Miracle-dz/UniFAR-FTUs huggingface dataset:
Configurable High-Frequency Alternating Magnetic Field Generator for Nanomedical Magnetic Hyperthermia Applications

This article shows the use of a high-frequency Full-Bridge inverter to design an alternating magnetic field generator for experimental studies on anti-cancer treatment with magnetic nanoparticles. In magnetic hyperthermia, nanoparticles are used to raise the pathological cancerous cell’s temperature high enough to induce their death by apoptosis, but not as high as to destroy them by thermal ablation of the whole tissue volume, consequently leaving healthy cells alive. Conventionally, sinusoidal alternating magnetic fields are used to heat the nanoparticles, which can be more easily produced than other waveforms. However, there are no theoretical nor experimental reasons to choose the sinusoidal waveform. This work aims to develop an improved power system to study the effect of different waveforms of the magnetic field on heat production when exciting magnetic nanoparticles, aiming at demonstrating that other waveforms can be much more efficient in producing heat than conventional sinusoids. To prove our hypothesis, we designed an inverter able to generate four waveforms at high frequencies and a fifth sinusoidal signal derived by a resonant capacitor, in the range of 100 kHz to 1 MHz and up to 10 mT of peak intensity. Also, we used SiC devices to process high currents at high switching frequencies efficiently. Additionally, to enhance the system efficiency, Zero-Voltage Switching is used to reduce switching losses and minimize electromagnetic noise and interference. The experimental results obtained with non-sinusoidal waveforms have shown a remarkable performance improvement compared to classical sine wave excitation. The nanoparticles’ heat dissipation depends on the applied alternating magnetic field’s signal slope, signal frequency, and peak field intensity. We conclude that further work deserves to be done to find the optimum work conditions in function of the used particle and biological environment to test if this type of magnetic field generator could overcome the conventional system’s performance.
Web-based videoconferencing in online teaching during the COVID-19 pandemic: University students’ perspectives

Web-based videoconferencing systems have become very popular means of online teaching during the educational shift from face-to-face to online due to the COVID-19 pandemic. Students around the world have been attending online classes through different web-based videoconferencing platforms instead of face-to-face lectures and seminars. The current study aims to investigate University students’ intentions to continue use web-based videoconferencing systems for their learning, when social distancing render unnecessary and Universities re-open after the pandemic is over. This study is part of our wider investigation on the impact of the COVID-19 pandemic on students’ experiences about different e-learning technologies and related pedagogies. The current study proposes a model based on the Technology Acceptance Model (TAM) and the Expectancy Confirmation Model (ECM) in order to explain and predict continuance intention to use videoconferencing systems in the post-COVID-19 era. Sixty-one students from a School of Education in a UK University completed an online survey. Structural equation modelling was used to analyze the data. The model explains and predicts 53% of the total variance in continuance intention to use videoconferencing systems for learning online in terms of perceived usefulness, social influence, satisfaction and confirmation. Satisfaction and perceived usefulness were found to be the most significant predictors of continuance intention to use. Implications for the use of web-based videoconferencing systems for online teaching and learning in the post-COVID 19 landscape are discussed.
Canopy Extraction and Height Estimation of Trees in a Shelter Forest Based on Fusion of an Airborne Multispectral Image and Photogrammetric Point Cloud

College of Information Science & Technology, Shihezi University, Shihezi, China Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Shihezi, China Industrial Technology Research Institute, Xinjiang Production and Construction Corps, Shihezi, China Agricultural Development Service Center of 150 Regiments, Shihezi, China College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
A Survey on the Relevance of the Performance of Model Transformations

In Model-Driven Engineering (MDE) model transformation languages are used to describe important operations on models. Such domain-speciﬁc languages are specially developed to describe transformation rules, according to which an output model should be generated from an input model. In comparison to these domain-speciﬁc languages, techniques to analyze and improve the performance of programs written in a general-purpose language, such as Java or C, are well known. However, are such techniques also needed for model transformation languages? Problem. Since these languages are only used in certain domains; the ﬁrst question is whether performance is at all relevant for model transformations and whether techniques similar to those used to analyze and improve the performance of general-purpose languages are needed. Research in the performance of model transformations focuses mainly on comparing the performance of different languages or different deﬁnition styles or optimizing the engine that executes the transformation. However, it is not clear to what extent these efforts can mitigate or prevent performance issues, and there is also a lack of studies that examine to what extend the performance of transformations is relevant. Method. In order to close this gap and to answer the initial question about the relevance of performance, we conducted an online survey. For this purpose, we developed a questionnaire and identiﬁed 649 authors as potential participants based on a Systematic Literature Review (SLR) on a selection of model transformation languages. Additionally, we were able to acquire four further potential participants by advertising our study. In total, 84 participants took part in our survey. We used statistical tests such as Kendall’s τ c , the Kruskal-Wallis-Test and the Mann-Whitney-U-Test to evaluate our hypotheses on relevant factors for the performance of model transformations. Results. The results show that speciﬁc performance is desired and that there is a willingness to improve performance. In this regard, we identiﬁed a need for insights necessary to better understand how a transformation is performed in order to be able to improve its performance. Furthermore, we investigated with the help of hypotheses tests the possible inﬂuencing factors that cause participants to try to analyze or improve the performance of model transformations. The main results of the hypotheses tests are that the satisfaction with the execution time, the size of the models used, the relevance of whether a speciﬁc execution time is not exceeded in the average case, and the knowledge of how a transformation engine executes a transformation are relevant factors.
A Spectroscopic Investigation of Photoelectrochemical Reactions at the Semiconductor - Electrolyte Interface for Artificial Photosynthesis

Author(s): Herlihy, David Michael | Advisor(s): Harris, Charles B | Abstract: The mechanism of water oxidation to molecular oxygen at the surface of strontiumtitanate, a representative transition-metal oxide photocatalyst, was investigatedusing ultrafast pump-probe spectroscopy and accompanying characterization methods.The dependence of the rate of initial charge transfer on applied potential was investigatedwith transient absorption spectroscopy, revealing a phenomenological transfer coefficientfor this single step of the multi-step water oxidation reaction. Time resolvedinfrared spectroscopy provided the first direct evidence of the molecular character of thefirst intermediate to be a titanium-bound oxyl radical, and revealed interfacial Fano couplingbetween the electrolyte, surface species, and catalyst electronic continuum. Furtherwork combining both transient absorption and time-resolved mid-infrared spectroscopyexposed a minimum of three distinct surface states at the strontium titanate surface,providing insight into the rich chemistry mediated at the catalyst surface. These resultshighlight the intricacies of charge transfer at the surface of these promising materials,provide insight into an important but difficult reaction necessary for any artificialphotosynthetic system, and offer a generalizable paradigm for considering heterogeneousphotochemistry. Finally, preliminary groundwork was done to uncover the mechanismof the carbon dioxide reduction half reaction in a novel inorganic-biological bacterialsystem with potential to produce green chemicals such as carbon-neutral liquid fuels.
T RACKING THE PROGRESS OF LANGUAGE MODELS BY EXTRACTING THEIR UNDERLYING KNOWLEDGE GRAPHS

The state of the art of language models, previously dominated by pre-trained word embeddings, is now being pushed forward by large pre-trained contextual representations. This success has driven growing interest to understand what these models encode inside their inner workings. Despite this, understanding their semantic skills has been elusive, often leading to unsuccessful, non-conclusive, or contradictory results among different works. In this work, we define a probing classifier that we use to extract the underlying knowledge graph of nine of the currently most influential language models, including word embeddings, context encoders, and text generators. This probe is based on concept relatedness, grounded on WordNet. Our results show that this knowledge is present in all the models, but has several inaccuracies. Furthermore, we show that the different pre-training strategies and architectures lead to different model biases. We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging for the different families of models. We hope our insights will motivate the future development of models that capture concepts more precisely.
Agricultural Development Assistance and Agricultural Production in Zimbabwe

Abstract: This paper examined the impact of agricultural development assistance (ODA) on agricultural production in Zimbabwe for the period 1980-2019. The examination is motivated by the volatility of agriculture output despite the steady flow of ODA to Zimbabwe. The study disaggregates and compares the impact of private (ODA) and public (government expenditure) agriculture investment on agriculture output. The study applied the Auto-Regressive Distributed Lag (ARDL) estimation technique on a time series model based on Solow’s (1956) growth theory. Results indicate that both ODA and government spending on agriculture have significantly positive output growth effects. Furthermore, an ARDL Bound Tests for cointegration confirmed the existence of a long run relationship amongst the variables of interest. Findings suggest that both development partners and the government should consider increasing investment in agriculture so as to improve agriculture production and the aggregate economy. Also, Zimbabwe should adopt policies and institutional reforms that create a viable environment for agriculture so as attract donor funding. The government should prioritize to meet the 10% budget goal which is in line with the Maputo declaration so as to increase government spending on agriculture.
Comparative evaluation of test characteristics of acetic acid, lugol’s iodine and toluidine blue stains in cervical cancer screening

Cervical cancer rank second in female cancer and India alone account for one fourth of the global cervical cancer burden. About 122,844 new cervical cancer cases are diagnosed annually in India and 67,477 die from it. Mortality due cervical cancer is also an indicator of health inequities, as 86% of all deaths due to cervical cancer are in developing, low and middle income countries. Invasive cervical cancer is preceded by a long phase of precancerous lesion that can be detected by screening and if treated can prevent the invasive cancers. Despite careful examination, small developing lesions often go unrecognized by clinicians. To aid visual inspection in addition to normal light wide range of commercial adjunctive aids are available. All these adjunctive aids will of great use in detecting small lesion with a certain amount of dysplasia which appears normal under routine visual inspection.
C ONTEXT -A GNOSTIC L EARNING U SING S YNTHETIC D ATA

We propose a novel setting for learning, where the input domain is the image of a map defined on the product of two sets, one of which completely determines the labels. Given the ability to sample from each set independently, we present an algorithm that learns a classifier over the input domain more efficiently than sampling from the input domain directly. We apply this setting to visual classification tasks, where our approach enables us to train classifiers on datasets that consist entirely of a single example of each class. On several standard benchmarks for real-world image classification, our approach achieves performance competitive with state-of-the-art results from the few-shot learning and domain transfer literature, while using significantly less data.
International Trade and the Risk in Bilateral Exchange Rates

Exchange rate volatility falls after a trade deal, driven by a decline in the systematic component of risk. The average trade deal increases trade by 50 percent over five years, reducing systematic risk by a third of a standard deviation across countries. We examine this connection in an Armington model where the structure of trade networks determines the risk in exchange rates. We estimate our model to current data and find i) that countries at the periphery of the world trade network benefit the most from lower trade barriers and ii) that a counterfactual experiment of a trade war between the US and China shows a global increase in currency risk, with effects concentrated among peripheral countries.
Gene Flow and Diversification in Himalopsyche martynovi Species Complex (Trichoptera: Rhyacophilidae) in the Hengduan Mountains

Simple Summary Himalopsyche is a group of aquatic insects endemic to the Hengduan Mountains, of which species are usually easily identifiable based on male genitalia, except for a few morphologically variable groups including the Himalopsyche martynovi complex. In order to clarify species boundaries within this complex, we investigated its evolutionary history (phylogenetics and gene flow analyses) using a large genomic dataset (~500,000 sites). We found three clades in the Himalopsyche martynovi complex, one of which being very variable morphologically while being involved in gene flow with other related lineages. When interpreted in the light of past geological, climatic and palaeohydrological changes, our study suggests that biological novelty—here, trait variation and recombination—may have been acquired via hybridization and represent a source of mountain biodiversity. Abstract The Hengduan Mountains are one of the most species-rich mountainous areas in the world. The origin and evolution of such a remarkable biodiversity are likely to be associated with geological or climatic dynamics, as well as taxon-specific biotic processes (e.g., hybridization, polyploidization, etc.). Here, we investigate the mechanisms fostering the diversification of the endemic Himalopsyche martynovi complex, a poorly known group of aquatic insects. We used multiple allelic datasets generated from 691 AHE loci to reconstruct species and RaxML phylogenetic trees. We selected the most reliable phylogenetic tree to perform network and gene flow analyses. The phylogenetic reconstructions and network analysis identified three clades, including H. epikur, H. martynovi sensu stricto and H. cf. martynovi. Himalopsyche martynovi sensu stricto and H. cf. martynovi present an intermediate morphology between H. epikur and H. viteceki, the closest known relative to the H. martynovi-complex. The gene flow analysis revealed extensive gene flow among these lineages. Our results suggest that H. viteceki and H. epikur are likely to have contributed to the evolution of H. martynovi sensu stricto and H. cf. martynovi via gene flow, and thus, our study provides insights in the diversification process of a lesser-known ecological group, and hints at the potential role of gene flow in the emergence of biological novelty in the Hengduan Mountains.
Deep Diffusion-Invariant Wasserstein Distributional Classiﬁcation

We denote Γ(f) ≡ Γ(f, f). The bilinear form Γ can be considered a generalization of the integration by parts formula, where ∫ fLg + Γ(f)dξ = 0 for the invariant measure ξ of L. Definition 4. (Relative Fisher Information) For Γ defined earlier, the relative Fisher information of νt with respect to NΣ is defined as I(νt|NΣ) = ∫ Pt[q] Γ(Pt[q])dNΣ. Definition 5. (Curvature-Dimension condition) We say that infinitesimal generator L induces the CD(ρ,∞) curvature-dimension condition if it satisfies Γ1(f) ≤ ρΓ2(f) for all f ∈ C∞ 0 . Example. (Multivariate Gaussian distribution)
Supplementary material to "Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at global scale"

Abstract. Many thousands of large dam reservoirs have been constructed worldwide during the last seventy years to increase reliable water supplies and support economic growth. Because reservoir storage measurements are generally not publicly available, so far there has been no global assessment of long-term dynamic changes in reservoir water volume. We overcame this by using optical (Landsat) and altimetry remote sensing to reconstruct monthly water storage for 6,743 reservoirs worldwide between 1984 and 2015. We relate reservoir storage to resilience and vulnerability and analyse their response to precipitation, streamflow and evaporation. We find reservoir storage has diminished substantially for 23 % of reservoirs over the three decades but increased for 21 %. The greatest declines were for dry basins in southeastern Australia (−29 %), the USA (−10 %), and eastern Brazil (−9 %). The greatest gains occurred in the Nile Basin (+67 %), Mediterranean basins (+31 %) and southern Africa (+22 %). Many of the observed reservoir changes were explained well by changes in precipitation and river inflows, emphasising the importance of multi-decadal precipitation changes for reservoir water storage, rather than changes in net evaporation or (demand-driven) dam water releases.
Phonetic Skills and Verbal Memory Capacity Predict Phonetic-based Word Learning: An Event-related Potential Study

Abstract The learning of new words is a challenge that accompanies human beings throughout the entire life span. Although the main electrophysiological markers of word learning have already been described, little is known about the performance-dependent neural machinery underlying this exceptional human faculty. Furthermore, it is currently unknown how word learning abilities are related to verbal memory capacity, auditory attention functions, phonetic discrimination skills, and musicality. Accordingly, we used EEG and examined 40 individuals, who were assigned to two groups (low [LPs] and high performers [HPs]) based on a median split of word learning performance, while they completed a phonetic-based word learning task. Furthermore, we collected behavioral data during an attentive listening and a phonetic discrimination task with the same stimuli to address relationships between auditory attention and phonetic discrimination skills, word learning performance, and musicality. The phonetic-based word learning task, which also included a nonlearning control condition, was sensitive enough to segregate learning-specific and unspecific N200/N400 manifestations along the anterior–posterior topographical axis. Notably, HPs exhibited enhanced verbal memory capacity and we also revealed a performance-dependent spatial N400 pattern, with maximal amplitudes at posterior electrodes in HPs and central maxima in LPs. Furthermore, phonetic-based word learning performance correlated with verbal memory capacity and phonetic discrimination skills, whereas the latter was related to musicality. This experimental approach clearly highlights the multifaceted dimensions of phonetic-based word learning and is helpful to disentangle learning-specific and unspecific ERPs.
A Neural Model for Text Segmentation

Text segmentation is the task of dividing a document of text into coherent and semantically meaningful segments which are contiguous. This task is important for other Natural Language Processing (NLP) applications like summarization, context understanding, and question-answering. The goal of this project is to successfully implement a text segmentation algorithm. We take a supervised learning approach to text segmentation and propose a neural model for this task. We aim to extend this task to podcasts by using existing transcription services. Our model obtained a Pk score (described below) of 6 . 54 on the Wiki-50 dataset, which was an improvement over our baseline score of 69 . 23 . In this paper we will discuss experiments with self-attention as a modiﬁcation to our model.
Predicting eWOM’s Influence on Purchase Intention Based on Helpfulness, Credibility, Information Quality and Professionalism

Product reviews co-written by many Internet reviewers can help consumers make purchase decisions and provide a basis for companies to improve their business strategies. For the company, the most important thing is to understand how the various factors of reviews influence the purchase intention. Therefore, we took this issue as the core and investigated the influence of eWOM on purchase intention based on helpfulness, credibility, information quality and professionalism. We adopted feature filtering algorithms and proposed an ensemble model to integrate these classification results to obtain the most accurate prediction. The empirical evaluation shows that the models based on the four importan aspects of reviews can effectively predict the degree of impact of reviews on purchase intention.
D ECOY - ENHANCED S ALIENCY M APS

Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier. Unfortunately, recent evidence suggests that many saliency methods poorly perform, especially in situations where gradients are saturated, inputs contain adversarial perturbations, or predictions rely upon inter-feature dependence. To address these issues, we propose a framework that improves the robustness of saliency methods by following a two-step procedure. First, we introduce a perturbation mechanism that subtly varies the input sample without changing its intermediate representations. Using this approach, we can gather a corpus of perturbed data samples while ensuring that the perturbed and original input samples follow the same distribution. Second, we compute saliency maps for the perturbed samples and propose a new method to aggregate saliency maps. With this design, we offset the gradient saturation influence upon interpretation. From a theoretical perspective, we show that the aggregated saliency map not only captures inter-feature dependence but, more importantly, is robust against previously described adversarial perturbation methods. Following our theoretical analysis, we present experimental results suggesting that, both qualitatively and quantitatively, our saliency method outperforms existing methods, in a variety of applications.