Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Birmingham Emulsifiers of Pickering-like characteristics at fluid interfaces

9 The influence of interfacial layer composition on the release kinetics of a model hydrophobic 10 active (dimethyl phthalate, DMP) from oil-in-water (o/w) emulsions relevant to foods is 11 reported. The present study considers various food-relevant emulsifiers known to form 12 colloidal particles in aqueous solutions. These range from a low molecular weight surfactant 13 and protein polyelectrolytes, to biopolymer complexes and solid particles: sodium stearoyl 14 lactylate (SSL); bovine serum albumin (BSA); sodium caseinate (NaCAS), chitosan (Ch); 15 BSA/Ch or NaCAS/Ch complexes; and silica (Pickering) nanoparticles (A200), were all 16 investigated. In all cases, DMP release from the oil droplets of the o/w emulsions was 17 controlled by the interfacial transport of the active rather than by its diffusion through the 18 globules’ interior. Release data followed first-order kinetics, where emulsions stabilised by soft 19 (protein/polysaccharide complexes) colloidal structures were shown to provide similar 20 (528 nm 2 s -1 ) or even lower interfacial rate constants (241 nm 2 s -1 ) to harder (silica) particulate 21 entities (625 nm 2 s -1 ). SSL (a lamellar-phase forming surfactant that has been previously 22 suggested to stabilise o/w emulsions via a mechanism closely resembling that of Pickering 23 particles) exhibited the lowest interfacial release rate (17 nm 2 s -1 ). Overall, the present study 24 contributes to current understanding on how emulsion interfacial architecture can be controlled 25 to provide desirable molecular release performances
Approximately Eﬃcient Cost Sharing via Double Auctions

A well-known result in the cost-sharing literature asserts the non-existence of mechanisms that are strategyproof, (weakly) budget balanced, and economically eﬃcient, even when budget balance and eﬃciency are sought only up to a multiplicative factor. An alternative measure of eﬃciency, approximate minimization of the social cost, can be achieved together with strategyproofness and budget balance, but in many settings of interest favors outcomes in which little or no service is provided. Focusing on a setting motivated by the provision and pricing of cloud services, we instead propose a mechanism that is strategyproof, budget balanced, and eﬃcient up to an additive term that is independent of the number of potential consumers of the service. All three properties are thus achieved exactly in the large-market limit. The mechanism is inspired by the truthful double auction of McAfee and enables more uneven cost shares and consequently a less aggressive reduction of service than traditional approaches.
On Multilingual Word Embeddings & their applications in machine translation

In this report we delve into recent groundbreaking work in the field of Word Embeddings in monolingual, bilingual and cross-lingual sense. Word embeddings lie to the heart of NLP and pre-trained embeddings serve useful when used in various NLP downstream tasks like Text Classification, Information Extraction, Dialogue generation, Question Answering, Named Entity Recognition (NER), Grammatical Error Correction (GEC) and so on. Therefore it is a must to improve both our understanding and also quality of word embeddings. Cross lingual word embeddings are important because we would like to learn shared embeddings between words in all languages and thus may leverage training data for any task in any language and projecting them into a common space. Our models would also obtain the capability to perform predictions in all other languages and therefore would be immensely helpful. Apart from that cross lingual word embeddings would also reduce any forms of language biases and therefore be better.
Estimating economic efficiency of preclinical diagnostics of Parkinson's disease with cost-utility approach

Neurodegenerative diseases, Parkinson's disease among them, set challenges to modern societies both in terms of premature deaths and resources spent on treatment of the diseases. At that, preventive care and early diagnostics in particular are potential directions towards higher economic efficiency of healthcare interventions in this area. Authors of this paper suggest a modification of the cost-utility approach to evaluate economic efficiency of an early diagnostics at the presymptomatic (prodromal) stage of PD, when its symptoms do not appear clinically yet. Such diagnostics, in combination with neuroprotective therapy for persons at high risk of PD, allows postponing its development until later years, and thereby ensuring an improvement in the quality of life of the population, as well as saving resources of the healthcare system and society as a whole. The authors rely on the diagnostic approach proposed by the research group headed by M. Ugryumov, which is currently at the stage of laboratory testing. Its implementation potentially leads to savings in both direct and indirect costs for PD treatment compared to the traditional approach, but increases testing costs, and also requires the development of new neuroprotective therapy for identified risk groups. The authors propose a modification of the cost-benefit assessment procedure to take into account the uncertainty associated with the lack of a final understanding of the scope and composition of the testing group at the preclinical stage. The condition for the economic efficiency of the preclinical diagnostic method in the developed procedure is the minimum permissible probability of detecting an increased risk of PD in the test group. To test their algorithm, the authors carry out calculations basing on the Russian data.
The changing demand in the labour market and challenges for The Vietnamese higher education system

Recently, due to the increasing integration of the internal economy into the regional and international markets, the demand of the Vietnamese labour market has changed significantly. Apart from the professional knowledge, employers are now looking for the candidates with adaptability, flexibility, who are open to learn and who have good generic skills. In order to best satisfy the needs of the labour market, the post-school educational system, especially the higher education system in Vietnam has expanded rapidly. New policies, reforms and innovations have also been applied in the system, all with the same aim: to increase the quality of training and to best satisfy the needs of the labour market. Nonetheless, the impact of those applications has remained modest. This article wants to discuss the challenges the Vietnamese educational system has to face and to tackle in order to find out more practical solutions to enhance the quality of its ‘education products’ to better satisfy the needs of the contemporary labour market.
SHAPE MODELING AND UNDERSTANDING AT THE UNIVERSITY OF GENOVA Geometry & Graphics Group

Introduction The Geometry & Graphics Group (G3) at the University of Genova has been active in the fields of geometric modeling, computer graphics, shape analysis, spatial data structures since 1990. The research of the group focuses on techniques for representing, manipulating, visualizing and reasoning on spatial objects. The reference applications are in scientific visualization, data analysis, geographic data processing, computer aided design and animation. This paper illustrates our recent research activities. Huge amounts of spatially-related digital data are being produced in acquisition and simulation processes. In such a scenario, our research has moved in two main directions: (i) geometric models for shapes, with related data structures and algorithms, to decrease memory requirements, increase the efficiency of operations, and better meet application needs; (ii) high-level shape descriptors based on topology, which disregard geometric details and focus on intrinsic properties like topological features and topological invariants. Both cases include the multi-scale scenario, in which the level of detail of a representation is adapted to user needs.
Remote sensing of chemical gas cloud emission by passive infrared scanning imaging system

Passive Fourier-transform infrared (Passive-FTIR) spectroscopy allows rapidly identification of the air pollution. However, for the localization of a leak and a complete assessment of the situation in the case of the release of a hazardous chemical gas or biological cloud, information about the position and the spatial distribution of a cloud is very important. In this work, a scanning imaging passive FTIR system, which composed of an interferometer, a data acquisition and processing software, a scanning system, a video system, and a laptop has been developed. The concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented, which involves the infrared radiative transfer model, radiometric calibration and absorption coefficient calculation. The concentration of the object gas is retrieved by using the nonlinear least squares method. And no background spectra are required. The remote sensing experiment of SF6 was carried out. The measuring result shows that, the column densities of all directions in which a target compound has been identified may be retrieved by a nonlinear least squares fitting algorithm and algorithm of radiation transfer, a false color image is displayed. The results are visualized by a video image, overlaid by false color concentration distribution image. The system has a high selectivity, and it allows visualization and quantification of pollutant clouds. The system allows mobile, real-time and fast measurements of chemical gas and biological clouds.
Depth image compression by colorization for Intel® RealSense™ Depth Cameras

Compression of RGB images is today a relatively mature field with many impressive codecs available to choose from that can achieve high compression ratios for lossless and lossy compression. However, for 3D depth images, the field is not as developed, although the goal remains the same – to compress both stilland movingimages in order to reduce system bandwidth and storage space requirements. In this white paper, we introduce a method by which depth from Intel RealSense depth cameras can be compressed using standard codecs, with the potential for 80x compression. Our approach is based on colorizing the depth image in a way that best reduces depth compression artefacts. We will define some key performance metrics, compare results obtained with different compression codecs, and introduce a post-processing technique that helps mitigate some of the worst depth artefacts.
ANALYSIS OF THE GENETIC DIVERSITY IN MAIZE LANDRACE CULTIVARS FROM NORTHERN RIO GRANDE DO SUL, BRAZIL

Maize landraces are important genetic resources for maize breeding. Many of these landrace varieties have not yet been properly studied in order to be distinguished from the others. In this study, multivariate statistical methods were used, beyond the analysis of variance, for estimating genetic dissimilarity among 27 maize landrace accessions. Principal component analysis and clustering analysis were performed using 16 evaluated quantitative characters. The ANOVA results reported the existence of significant differences among the tested accessions for 14 evaluated characters. Two principal components almost explained 49% of found experimental variance. Four different clusters were formed by the used clustering analysis, whose results were plotted into a dendrogram. The graphical integration of this dendrogram with the PCA allowed to conclude that the variation found may be due to the genotypic distinctions existing among the four groups of accesses determined in this study.
Building a Hotel Concierge Bot: an industrial case study

We describe our experience in developing ConciergeBot , an industrial strength question-answering bot for hotels. The bot automatically suggests answers for information-seeking questions over an input knowledge base of facts about the hotel and its amenities. We demonstrate how Concierge-Bot handles unique challenges that arise in our setting. More speciﬁcally, we show how our system trains eﬀective models with limited training data, how it can be deployed in diﬀerent hotels with almost no hotel-speciﬁc tuning, and how it manages heterogeneity in questions and data. Our experiments validate that ConciergeBot achieves high precision (78%) and good recall (71%) with as few as 1,300 questions for training purposes
Immediate ambiguity resolution in speech perception based on prior acoustic experience

Speech perception relies on the rapid resolution of uncertainty. Here we explore whether auditory experiences contribute to this process of ambiguity resolution. ~8000 participants were surveyed online for their (i) subjective percept of a speech stimulus with ambiguous formant allocation; (ii) demographic profile and auditory experiences. Both linguistic and non-linguistic auditory experiences significantly predict speech perception. Listeners were more likely to perceive the ambiguous stimulus in accordance with their own name, and were biased towards lower formant allocation as a function of being exposed to lower auditory frequencies in their environment. Overall, our results show that the subjective interpretation of an ambiguous stimulus in the auditory domain is determined by prior acoustic exposure, suggesting the operation of an exposure-dependent mechanism impacting sensitivity that resolves ambiguity in speech perception. Significance statement Organisms must contend with fundamental uncertainty due to missing information in sensory inputs, resulting in perceptual ambiguity. There are several known ways in which perceptual ambiguity of speech is resolved, most importantly by the immediate linguistic context. Here, we show that prior acoustic experience be it speech specific or more general determines how listeners resolve a segment of ambiguous speech, the LaurelYanny stimulus. This is important both because it suggests that the brain uses the principle described in this paper to resolve ambiguous speech in general and that divergent auditory inputs will yield immediate disagreement about the conscious percept, given ambiguous stimulus situations.
Sphere tensiometry: an evaluation and critique

An absolute gravimetric method of measuring surface and interfacial tensions of liquids by pulling a sphere through the interface is examined. The method also permits simultaneous measurement of the contact angle of the liquid on the sphere surface; this enables corrections to be made for incomplete wetting of the solid by liquids in measuring the interfacial tension, a feature which the conventional ring and plate methods lack. Simultaneous measurements of the interfacial tension and the difference in phase densities across the interface are in principle also possible. Preliminary experimental results are presented, and the advantages and disadvantages of the method are critically discussed.
Plankton Image Classiﬁcation

This paper is in response to the National Data Science Bowl’s automated Plankton image classiﬁcation problem. It evaluates the performance of many different experiments run on medium-architecture Convolutional Neural Networks. Experiments evaluated include different architectures, feature extraction from multiple architectures followed by linear and non-linear classiﬁcation, data augmentation and model ensembles. The experiments together yield a classiﬁ-cation accuracy of 70% and multi-label log loss of 1.15 on validation set. The learned approaches can be ported to deep ConvNets as future work for signiﬁcant improvements.
Assessment of China‘s 1985-2017 Higher Education Policy Reform: Nation-Building Directives

The Chinese Dream presented by president Xi Jinping focused on the renewal of the Chinese nation, the prosperity of the country and the well-being of its people. Incorporating the global influence ambition of China into nation-building and education, this study employed a macro policy analysis approach to synthesize the effects of the state-led ideals on nation-building and the roles to be played by higher education. A total of nine comprehensive policies, covering the period 1985 to 2017, and receptive to globalization, nation-building and higher education were selected. The findings revealed that nation-building in the post-Moa era is built upon three critical priority determinants labelled global influence, socialist market economy and talent management. These core nation-building determinants delineate specific higher education guidelines in terms of citizen education, science and technology, talent management, and innovation amidst accelerating global modernization and freedom-of-mind requirement for the establishment of a socialist consultative democracy. The findings further offer direction to higher education for the provision of a fit-for-purpose workforce capable of operating in both a modern global and local market.
Actors and Agency in Multilingual Education Policy and Planning: A Case Study in a Chinese University

Although there has been a not quite short history of language policy and planning, the attention is more paid to the macro-level policy and planning of the national government. However, recent studies have begun to move the eyes to the individual actors in the local context, thus the mesoand micro-level implementation of language policy and planning become the new topic in this field. This study adopts the new framework to analyze the individual actors’ agency in the process of implementing the macro-level multilingual education policy and planning of learning languages other than English (LOTEs). In response to the macro-level language policy and planning shifting, the individual actors conduct agentive or constrained actions to propel or impede the procedure of macro-level language policy and planning. The findings of this study highlight the importance of constructing the dialogue mechanism among the individual actors at the macro-, mesoand microlevel.
Estimation of sedimentary cover thickness by using spectral analysis of gravity anomalies

In this paper, power spectrum analysis has been used for estimating the depth of geological interfaces. The computer code was tested for synthetic models and implemented to gravity survey data to estimate the thickness of sedimentary cover of several Meso-cenozoic basins of Mongolia. The depth estimation results of the basins’ sedimentary cover were in good agreement with other prior geological and geophysical interpretation results of the study areas.
Styles of coping with stress presented by female and male students of Physical Education during the pandemic

The aim of the study was to determine the intensity of coping strategies used by students of Physical Education during a pandemic. Women and men were compared in terms of strategies considered positive and negative. The study used the psychological tool: Inventory for the Measurement of Coping with Stress. The participants were students of Physical Education (aged 25.69 ± 5.908); 26 women, 28 men. Results: the examined women show a higher intensity of both positive and negative coping strategies. The most common strategy among students is positive re-evaluation. Conclusions: The surveyed students experiencing difficult situations choose rather positive than negative strategies of coping with the problem. Women showing a higher level of coping strategies may be more aware of a difficult situation or experience a pandemic more intensely in relation to men.
Detecting Twitter Trolls Using Natural Language Processing Techniques Trained on Message Bodies

—Internet trolls based in Russia effectively shaped social media opinions during the 2016 U.S. presidential election. This paper is an empirical study of natural language processing techniques for identifying similar online trolls. The machine learning approaches used in this paper focus only on the text contained in the message itself and not with other meta-data associated with the posting accounts. Actual tweets attributed to the trolls were evaluated using 27 different machine learning models in a binary classiﬁcation task. The results show that the support vector machine model, trained on a unigram language model, is the most effective choice. We conclude that while natural language processing techniques are capable of successfully identifying online trolls, they alone are not a sufﬁcient ﬁltering mechanism to solve the problem. Rather, natural language processing can be a useful tool in a larger investigative effort.
A molecular theory of crystal nucleation from the melt

We construct a molecular theory for the homogeneous nucleation of crystals from pure liquids by combining an order parameter theory of freezing with a square gradient approximation for the nonlocal dependence of free energy on density. We show the results of our theory for the free energy, radius, and shape of crystal nuclei as a function of supercooling, and compare with the conventional nucleation theory based on the capillarity approximation. We argue that the simple picture given by the conventional approach is qualitatively reasonable, although calculated values of the liquid–solid surface free energy may be inaccurate. We point out some of the limitations of our approach and stress the need for additional structural data on supercooled liquids.
Traditional Knowledge Tracing Models for Clustered Students

Against the background of the worldwide COVID-19 pandemic, online learning has currently become one of the dominant educational forms. For more effective online learning, knowledge tracing that can dynamically estimate the knowledge state of learners should be paid more attention. This study gives an overall introduction of knowledge tracing models. We illustrate the development of the Bayesian Knowledge Tracing model and clarify its concrete mathematic principle. In addition, an individualized method based on clustered students for the Bayesian Knowledge Tracing model is initially proposed, which changes the individualization level from a group of all students to subgroups. To confirm whether this individualized method can be generalized to other knowledge tracing models, we also test it on logistic knowledge tracing models. Therefore, we provide an introduction about the principles of three logistic knowledge tracing models. We evaluate our method on the four models with two international public educational datasets. The results show that all knowledge tracing models for clustered students outperform the original models without clustering. The Bayesian Knowledge Tracing model and item response theory for clustered students both gain good improvement in their performance, while the Performance Factors Analysis for clustered students has only few improvements. The reason for the experimental results is discussed at the end. Overall, this paper provides a general method for further promoting the performance of traditional knowledge tracing models that requires more studies.