bluuebunny/crossref_metadata_2025 huggingface dataset:
The potential therapeutic benefits of low frequency haemodynamic oscillations
Authors: Anderson Garen K. , Rickards Caroline A.
Published: July 2022

Haemodynamic oscillations occurring at frequencies below the rate of respiration have been observed experimentally for more than a century. Much of the research regarding these oscillations, observed in arterial pressure and blood flow, has focused on mechanisms of generation and methods of quantification. However, examination of the physiological role of these oscillations has been limited. Multiple studies have demonstrated that oscillations in arterial pressure and blood flow are associated with the protection in tissue oxygenation or functional capillary density during conditions of reduced tissue perfusion. There is also evidence that oscillatory blood flow can improve clearance of interstitial fluid, with a growing number of studies demonstrating a role for oscillatory blood flow to aid in clearance of debris from the brain. The therapeutic potential of these haemodynamic oscillations is an important new area of research which may have beneficial impact in treating conditions such as stroke, cardiac arrest, blood loss injuries, sepsis, or even Alzheimer's disease and vascular dementia.

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Enfermería en la salud sexual y reproductiva para jóvenes y adolescentes
Authors: Herrera Zuleta Ivett Adriana , Rodriguez Ruiz María Fernanda , Fernández Durán Valentina , Sánchez Cabrera Leidy Tatiana
Published: July 2022

Introducción: los programas que han evidenciado ser exitosos en la prevención del embarazo adolescente, enfermedades de transmisión sexual y virus de inmunodeficiencia adquirida (VIH) se caracterizan por ofrecer una atención integral que incluye: educación sexual, promoción de habilidades para la vida, acceso a asesoría en salud y planificación familiar. La población rural presenta situaciones sociales y culturales específicas que la hacen distintiva: bajos niveles de educación, accesibilidad y articulación con sectores, lo cual conlleva desarrollar un abordaje diferente por parte del enfermero en el territorio. Objetivo: describir la experiencia del enfermero(a) en el abordaje de la salud sexual y reproductiva en jóvenes y adolescentes de áreas rurales. Método: estudio cualitativo con enfoque fenomenológico. Resultados: surgieron cuatro categorías: la primera enfatiza en la educación en salud sexual y reproductiva para jóvenes y adolescentes, una apuesta dinámica para el abordaje en el territorio rural. La sigue el abordaje de la salud sexual y reproductiva en los jóvenes y adolescentes rurales. La tercera supone la construcción de la experiencia en el territorio y la cuarta arañe a la pandemia que rediseñó la atención. Conclusión: la enfermería debe articularse a otros actores para potencializar las actividades destinadas a población adolescente en el área rural, para lo cual debe desarrollar habilidades y conocer el territorio en el cual realiza la intervención para comprender sus cosmovisiones. Y al mismo tiempo, apoyarse en estrategias de atención diferencial, las cuales demostraron –según las vivencias– proveer la mejor experiencia en salud sexual y reproductiva.
Future Leadership
Authors: Bryant-Lukosius Denise
Published: July 2022

To coin a phrase from the lyrics of an old song, “what the world needs now” to improve global health is nursing leadership. Several policy reports from the World Health Organization1,2 and the Pan America Health Organization (PAHO)3 emphasize the strategic importance of nursing leadership and the strengthening of nursing leadership at all levels of health systems for improving access and quality of care and population health outcomes. Similarly, these reports recognize the enhanced clinical expertise provided by advanced practice nurses and advocate for their expanded use and optimized scope of practice to deliver needed health care services in all countries, but especially in lower- and middle-income countries where advanced practice nurses may be few or non-existent, and where there is high health inequity.
Effects of N2 and O2 plasma treatments of quartz surfaces exposed to H2 plasmas
Authors: Zhou Yingliang , Li Hanyang , Jung Ji-Eun , Nam Sang Ki , Donnelly Vincent M.
Published: July 2022

This paper presents a study of methods for reducing the erosion of SiO2 in a high-power density (10–40 W/cm3), purely inductive H2/Ar plasma, using a toroidal transformer-coupled plasma source operated at 0.5 Torr. Quartz samples were exposed to plasma densities of 1–3 × 1013 cm−3 and H atom temperatures of 4000–8000 K [electron densities and H translational temperatures were measured by Stark and Doppler broadening of H Balmer-β (Hβ) emission at 486.1 nm]. Laser interferometry was employed to monitor time-resolved temperatures of the quartz substrate. Etching rates were measured by stylus profilometry, and roughness was quantified by atomic force microscopy (AFM). For 5 min discontinuous H2/Ar plasma exposure (0.5 Torr, 16 W/cm3, 1 min plasma-on, 9 min plasma-off per cycle with five cycles), the etching rate during the plasma-on time was 224 nm/min. This was much higher than the 16 nm/min rate observed for a continuous 1 h H2/Ar plasma exposure. This trend was ascribed to the higher substrate temperatures reached with continuous plasma operation and a negative dependence of the etching rate on temperature, described by an activation energy of roughly −5 kcal/mol. When exposure to 1 min H2/Ar plasmas was alternated with 1 min O2/Ar plasma treatments and 12 min plasma-off periods, the etching rate was reduced to near-zero and the extent of surface roughness was reduced by at least fivefold. N2/Ar plasma treatments were less effective in reducing the etching rate (to 57 nm/min), while the roughness to the surface caused by exposure to the H2/Ar 1 min plasmas was nearly eliminated. A mechanism for the erosion process is proposed, involving the penetration of H atoms below the surface where they insert into Si–O–Si linkages to form SiH and SiOH groups. This opening of the SiO2 network allows easier penetration of H, further bond breakage, and crack propagation that eventually leads to the shedding of small silica particles. Periodic exposure to O atoms hydroxalizes these subsurface regions and subsequently reforms Si–O–Si linkages, accompanied by the formation of H2O that presumably diffuses to the surface and desorbs.
Presynaptic supervision of cortical spine dynamics in motor learning
Authors: Sohn Jaerin , Suzuki Mototaka , Youssef Mohammed , Hatada Sayuri , Larkum Matthew E. , Kawaguchi Yasuo , Kubota Yoshiyuki
Published: July 2022

In mammalian neocortex, learning triggers the formation and turnover of new postsynaptic spines on pyramidal cell dendrites. However, the biological principles of spine reorganization during learning remain elusive because the identity of their presynaptic neuronal partners is unknown. Here, we show that two presynaptic neural circuits supervise distinct programs of spine dynamics to execute learning. We imaged spine dynamics in motor cortex during learning and performed post hoc identification of their afferent presynaptic neurons. New spines that appeared during learning formed small transient contacts with corticocortical neurons that were eliminated on skill acquisition. In contrast, persistent spines with axons from thalamic neurons were formed and enlarged. These results suggest that pyramidal cell dendrites in motor cortex use a neural circuit division of labor during skill learning, with dynamic teaching contacts from top-down intracortical axons followed by synaptic memory formation driven by thalamic axons. Dual spine supervision may govern diverse skill learning in the neocortex.
A quantitative biology approach correlates neuronal toxicity with the largest inclusions of TDP-43
Authors: Cascella Roberta , Bigi Alessandra , Riffert Dylan Giorgino , Gagliani Maria Cristina , Ermini Emilio , Moretti Matteo , Cortese Katia , Cecchi Cristina , Chiti Fabrizio
Published: July 2022

A number of neurodegenerative conditions are associated with the formation of cytosolic inclusions of TDP-43 within neurons. We expressed full-length TDP-43 in a motoneuron/neuroblastoma hybrid cell line (NSC-34) and exploited the high-resolution power of stimulated emission depletion microscopy to monitor the changes of nuclear and cytoplasmic TDP-43 levels and the formation of various size classes of cytoplasmic TDP-43 aggregates with time. Concomitantly, we monitored oxidative stress and mitochondrial impairment using the MitoSOX and MTT reduction assays, respectively. Using a quantitative biology approach, we attributed neuronal dysfunction associated with cytoplasmic deposition component to the formation of the largest inclusions, independently of stress granules. This is in contrast to other neurodegenerative diseases where toxicity is attributed to small oligomers. Using specific inhibitors, markers, and electron microscopy, the proteasome and autophagy were found to target mainly the largest deleterious inclusions, but their efficiency soon decreases without full recovery of neuronal viability.
Rapid discovery of stable materials by coordinate-free coarse graining
Authors: Goodall Rhys E. A. , Parackal Abhijith S. , Faber Felix A. , Armiento Rickard , Lee Alpha A.
Published: July 2022

A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the stability and functional properties of materials to be accurately predicted. However, most of these approaches use atomic coordinates as input and are thus bottlenecked by crystal structure identification when investigating previously unidentified materials. Our approach solves this bottleneck by coarse-graining the infinite search space of atomic coordinates into a combinatorially enumerable search space. The key idea is to use Wyckoff representations, coordinate-free sets of symmetry-related positions in a crystal, as the input to a machine learning model. Our model demonstrates exceptionally high precision in finding unknown theoretically stable materials, identifying 1569 materials that lie below the known convex hull of previously calculated materials from just 5675 ab initio calculations. Our approach opens up fundamental advances in computational materials discovery.
Arctic Ocean Amplification in a warming climate in CMIP6 models
Authors: Shu Qi , Wang Qiang , Årthun Marius , Wang Shizhu , Song Zhenya , Zhang Min , Qiao Fangli
Published: July 2022

Arctic near-surface air temperature warms much faster than the global average, a phenomenon known as Arctic Amplification. The change of the underlying Arctic Ocean could influence climate through its interaction with sea ice, atmosphere, and the global ocean, but it is less well understood. Here, we show that the upper 2000 m of the Arctic Ocean warms at 2.3 times the global mean rate within this depth range averaged over the 21st century in the Coupled Model Intercomparison Project Phase 6 Shared Socioeconomic Pathway 585 scenario. We call this phenomenon the “Arctic Ocean Amplification.” The amplified Arctic Ocean warming can be attributed to a substantial increase in poleward ocean heat transport, which will continue outweighing sea surface heat loss in the future. Arctic Amplification of both the atmosphere and ocean indicates that the Arctic as a whole is one of Earth’s regions most susceptible to climate change.
Ancient herpes simplex 1 genomes reveal recent viral structure in Eurasia
Authors: Guellil Meriam , van Dorp Lucy , Inskip Sarah A. , Dittmar Jenna M. , Saag Lehti , Tambets Kristiina , Hui Ruoyun , Rose Alice , D’Atanasio Eugenia , Kriiska Aivar , Varul Liivi , Koekkelkoren A. M. H. C. , Goldina Rimma D. , Cessford Craig , Solnik Anu , Metspalu Mait , Krause Johannes , Herbig Alexander , Robb John E. , Houldcroft Charlotte J. , Scheib Christiana L.
Published: July 2022

Human herpes simplex virus 1 (HSV-1), a life-long infection spread by oral contact, infects a majority of adults globally. Phylogeographic clustering of sampled diversity into European, pan-Eurasian, and African groups has suggested the virus codiverged with human migrations out of Africa, although a much younger origin has also been proposed. We present three full ancient European HSV-1 genomes and one partial genome, dating from the 3rd to 17th century CE, sequenced to up to 9.5× with paired human genomes up to 10.16×. Considering a dataset of modern and ancient genomes, we apply phylogenetic methods to estimate the age of sampled modern Eurasian HSV-1 diversity to 4.68 (3.87 to 5.65) ka. Extrapolation of estimated rates to a global dataset points to the age of extant sampled HSV-1 as 5.29 (4.60 to 6.12) ka, suggesting HSV-1 lineage replacement coinciding with the late Neolithic period and following Bronze Age migrations.
SMAD4, activated by the TCR-triggered MEK/ERK signaling pathway, critically regulates CD8
+
T cell cytotoxic function
Authors: Liu Xinwei , Hao Jing , Wei Peng , Zhao Xiaohong , Lan Qiuyan , Ni Lu , Chen Yongzhen , Bai Xue , Ni Ling , Dong Chen
Published: July 2022

Transforming growth factor–β is well known to restrain cytotoxic T cell responses to maintain self-tolerance and to promote tumor immune evasion. In this study, we have investigated the role of SMAD4, a core component in the TGF-β signaling pathway, in CD8
+
T cells. Unexpectedly, we found that SMAD4 was critical in promoting CD8
+
T cell function in both tumor and infection models. SMAD4-mediated transcriptional regulation of CD8
+
T cell activation and cytotoxicity was dependent on the T cell receptor (TCR) but not TGF-β signaling pathway. Following TCR activation, SMAD4 translocated into the nucleus, up-regulated genes encoding TCR signaling components and cytotoxic molecules in CD8
+
T cells and thus reinforced T cell function. Biochemically, SMAD4 was directly phosphorylated by ERK at Ser
367
residue following TCR activation. Our study thus demonstrates a critical yet unexpected role of SMAD4 in promoting CD8
+
T cell–mediated cytotoxic immunity.
Mapping pure plastic strains against locally applied stress: Revealing toughening plasticity
Authors: Edwards Thomas E. J. , Maeder Xavier , Ast Johannes , Berger Luisa , Michler Johann
Published: July 2022

The deformation of all materials can be separated into elastic and plastic parts. Measuring the purely plastic component is complex but crucial to fully characterize, understand, and engineer structural materials to “bend, not break.” Our approach has mapped this to answer the long-standing riddle in materials mechanics: The low toughness of body-centered cubic metals, where we advance an experimentally led mitigative theory. At a micromechanically loaded crack, we measured in situ the stress state applied locally on slip systems, and the dislocation content, and then correlatively compared with the occurrence—or not—of toughness-inducing local plasticity. We highlight limitations and potential misinterpretations of commonly used postmortem transmission electron imaging. This should enable better-informed design for beneficial plasticity and strength in crystalline and amorphous solids alike.
Meta-optic accelerators for object classifiers
Authors: Zheng Hanyu , Liu Quan , Zhou You , Kravchenko Ivan I. , Huo Yuankai , Valentine Jason
Published: July 2022

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic–based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.
Performing calculus with epsilon-near-zero metamaterials
Authors: Li Hao , Fu Pengyu , Zhou Ziheng , Sun Wangyu , Li Yue , Wu Jiamin , Dai Qionghai
Published: July 2022

Calculus is a fundamental subject in mathematics and extensively used in physics and astronomy. Performing calculus operations by analog computing has received much recent research interest because of its high speed and large data throughput; however, current analog calculus frameworks suffer from bulky sizes and relatively low integration densities. In this work, we introduce the concept of an epsilon-near-zero (ENZ) metamaterial processing unit (MPU) that performs differentiation and integration on analog signals to achieve extreme miniaturization at the subwavelength scale by generating desired dispersions of the ENZ metamaterials with photonic doping. To show the feasibility of this proposal, we further build an experimental analog image edge extraction system with a differentiating ENZ-MPU as its compute core. With a computing density theoretically analyzed to be several tera-operations per second and square micrometer, the proposed ENZ-MPU is scalable and configurable for more complex computations, providing an effective solution for analog calculus operators with extreme computing density and data throughput.
Science Robotics
: Helping build better robots for a better future
Authors: Nelson Bradley J.
Published: July 2022

To build better machines, roboticists need to ask the right questions.
Impacts of Rotation-Fallow Practices on Bacterial Community Structure in Paddy Fields
Authors: Li Na , Li Xinmei , Li Shujuan , Guo Shujian , Wan Ziwei , Huang Guoqin , Xu Huifang
Published: July 2022

Soil nutrients and microbial community play a central role in determining crop productivity. Therefore, elucidating the microbial mechanisms associated with different cropping systems is indispensable for understanding the sustainability of agroecosystem.
Cytokinin Regulates Energy Utilization in Botrytis cinerea
Authors: Anand Gautam , Gupta Rupali , Bar Maya
Published: July 2022

The hormone cytokinin (CK) is a plant developmental regulator. Previous research has highlighted the involvement of CK in plant defense.
Diaphorin, a Polyketide Produced by a Bacterial Symbiont of the Asian Citrus Psyllid, Inhibits the Growth and Cell Division of Bacillus subtilis but Promotes the Growth and Metabolic Activity of Escherichia coli
Authors: Tanabe Nozomu , Takasu Rena , Hirose Yuu , Kamei Yasuhiro , Kondo Maki , Nakabachi Atsushi
Published: July 2022

Certain secondary metabolites, including antibiotics, evolve to mediate interactions among organisms. These molecules have distinct spectra for microorganisms and are often more effective against Gram-positive bacteria than Gram-negative ones.
Wolbachia w
Pip Blocks Zika Virus Transovarial Transmission in
Aedes albopictus
Authors: Guo Yan , Guo Jiatian , Li Yifeng
Published: July 2022

Area-wide application of
Wolbachia
to suppress mosquito populations and their transmitted viruses has achieved success in multiple countries. However, the mass release of
Wolbachia
-infected male mosquitoes involves a potential risk of accidentally releasing fertile females. In this study, we employed ovarian cells of the
Ae. albopictus
GUA, HC, and GT lines, which exhibit key traits, and compared them to better understand how
Wolbachia
inhibits ZIKV transovarial transmission.
Guanidinoacetic acid provides superior cardioprotection to its combined use with betaine and (or) creatine in HIIT-trained rats
Authors: Prokic Veljko Z. , Rankovic Marina R. , Draginic Nevena D. , Andjic Marijana M. , Sretenovic Jasmina Z. , Zivkovic Vladimir I. , Jeremic Jovana N. , Milinkovic Milica V. , Bolevich Sergey , Jakovljevic Vladimir L.J. , Pantovic Suzana B.
Published: July 2022

This study aimed to determine how guanidinoacetic acid (GAA) or its combined administration with betaine (B) or creatine (C) influences the cardiac function, morphometric parameters, and redox status of rats subjected to high-intensity interval training (HIIT). This research was conducted on male Wistar albino rats exposed to HIIT for 4 weeks. The animals were randomly divided into five groups: HIIT, HIIT + GAA, HIIT + GAA + C, HIIT + GAA + B, and HIIT + GAA + C + B. After completing the training protocol, GAA (300 mg/kg), C (280 mg/kg), and B (300 mg/kg) were applied daily per os for 4 weeks. GAA supplementation in combination with HIIT significantly decreased the level of both systemic and cardiac prooxidants ([Formula: see text], H2O2, [Formula: see text], and thiobarbituric acid reactive substances) compared with nontreated HIIT ( p < 0.05). Also, GAA treatment led to an increase in glutathione and superoxide dismutase levels. None of the treatment regimens altered cardiac function. A larger degree of cardiomyocyte hypertrophy was observed in the HIIT + GAA group, which was reflected through an increase of the cross-sectional area of 27% ( p < 0.05) and that of the left ventricle wall thickness of 27% ( p < 0.05). Since we showed that GAA in combination with HIIT may ameliorate oxidative stress and does not alter cardiac function, the present study is a basis for future research exploring the mechanisms of cardioprotection induced by this supplement in an HIIT scenario.
Radion dynamics, heavy Kaluza–Klein resonances and gravitational waves
Authors: Megías Eugenio , Nardini Germano , Quirós Mariano
Published: July 2022

In this paper, we study the confinement/deconfinement phase transition of the radion field in a warped model with a polynomial bulk potential. The backreaction of the radion on the metric is taken into account by using the superpotential formalism, while the radion effective potential is obtained from a novel formulation which can incorporate the backreaction. The phase transition leads to a stochastic gravitational wave background that depends on the energy scale of the first Kaluza–Klein resonance, [Formula: see text]. This work completes previous studies in the following aspects: (i) we detail the evaluation of the radion spectrum; (ii) we report on the mismatches between the thick wall approximation and the numerical bounce solution; (iii) we include a suppression factor in the spectrum of sound waves accounting for their finite lifetime; and (iv) we update the bound on [Formula: see text] in view of the O3 LIGO and Virgo data. We find that the forthcoming gravitational wave interferometers can probe scenarios where [Formula: see text], while the O3-run bounds rule out warped models with [Formula: see text] exhibiting an extremely strong confinement/deconfinement phase transition.
ALP-portal majorana dark matter
Authors: Gola Shivam , Mandal Sanjoy , Sinha Nita
Published: July 2022

Axion like particles (ALPs) and right-handed neutrinos (RHNs) are two well-motivated dark matter (DM) candidates. However, these two particles have a completely different origin. Axion was proposed to solve the strong CP problem, whereas RHNs were introduced to explain light neutrino masses through seesaw mechanisms. We study the case of ALP portal RHN DM (Majorana DM) taking into account existing constraints on ALPs. We consider the leading effective operators mediating interactions between the ALP and Standard Model (SM) particles and three RHNs to generate light neutrino masses through type-I seesaw. Further, ALP-RHN neutrino coupling is introduced to generalize the model which is restricted by the relic density and indirect detection constraint.
Impact of generalized fourier law in thermal flux convective flow over a vertical plate: Analysis of fractional derivative
Authors: Raza Ali , Hejazi Hala A. , Khan M. Ijaz
Published: July 2022

In this paper, a convective fluid flow mixed with different nanoparticles (NPs) flowing on a vertical plate is studied. The impacts of slip boundary wall, Newtonian heating, and magnetic field are also pondered. Using Fourier’s rule of heat conductivity and Caputo-time (CT) fractional derivative (FD), the nondimensional governing equations for this mathematical model are obtained. The Laplace transformation (LT) technique is used to get a generalized solution to the equations of momentum and energy. The physical interpretation of velocity distribution in some particular cases is well recognized in the past research works and is also highlighted to scrutinize some further physical perception into the subject and to fortify the novelty of this research communication. Furthermore, graphical depictions are created using the mathematica program to assess the physical importance of the considered topic. As a result, we concluded that the blood-based aluminium oxide ([Formula: see text] NPs show more decaying behavior as compared to titanium dioxide ([Formula: see text] NPs for temperature and velocity profile. Moreover, changing the fractional parameter’s value for a long period of time improves thermal and velocity profiles.
Highly dispersive optical and other soliton solutions to fiber Bragg gratings with the application of different mechanisms
Authors: Rehman Shafqat-Ur- , Bilal Muhammad , Ahmad Jamshad
Published: July 2022

This work devotes to explore abundant optical and other soliton solutions to fiber Bragg gratings with dispersive reflectivity having Kerr law of nonlinear refractive index. Bragg gratings are no doubt a technological spectacle that sustained balance between dispersion and nonlinear effects that leads to a stable transmission of solitons across intercontinental distances. Three reliable integration norms namely ([Formula: see text])-expansion function method, generalized tanh method and generalized Kudryashov method are engaging to devise optical dark, singular, combo, complex and periodic solutions. The reported solutions contain key applications in engineering and physics. These solutions define the wave performance of the governing models, actually. By the choice of suitable parametric values, the dynamics of the evaluated results are exemplified by sketching their 2D, 3D and contour profiles to understand the real phenomena for such sort of nonlinear models. The novelty of the gained outcomes is manifested by a detailed comparison with the results that already exist. The outcomes disclose that our schemes are very active reliable, concise, outspoken, appropriate, useful and skilled for attaining the exact solutions of such kind of problems which minimize the computational work and give it a wide range of applications.
Intrusion Detection Algorithm Based on Convolutional Neural Network and Light Gradient Boosting Machine
Authors: Wang Qian , Zhao Wenfang , Wei Xiaoyu , Ren Jiadong , Gao Yuying , Zhang Bing
Published: July 2022

Aiming at the limitations of existing algorithms of network intrusion detection in dealing with complex data of imbalance and high dimensionality, this paper proposes an intrusion detection algorithm based on convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM). First, the data-type conversion, oversampling technology and image data conversion are included in the data preprocessing to make the data balanced and adapt to the input format. Then, by the convolutional layer, pooling layer and fully connected layer of the CNN model, the main features are abstracted from the converted image data. Finally, data of the main features is used for training and testing the LightGBM model, so as to get the final classification results. This paper uses KDDCUP99 dataset to carry out multi-classification experiments. By comparing the experiments before and after balancing the dataset, and comparing with similar algorithms, it verifies the superiority of the proposed algorithm in the classification performance of intrusion detection, especially for the minority attack classes.
Characterizing and Detecting Methods to be Benchmarked under Performance Unit Test
Authors: Chen Jie , Hu Haiyang , Yu Dongjin
Published: July 2022

Continuous integration is a growing trend in the software engineering community and industry. Performance testing is becoming more important in this context. To support precise and fine-grained monitoring, performance unit tests are applied for small software components. However, the benchmarks for performance unit testing are still insufficient, which means that benchmark coverage is low and there is a room for improvement. Therefore, focusing on the most important parts of the software, such as methods, and ensuring that their performance is monitored closely with performance unit tests can greatly reduce the amount of work that needs to be done for testing and to prepare benchmarks. This paper aims to provide an assisting approach for detecting methods that need to be benchmarked in performance unit tests. We start by defining 30 features to characterize the methods in the projects and show that they can be used to tell the benchmarked methods (short for BDMs) from those that are not. Then, using the proposed features, we build machine learning-based models to detect BDMs. We perform an experiment with 10 open source projects from GitHub to see how well our approach works. First, we use seven binary classification techniques to evaluate the prediction performance of our machine learning models. We find that Random Forest makes the best predictions where AUC and MCC are between 0.77 and 0.89 and 0.5 and 0.75, respectively. In terms of cost effectiveness, the experiment reveals that by inspecting only 5% of the candidate methods detected by our model, 43% of the total real BDMs can be retrieved. Second, we conduct feature importance evaluations for individual features and feature categories. We find that eight features related to Scope, History, and Complexity are individually important for good predictions and that the combination of all features in the Scope category is paramount for our model, while the combination of features in the Control Flow category is less important. Third, we investigate the performance of our detection approach with different feature selection strategies and data sources. Our results show that we can make good predictions about whether a method needs to be benchmarked by using machine learning models. Practitioners can use our method and the results of the study to deal with BDMs detection effectively.
BLOW-UP PROBLEMS FOR GENERALIZED FUJITA-TYPE EQUATIONS ON GRAPHS
Authors: WU YITING
Published: July 2022

In this paper, we study the blow-up problem for Fujita-type equations with a general absorption term [Formula: see text] on finite graphs and locally finite graphs, respectively. We prove that if [Formula: see text] satisfies appropriate conditions, then the solutions of the equation blow up in finite time. The obtained results are generalization of those given by the author in previous papers. At the end of the paper, we provide some numerical experiments to illustrate the applicability of the obtained results.
Lie symmetry analysis of time fractional Burgers equation, Korteweg-de Vries equation and generalized reaction-diffusion equation with delays
Authors: Yu Jicheng
Published: July 2022

In this paper, Lie symmetry analysis method is applied to time fractional Burgers equation, Korteweg-de Vries equation and generalized reaction-diffusion equation with delays, respectively. The Lie symmetries for fractional partial differential equations with delays (DFPDEs) are obtained, and the group classifications of the equations are established. The obtained group generators are used to reduce the DFPDEs to fractional ordinary differential equations with delays (DFODEs). Some exact solutions constructed for the DFODEs generate group-invariant solutions of the discussed DFPDEs.
High-Performance Polynomial Root Finding for Graphics
Authors: Yuksel Cem
Published: July 2022

We present a computationally-efficient and numerically-robust algorithm for finding real roots of polynomials. It begins with determining the intervals where the given polynomial is monotonic. Then, it performs a robust variant of Newton iterations to find the real root within each interval, providing fast and guaranteed convergence and satisfying the given error bound, as permitted by the numerical precision used.

For cubic polynomials, the algorithm is more accurate and faster than both the analytical solution and directly applying Newton iterations. It trivially extends to polynomials with arbitrary degrees, but it is limited to finding the real roots only and has quadratic worst-case complexity in terms of the polynomial's degree.

We show that our method outperforms alternative polynomial solutions we tested up to degree 20. We also present an example rendering application with a known efficient numerical solution and show that our method provides faster, more accurate, and more robust solutions by solving polynomials of degree 10.
PLOC++
Authors: Benthin Carsten , Drabinski Radoslaw , Tessari Lorenzo , Dittebrandt Addis
Published: July 2022

We propose a novel version of the GPU-oriented massively parallel locally-ordered clustering (PLOC) algorithm for constructing bounding volume hierarchies (BVHs). Our method focuses on removing the weaknesses of the original approach by simplifying and fusing different phases, while replacing most performance critical parts by novel and more efficient algorithms. This combination allows for outperforming the original approach by a factor of 1.9 - 2.3x.
Temporally Stable Real-Time Joint Neural Denoising and Supersampling
Authors: Thomas Manu Mathew , Liktor Gabor , Peters Christoph , Kim Sungye , Vaidyanathan Karthik , Forbes Angus G.
Published: July 2022

Recent advances in ray tracing hardware bring real-time path tracing into reach, and ray traced soft shadows, glossy reflections, and diffuse global illumination are now common features in games. Nonetheless, ray budgets are still limited. This results in undersampling, which manifests as aliasing and noise. Prior work addresses these issues separately. While temporal supersampling methods based on neural networks have gained a wide use in modern games due to their better robustness, neural denoising remains challenging because of its higher computational cost.

We introduce a novel neural network architecture for real-time rendering that combines supersampling and denoising, thus lowering the cost compared to two separate networks. This is achieved by sharing a single low-precision feature extractor with multiple higher-precision filter stages. To reduce cost further, our network takes low-resolution inputs and reconstructs a high-resolution denoised supersampled output. Our technique produces temporally stable high-fidelity results that significantly outperform state-of-the-art real-time statistical or analytical denoisers combined with TAA or neural upsampling to the target resolution.
Virtual Blue Noise Lighting
Authors: Li Tianyu , Wang Wenyou , Lin Daqi , Yuksel Cem
Published: July 2022

We introduce virtual blue noise lighting, a rendering pipeline for estimating indirect illumination with a blue noise distribution of virtual lights. Our pipeline is designed for virtual lights with non-uniform emission profiles that are more expensive to store, but required for properly and efficiently handling specular transport.

Unlike the typical virtual light placement approaches that traverse light paths from the original light sources, we generate them starting from the camera. This avoids two important problems: wasted memory and computation with fully-occluded virtual lights, and excessive virtual light density around high-probability light paths. In addition, we introduce a parallel and adaptive sample elimination strategy to achieve a blue noise distribution of virtual lights with varying density. This addresses the third problem of virtual light placement by ensuring that they are not placed too close to each other, providing better coverage of the (indirectly) visible surfaces and further improving the quality of the final lighting estimation.

For computing the virtual light emission profiles, we present a photon splitting technique that allows efficiently using a large number of photons, as it does not require storing them. During lighting estimation, our method allows using both global power-based and local BSDF important sampling techniques, combined via multiple importance sampling. In addition, we present an adaptive path extension method that avoids sampling nearby virtual lights for reducing the estimation error.

We show that our method significantly outperforms path tracing and prior work in virtual lights in terms of both performance and image quality, producing a fast but biased estimate of global illumination.
Review of Complexity Dichotomies for Counting Problems Volume 1
Authors: Green Frederic
Published: July 2022

This book is a definitive account of a field of research that has blossomed remarkably over the past two decades. Many of its chief developments are presented here, indeed in many cases having been obtained by its authors.
Review of The Geometry of Uncertainty by Fabio Cuzzolin
Authors: Galinkin Erick
Published: July 2022

The Geometry of Uncertainty is unlike any book on mathematics and computer science I've ever read. It's certainly not a textbook in the traditional sense - there are no exercises, and very little is presented as the "right way" to do something. In many ways, The Geometry of Uncertainty is like a survey paper: it critically analyzes decades of research and while some of the author's preferences are reflected, many perspectives are presented with little direct guidance about which approach ought to be favored. People have long used the phrase "The Bible of X" to describe the most well-known or well-respected text in a field. However, calling The Geometry of Uncertainty "The Bible of Evidence Theory" is truly apt in the sense that you come into the book with questions, and you leave the book with parables, historical insight, and a whole host of new, better informed questions. Truly, the book is not merely an introduction to belief function theory, it is instead a truly comprehensive view of an entire field of study from its conception up to the most bleeding edge research.
Open Problems Column
Authors: Gasarch William
Published: July 2022

This issue's Open Problem Column is by William Gasarch, Nathan Hayes, Anthony Ostuni, and Davin Park. It is The complexity of chromatic number when restricted to graphs with either bounded genus or bounded crossing number.
SIGACT News Complexity Theory Column 113
Authors: Hemaspaandra Lane A.
Published: July 2022

My deepest thanks to Nutan Limaye, Srikanth Srinivasan, and Sebastien Tavenas for their fascinating article, Lower bounds Against Constant-Depth Algebraic Circuits.
Online List Scheduling for Makespan Minimization
Authors: Dwibedy Debasis , Mohanty Rakesh
Published: July 2022

In the modern interactive computing era, computational problems such as scheduling, rout- ing, sequencing, and resource management are online in nature. In the online framework, at the outset, an algorithm receives and processes inputs one by one in order without the knowledge of future inputs, unlike in an offline framework, where an algorithm knows the entire input sequence at the beginning. The design of mathematical models and efficient al- gorithms for the online framework constitutes a challenging and competitive area of research in Theoretical Computer Science. The researchers have considered competitive analysis as a well-de ned and standard method to measure the performance of online algorithms.
Taxonomy of Machine Learning Safety: A Survey and Primer
Authors: Mohseni Sina , Wang Haotao , Xiao Chaowei , Yu Zhiding , Wang Zhangyang , Yadawa Jay
Published: July 2022

The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations. Research explores different approaches to improve ML dependability by proposing new models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. However, there is a missing connection between ongoing ML research and well-established safety principles. In this article, we present a structured and comprehensive review of ML techniques to improve the dependability of ML algorithms in uncontrolled open-world settings. From this review, we propose the
Taxonomy of ML Safety
that maps state-of-the-art ML techniques to key engineering safety strategies. Our taxonomy of ML safety presents a safety-oriented categorization of ML techniques to provide guidance for improving dependability of the ML design and development. The proposed taxonomy can serve as a safety checklist to aid designers in improving coverage and diversity of safety strategies employed in any given ML system.
Remote Electronic Voting in Uncontrolled Environments: A Classifying Survey
Authors: Heinl Michael P. , Gölz Simon , Bösch Christoph
Published: July 2022

Remote electronic voting, often called online or Internet voting, has been subject to research for the last four decades. It is regularly discussed in public debates, especially in the context of enabling voters to conveniently cast their ballot from home using their personal devices. Since these devices are not under the control of the electoral authority and could be potentially compromised, this setting is referred to as an “uncontrolled environment” for which special security assumptions have to be considered.

This paper employs general election principles to derive cryptographic, technical, and organizational requirements for remote electronic voting. Based on these requirements, we have extended an existing methodology to assess online voting schemes and develop a corresponding reference attacker model to support the preparation of tailored protection profiles for different levels of elections. After presenting a broad survey of different voting schemes, we use this methodology to assess and classify those schemes comparatively by leveraging four election-specific attacker models.
A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning
Authors: Hossain Tahera , Shen Wanggang , Antar Anindya , Prabhudesai Snehal , Inoue Sozo , Huan Xun , Banovic Nikola
Published: July 2022

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this article, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: (1)pre-hocexperiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and (2)post-hocdataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. We validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of the effects of dataset size on IRL model parameters.
On the Design of a Matrix Adaptation Evolution Strategy for Optimization on General Quadratic Manifolds
Authors: Spettel Patrick , Beyer Hans-Georg
Published: July 2022

An evolution strategy design is presented that allows for an evolution on general quadratic manifolds. That is, it covers elliptic, parabolic, and hyperbolic equality constraints. The peculiarity of the presented algorithm design is that it is an interior point method. It evaluates the objective function only for feasible search parameter vectors and it evolves itself on the nonlinear constraint manifold. Such a characteristic is particularly important in situations where it is not possible to evaluate infeasible parameter vectors, e.g., in simulation-based optimization. This is achieved by a closed form transformation of an individual’s parameter vector, which is in contrast to iterative repair mechanisms. This constraint handling approach is incorporated into a matrix adaptation evolution strategy making such algorithms capable of handling problems containing the constraints considered. Results of different experiments are presented. A test problem consisting of a spherical objective function and a single hyperbolic/parabolic equality constraint is used. It is designed to be scalable in the dimension. As a further benchmark, the Thomson problem is used. Both problems are used to compare the performance of the developed algorithm with other optimization methods supporting constraints. The experiments show the effectiveness of the proposed algorithm on the considered problems. Additionally, an idea for handling multiple constraints is discussed. And for a better understanding of the dynamical behavior of the proposed algorithm, single run dynamics are presented.
LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans
Authors: Joshi Amogh Manoj , Nayak Deepak Ranjan , Das Dibyasundar , Zhang Yudong
Published: July 2022

Recent years have witnessed a rise in employing deep learning methods, especially
convolutional neural networks (CNNs)
for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a
lightweight multi-scale CNN
called
LiMS-Net
is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model.