic regression adjusting for age as a continuous variable, and for sex where possible. An estimate of hours worked with pesticides in the previous year was calculated for each participant.

Results

At baseline, 608 (10.4%) had doctor diagnosed asthma. In 2018 the figure was 297 (11.5% of the follow-up population); the incidence of new asthma cases between surveys was 1.7 cases per 1000 participants per year. At follow-up, 18.1% reported wheeze in the last 12 months, 73.2% of those with self-reported asthma noted it to be persistent and using a more specific definition of asthma (doctor diagnosed asthma with at least one asthma-related symptom in the last year); 6.8% (95% CI 5.9% to 7.9%) fulfilled this definition. At follow-up, 127 participants felt that their asthma was caused or made worse by their work, with 77 (63.6%) nominating organic dust, 13 (10.7%) unspecified dust, 12 (9.9%) chemicals, 9 (7.4%) mixed exposures, 7 (5.8%) physical agents and 3 (2.5%) fumes or other irritants. There was little or no association between high pesticide exposure and doctor diagnosed asthma or self-reported recent wheeze, although there was an elevated risk for work-related wheeze (OR for high exposure=2.67; 95% CI 1.16 to 6.18). High pesticide exposure (high vs low exposure category OR 2.68, 95% CI 1.28 to 5.60) was also associated with work-related chest tightness. Exposure to organic dusts was associated (significantly, p=0.026) with persistent asthma when adjusted for the effects of age and smoking.

Conclusions

This large study of pesticide workers has identified expected levels of doctor diagnosed asthma, and high levels of self-reported respiratory symptoms. Pesticide exposure was associated with an increased risk of self-reported work-related wheeze, but not with asthma or wheeze in general. Further work is needed to identify more clearly which exposures within a complex mixed exposure profile are likely causative in order to best focus interventions to reduce work-related asthma and related conditions.
Factors associated with low levels of HIV testing among young men who have sex with men (MSM) participating in EMIS-2017 in Spain
Authors: Gallego Nuria , Diaz Asuncion , Folch Cinta , Meyer Sebastian , Vazquez Maria , Casabona Jordi , Hernando Victoria
Published: February 2022

Purpose

The European Men who have sex with men Internet Survey looked over the characteristics and needs of men who have sex with men (MSM) across Europe. Our objective was to estimate the prevalence of HIV testing and its associated factors among MSM younger than 25 years old participating in the EMIS-2017 in Spain.

Methods

Multivariable regression model was used to compare those who had been tested for HIV within the last 12 months and those that had not.

Results

Of 2313 participants, 1070 (46.3%) had been tested for HIV in the past 12 months. Increased age (age 19–21 years, aOR=3.38 (95% CI 2.57 to 4.44); age 22–24 years, aOR=5.26 (4.06 to 6.92) compared with age 16–18 years); being migrant (Latin America: aOR=1.34 (0.98 to 1.84); Europe, North America and Mediterranean countries (aOR=1.56 (0.98 to 2.51) compared with those from Spain); living more openly with one’s sexuality (out to some people (aOR=1.53 (1.19 to 1.96)); out to all or almost all people (aOR=2.24 (1.75 to 2.87) compared with those out to none or a few people); having had one condomless steady partners in the las year ((aOR=1.59 (1.26 to 2.02)); having had condomless non-steady partners in the last year (one: aOR=1.76 (1.35 to 2.29)); two or more partners: aOR=2.37 (1.84 to 3.04)); and having practised sex work in the past year (aOR=1.52 (1.07 to 2.13)) were associated with increased odds of HIV testing. Living in a smaller city was associated with less likelihood of HIV testing (<1 00 000 inhabitants: aOR=0.51 (95% CI 0.41 to 0.64); 100 000–500 000: aOR=0.68 (95% CI 0.54 to 0.86) compared with more than 500 000).

Conclusion

Young MSM showed low HIV testing rate. Future programming specifically targeting this population, especially those middle adolescents, living in a medium-small city and having less ‘outness’, can help increase HIV testing and prevent access barriers.
Provincial-scale digital soil mapping using a random forest approach for British Columbia
Authors: Heung Brandon , Bulmer Chuck E. , Schmidt Margaret G. , Zhang Jin
Published: February 2022

Although British Columbia (BC), Canada, has a rich history of producing conventional soil maps (CSMs) between 1925 and 2000, the province still lacks a detailed soil map with a comprehensive coverage due to the cost and time required to develop such a product. This study builds on previous digital soil mapping (DSM) research in BC and develops provincial-scale maps. Soil taxonomic classes (e.g., great groups and order) and parent material classes were mapped at a 100 m spatial resolution for BC (944 735 km2). Training points were generated from detailed and semi-detailed soil survey maps. The training points were intersected with 26 topographic indices for mapping parent materials with an additional 9 climatic and vegetation indices for mapping soil classes. The soil–environmental relationships were inferred using the random forest (RF) classifier. The fitted models were used to predict 23 soil great groups, 9 soil orders, and 10 parent material classes. Accuracy assessments were performed using n = 14 570 validation points for parent material classes and n = 14 316 validation points for soil classes, acquired from the BC Soil Information System. The accuracy rates for soil great groups, orders, and parent material classes were 55%, 62%, and 69%, respectively, and kappa coefficients were 0.37, 0.41, and 0.59, respectively. This study demonstrated that when RF was trained using CSMs, the accuracy for the resulting DSM was higher than the original CSM. To assess prediction uncertainties, ignorance uncertainty maps were developed using class-probability layers generated by the RF models.
Perturbative structure of two- and four-point functions of color charge in a non-Gaussian small-x action
Authors: Giannini Andre V. , Nara Yasushi
Published: February 2022

We compute the perturbative expansion of the two- and four-point functions of color charges in the Color Glass Condensate framework considering the quartic correction to the McLerran–Venugopalan (MV) model of Gaussian color charge fluctuations. Expressions for these correlators in the perturbative expansion for small and large non-Gaussian color charge fluctuations are derived for arbitrary orders in perturbation theory. We explicitly show that the perturbative series does not converge at higher orders as expected. We apply the Borel–Padé resummation method to our problem to construct a convergent series. It is shown that the fully non-perturbative solution can be described by the Borel–Padé approximants constructed from the first few terms of the perturbative series for small non-Gaussian fluctuations.
Hadamard states for quantized Dirac fields on Lorentzian manifolds of bounded geometry
Authors: Gérard Christian , Stoskopf Théo
Published: February 2022

We consider Dirac equations on even-dimensional Lorentzian manifolds of bounded geometry with a spin structure. For the associated free quantum field theory, we construct pure Hadamard states using global pseudodifferential calculus on a Cauchy surface. We also give two constructions of Hadamard states for Dirac fields for arbitrary spacetimes with a spin structure.
Regulating the Imbalance of Gut Microbiota by Smilax china L. Polyphenols to Alleviate Dextran Sulfate Sodium-induced Inflammatory Bowel Diseases
Authors: Xu Meng , Xue Hui , Qiao Gaoxiang , Liao Mingfu , Kong Li , Zhang Qingfeng , Lin Lezhen , Yang Licong , Zheng Guodong
Published: February 2022

Smilax china L. is used not only as a kind of traditional Chinese herbal medicinal ingredients with various pharmacological properties, but also as food in certain parts of China. However, it is by far still unclear whether Smilax china L. polyphenols (SCP), as important bioactive constituents in Smilax china L., have effects on inflammatory bowel diseases (IBD). This study investigated the impact of SCP on the dextran sulfate sodium (DSS)-induced IBD and gut microbiota in mice. SCP treatments ameliorated typical symptoms of IBD as what was reflected through suppressing body weight loss, colonic shortening, intestinal barrier damage, and increasing intestinal disease activity index. SCP treatments simultaneously decreased the release of proinflammatory cytokines and oxidative stress, as well as promoted the release of anti-inflammatory factors. Furthermore, SCP ameliorated the ecological imbalance of gut microbiota and regulated the key bacteria associated with IBD (including Akkermansiaceae, Ruminococcaceae, Acidaminococcaceae, Muribaculaceae, and Anaeroplasmataceae). In general, SCP may improve DSS-induced IBD in mice by regulating inflammatory factors, inhibiting oxidative stress, reducing intestinal tissue damage, and regulating the ecological imbalance of intestinal microbiota. Thus, SCP might serve as a potential therapeutic agent against the inflammation-driven diseases.
Anticancer Effects ofTaraxacumvia Cell Cycle Arrest, Necrosis, Apoptosis, and Endoplasmic Reticulum Stress
Authors: Lin Chien-Jung , Liu Shu-Ting , Yang Rong-Chi , Wang Li-Hsiang , Tsai Chen-Chen , Chen Teng-Wei , Huang Shih-Ming
Published: February 2022

Dandelion (Taraxacum species) is a wild plant with over 2500 species. Flavonoids, phenolic compounds, saponins, sesquiterpenes, and sugars have been detected in the organs of Taraxacum, and for centuries it has been used in traditional medicine for the relief and treatment of various diseases. However, details of its working mechanism remain unclear. Bioactive compounds in herbal extracts generally have low yields, which makes their isolation and purification intensive in terms of time and cost. Here, to assess their versatility and safety, we applied aqueous extracts of two species of Taraxacum, T. mongolicum and T. formosanum, including extracts of both fresh and dried T. formosanum, to compare their potential antitumor effects on HeLa human cervical cancer cells, three liver cancer cell lines, and one normal liver cell line. After being treated with a lower dose of Taraxacum, the upregulation of subG1 and S populations, as well as increased levels of p-eIF2[Formula: see text]-to-eIF2[Formula: see text] ratio, were observed in HeLa cells, whereas the downregulation of S population and the absence of mRNA expressions were detected in HeLa cells when being treated with a higher dose of Taraxacum. These results indicated that Taraxacumcould induce apoptosis and endoplasmic reticulum stress while suppressing proliferation, transcription, colony formation, migration, and invasion. What’s more, we also found that the effects of fresh T. formosanum were much stronger than that of T. mongolicumin HeLa cells. Based on these results, we suggest that T. formosanum may contain specific compound(s) that are potentially useful for cancer therapy. However, much work remains to identify these effective compounds for the future application of Taraxacumto cancer therapy.
Powder sintered flat micro-heat pipe with wettability modification
Authors: Zhao Zhengang , Wang Yaxin , Li Lei , Peng Guohong , Zhang Dacheng
Published: February 2022

Flat micro-Heat Pipe (FHP) provides an excellent solution to the heat dissipation problem of an electronic chip by the gas–liquid two-phase flow. Therefore, the liquid channels and the gas channels are part of the FHP. It determines the performance of the FHP. To this end, these channels were optimized by infiltration modification to accelerate the gas-liquid circulation. In this work, the wick underwent hydrophilic treatment by chemical corrosion and the solutions selected were KOH and (NH4)2S2O8. The shell surface undergoes hydrophobic treatment by electrodeposition with a mixture of zinc-nickel ions. The thermal transfer performance of the Hydrophilic Wick FHP (HW-FHP) and the Hydrophobic Shells FHP (HS-FHP) was analyzed. The results show that the maximum thermal power of the HW-FHP is 10.8 W, 9.1% higher than that of the Untreated FHP (UFHP). The HS-FHP is 11.9 W, 20% higher than that of the UFHP. The HW has a high capillary force, resulting in faster fluid flow. The vapor flow channels made up of the HS have a low resistance to the vapor flow. The hydrophobic surface can reduce the droplet diameter and prevent blocked vapor flow channels.
Forecasting extreme events in collective dynamics: An analytic signal approach to detecting discrete scale invariance
Authors: Viswanathan G. M.
Published: February 2022

A challenging problem in physics concerns the possibility of forecasting rare but extreme phenomena such as large earthquakes, financial market crashes and material rupture. A promising line of research involves the early detection of precursory log-periodic oscillations to help forecast extreme events in collective phenomena where discrete scale invariance plays an important role. Here we investigate two distinct approaches towards the general problem of how to detect log-periodic oscillations in arbitrary time series without prior knowledge of the location of the moveable singularity. We first show that the problem has a definite solution in Fourier space; however, the technique involved requires an unrealistically large signal-to-noise ratio. Then we show that the quadrature signal obtained via analytic continuation onto the imaginary axis, using the Hilbert transform, necessarily retains the log-periodicities found in the original signal. This finding allows the development of a new method of detecting log-periodic oscillations that relies on calculation of the instantaneous phase of the analytic signal. We then illustrate the method by applying it to the stock market crash of 1987 and explore the important role played by positive feedback mechanisms in relation to economic bubble formation, financial crashes, log-periodic dynamics and analytic behavior. Finally, we discuss the relevance of these findings for parametric rather than nonparametric estimation of critical times.
A Low Power, Low Phase Noise, 11.8 GHz LC-VCO in 65 nm CMOS for 5G NR Based C-V2X Applications
Authors: Kayanselçuk Toprak , Zencir Ertan
Published: February 2022

An 11.8-GHz low-power, low phase noise, 65-nm CMOS LC-VCO for V2X communication standards is presented in this work. To achieve low-phase noise performance to fulfill the 5G NR C-V2X standards, several adjustments are made to the conventional LC-VCO topologies. These adjustments are briefly explained in this work. Presented VCO consumes 3.046[Formula: see text]mA current from a 1.2-V power supply. It has [Formula: see text]116.13[Formula: see text]dBc/Hz phase noise at 1[Formula: see text]MHz offset and tuning range between 11.64[Formula: see text]GHz and 11.94[Formula: see text]GHz. A frequency divider circuit is designed and implemented to the presented VCO to satisfy the frequency spectrum of V2X radio access technologies. To the best of authors’ knowledge, this work is the first VCO implemented at 11.8 GHz in a 65-nm CMOS process with the reported phase noise number for 5G NR C-V2X applications.
Multi-Scale Correlation for Deep Homography Estimation
Authors: Ke Nan , Shang Zhaowei , Zhao Lingzhi , Wang Yingxin , Zhou Mingling
Published: February 2022

In this paper, we propose a novel multi-scale correlation network (MSCNet) for homography estimation from coarse to fine. First, we extract multi-scale features through a siamese network to generate global and local correlations from feature maps of different scales. Second, we use a group dilated deconvolution block to capture global mapping by increasing the receptive fields in terms of different levels. Third, we employ the channel and spatial attention mechanism to achieve local refinement for small displacements. Finally, we adopt a knowledge distillation strategy to lightweight our model while maintaining relatively high estimation performance. Experimental results on Microsoft Common Objects in Context (MSCOCO) dataset show that our proposed MSCNet outperforms the state-of-the-art approaches in terms of accuracy and parameter count.
Direct decompositions of groups of piecewise linear homeomorphisms of the unit interval
Authors: Sato Takamichi
Published: February 2022

We study subgroups of the group [Formula: see text] of piecewise linear orientation-preserving homeomorphisms of the unit interval [Formula: see text] that are differentiable everywhere except at finitely many real numbers, under the operation of composition. We provide a criterion for any two subgroups of [Formula: see text] which are direct products of finitely many indecomposable non-commutative groups to be non-isomorphic. As its application, we give a necessary and sufficient condition for any two subgroups of the R. Thompson group [Formula: see text] that are stabilizers of finite sets of numbers in the interval [Formula: see text] to be isomorphic, thus solving a problem by G. Golan and M. Sapir. We also show that if two stabilizers are isomorphic, then they are conjugate inside a certain group [Formula: see text].
Nonstatic hyperbolically symmetric fluids
Authors: Herrera L.
Published: February 2022

In this paper, we present the general properties of dynamic dissipative fluid distribution endowed with hyperbolical symmetry. All the equations required for its analysis are exhibited and used to contrast the behavior of the system with the spherically symmetric case. Several exact solutions are exhibited and prospective applications to astrophysical and cosmological scenarios are discussed.
Sphaleron transition rates and the chiral magnetic effect
Authors: Kapusta Joseph I. , Rrapaj Ermal , Rudaz Serge
Published: February 2022

The chiral magnetic effect is a novel quantum phenomenon proposed for high-energy nuclear collisions but which has yet to be observed. We quantify the axial charge relaxation time, due to sphalerons, which enters in simulations of this effect. An extrapolation of weak coupling calculations of the sphaleron rate yields rather different relaxation times than strong coupling AdS/CFT calculations. The AdS/CFT relaxation time is the larger one of the two by an order of magnitude, but the weak coupling relaxation time may not be reliable because it is only marginally bigger than the microscopic thermalization time. The role of quark masses has yet to be accurately assessed.
TUNING OF TWO-DEGREE-OF-FREEDOM IMC BASED ON A STRANGE NONCHAOTIC OPTIMIZATION APPROACH FOR LARGE TIME-DELAY PROCESSES
Authors: SHEN YUNZHU , TANG WEI
Published: February 2022

The internal model control (IMC) has a good control effect for large time-delay systems, but the parameter tuning of the controller is a challenge for researchers. As a typical fractal (strange) attractor, a strange nonchaotic attractor has the dynamic characteristics of randomness and ergodicity. Based on these characteristics, an optimization method is proposed to tune the parameters of an internal model controller for a large time-delay process, which is named a strange nonchaotic optimization algorithm (SNOA). SNOA has the advantages of easy implementation, short execution time and having robust mechanisms to avoid local optimization. It can find the optimal parameters of the internal model controller in large time-delay systems. The results show that SNOA can effectively improve the dynamic stability of the large time-delay systems. It has also proved to be superior to chaotic optimization (CO) algorithm and particle swarm optimization (PSO) algorithm.
A GENERALIZED METHOD AND ITS APPLICATIONS TO n-DIMENSIONAL FRACTIONAL PARTIAL DIFFERENTIAL EQUATIONS IN FRACTAL DOMAIN
Authors: YUE CHAO , MA WEN-XIU , LI KUN
Published: February 2022

A generalized local fractional Riccati differential equation (LFRDE) method, which is a combination of the local fractional Riccati differential equation method and the transformed rational function approach, is presented to obtain the non-differentiable exact traveling wave solutions for n-dimensional fractional partial differential equations. In order to verify the effectiveness of the method, the seventh-order local fractional Sawada–Kotera–Ito equation, the local fractional sine-Gordon equation and the local fractional Kadomtsev–Petviashvili equation in fractal domain are first considered. The obtained results show that the presented method involving fractal special functions, are powerful and effective for obtaining exact solutions of nonlinear fractional partial differential equations in fractal domains.
A REVISIT TO STABILITY OF SCHAUDER BASES: FRACTALIZING MULTIVARIATE FABER–SCHAUDER SYSTEM
Authors: NAVASCUÉS M. A. , VISWANATHAN P.
Published: February 2022

Let [Formula: see text] be a Banach space with a Schauder basis [Formula: see text], and [Formula: see text] be the identity operator on [Formula: see text]. It is known, at least in essence, that if [Formula: see text] is a sequence of bounded linear operators on [Formula: see text] such that [Formula: see text] then [Formula: see text] is also a basis. The first part of this work acts as an expository note to formally record the aforementioned stability result. In the second part, we apply this stability result to construct a Schauder basis consisting of bivariate fractal functions for the space of continuous functions defined on a rectangle. To this end, fractal perturbations of the elements in the classical bivariate Faber–Schauder system are formulated using a sequence of bounded linear fractal operators close to the identity operator in accordance with the stability result mentioned above. This illustration, although emphasized only for the bivariate case, can easily be extended to higher dimensions. Further, the perturbation technique used here acts as a companion for a few researches on fractal bases in the univariate setting.
Analysis of Deep Entrepreneurial Model in College Students Based on Mixed Space and Time Big Data Stream
Authors: Huang Lin
Published: February 2022

Due to the homogenization of massive entrepreneurial undertaking by college students, they face a series of dilemmas such as the low utilization rate, poor user stickiness, failure to support various activities of the teachers and students, inability to maintain the sustainable development and so on. The solution is to establish a deep entrepreneurship mode of college students based on the mixed space and time big data stream. In this paper, a new idea of building an information platform based on the mixed space and time big data stream is explored. A new method for the construction of platform with the data as the core and the project as the orientation is put forward, which is composed of the data source analysis, data processing analysis and model establishment, innovation and entrepreneurship project module. Hence, a system platform is established with the “data” as the center and the functions expanded continuously by using the data mining technology, which will play an active role in the sustainable development of projects, the self-cognition of teachers and students, the discovery of enterprise talents, the decision making of college education and other aspects.
New conserved tensors and Brans–Dicke-type field equation, using integrability condition
Authors: Sanyal Abhik Kumar , Modak Bijan , Chakrabortty Manas
Published: February 2022

In this paper, we explore some new off-shell and on-shell conserved quantities for a scalar field in Minkowski space, using integrability condition. The off-shell conserved tensors are related to the kinematics of the field, while a linear combination of the off-shell and the on-shell conserved tensors ends up with the energy–momentum tensor for the scalar field. In the curved background, using Ricci and Bianchi identities, scalar–tensor theory of gravity, that resembles somewhat with Brans–Dicke-type field equations, emerges without requiring the principle of equivalence. Further, starting from the curvature scalar and using these identities, the field equations for modified gravity (Einstein–Hilbert action in the presence of higher-order terms) follow.
Propagation and reaction–diffusion models with free boundaries
Authors: Du Yihong
Published: February 2022

In this short survey, we describe some recent developments on the modeling of propagation by reaction-differential equations with free boundaries, which involve local as well as nonlocal diffusion. After the pioneering works of Fisher, Kolmogorov–Petrovski–Piskunov (KPP) and Skellam, the use of reaction–diffusion equations to model propagation and spreading speed has been widely accepted, with remarkable progresses achieved in several directions, notably on propagation in heterogeneous media, models for interacting species including epidemic spreading, and propagation in shifting environment caused by climate change, to mention but a few. Such models involving a free boundary to represent the spreading front have been studied only recently, but fast progress has been made. Here, we will concentrate on these free boundary models, starting with those where spatial dispersal is represented by local diffusion. These include the Fisher–KPP model with free boundary and related problems, where both the one space dimension and high space dimension cases will be examined; they also include some two species population models with free boundaries, where we will show how the long-time dynamics of some competition models can be fully determined. We then consider the nonlocal Fisher–KPP model with free boundary, where the diffusion operator [Formula: see text] is replaced by a nonlocal one involving a kernel function. We will show how a new phenomenon, known as accelerated spreading, can happen to such a model. After that, we will look at some epidemic models with nonlocal diffusion and free boundaries, and show how the long-time dynamics can be rather fully described. Some remarks and comments are made at the end of each section, where related problems and open questions will be briefly discussed.
Boundedness of analytic functions in the unit disc using the theory of differential subordination
Authors: Krishnan Priya G. , Saikrishnan P. , Ravichandran V.
Published: February 2022

By using the theory of differential subordination, we investigate differential inequalities, satisfied by an analytic function [Formula: see text] defined on the unit disc, that imply the boundedness of the function [Formula: see text]. Our results are applied to find sufficient conditions for the normalized analytic functions to be either starlike or close-to-convex.
On a novel seventh convergence order method for solving nonlinear equations and its extensions
Authors: Regmi Samundra , Argyros Ioannis K. , George Santhosh , Argyros Christopher
Published: February 2022

We extend the applicability of a novel seventh-order method for solving nonlinear equations in the setting of Banach spaces. This is done by using assumptions only on the first derivative that does appear on the method, whereas in earlier works up to the eighth derivatives (not on the scheme) were used to establish the convergence. Our technique is so general that it can be used to extend the usage of other schemes along the same lines.
Mersenne Beam-Compressor with Field-Flattener Optics as Main Telescope Design for Extremely Large Telescopes and Telescope Arrays
Authors: Gadey Nishant Neeraj
Published: February 2022

The Mersenne beam-compressor is the fundamental arrangement for two mirror reflecting telescopes. The Mersenne beam-compressor consists of two confocal paraboloids. The design produces an aberration-free, collimated output beam, which makes it easy to relocate the beam to any distant location. This property of Mersenne beam compressors makes them near-ideal for telescopes with requirements for ultra-stable instruments and interferometric applications. The only problem against such application is the Petzval field curvature that is induced in the output beam. This becomes very pronounced at very high compression ratios, which are required for interferometry with large aperture telescopes. The high compression ratio is a problem because, such large aperture telescopes are also required to deliver good performance at Nasmyth and Coude locations, to be feasible. This is not possible at high compression ratios using conventional design of Mersenne beam-compressor, due to prohibitively strong field curvature. Here, we propose powered and un-powered field-flattener optics to address the problem of field curvature in Mersenne beam-compressor. After passing through this set of optics, the output beam is completely aberration free. This arrangement can produce very high compression ratios, which are useful for various special applications. We also illustrate the performance of such a system using example designs. The performance analysis shows that such a design can give considerably high performance at distant locations of foci as compared to existing relay systems.
SDR Pathfinder for Understanding Transient and Noise-Level Interference in the Karoo (SPUTNIK)
Authors: Josaitis Alec T. , DeBoer David R. , de Lera Acedo Eloy
Published: February 2022

In this work, the SDR Pathfinder for Understanding Transient and Noise-level Interference in the Karoo (SPUTNIK) is presented. We describe how a low-cost radio frequency interference (RFI) monitoring system, using solely consumer-off-the-shelf (COTS) components, directly contributes to the analysis efforts of a precision 21[Formula: see text]cm cosmology instrument. A SPUTNIK system overview is provided, as well as a generalized software-defined radio (SDR) internal calibration technique to achieve wideband, [Formula: see text][Formula: see text]dBm-level accuracy and a measured dynamic range of [Formula: see text][Formula: see text]dB.
An Evolutive Frequent Pattern Tree-based Incremental Knowledge Discovery Algorithm
Authors: Liu Xin , Zheng Liang , Zhang Weishan , Zhou Jiehan , Cao Shuai , Yu Shaowen
Published: February 2022

To understand current situation in specific scenarios, valuable knowledge should be mined from both historical data and emerging new data. However, most existing algorithms take the historical data and the emerging data as a whole and periodically repeat to analyze all of them, which results in heavy computation overhead. It is also challenging to accurately discover new knowledge in time, because the emerging data are usually small compared to the historical data. To address these challenges, we propose a novel knowledge discovery algorithm based on double evolving frequent pattern trees that can trace the dynamically evolving data by an incremental sliding window. One tree is used to record frequent patterns from the historical data, and the other one records incremental frequent items. The structures of the double frequent pattern trees and their relationships are updated periodically according to the emerging data and a sliding window. New frequent patterns are mined from the incremental data and new knowledge can be obtained from pattern changes. Evaluations show that this algorithm can discover new knowledge from evolving data with good performance and high accuracy.
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
Authors: Liu Zelei , Chen Yuanyuan , Yu Han , Liu Yang , Cui Lizhen
Published: February 2022

Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important to attract high-quality data owners with appropriate incentive schemes. As an important building block of such incentive schemes, it is essential to fairly evaluate participants’ contribution to the performance of the final FL model without exposing their private data. Shapley Value (SV)–based techniques have been widely adopted to provide a fair evaluation of FL participant contributions. However, existing approaches incur significant computation costs, making them difficult to apply in practice. In this article, we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to address this challenge. It reconstructs FL models from gradient updates for SV calculation instead of repeatedly training with different combinations of FL participants. In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required. This is accomplished through extensive experiments under diverse realistic data distribution settings. The results demonstrate that GTG-Shapley can closely approximate actual Shapley values while significantly increasing computational efficiency compared with the state-of-the-art, especially under non-i.i.d. settings.
Sampling a Near Neighbor in High Dimensions — Who is the Fairest of Them All?
Authors: Aumüller Martin , Har-Peled Sariel , Mahabadi Sepideh , Pagh Rasmus , Silvestri Francesco
Published: February 2022

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points
S
and a radius parameter
r
> 0, the r-near neighbor (
r
-NN) problem asks for a data structure that, given any query point
q
, returns a point
p
within distance at most
r
from
q
. In this paper, we study the
r
-NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance
r
from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014).
Locality sensitive hashing (LSH)
, the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee.

In this work, we show that
LSH
based algorithms can be made fair, without a significant loss in efficiency. We propose several efficient data structures for the exact and approximate variants of the fair NN problem. Our approach works more generally for sampling uniformly from a sub-collection of sets of a given collection and can be used in a few other applications. We also develop a data structure for fair similarity search under inner product that requires nearly-linear space and exploits locality sensitive filters. The paper concludes with an experimental evaluation that highlights the unfairness of state-of-the-art NN data structures and shows the performance of our algorithms on real-world datasets.
An OpenMP Runtime for Transparent Work Sharing across Cache-Incoherent Heterogeneous Nodes
Authors: Lyerly Robert , Bilbao Carlos , Min Changwoo , Rossbach Christopher J. , Ravindran Binoy
Published: February 2022

In this work, we present
libHetMP
, an OpenMP runtime for automatically and transparently distributing parallel computation across heterogeneous nodes.
libHetMP
targets platforms comprising CPUs with different instruction set architectures (ISA) coupled by a high-speed memory interconnect, where cross-ISA binary incompatibility and non-coherent caches require application data be marshaled to be shared across CPUs. Because of this, work distribution decisions must take into account both relative compute performance of asymmetric CPUs and communication overheads.
libHetMP
drives workload distribution decisions without programmer intervention by measuring performance characteristics during cross-node execution. A novel HetProbe loop iteration scheduler decides if cross-node execution is beneficial and either distributes work according to the relative performance of CPUs when it is or places all work on the set of homogeneous CPUs providing the best performance when it is not. We evaluate
libHetMP
using compute kernels from several OpenMP benchmark suites and show a geometric mean 41% speedup in execution time across asymmetric CPUs. Because some workloads may showcase irregular behavior among iterations, we extend
libHetMP
with a second scheduler called HetProbe-I. The evaluation of HetProbe-I shows it can further improve speedup for irregular computation, in some cases up to a 24%, by triggering periodic distribution decisions.
Unified Holistic Memory Management Supporting Multiple Big Data Processing Frameworks over Hybrid Memories
Authors: Chen Lei , Zhao Jiacheng , Wang Chenxi , Cao Ting , Zigman John , Volos Haris , Mutlu Onur , Lv Fang , Feng Xiaobing , Xu Guoqing Harry , Cui Huimin
Published: February 2022

To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy inefficient. Emerging
non-volatile memory (NVM)
technologies offer high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in
managed languages
and executed on top of a
managed runtime
that already performs various dimensions of memory management. Supporting hybrid physical memories adds a new dimension, creating unique challenges in data replacement. This article proposes Panthera, a
semantics-aware, fully automated
memory management technique for Big Data processing over hybrid memories. Panthera analyzes user programs on a Big Data system to infer their coarse-grained access patterns, which are then passed to the Panthera runtime for efficient data placement and migration. For Big Data applications, the coarse-grained data division information is accurate enough to guide the GC for data layout, which hardly incurs overhead in data monitoring and moving. We implemented Panthera in OpenJDK and Apache Spark. Based on Big Data applications’ memory access pattern, we also implemented a new profiling-guided optimization strategy, which is
transparent
to applications. With this optimization, our extensive evaluation demonstrates that Panthera reduces energy by 32–53% at less than 1% time overhead on average. To show Panthera’s applicability, we extend it to QuickCached, a pure Java implementation of Memcached. Our evaluation results show that Panthera reduces energy by 28.7% at 5.2% time overhead on average.
Graph Neural News Recommendation with User Existing and Potential Interest Modeling
Authors: Qiu Zhaopeng , Hu Yunfan , Wu Xian
Published: February 2022

Personalized news recommendations can alleviate the information overload problem. To enable personalized recommendation, one critical step is to learn a comprehensive user representation to model her/his interests. Many existing works learn user representations from the historical clicked news articles, which reflect their existing interests. However, these approaches ignore users’ potential interests and pay less attention to news that may interest the users in the future. To address this problem, we propose a novel
G
raph neural news
R
ecommendation model with user
E
xisting and
P
otential interest modeling, named GREP. Different from existing works, GREP introduces three modules to jointly model users’ existing and potential interests: (1)
Existing Interest Encoding
module mines user historical clicked news and applies the multi-head self-attention mechanism to capture the relatedness among the news; (2)
Potential Interest Encoding
module leverages the graph neural network to explore the user potential interests on the knowledge graph; and (3)
Bi-directional Interaction
module dynamically builds a news-entity bipartite graph to further enrich two interest representations. Finally, GREP combines the existing and potential interest representations to represent the user and leverages a prediction layer to estimate the clicking probability of the candidate news. Experiments on two real-world large-scale datasets demonstrate the state-of-the-art performance of GREP.
Providing Reliable Service for Parked-vehicle-assisted Mobile Edge Computing
Authors: Zhou Ao , Ma Xiao , Gao Siyi , Wang Shangguang
Published: February 2022

Nowadays, a growing number of computation-intensive applications appear in our daily life. Those applications make the loads of both the core network and the mobile devices, in terms of energy and bandwidth, hugely increase. Offloading computation-intensive tasks to edge cloud is proposed to address this issue. Since edge clouds have limited computation resources compared with the remote cloud, they would get over-loaded because of the heavy computation burden. Parked-vehicle-assisted mobile edge computing becomes one of the promising solutions for this problem. However, several critical issues in parked-vehicle-assisted mobile edge computing would result in low reliable edge service. The open environment would bring about uncertainty, and the data privacy is hard to ensure. In addition, different from edge cloud, each parked vehicle only has limited parking duration and can leave unexpectedly for personal reasons. Moreover, edge cloud and vehicle adopt different execution models of computation and communication. The heterogeneous environment may result in negative effect on cooperativeness. Ignoring those issues can result in substantial performance degradation. To tackle this challenge and explore the benefits of parked-vehicle-assisted offloading, we study the task offloading and resource-allocation problem by fully considering the above issues. First, we propose a resource-management scheme to address the privacy issue. Second, we review the execution model of computation and communication in parked-vehicle-assisted computation offloading. Then, we formulate the problem into a mixed-integer nonlinear programming. The problem is hard to tackle due to its non-convex nature, which means that the time complexity of finding global optimal solution is unaffordable. Finally, we decompose the original problem into two sub-problems with lower complexity, and related algorithms are given to deal with the sub-problems. Simulation results demonstrate the effectiveness of the proposed solution.
Asymmetric Multi-Task Learning with Local Transference
Authors: Oliveira Saullo H. G. , Gonçalves André R. , Von Zuben Fernando J.
Published: February 2022

In this article, we present the Group Asymmetric Multi-Task Learning (GAMTL) algorithm that automatically learns from data how tasks transfer information among themselves at the level of a subset of features. In practice, for each group of features GAMTL extracts an asymmetric relationship supported by the tasks, instead of assuming a single structure for all features. The additional flexibility promoted by local transference in GAMTL allows any two tasks to have multiple asymmetric relationships. The proposed method leverages the information present in these multiple structures to bias the training of individual tasks towards more generalizable models.

The solution to the GAMTL’s associated optimization problem is an alternating minimization procedure involving tasks parameters and multiple asymmetric relationships, thus guiding to convex smaller sub-problems. GAMTL was evaluated on both synthetic and real datasets. To evidence GAMTL versatility, we generated a synthetic scenario characterized by diverse profiles of structural relationships among tasks. GAMTL was also applied to the problem of Alzheimer’s Disease (AD) progression prediction. Our experiments indicated that the proposed approach not only increased prediction performance, but also estimated scientifically grounded relationships among multiple cognitive scores, taken here as multiple regression tasks, and regions of interest in the brain, directly associated here with groups of features. We also employed stability selection analysis to investigate GAMTL’s robustness to data sampling rate and hyper-parameter configuration. GAMTL source code is available on GitHub:
https://github.com/shgo/gamtl
.
A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments
Authors: Di Kai , Zhou Yifeng , Yan Fuhan , Jiang Jiuchuan , Yang Shaofu , Jiang Yichuan
Published: February 2022

As an essential problem in robotics, foraging means that robots collect objects from a given environment and return them to a specified location. On many occasions, robots are required to perform foraging tasks in adversarial environments, such as battlefield rescue, where potential adversaries may damage robots with a certain probability. The longer an individual robot moves through adversarial environments, the higher the probability of being damaged by adversaries. The robot system can gain utility only when the robot brings carried objects back to a predetermined home station. Such a risk of being damaged makes returning home at different locations potentially relevant to the expected utility produced by the robot. Thus, the individual robot faces a dilemma when it responds to the potential risks in adversarial environments: whether to return the carried resources home or continue foraging tasks. In this article, two fundamental environment settings are discussed, homogeneous cases and heterogeneous cases. The former is analyzed as having both the optimal substructure property and the non-aftereffect property. Then, we present a dynamic programming (DP) algorithm that can find an optimal solution with polynomial time complexity. For the latter, it is proven that finding an optimal solution is

\( \mathcal {NP} \)

-hard. We then propose a heuristic algorithm: A division hierarchical path planning (DHPP) algorithm that is based on the idea of dividing the foraging routes generated initially into a certain number of subroutes to dilute risks. Finally, these algorithms are extensively evaluated in simulations, concluding that in adversarial environments, they can significantly improve the productivity of an individual robot before it is damaged.
Super-Resolution Microscopy for Structural Cell Biology
Authors: Liu Sheng , Hoess Philipp , Ries Jonas
Published: February 2022

Super-resolution microscopy techniques, and specifically single-molecule localization microscopy (SMLM), are approaching nanometer resolution inside cells and thus have great potential to complement structural biology techniques such as electron microscopy for structural cell biology. In this review, we introduce the different flavors of super-resolution microscopy, with a special emphasis on SMLM and MINFLUX (minimal photon flux). We summarize recent technical developments that pushed these localization-based techniques to structural scales and review the experimental conditions that are key to obtaining data of the highest quality. Furthermore, we give an overview of different analysis methods and highlight studies that used SMLM to gain structural insights into biologically relevant molecular machines. Ultimately, we give our perspective on what is needed to push the resolution of these techniques even further and to apply them to investigating dynamic structural rearrangements in living cells.