Miracle-dz/UniFAR-FTUs huggingface dataset:
Central-Eye: Gaze Tracking Research on Visual Rendering Method in Industrial Virtual Reality Scene

With development of computing technology, virtual reality devices are widely used in Internet-enabled industrial platforms. The problem of virtual reality stuns caused by the increase in visual frequency and real-world scenarios in industrial applications is still remaining the core problem. We proposes a new method called Central-Eye. It adopts a new context-aware solution for eye tracking to obtain the focus of the human eye on the screen, and then based on the distribution of human visual acuity, down-sampling the rendering in the visual edge area to increase the rendering speed to solve the user experience problem. Aiming at the characteristics of industrial virtual reality equipment with large field of view, high frame rate requirements, and eyepiece distortion, we propose a new gaze tracking method combining pattern recognition with local linear fitting. From experimental evaluation, we finally conclude the research that Central-Eye method can virtually improve the user experiences.
Complex Chemical Data Classification and Discrimination Using Locality Preserving Partial Least Squares Discriminant Analysis

Partial least squares discriminant analysis (PLS-DA) is a well-known technique for feature extraction and discriminant analysis in chemometrics. Despite its popularity, it has been observed that PLS-DA does not automatically lead to extraction of relevant features. Feature learning and extraction depends on how well the discriminant subspace is captured. In this paper, discriminant subspace learning of chemical data is discussed from the perspective of PLS-DA and a recent extension of PLS-DA, which is known as the locality preserving partial least squares discriminant analysis (LPPLS-DA). The objective is twofold: (a) to introduce the LPPLS-DA algorithm to the chemometrics community and (b) to demonstrate the superior discrimination capabilities of LPPLS-DA and how it can be a powerful alternative to PLS-DA. Four chemical data sets are used: three spectroscopic data sets and one that contains compositional data. Comparative performances are measured based on discrimination and classification of these data sets. To compare the classification performances, the data samples are projected onto the PLS-DA and LPPLS-DA subspaces, and classification of the projected samples into one of the different groups (classes) is done using the nearest-neighbor classifier. We also compare the two techniques in data visualization (discrimination) task. The ability of LPPLS-DA to group samples from the same class while at the same time maximizing the between-class separation is clearly shown in our results. In comparison with PLS-DA, separation of data in the projected LPPLS-DA subspace is more well defined.
Measuring the Contextual Dimension of User Experience: Development of the User Experience Context Scale (UXCS)

The context of use has been highlighted for a long time as being a key factor impacting User Experience (UX). Yet current UX evaluation tools, especially questionnaires, rarely encompass an explicit investigation of the context. With the ever-growing trend for mobile products and ubiquitous computing, the absence of a dedicated measurement tool becomes critical. Based on a review of relevant literature and a fine-grained categorization of contextual factors, we developed the UX Context Scale (UXCS), a 30-items instrument allowing for a measure of context properties, as perceived by the user. We report on the development of the scale and present a first validation study (N = 137). A principal component analysis on the subjective items reveals a 6-components structure: Physical Context, Social Context, Internal Context, Perceived Resources, Task Context, and Temporal Context. Reliability of each subscale is high and further analyses confirm the relevance of this tool for UX evaluation.
Improving Multi-set Query Processing Via a Learned Oracle

Multi-set query is a fundamental problem in computer systems and applications. Most traditional solutions for multi-set query are based on hash tables or bloom filters. However, when the sizes of multi-sets are large, these solutions cannot achieve small memory usage, fast query speed and high accuracy at the same time. In this work, we study the problem of using a learned oracle to improve the performance of traditional multi-set query processing empirically. The key idea is to train an oracle to predict which set contains a query item e as a classification problem. To ensure an exact query result, we combine the learned oracle with a standard bloom filter and an exact-match index to catch items that are not correctly identified by the oracle. When the oracle is both small and efficient, the whole query performance can be improved. In our framework, we treat the learned oracle as a complete black box, and is not dependent on its inner workings. Theoretical proofs and experimental results show that compared to the state-of-the-art, the error rate of our approach is 0% even with much less memory usage and a comparable speed.
Accepting Variation and Embracing Uncertainty: Creating a Regional Pottery Typology in South Asian Archaeology

Abstract This article presents the results of the analysis of archaeological ceramics collected during landscape surveys in the Vidarbha of Maharashtra, India; and offers the first attempt at a regional pottery typology for this area. Here, as in many other parts of South Asia, the pottery from archaeological sites have been subject to considerable scrutiny. Yet, so far approaches to their study have focussed on mainly their surface colour and feel. This has resulted in overly simplistic typologies that do not (and cannot) accommodate the full range of variation that exists within a ceramic assemblage, and so limit their value as archaeological evidence. Addressing this, we apply a chaîne opératoire-based approach to the analysis of a ceramic assemblage that we have been developing in this region. This results in a much more complex and detailed pottery typology than has so far been achieved. Throughout this study we also identify points of comparison with familiar parallels published elsewhere. In doing so, the resulting typology, while by no means the final word on the matter, provides a valuable and flexible resource that others working in this region and neighbouring areas can use for their own analyses and research. Moreover, in shifting the bases of categorisation and classification to the ways that pottery was made, we are able to incorporate far more of the variation that exists in the material itself. Indeed, the amount of variation can be somewhat bewildering in comparison to the standard (limiting) typological categories that populate earlier reports, and forces us to question those frameworks. Yet, we argue that it is precisely this sort of uncertainty that has to be embraced if the study of archaeological ceramics for the region of the ancient Vidarbha is going to continue to develop as a meaningful area of archaeological enquiry.
Sheet diagrams for bimonoidal categories

Bimonoidal categories (also known as rig categories) are categories with two monoidal structures, one of which distributes over the other. We formally define sheet diagrams, a graphical calculus for bimonoidal categories that was informally introduced by Staton. Sheet diagrams are string diagrams drawn on a branching surface, which is itself an extruded string diagram. Our main result is a soundness and completeness theorem of the usual form for graphical calculi: we show that sheet diagrams form the free bimonoidal category on a signature.
Mitochondrial Ca2 + Is Related to Mitochondrial Activity and Dynamic Events in Mouse Oocytes

Mitochondrial energy insufficiency is strongly associated with oocyte activation disorders. Ca2+, especially that in the mitochondrial matrix, plays a pivotal role in mitochondrial energy supplementation, but the underlying mechanisms are still only poorly understood. An encoded mitochondrial matrix Ca2+ probe (Mt-GCaMP6s) was introduced to observe mitochondrial Ca2+ ([Ca2+]m) dynamic changes during oocyte maturation and activation. We found that active mitochondria surrounding the nucleus showed a higher [Ca2+]m than those distributed in the cortex during oocyte maturation. During oocyte partheno-activation, the patterns of Ca2+ dynamic changes were synchronous in the cytoplasm and mitochondria. Such higher concentration of mitochondrial matrix Ca2+ was closely related to the distribution of mitochondrial calcium uptake (MICU) protein. We further showed that higher [Ca2+]m mitochondria around the chromosomes in oocytes might have a potential role in stimulating mitochondrial energy for calmodulin-responsive oocyte spindle formation, while synchronizing Ca2+ functions in the cytoplasm and nuclear area are important for oocyte activation.
In silico Functional Analysis of FLC and FT-Genes Responsible for Postponingand Accelerating the Onset of Flowering

In silico functional analyses of FLOWERING LOCUS C (FLC) and FLOWERING LOCUS T (FT) genes were investigated herein. The recognition of cis-regulatory modules and their organization are a prerequisite to information on the regulation of gene expression. Accordingly, this study carried out a promoter analysis on FT and FLC genes in Arabidopsis and its orthologs in Prunus persica by PlantPan and PLANCARE servers in which the integrating transcription factor binding site (TFBs) was involved in hormonal and stress signals. Light responsive elements were also studied. Among the responsive elements identified in FT and FLC, Arabidopsis had the highest number of responsive elements. This is partly because P. persica is day-neutral, whereas Arabidopsis is a facultative long day plant. The results of motif recognition in FT and FLC proteins with the MEME server showed that FT is highly conserved, but the number of motifs suggests that the FLC protein had far more numerous motifs, which is likely due to its higher amino acid length. Some specified TFs like AGAMOUS (AG), AGAMOUS LIKE (AGL), LEAFY (LFY) and APETALA1 (AP1) were found by the PlantPan web server in FT and FLC promoters’ analysis. They were also present in the protein-protein interaction network analysis which raises the question whether or not FT or FLC can be positive or negative regulators of their own expression in a feedback loop.
Predicting Sex From EEG: Validity and Generalizability of Deep-Learning-Based Interpretable Classifier

Explainable artificial intelligence holds a great promise for neuroscience and plays an important role in the hypothesis generation process. We follow-up a recent machine learning-oriented study that constructed a deep convolutional neural network to automatically identify biological sex from EEG recordings in healthy individuals and highlighted the discriminative role of beta-band power. If generalizing, this finding would be relevant not only theoretically by pointing to some specific neurobiological sexual dimorphisms, but potentially also as a relevant confound in quantitative EEG diagnostic practice. To put this finding to test, we assess whether the automatic identification of biological sex generalizes to another dataset, particularly in the presence of a psychiatric disease, by testing the hypothesis of higher beta power in women compared to men on 134 patients suffering from Major Depressive Disorder. Moreover, we construct ROC curves and compare the performance of the classifiers in determining sex both before and after the antidepressant treatment. We replicate the observation of a significant difference in beta-band power between men and women, providing classification accuracy of nearly 77%. The difference was consistent across the majority of electrodes, however multivariate classification models did not generally improve the performance. Similar results were observed also after the antidepressant treatment (classification accuracy above 70%), further supporting the robustness of the initial finding.
Molecular Profiling–Based Precision Medicine in Cancer: A Review of Current Evidence and Challenges

Matched therapy based on next-generation sequencing is now a part of routine care to guide the treatment of patients with advanced solid tumors. However, whether and to what extent patients can benefit from this strategy on a large scale remains uncertain. In the past decade, several clinical studies were performed in this field, among which only one was a randomized trial. We reviewed the literature on this topic and summarize the existing data about the efficacy of this treatment strategy. Currently, the evidence is promising but not solid. Multiple ongoing trials are also summarized. We also discuss the limitations of this treatment strategy and certain unsolved important problems, including how to select the sample and target level, how to interpret the results, and the problem of drug accessibility. All these issues should receive more attention in future clinical trial design and the application of target therapy in cancer treatment.
Brassinosteroid Signaling Converges With Auxin-Mediated C3H17 to Regulate Xylem Formation in Populus

Brassinosteroid (BR) signaling has long been reported to have an effect on xylem development, but the detailed mechanism remains unclear, especially in tree species. In this study, we find PdC3H17, which was demonstrated to mediate xylem formation driven by auxin in our previous report, is also involved in BR-promoted xylem development. Y1H analysis, EMSA, and transcription activation assay confirmed that PdC3H17 was directly targeted by PdBES1, which is a key transcriptional regulator in BR signaling. Tissue specificity expression analysis and in situ assay revealed that PdC3H17 had an overlapping expression profile with PdBES1. Hormone treatment examinations verified that xylem phenotypes in PdC3H17 transgenic plants, which were readily apparent in normal condition, were attenuated by treatment with either brassinolide or the BR biosynthesis inhibitor propiconazole. The subsequent quantitative real-time polymerase chain reaction (qRT-PCR) analyses further revealed that BR converged with PdC3H17 to influence transcription of downstream xylem-related genes. Additionally, the enhancement of xylem differentiation by auxin in PdC3H17 overexpression plants was significantly attenuated compared with wild-type and dominant negative plants due to BR deficiency, which suggested that the BR- and auxin-responsive gene PdC3H17 acted as an mediation of these two hormones to facilitate xylem development. Taken together, our results demonstrate that BR signaling converges with auxin-mediated PdC3H17 to regulate xylem formation in Populus and thus provide insight into the regulation mechanism of BRs and the crosstalk with auxin signaling on xylem formation.
Changing Epidemiology of Presumptive Community-associated-methicillin-resistant Staphylococcus Aureus in Slovenia in 2014-2015 Compared To 2010

Abstract Introduction Although the distinction between the Community-Associated-Methicillin-Resistant Staphylococcus aureus (CA-MRSA) and Hospital-Associated-Methicillin-Resistant S. aureus (HA-MRSA) has blurred in recent years, the CA-MRSA is an important group because of its potential to cause fulminant and severe infections. Its importance has further increased with the emergence of Livestock-Associated-Methicillin-Resistant S. aureus (LA-MRSA). Methods In the present study we analysed clonal distributions and virulence factors in presumptive CA-MRSA isolated from January 2014 to December 2015 and compared the results with our previous study from 2010. Phenotypic definition for presumptive CA-MRSA was based on resistance to cefoxitin and oxacillin and susceptibility to at least two of the following four antibiotics: ciprofloxacin, erythromycin, clindamycin and gentamicin. Results In 2014 and 2015 altogether 304 MRSA isolates fulfilled our screening phenotypic definition, 45 isolates were cultivated from clinical specimens and 259 from screening specimens. Sequence types ST398, LA-MRSA and mecC MRSA increased significantly in 2015 compared to 2010 (p-value <0.05) and were spread over Slovenia. Conclusion The clonal distribution of presumptive CA-MRSA has changed within the study period in Slovenia. In 2015 the most frequent clone among clinical and screening specimens was a pig-associated clone, ST398, but the number of confirmed ST398 infections remains low. While previously ST398 and mecC positive MRSA strains were geographically limited, they have spread throughout the country since 2010.
ATRO: Adversarial Training with a Rejection Option

This paper proposes a classification framework with a rejection option to mitigate the performance deterioration caused by adversarial examples. While recent machine learning algorithms achieve high prediction performance, they are empirically vulnerable to adversarial examples, which are slightly perturbed data samples that are wrongly classified. In real-world applications, adversarial attacks using such adversarial examples could cause serious problems. To this end, various methods are proposed to obtain a classifier that is robust against adversarial examples. Adversarial training is one of them, which trains a classifier to minimize the worst-case loss under adversarial attacks. In this paper, in order to acquire a more reliable classifier against adversarial attacks, we propose the method of Adversarial Training with a Rejection Option (ATRO). Applying the adversarial training objective to both a classifier and a rejection function simultaneously, classifiers trained by ATRO can choose to abstain from classification when it has insufficient confidence to classify a test data point. We examine the feasibility of the framework using the surrogate maximum hinge loss and establish a generalization bound for linear models. Furthermore, we empirically confirmed the effectiveness of ATRO using various models and real-world datasets.
Robust Optimization Approaches for Portfolio Selection: A Computational and Comparative Analysis

The field of portfolio selection is an active research topic, which combines elements and methodologies from various fields, such as optimization, decision analysis, risk management, data science, forecasting, etc. The modeling and treatment of deep uncertainties for future asset returns is a major issue for the success of analytical portfolio selection models. Recently, robust optimization (RO) models have attracted a lot of interest in this area. RO provides a computationally tractable framework for portfolio optimization based on relatively general assumptions on the probability distributions of the uncertain risk parameters. Thus, RO extends the framework of traditional linear and non-linear models (e.g., the well-known mean-variance model), incorporating uncertainty through a formal and analytical approach into the modeling process. Robust counterparts of existing models can be considered as worst-case re-formulations as far as deviations of the uncertain parameters from their nominal values are concerned. Although several RO models have been proposed in the literature focusing on various risk measures and different types of uncertainty sets about asset returns, analytical empirical assessments of their performance have not been performed in a comprehensive manner. The objective of this study is to fill in this gap in the literature. More specifically, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005-2016.
Research on the Influencing Factors of User Trust Based on Artificial Intelligence Self Diagnosis System

Artificial intelligence technology has gradually become an important auxiliary means for people to obtain medical consultation. However, many researches reflect users' doubts about the feedback results of AI system. Therefore, this paper focuses on the design of the user-friendly intelligent self diagnosis system, using interdisciplinary research methods. Firstly, based on the psychological questionnaire and interview, this paper studies the public's cognition of the intelligent self diagnosis system, and discusses how to effectively improve the user's recognition and trust of the intelligent self diagnosis system. It is found that the credibility of the intelligent self diagnosis system can be improved by further improving the transparency of the system. For example, users can be provided with four different types of information, including system reasoning, system reliability, information source and personalized information. On the basis of interviews, 48 volunteers were invited to carry out a comparative experiment in groups to evaluate the impact of medical interpretation of intelligent self diagnosis system on patients' perception and trust under different transparency and accuracy models. We found that the improvement of system reliability information (i.e. accuracy model and confidence score) composed of accuracy model and confidence score strengthens patients' cognition of AI system and improves the user trust of the system; while the detailed display of system reasoning and logic can improve the comprehensibility of medical advice output of the system, but does not substantially improve the user trust. At the end of this paper, some suggestions are put forward to design an interpretable and reliable intelligent self diagnosis system.
Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models

A systematic approach to finding variational approximation in an otherwise intractable non-conjugate model is to exploit the general principle of convex duality by minorizing the marginal likelihood that renders the problem tractable. While such approaches are popular in the context of variational inference in non-conjugate Bayesian models, theoretical guarantees on statistical optimality and algorithmic convergence are lacking. Focusing on logistic regression models, we provide mild conditions on the data generating process to derive non-asymptotic upper bounds to the risk incurred by the variational optima. We demonstrate that these assumptions can be completely relaxed if one considers a slight variation of the algorithm by raising the likelihood to a fractional power. Next, we utilize the theory of dynamical systems to provide convergence guarantees for such algorithms in logistic and multinomial logit regression. In particular, we establish local asymptotic stability of the algorithm without any assumptions on the data-generating process. We explore a special case involving a semi-orthogonal design under which a global convergence is obtained. The theory is further illustrated using several numerical studies.
Integrating Instructional Design Principles in Serious Games Authoring Tools: Insights from Systematic Literature Review

Serious Games (SGs) have been widely used for educational purposes. However, there is still a lack of standardised SG design models. Rather than creating these models from scratch, established instructional design principles (IDPs) can be adopted so as to ensure the efficacy of SGs. The use of IDPs may enable educators, who are well-informed about the process of designing instructions, to be involved in designing SGs. In the literature, several SG authoring tools that remove the need for programming have been proposed. Nonetheless, the complex process of game design and development will still impede the potential of authoring tools. To gain a deeper understanding of how IDPs have been integrated in SG design models, we have conducted a systematic literature review. With the use of four databases, 165 unique articles were retrieved. After a meticulous filtering process, eight were critically analysed. Results thereof allowed us to make informed recommendations on the integration of IDPs within SG authoring tools.