Miracle-dz/UniFAR-FTUs huggingface dataset:
Unifying Diffusion Models on Networks and Their Influence Maximisation

Information diﬀusion in social networks is a central theme in computational social sciences, with important theoretical and practical implications, such as the inﬂuence maximisation problem for viral marketing. Two widely adopted diﬀusion models are the independent cascade model where nodes adopt their behaviour from each neighbour independently, and the linear threshold model where collective eﬀort from the whole neighbourhood is needed to inﬂuence a node. However, both models suﬀer from certain drawbacks, including a binary state space, where nodes are either active or not, and the absence of feedback, as nodes can not be inﬂuenced after having been activated. To address these issues, we consider a model with continuous variables that has the additional advantage of unifying the two classic models, as the extended independent cascade model and the extended linear threshold model are recovered by setting appropriate parameters. For the associated inﬂuence maximisation problem, the objective function is no longer submodular, a feature that most approximation algorithms are based on but is arguably strict in practice. Hence, we develop a framework, where we formulate the inﬂuence maximisation problem as a mixed integer nonlinear programming and adopt derivative-free methods. Furthermore, we propose a customised direct search method speciﬁcally for the proposed diﬀusion model, with local convergence. We also show that the problem can be exactly solved in the case of linear dynamics by selecting nodes according to their Katz centrality. We demonstrate the rich behaviour of the newly proposed diﬀusion model and the close-to-optimal performance of the customised direct search numerically on both synthetic and real networks.
Coupling ModMax theory precursor with scalars, and BIon-type solutions

Recently, the most general theory of electromagnetism invariant under duality and conformal invariance was written, and dubbed ModMax. It arises from a generalization of Born-Infeld (BI) theory by taking the inﬁnite tension limit, T → ∞ . In this note we show that this generalization can be obtained from a brane-like construction, just like BI, and can thus be coupled to scalars in the same way to obtain a DBI-like action. All the BIon and catenoid solutions, and their interpolations, are still solutions of the generalized DBI-like action, suggesting that an interpretation within string theory could be possible. We also show that Ra˜nada’s knotted solutions (with nonzero helicities) are still valid, both in the ModMax theory, and in its precursor.
Visual-Semantic Transformer for Scene Text Recognition

Modeling semantic information is helpful for scene text recognition. In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (VST). The VST first explicitly extracts primary semantic information from visual feature maps with a transformer module and a primary visual-semantic alignment module. The semantic information is then joined with the visual feature maps (viewed as a sequence) to form a pseudo multi-domain sequence combining visual and semantic information, which is subsequently fed into an transformer-based interaction module to enable learning of interactions between visual and semantic features. In this way, the visual features can be enhanced by the semantic information and vice versus. The enhanced version of visual features are further decoded by a secondary visual-semantic alignment module which shares weights with the primary one. Finally, the decoded visual features and the enhanced semantic features are jointly processed by the third transformer module obtaining the final text prediction. Experiments on seven public benchmarks including regular/ irregular text recognition datasets verifies the effectiveness our proposed model, reaching state of the art on four of the seven benchmarks.
Sensitivity Analysis of the Soil Inputs for the Growth Model Peanut

The peanut growth model, PEANUT, requires several inputs that describe the physical characteristics of the soil. A sensitivity analysis was conducted to determine the model’s response to changes in the soil inputs. For the soil water evaporation and water distribution inputs, one input was varied per simulation, while the other variables were set equal to values previously determined for a Norfolk sandy loam soil. Sensitivity to changes in the soil water characteristic function was obtained by fitting the function to data for five different Norfolk soils. The effects of changes in the inputs were considered by conducting growth simulations with 16 years of weather data. Changes in all of the soil water parameters, with the exception of the soil water storage parameters, had little effect on the predicted pod masses; however, a greater effect on predicted pod mass due to changes in the soil water parameters was present in years with adequate rainfall in the early season followed by drier conditions in mid to late season.
Hot electron degradation of the DC and RF characteristics of AlGaAs/InGaAs/GaAs PHEMT's

This paper reports on hot electron (HE) degradation of 0.25-/spl mu/m Al/sub 0.25/Ga/sub 0.75/As/In/sub 0.2/Ga/sub 0.8/As/GaAs PHEMT's by showing the effects of the hot electron stress on both the dc and rf characteristics. The changes of dc and rf behavior after stress turn out to be strongly correlated. Both can be attributed to a decrease of the threshold voltage yielding different effects on the device gain depending on the bias point chosen for device operation and on the bias circuit adopted: a fixed current bias scheme will minimize the changes induced by the stress. The work also presents a study of the dependence of device degradation on the stress bias condition.
CloudWalker: 3D Point Cloud Learning by Random Walks for Shape Analysis

Point clouds are gaining prominence as a method for representing 3D shapes, but its irregular structure poses a challenge for deep learning methods. In this paper we pro-pose CloudWalker, a novel method for learning 3D shapes using random walks. Previous works attempt to adapt Convolutional Neural Networks (CNNS) or impose a grid or mesh structure to 3D point clouds. This work presents a different approach to represent and learn the shape from a given point set. The key idea is to impose structure on the point set by multiple random walks through the cloud for exploring different regions of the 3D object. Then we learn a per-point and per-walk representation and aggregate multiple walk predictions at inference. Our approach achieves state-of-the-art results for two 3D shape analysis tasks: classiﬁcation and retrieval. Furthermore, we pro-pose a shape complexity indicator function that uses cross-walk and inter-walk variance measures to subdivide the shape space.
High-throughput microparticle separation using gradient traveling wave dielectrophoresis

This paper describes highly efficient and high-throughput microparticle separation using gradient traveling wave dielectrophoresis (TwDEP) with a multilayered microelectrode design. Although cell separation based on dielectrophoresis is a very useful and versatile method, its throughput is less than that of a commercially available magnetic activated cell sorter (MACS). Further, in TwDEP-based cell sorters, the microdevices must have a large area to achieve high-throughput separation. However, increasing the TwDEP device area, which is critical for achieving throughput, has limitations: the resistance of microelectrodes also increases. In this study, we have successfully developed a novel gradient TwDEP chip with an extremely large area (31 × 25 mm2) using a unique multilayered bus bar design. The proposed bus bar design, which divides four ac input signals into two groups (0° and 270° phases and 90° and 180° phases), makes it possible to maintain low resistance in microelectrodes for TwDEP despite the increase in the device area. In addition, a microelectrode track design with gradually increasing gaps from 10 to 40 µm between the electrodes was introduced; as a result, the TwDEP force and negative DEP force that balance the gravitational force decrease gradually along the microelectrode track. Finally, the microparticles could be trapped at specific locations depending on their physical properties. We demonstrated the feasibility of our suggestion using latex microparticles (3 µm, 6 µm, 10 µm and 20 µm) and showed the potential of high-throughput separation with the TwDEP technique.
Research on Earthwork Calculating Methods Based on 3D Laser Scanning Technology

Terrestrial laser scanner (TLS) can provide the measurement of a large number of physical points distributed on the observed surface. A fast earthwork calculating method is proposed based on the redundant number of acquired points, which leads to a very accurate and high resolution reconstruction of the observed surfaces. This paper describes the three main steps of the method, namely the acquisition of the earthwork data based on TLS, the pre-processing of point cloud data, the earthwork calculation and accuracy evaluation based on point cloud data. Furthermore, it illustrates the performance of the proposed method with a validation experiment.
KPDrop: An Approach to Improving Absent Keyphrase Generation

Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. The generated keyphrases can be either present or absent from the text of the given document. While the extraction of present keyphrases has re-ceived much attention in the past, only recently a stronger focus has been placed on the generation of absent keyphrases. However, generating absent keyphrases is very challenging; even the best methods show only a modest de-gree of success. In this paper, we propose an approach, called keyphrase dropout (or KPD ROP ), to improve absent keyphrase generation. We randomly drop present keyphrases from the document and turn them into artiﬁcial absent keyphrases during training. We test our approach extensively and show that it con-sistently improves the absent performance of strong baselines in keyphrase generation.
Aerosol tracer testing in Boeing 767 and 777 aircraft to simulate exposure potential of infectious aerosol such as SARS-CoV-2

The COVID-19 pandemic has reintroduced questions regarding the potential risk of SARS-CoV-2 exposure amongst passengers on an aircraft. Quantifying risk with computational fluid dynamics models or contact tracing methods alone is challenging, as experimental results for inflight biological aerosols is lacking. Using fluorescent aerosol tracers and real time optical sensors, coupled with DNA-tagged tracers for aerosol deposition, we executed ground and inflight testing on Boeing 767 and 777 airframes. Analysis here represents tracer particles released from a simulated infected passenger, in multiple rows and seats, to determine the exposure risk via penetration into breathing zones in that row and numerous rows ahead and behind the index case. We completed over 65 releases of 180,000,000 fluorescent particles from the source, with 40+ Instantaneous Biological Analyzer and Collector sensors placed in passenger breathing zones for real-time measurement of simulated virus particle penetration. Results from both airframes showed a minimum reduction of 99.54% of 1 micron aerosols from the index source to the breathing zone of a typical passenger seated directly next to the source. An average 99.97 to 99.98% reduction was measured for the breathing zones tested in the 767 and 777, respectively. Contamination of surfaces from aerosol sources was minimal, and DNA-tagged 3 micron tracer aerosol collection techniques agreed with fluorescent methodologies.
Phasor Modeling Approaches and Simulation Guidelines of Voltage-Source Converters in Grid-Integration Studies

With the large integration of converter-interfaced generation (CIG) and widespread of power electronic interfaced technologies, power system dynamic behaviour is becoming progressively faster. As a consequence, is often unclear when to use EMT or Phasor models in grid integration studies. Therefore, this paper presents useful simulation guidelines of Voltage-Source Converters (VSCs) used in AC grid integration studies. It also presents several EMT and Phasor models suited to simulate CIG and renewable energy resources connected to power systems. Several modelling approaches and suitability analyses were provided based on a comprehensive comparative study among the models. Various studies were performed in a small system, composed of two generators, and a CIGRE benchmark system, both modelled in Simulink. We address a gap related to the suitability of CIGs phasor models in studies where the boundary between electromagnetic and electromechanical transients overlap. An insightful analysis of the adequate simulation time step for each model and study is also provided.
Changepoint Analysis of Topic Proportions in Temporal Text Data

Changepoint analysis deals with unsupervised detection and/or estimation of time-points in time-series data, when the distribution generating the data changes. In this article, we consider \emph{offline} changepoint detection in the context of large scale textual data. We build a specialised temporal topic model with provisions for changepoints in the distribution of topic proportions. As full likelihood based inference in this model is computationally intractable, we develop a computationally tractable approximate inference procedure. More specifically, we use sample splitting to estimate topic polytopes first and then apply a likelihood ratio statistic together with a modified version of the wild binary segmentation algorithm of Fryzlewicz et al. (2014). Our methodology facilitates automated detection of structural changes in large corpora without the need of manual processing by domain experts. As changepoints under our model correspond to changes in topic structure, the estimated changepoints are often highly interpretable as marking the surge or decline in popularity of a fashionable topic. We apply our procedure on two large datasets: (i) a corpus of English literature from the period 1800-1922 (Underwoodet al., 2015); (ii) abstracts from the High Energy Physics arXiv repository (Clementet al., 2019). We obtain some historically well-known changepoints and discover some new ones.
Simulating thermal density operators with cluster expansions and tensor networks

We provide an efficient approximation for the exponential of a local operator in quantum spin systems using tensor-network representations of a cluster expansion. We benchmark this cluster tensor network operator (cluster TNO) for one-dimensional systems, and show that the approximation works well for large real- or imaginary-time steps. We use this formalism for representing the thermal density operator of a two-dimensional quantum spin system at a certain temperature as a single cluster TNO, which we can then contract by standard contraction methods for two-dimensional tensor networks. We apply this approach to the thermal phase transition of the transverse-field Ising model on the square lattice, and we find through a scaling analysis that the cluster-TNO approximation gives rise to a continuous phase transition in the correct universality class; by increasing the order of the cluster expansion we find good values of the critical point up to surprisingly low temperatures.
An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All

Artifical Intelligence (AI) in Education has great potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning. We believe this is a crucial moment for setting the foundations of AI in education in the beginning of this Fourth Industrial Revolution. This report aims to synthesize how AI might change (and is already changing) how we learn, as well as what technological features are crucial for these AI systems in education, with the end goal of starting this pressing dialogue of how the future of AI in education should unfold, engaging policy makers, engineers, researchers and obviously, teachers and learners. This report also presents the advances within the X5GON project, a European H2020 project aimed at building and deploying a cross-modal, cross-lingual, cross-cultural, cross-domain and cross-site personalised learning platform for Open Educational Resources (OER).
Forex Trading Volatility Prediction using Neural Network Models

In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques. We show step-by-step how to construct the deep-learning network by the guidance of the empirical patterns of the intra-day volatility. The numerical results show that the multiscale Long Short-Term Memory (LSTM) model with the input of multi-currency pairs consistently achieves the state-of-the-art accuracy compared with both the conventional baselines, i.e. autoregressive and GARCH model, and the other deep learning models.
A new speed observer for control system of induction motor

A new speed observer for control system of induction motor is proposed in the paper. Disturbances are defined, estimated and used in the observer instead of products of rotor angular velocity and rotor flux vector components. The disturbances and rotor flux vector components are used to calculate the rotor angular velocity. Very low errors between the estimated and real rotor angular velocity in steady states and transients have been observed in the new observer for values very close or equal to zero. Properties of the control system with new speed observer have been investigated and results of simulations are presented.
Construction and validation of a metabolic gene-associated prognostic model for cervical carcinoma and the role on tumor microenvironment and immunity

Metabolic reprogramming is a common feature of tumor cells and is associated with tumorigenesis and progression. In this study, a metabolic gene-associated prognostic model (MGPM) was constructed using multiple bioinformatics analysis methods in cervical carcinoma (CC) tissues from The Cancer Genome Atlas (TCGA) database, which comprised fifteen differentially expressed metabolic genes (DEMGs). Patients were divided into a high-risk group with shorter overall survival (OS) and a low-risk group with better survival. Receiver operating characteristic (ROC) curve analysis showed that the MGPM precisely predicted the 1-, 3- and 5-year survival of CC patients. As expected, MGPM exhibited a favorable prognostic significance in the training and testing datasets of TCGA. And the clinicopathological parameters including stage, tumor (T) and metastasis (M) classifications had significant differences in low- and high-risk groups, which further demonstrated the MGPM had a favorite prognostic prediction ability. Additionally, patients with low-ESTMATEScore had a shorter OS and when those combined with high-risk scores presented a worse prognosis. Through “CIBERSORT” package and Wilcoxon rank-sum test, patients in the high-risk group with a poor prognosis showed lower levels of infiltration of T cell CD8 (P < 0.001), T cells memory activated (P = 0.010) and mast cells resting (P < 0.001), and higher levels of mast cells activated (P < 0.001), and we also found these patients had a worse response for immunosuppressive therapy. These findings demonstrate that MGPM accurately predicts survival outcomes in CC patients, which will be helpful for further optimizing immunotherapies for cancer by reprogramming its cell metabolism.
Opinion on Updating the Taxonomic Names of Aspergillus terreus Strains IFO 6365 and TN-484

In their letter to the editors, Dr. Houbraken et al. pointed out that the correct identification of a fungal strain is important and reports with inaccurate strain identification confuse researchers (1). They also proposed a procedure to prevent this issue of inaccurate strain identification. I totally understand the reason for raising this issue and the need for the proposed procedure to ensure accuracy. Therefore, I wish to respond to the points highlighted in the letter. It was incorrect to claim that strain IFO 6365 (2) and its mutant strain, TN-484 (3), were Aspergillus terreus; these were consistently and correctly identified as Aspergillus pseudoterreus. Although I agree with the importance of listing fungal strains accurately, there is also a need to stress the importance of consistency among all our previous reports to ensure the reader’s understanding. First and foremost, I would like to clarify the identity of strain IFO 6365 (=NBRC 6365). In actuality, this strain was not isolated and identified by our research team but was instead obtained from an established microbial preservation institution in Japan. This particular strain was initially registered in 1958 with the Institute for Fermentation, Osaka (IFO), Japan, a former institution focused on research related to microbial conservation; this strain was subsequently transferred to the Biological Resource Center, National Institute of Technology and Evaluation (NBRC), and has since been distributed as strain NBRC 6365 (4). Therefore, it is not possible to change the strain name at our own discretion; we shall discuss the notation of this fungal strain with the relevant taxonomic experts at NBRC. Meanwhile, strain TN-484 was obtained by introducing a mutation into strain IFO 6365; hence, the taxonomic classification of TN-484 should be identical to that of strain IFO 6365. Second, I would like to stress the importance of consistency in fungal strain notation in all our reports. We have published numerous articles on strains IFO 6365 and TN-484 since 1993 (5). Additionally, A. pseudoterreus strain NRRL 4017, as mentioned in the Comment Letter (1), was only listed in 2011 (6). The Agricultural Research Service (NRRL) Culture Collection catalog (version as of summer 2021) specifies that a synonym of strain NRRL 4017 is Aspergillus terreus Thom. Most researchers understand that the strain IFO 6365 that is described in past and present reports is the same strain, and to ensure clarity and consistency, the synonym shall be included in our future reports.
On-line fault detection of aluminium electrolytic capacitors, in step-down DC-DC converters, using input current and output voltage ripple

The aim of this study is to present a simple on-line fault detection technique that is able to prevent structural failures in aluminium electrolytic capacitors used in the output filter of step-down DC–DC converters. The aluminium electrolytic capacitors equivalent circuit consists of an ideal resistance in series with an ideal capacitor and an ideal inductor. The first two elements change with frequency, temperature and aging. Most manufacturers define the end of life limit of these capacitors when their internal resistance (equivalent series resistance (ESR)) doubles or their capacitance (C) reduces by 20%, when compared with the initial values. The proposed on-line fault detection technique is able to estimate the capacitor ESR value during converter operation. For that purpose, a simple relationship between the input current and the output voltage ripple is used. The measurement system is composed of a digital oscilloscope that is connected to a microcomputer with ‘Matlab’ software. Several simulation and experimental results are presented to validate the proposed fault detection technique.