Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic Behavior of Coarse-grained Models for Opinion Dynamics on Large Networks

In this paper, we propose a general mathematical framework to represent many multi-agent signalling systems in recent works. Our goal is to apply previous results in monotonicity to this class of systems and study their asymptotic behavior. Hence we introduce a suitable partial order for these systems and prove nontrivial extensions of previous results on monotonicity. We also derive a convenient sufficient condition for a signalling system to be monotone and test our condition on the Naming Games, NG and K-NG on complete networks both with and without committed agents. We also give a counter example which fails to satisfy our condition. Next we further extend our conclusions to systems on sparse random networks. Finally we discuss several meaningful consequences of monotonicity which narrows down the possible asymptotic behavior of signalling systems in mathematical sociology and network science.
Hamiltonian stability of Hamiltonian minimal Lagrangian submanifolds in pseudo- and para-K\"ahler manifolds

Let L be a Lagrangian submanifold of a pseudo- or para-K\"ahler manifold which is H-minimal, i.e. a critical point of the volume functional restricted to Hamiltonian variations. We derive the second variation of the volume of L with respect to Hamiltonian variations. We apply this formula to several cases. In particular we observe that a minimal Lagrangian submanifold L in a Ricci-flat pseudo- or para-K\"ahler manifold is H-stable, i.e. its second variation is definite and L therefore a local extremizer of the volume with respect to Hamiltonian variations. We also give a stability criterion for spacelike minimal Lagrangian submanifolds in para-K\"ahler manifolds, similar to Oh's stability criterion for minimal Lagrangian manifolds in K\"ahler-Einstein manifolds. Finally, we determine the H-stability of a series of examples of H-minimal Lagrangian submanifolds: the product of n circles of arbitrary radii in complex space C^n is H-unstable with respect to any indefinite flat Hermitian metric, while the product of n hyperbolas in para-complex vector space D^n is H-stable for n=1,2 and H-unstable for n > 2. Recently, minimal Lagrangian surfaces in the space of geodesics of three-dimensional space forms have been characterized; on the other hand, a class of H-minimal Lagrangian surfaces in the tangent bundle of a Riemannian, oriented surface has been identified. We discuss the H-stability of all these examples.
EEG- and EMG-Driven Poststroke Rehabilitation: A Review

Intelligent poststroke rehabilitation has attracted great attention worldwide, since the high incidence rate of stroke with the aging of the population. It is well known that effective rehabilitation training can help the rehabilitation of neuromuscular injuries. In recent decades, biological signal-based closed-loop rehabilitation has significantly progressed and attracted widespread attention in clinics and academia, achieving relatively promising results. These biological signals are mainly electromyographic (EMG) signals and electroencephalographic (EEG) signals. First, this article briefly overviews how to use EMG and EEG to be involved in rehabilitation. Special attention is paid to the detailed changes in the EMG signal, EEG signal, and brain rhythm after stroke, regarding technology-based intervention in stroke rehabilitation including intention cognitive, function rehabilitation devices, gesture decoded, and motor imagery (MI). Finally, the feasibility of state-of-the-art motor function rehabilitation with EEG and EMG signals is analyzed. Our results show that rehabilitation with EEG and EMG signals is relatively more favorable than a single signal. Though the challenges may be tough, new theories and technological approaches able to exploit the full potential of EEG and EMG.
sgRNA-shRNA Structure Mediated SNP Site Editing on Porcine IGF2 Gene by CRISPR/StCas9

The SNP within intron 3 of the porcine IGF2 gene (G3072A) plays an important role for muscle growth and fat deposition in pigs. In this study, the StCas9 derived from Streptococcus thermophilus together with the Drosha-mediated sgRNA-shRNA structure were combined to boost the G to A base editing on the IGF2 SNP site, which we called “SNP editing. ” The codon-humanized StCas9 as we previously reported was firstly compared with the prevalently used SpCas9 derived from Streptococcus pyogenes using our idiomatic surrogate report assay, and the StCas9 demonstrated a comparable targeting activity. On the other hand, by combining shRNA with sgRNA, simultaneous gene silencing and genome targeting can be achieved. Thus, the novel IGF2.sgRNA-LIG4.shRNA-IGF2.sgRNA structure was constructed to enhance the sgRNA/Cas9-mediated HDR-based IGF2 SNP editing by silencing the LIG4 gene, which is a key molecule of the HDR’s competitive NHEJ pathway. The sgRNA-shRNA/StCas9 all-in-one expression vector and the IGF2.sgRNA/StCas9 as control were separately used to transfect porcine PK15 cells together with an ssODNs donor for the IGF2 SNP editing. The editing events were detected by the RFLP assay, Sanger sequencing as well as Deep-sequencing, and the Deep-sequencing results finally demonstrated a significant higher HDR-based editing efficiency (16.38%) for our sgRNA-shRNA/StCas9 strategy. In short, we achieved effective IGF2 SNP editing by using the combined sgRNA-shRNA/StCas9 strategy, which will facilitate the further production of base-edited animals and perhaps extend for the gene therapy for the base correction of some genetic diseases.
A Full-Wave Discontinuous Galerkin Time-Domain Finite Element Method for Electromagnetic Field Mode Analysis

The accurate analysis and the characterization of guided-wave structures are important steps in the design of microwave and optical systems. A numerical methodology based on a three-dimensional discontinuous Galerkin time-domain (DGTD) finite element method is used to estimate the resonant frequencies of various structures in a single run, with several polychromatic sources (dipole, plane wave, etc). The proposed approach leads to simulations that are fast and accurate, this being the result of the local discretization strategy of the full-wave time-domain Maxwell’s equations on unstructured tetrahedral meshes that is easy to parallelize. The stability of the overall numerical scheme is obtained using an upwind numerical flux and an implicit second-order accurate time integration scheme. The numerical methodology is verified based on two problems with closed-form solutions. First, homogeneous and non-homogeneous waveguides are studied using the proposed methodology. The WR-284 waveguide is used, and cut-off frequencies of the first 7 modes are computed and compared with analytical solutions. Propagation characteristics of mode-selective transmission lines (MSTL) are then investigated. These MSTL simulation results are verified using measurement results of fabricated MSTL, microstrip, and RWG prototypes up to 110 GHz. Simulation results show the flexibility, the robustness, and the high accuracy of the proposed numerical strategy.
A MINOR MERGER CAUGHT IN THE ACT OF FUELING THE ACTIVE GALACTIC NUCLEUS IN Mrk 509

In recent observations by the Hubble Space Telescope (HST) as part of a campaign to discover locations and kinematics of AGN outflows, we found that Mrk 509 contains a 3″ (∼2100 pc) linear filament in its central region. Visible in both optical continuum and [O iii] imaging, this feature resembles a “check mark” of several knots of emission that travel northwest to southeast before jutting toward the nucleus from the southwest. Space Telescope Imaging Spectrograph (STIS/HST) observations along the inner portion of the filament reveal redshifted velocities, indicating that the filament is inflowing. We present further observations of the nucleus in Mrk 509 using the Gemini Near-Infrared Integral Field Spectrograph, from which we conclude that this structure cannot be related to previously studied, typical narrow line region outflows and instead embodies the remains of an ongoing minor merger with a gas-rich dwarf galaxy, therefore providing a great opportunity to study the fueling of an AGN by a minor merger in progress.
An Alternating Direction Method of Multipliers (ADMM) Based Optimal Electric Vehicle Fleets Charging In Active Electric Distribution Network

This paper presents an ADMM (Alternating Direction Method of Multipliers) based scheduling algorithm for electric vehicle charging considering power grid impact. Different scheduling approaches for vehicle charging are developed that allow for demand management, such as valley filling and peak shaving by considering real-time pricing (RTP). The main advantage of the proposed algorithm is that it allows dividing a large number of vehicle and grid nodes into decentralized sub-problem which allows for a fast and efficient solution. The methods used in this research attempt to change consumers' electricity consumption behavior in order to lower peak demand and thereby increase energy efficiency and power system stability. Results show that improvement features a vital impact on demand management and cost objectives.
Semidiscrete variable time-step \theta-scheme for nonmonotone evolution inclusion

In this paper we show the convergence of a semidiscrete time stepping \theta-scheme on a time grid of variable length to the solution of parabolic operator differential inclusion in the framework of evolution triple. The multifunction is assumed to be strong-weak upper-semicontinuous and to have nonempty, closed and convex values, while the quasilinear operator present in the problem is required to be pseudomonotone, coercive and satisfy the appropriate growth condition. The convergence of piecewise constant and piecewise linear interpolants constructed on the solutions of time discrete problems is shown. Under an additional assumption on the sequence of time grids and regularity of quasilinear operator strong convergence results are obtained.
Twin-Screw Extrusion as Hydrothermal Technology for the Development of Gluten-Free Teff Flours: Effect on Antioxidant, Glycaemic Index and Techno-Functional Properties

Gluten-free products (GFP) currently are the fastest-growing category of baked goods probably due to the high worldwide incidence of celiac disease (CD). Refined rice is one of the most used cereal flour for GFP development, due to its high content in starch and good technological aptitude. However, its low content in fibre, protein and minerals has awakened a recent interest as alternative to balance the GF flour formulas. Teff is a cereal with high levels in fibre and antioxidants compounds but the lack of gluten results in very limited techno-functional properties. Extrusion is a thermal technology that allows to combine flours, overcoming negative impacts on quality characteristics. This study evaluated the effect of twin-screw extruder on rice-teff (white and brown) mixtures with different teff concentrations (25, 50 and 75%) on their antioxidant, glycaemic index and techno-functional properties. The results showed than the high shear–temperature process produced important modifications on the flour, which were confirmed using scanning electron microscopy (SEM). Significant increases in total dietary fibre (16 to 100% increase) were observed in teff containing flours, due to carbohydrate–lipid–protein complexes, which lead to resistant starch, with no significant increase in rice flour. Hydration and pasting properties were significantly (p > 0.05) affected by extrusion, and the effect was related to the concentration of teff used. The thermal process showed a decrease in total phenol (TP) content for rice; however, extrusion enhanced the release of total phenol in rice-teff blends, which was reflected on the antioxidant activities of blend flours, especially those prepared with brown teff.
The Internet of Things Network Penetration Testing Model Using Attack Graph Analysis

Penetration testing (pen-testing) is one of the most effective approaches to increase the security level of information systems. Although pen-testing is a very popular approach, the process requires a significant investment of time and extensive financial resources. Existing pen-testing models have focused heavily on handling IoT security breaches and improving attack path analysis. However, they have their limitations as they are too general and unable to detect multi-stage, multi-host, or zero-day vulnerabilities in IoT devices. In this proposed study, an algorithm is developed to generate all attack paths for vulnerable IoT devices. The techniques to optimize the attack paths are defined. A second algorithm is developed to identify the criticality of the paths, nodes, and vulnerabilities before it is used to optimize the target graphs. The developed model is expected to be an end-to-end, accurate, flexible, and automatic pen-testing model using attack graphs for detecting all possible paths that can be used by an attacker to penetrate the target system.
Sinicizing European Languages: Lexicographical and Literary Practices of Pidgin English in Nineteenth-Century China

Abstract:This article reconsiders the social, economic, and literary significance of Chinese Pidgin English (CPE) in Chinese society by exploring lexicographical and literary practices of pidgin in nineteenth-century China. Resituating the history of CPE in Chinese language history, this article problematizes the concept of pidgin and pursues three arguments. First, the author maintains that CPE arose from the marginalized status of the Euro-American traders who were restricted from learning the Chinese language in Canton. Second, by exploring foreign-language glossaries, this article foregrounds the key role of sinographs and Chinese topolects in mediating and remolding foreign languages. Last, by examining the appropriation of foreign sounds in Cantonese folk songs and Pan Youdu's poetry, this article demonstrates the complex flow of these sounds among different languages and the power of pidgin in transgressing linguistic boundaries.
Stochastic differential equations with a fractionally filtered delay: A semimartingale model for long-range dependent processes

In this paper we introduce a model, the stochastic fractional delay differential equation (SFDDE), which is based on the linear stochastic delay differential equation and produces stationary processes with hyperbolically decaying autocovariance functions. The model departs from the usual way of incorporating this type of long-range dependence into a short-memory model as it is obtained by applying a fractional filter to the drift term rather than to the noise term. The advantages of this approach are that the corresponding long-range dependent solutions are semimartingales and the local behavior of the sample paths is unaffected by the degree of long memory. We prove existence and uniqueness of solutions to the SFDDEs and study their spectral densities and autocovariance functions. Moreover, we define a subclass of SFDDEs which we study in detail and relate to the well-known fractionally integrated CARMA processes. Finally, we consider the task of simulating from the defining SFDDEs.
Hand-to-mouth work culture and the COVID-19 lockdown restrictions: experiences of selected informal sector workers in Kumasi, Ghana

This study investigated the economic impacts of the COVID-19 restrictions on businesses and livelihoods. Study participants included 25 informal sector workers who were sampled purposively and conveniently. Qualitative thematic analysis was used for interpreting the data generated from the study. The findings revealed that these workers were adversely affected economically by the COVID-19 lockdown and other restrictions, disrupting their business operations, and plunging their livelihoods into extreme poverty. It was also revealed that the high number of informal sector workers in the 'hand-to-mouth' work culture category in Kumasi cited to have flouted the COVID-19 lockdown regulation due to the unbearable economic surge it had inflicted on their livelihood support systems. The study suggests that immediate economic mitigation strategies set out by the Coronavirus Alleviation Programme (CAP) as well as financial agencies must factor the economic needs of the informal sector workers in the hand-to-mouth work culture category and offer them robust financial relief to assist them recover from economic impacts due to the COVID-19.
One-Class Convolutional Neural Networks for Water-Level Anomaly Detection

Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging from leakage to destruction of infrastructures, leading to economic and life losses. Thus, apprehending the nature of abnormalities that may interrupt or aggravate the service or cause the destruction is at the core of their business model. Normally, companies use sensor networks to monitor these systems and record operational data including any fluctuations in water levels considered abnormalities. Detecting abnormalities allows water companies to enhance the service’s sustainability, quality, and affordability. This study investigates a 2D-CNN-based method for detecting water-level abnormalities as time-series anomaly pattern detection in the One-Class Classification (OCC) problem. Moreover, since abnormal data are usually scarce or unavailable, we explored a cheap method to generate synthetic temporal data and use them as a target class in addition to the normal data to train the CNN model for feature extraction and classification. These settings allow us to train a model to learn relevant pattern representations of the given classes in a binary classification fashion using cross-entropy loss. The ultimate goal of these investigations is to determine if any 2D-CNN-based model can be trained from scratch or if transfer learning of any pre-trained CNN model can be partially trained and used as the base network for one-class classification. The evaluation of the proposed One-Class CNN and previous approaches have shown that our approach has outperformed several state-of-the-art approaches by a significant margin. Additionally, in this paper, we mention two interesting findings: using synthetic data as the pseudo-class is a promising direction, and transfer learning should be dealt with considering that underfitting can happen because the transferred model is too complicated for training data.
Study on Strategic Interaction between Government and Farmers in Rural Passive Energy Transformation

In the implementation of public environmental policies, the deviation between the behavioral intention of farmers and the results of policy implementation is widespread. To reveal the universality, and break through existing research perspectives, this paper, starting from the interaction between the government and farmers, builds a rural passive energy transformation mechanism conceptual model. Using the policy of “coal to gas” in northern China area as a case, a comprehensive analysis of the influencing factors of peasant household behavior response is presented, using a structural equation to compute the interaction strength between the two agents. The results of the study show that: (1) the standardized path coefficients of household behavioral intention and government policy characteristics on household behavioral response are 0.458 and 0.554, respectively. The effect of government is stronger than that of households, which highlights the change effect of government on household behavioral responses and explains the reason for the deviation between behavioral intention and behavioral response; and (2) The standardized correlation coefficient between government policy characteristics and farmers’ behavioral intention is 0.858, indicating that the interaction between government and farmers has a significant impact on policy results, and verifies the important role of research on the interactions between government and farmers. Therefore, in order to improve the effect of rural energy transformation, it is necessary to strengthen the interactions between the government and farmers, to smooth the channels of farmers’ demands, and to form a pattern of air pollution control with government guidance and full participation.
A Limit Theorem for Stochastically Decaying Partitions at the Edge

In this paper, we study the asymptotic behavior of the first, second, and so on rows of stochastically decaying partitions. We establish that, with appropriate scaling in time and length, the sequence of rows converges to the Airy$_2$ line ensemble. This result was first established, in a more general setting, by Borodin and Olshanski, who relied on the determinantal structure of the Poissonized correlation functions. Our argument is based on a different, combinatorial approach, developed by Okounkov. This approach may be useful in other problems in which no determinantal structure is available, and also highlights the similarity between random partitions and random matrices.
CTNI-17. A MULTI-INSTITUTIONAL RANDOMIZED CLINICAL TRIAL COMPARING ASSAY - GUIDED CHEMOTHERAPY WITH PHYSICIAN-CHOICE TREATMENT FOR RECURRENT HIGH-GRADE GLIOMA (NCT03632135)

The presence of therapy-resistant cancer stem cells (CSCs) in recurrent high-grade glioma (HGG) patients contributes to poor clinical outcomes. The ChemoID functional anti-cancer assay targets cancer stem cells along with the bulk of the tumor cells. This trial aims to determine if ChemoID assay-guided treatment improves survival rates for recurrent HGG patients compared to the empirically physician-selected treatment. Patients with grade-III/IV recurrent glioma who failed standard of care (SOC) therapy were randomized (1:1) between two intervention groups. They received one of fourteen mono or combination chemotherapies based on the ChemoID assay or physician choice. The study met the primary outcome in the first interim analysis of 50 patients as per protocol. The ChemoID group had an improved survival rate (vs physician-choice). Median OS (mOS) was 12.5 months in the ChemoID group (95% CI, 10.2-14.7) vs 9 months in the physician-choice (95% CI, 4.2-13.8; log-rank P = .010). Mortality risk was lower in the ChemoID group (HR = 0.44; 95% CI, 0.24-0.81; P = .008). Median progression-free survival was 10.1 months in the ChemoID group vs 3.5 months in the physician choice (95% CI, 4.8-15.4 vs 1.9-5.1; log-rank < 0.001). Risk of progression was lower in the ChemoID group (HR = 0.25; 95% CI, 0.14-0.44; P < 0.001). The intention to treat (ITT) analysis of 78 patients showed substantially improved OS. The ChemoID group had a statistically significant longer median survival of 4.5 months. mOS was 12.0 months in the ChemoID group (95% CI, 10.8-13.2) vs 7.5 in the physician-choice group (95% CI, 3.5-11.5; log-rank P = .009). The ChemoID group had a decreased mortality risk (HR = 0.52; 95% CI, 0.24-0.81; P = .008). Compared with the physician-choice, the ChemoID group had a significantly longer OS in the ITT population. Our findings support that screening standard cytotoxic chemotherapies with a patient-specific anti-cancer assay improves survival outcomes in recurrent HGG patients.