Miracle-dz/UniFAR-FTUs huggingface dataset:
Can oligodendrocyte transcriptional factor-2 (Olig2) be used as an alternative for 1p/19q co-deletions to distinguish oligodendrogliomas from other glial neoplasms?

AIM OF THE STUDY Oligodendrocyte transcriptional factor-2 (Olig2) is an essential marker for oligodendrocytes expression. We aimed to explore the expression of Olig2 in different glial neoplasms and to investigate if diffuse Olig2 expression can replace 1p19q co-deletion for the diagnosis of oligodendroglioma. MATERIAL AND METHODS Olig2 was performed on 53 samples of different glial neoplasms using immunohistochemistry (IHC). 1p/19q deletions were investigated using fluorescence in situ hybridization (FISH). RESULTS Olig2 labelling of different glial neoplasms revealed various expressions, in which 26 tumours showed diffuse expression (≥ 60%) and 23 tumours showed partial focal expression (< 50%). Four tumours showed no expression. Of the 26 tumours, 6 oligodendrogliomas had 1p19q co-deletion and the remaining 3 oligodendrogliomas showed no co-deletion. Three non-oligodendroglial tumours were found to have 19q deletion. The FISH of the remaining tumours (14/26) showed no aberrations. There was no significant difference in the final diagnosis by using 1p19q co-deletion test among glial neoplasms with diffuse Olig2 expression (p = 0.248). CONCLUSIONS Olig2 marker cannot be used as an alternative diagnostic method for 1p19q co-deletion to distinguish oligodendrogliomas from other glial neoplasms. Although some glial tumours showed diffuse Olig2 expression, 1p19q co-deletion testing is the best diagnostic method.
Plasma MMP-9 Levels as the Future Risk of Conversion to Dementia in ApoE4-Positive MCI Patients: Investigation Based on the Alzheimer’s Disease Neuroimaging Initiative Database

Background Matrix metalloproteinase 9 (MMP-9) has been reported to be correlated with declines in hippocampal volume and cognitive function in ApoE4-positive MCI patients. Objectives The present study was aimed to investigate the effects of plasma matrix MMP-9 on the conversion risk between mild cognitive impairment (MCI) patients with and without ApoE4. Design and Setting Retrospective observational study using the data extracted from the Alzheimer’s Disease Neuroimaging Initiative database. Participants We included 211 ApoE4-positive MCI subjects (ApoE4+ MCI) and 184 ApoE4-negative MCI subjects (ApoE4-MCI). Measurements We obtained demographic and data including plasma MMP-9 levels at baseline and longitudinal changes in Clinical Dementia Rating (CDR) up to 15 years. We compared conversion rates between ApoE4+ MCI and ApoE4- MCI by the Log-rank test and calculated the hazard ratio (HR) for covariates including age, sex, educational attainment, drinking and smoking histories, medications, and plasma MMP-9 levels using a multiple Cox regression analysis of ApoE4+ MCI and ApoE4- MCI. Results No significant differences were observed in baseline plasma MMP-9 levels between ApoE4+ MCI and ApoE4- MCI. High plasma MMP-9 levels increased the conversion risk significantly more than low plasma MMP-9 levels (HR, 2.46 [95% CI, 1.31–4.48]) and middle plasma MMP-9 levels (HR, 1.67 [95% CI, 1.04–2.65]) in ApoE4+ MCI, but not in ApoE4- MCI. CONCLUSION: Plasma MMP-9 would be the risk of the future conversion to dementia in ApoE4+ MCI.
First record of Centronycteris centralis (Chiroptera: Emballonuridae) from Honduras

Centronycteris centralis Thomas, 1912 has been included in lists of Honduran mammals; however, there are no verified records of the species for the country. We present the first verified record based on acoustic sampling at the Texiguat Wildlife Refuge, northern Honduras. This bat is a forest specialist that needs protected areas for its survival. Although this new record does not extend the distribution of the species, it confirms its presence in Honduras and highlights the relevance of the protected area housing the species. With the current record of C. centralis, there are now 112 confirmed bat species for Honduras.
Musical agency and collaboration in the digital age

In 2019, the musician Holly Herndon released her third full-length album, Proto. In addition to input from two other human artists, the album had a fourth collaborator: an artificial neural network named Spawn. The software had been trained over several years to generate and manipulate the cavernous choral soundscapes that brought Proto widespread critical acclaim. Spawn’s role in each stage of the music-making process was neither completely predictable nor completely under Herndon’s control; her vocal contribution – its tone, pitch, rhythm and dynamics – was often novel, original and surprising.1 Herndon describes Spawn as ‘a performer . an ensemble member. So I would say that I collaborated with a human and an inhuman ensemble’ (Funai 2019). Here, we consider how seriously we ought to take assertions like this one. Can we really conceive of AI systems as legitimate collaborators in the skilled project of making art? Do they have the kinds of creative agency, autonomy and expressive power that characterize membership of an artistic ensemble? 2 7
EXTRACTION OF TRAFFIC FEATURES IN SOFTWARE- DEFINED NETWORKS USING AN SDN CONTROLLER

Machine learning methods can be used to solve the problems of detecting and countering attacks onsoftware-defined networks. For such methods, it is necessary to prepare a large amount of initial datafor training. Mininet is used as a modeling environment for SDN. The main tasks of modeling asoftware-defined network are studying traffic within the network, as well as practicing variousscenarios of attacks on network elements. The SDN controller ONOS (Open Network OperatingSystem) is used as the network controller. Various network topologies are considered in the modeling.The possibility of analyzing information about traffic within the network using an SDN controller inreal time is investigated, as well as the possibility of collecting information in the form of a set offeatures. Modeling of software-defined networks under different initial conditions and for differentattack scenarios can be carried out on a distributed computing system. Since the computationalproblem to be solved can be divided according to the data into many autonomous tasks, it is possibleto use desktop grid system and voluntary distributed computing to speed up the process.
Semiparametric Goodness-of-Fit Test for Clustered Point Processes with a Shape-Constrained Pair Correlation Function

Abstract Specification of a parametric model for the intensity function is a fundamental task in statistics for spatial point processes. It is, therefore, crucial to be able to assess the appropriateness of a suggested model for a given point pattern dataset. For this purpose, we develop a new class of semiparametric goodness-of-fit tests for the specified parametric first-order intensity, without assuming a full data generating mechanism that is needed for the existing popular Monte Carlo tests. The proposed tests crucially rely on accurate nonparametric estimation of the second-order properties of a point process. To address this we propose a new nonparametric pair correlation function (PCF) estimator for clustered spatial point processes under some mild shape constraints, which is shown to achieve uniform consistency. The proposed test statistics are computationally efficient owing to closed-form asymptotic distributions and achieve the nominal size even for testing composite hypotheses. In practice, the proposed estimation and testing procedures provide effective tools to improve parametric intensity function modeling, which is demonstrated through extensive simulation studies as well as a real data analysis of street crime activity in Washington DC. Supplementary materials for this article are available online.
SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

The classic model of Markowitz for designing investment portfolios is an optimization problem with two objectives: maximize returns and minimize risk. Various alternatives and improvements have been proposed by different authors, who have contributed to the theory of portfolio selection. One of the most important contributions is the Sharpe Ratio, which allows comparison of the expected return of portfolios. Another important concept for investors is diversification, measured through the average correlation. In this measure, a high correlation indicates a low level of diversification, while a low correlation represents a high degree of diversification. In this work, three algorithms developed to solve the portfolio problem are presented. These algorithms used the Sharpe Ratio as the main metric to solve the problem of the aforementioned two objectives into only one objective: maximization of the Sharpe Ratio. The first, GENPO, used a Genetic Algorithm (GA). In contrast, the second and third algorithms, SAIPO and TAIPO used Simulated Annealing and Threshold Accepting algorithms, respectively. We tested these algorithms using datasets taken from the Mexican Stock Exchange. The findings were compared with other mathematical models of related works, and obtained the best results with the proposed algorithms.
Desenvolvimento de uma luva com rastreamento de dedos e monitoramento fisiológico para aplicações de Realidade Virtual

VR Wear é um protótipo vestível no qual se busca provar o conceito de rastreamento de dedos, de baixo custo, para ambientes virtuais imersivos projetados na game engine Unity. O dispositivo também permite o acoplamento de sensores de monitoramento fisiológico, fornecendo um acessório multifuncional capaz de ser utilizado em aplicações interativas de diferentes propósitos. Este artigo descreve projetos similares, os conceitos relacionados, o projeto e o desenvolvimento do VR Wear, e apresenta testes iniciais do equipamento.
THE IMPACT OF DIGITAL TECHNOLOGIES ON SELF-LEARNING IN MATHEMATICS

During the pandemic, digital technologies had a major impact on self-directed learning in mathematics. Without digital skills and self-directed learning, everyday learning is unthinkable, as learning in 2020 has largely taken place remotely. It is important for students to acquire these skills in order to learn fully and qualitatively at a distance. These acquired skills are and will be useful in full-time studies and later in life. Digital literacy allows the student to develop self-directed learning and self-directed learning develops digital literacy. Both of these skills are interrelated and help you learn math. The student's skills are greatly influenced by internal and external life factors, which unfortunately cannot be influenced by the educational institution. Thus, these negative factors also affect the student's motivation and discipline for learning and learning outcomes are low. The research analyzes the scientific literature and emphasizes the author's personal experience.
Foreword

In recent years, the United States has experienced a tremendous resurgence in hydrocarbon production, largely driven by recent technological advancements that have enabled natural gas and crude oil to be economically produced from shale and other low-permeability formations. In the process, the United States has become the world's largest producer of natural gas. According to British Petroleum's statistical world energy review, the United States has surpassed the Russian Federation in reaching a newall-time high of 688 billion cubic meters of produced natural gas in 2013 (British Petroleum 2014). At the same time, the United States overtook Saudi Arabia to become one of the largest crude oil producers in the world (Smith 2014)-U.S. output exceeded 11 million barrels per day during the first five months of 2014 (Energy Information Administration 2014a).
Modeling and analysis of magnetic field distribution of square pane permanent magnet for intelligent ball joint

The reasonable permanent magnetic field distribution has an important influence on improving the measuring accuracy in intelligent ball joint. In view of the defects on the ring permanent magnet in the previous experiment scheme, a new method on Square Pane Permanent Magnet (SPPM) is put forward. It possesses distinct advantages on orientation identification and model simplification. This paper proposes an optimized theory model of the magnetic field distribution of SPPM and gives the magnetic field theoretical expressions. The experiments have shown that the experimental data basically agreed with the theory value which is less than 4.3% error in full scale. This result verified the correctness of the analytic work and paves the way for improving the measurement accuracy in intelligent ball joint.
Disorder-and cognitive demand-specific neurofunctional alterations during social emotional working memory in generalized anxiety disorder and major depressive disorder

Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD) are both characterized by cognitive and social impairments. Determining disorder-specific neurobiological alterations in GAD and MDD by means of functional magnetic resonance imaging (fMRI) may promote determination of precise diagnostic markers. This study aimed to examine disorder-specific behavioral and neural alterations at the intersection of social and cognitive processing in treatment-naive first-episode GAD (n=35) and MDD (n=37) patients compared to healthy controls (n=35) by employing a social-emotional n-back fMRI paradigm. No behavioral differences between patients and healthy controls were observed. However, GAD patients exhibited decreased bilateral dorsomedial prefrontal cortex (dmPFC) engagement during the 0-back condition yet increased dmPFC engagement during the 1-back condition compared to MDD and healthy participants. In contrast, MDD patients exhibited increased dmPFC-insula coupling during 0-back, yet decreased coupling during 1-back, compared to GAD and healthy participants. Dimensional symptom-load analysis confirmed that increased dmPFC-insula connectivity during 0-back was positively associated with depressive symptom load. These findings suggest that the dmPFC engaged in integrating of affective and cognitive components and self-other processing exhibits GAD-specific neurofunctional dysregulations whereas functional dmPF communication with insula, a region involved in salience processing, may represent an MDD-specific neurofunctional deficit.
Point-NeRF: Point-based Neural Radiance Fields

Volumetric neural rendering methods like NeRF [34] generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30× faster training time. Point-NeRF can be combined with other 3D re-construction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.
VisQualdex: a Comprehensive Guide to Good Data Visualization

The rapid influx of low-quality data visualizations is one of the main challenges in today’s commu- nication. Misleading, unreadable, or confusing visualizations spread misinformation. Furthermore, they fail to deliver their message to the viewer. The scale of the problem is big enough that there already exist public fora gathering tens of thousands of users criticizing graphics and charts (reddit.com/r/dataisugly) made with obvious mistakes. Current attempts at data visualization appear mostly as simple and overgeneralized checklists, and often lack systematicity and versatility. The lack of proper tooling for evaluating data visualization quality further heightens the problem. Therefore, this paper proposes VisQualdex, a systematic set of guidelines for static data visual- ization. The codex categorization is based and inspired by the theory of Grammar of Graphics. It contains dozens of criteria designed to catch various errors and mistakes of different categories and magnitude. Furthermore, it has been peer-reviewed and tested by experts of data visualization, data science, graphics design, information technology and computer science. To apply theory in the real world and increase the practical impact of VisQualdex, this contribution also introduces a practical tool. The implementation of the guidelines is available in the form of the web server, https://visqual.onrender. com, developed as a single page application in JavaScript using Vue.js and Material Design principles.
Conversational Information Seeking

Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.
Minimal Motifs of Dynamic Robustness (Part II of Dynamic ACR Quadrilogy)

Networks with absolute concentration robustness (ACR) are ones that have a special translate of a coordinate hyperplane, which contains all steady states (static ACR) or attracts all trajectories (dynamic ACR). The implication for the underlying biological system is robustness in the concentration of one of the species independent of the initial conditions as well as independent of the concentration of all other species. Identifying network conditions for dynamic ACR is a challenging problem. We lay the groundwork in this paper by studying small reaction networks, those with 2 reactions and 2 species. We give a complete classification by ACR properties of these minimal reaction networks. The dynamics is rich even within this simple setting. Insights obtained from this work will help illuminate the properties of more complex networks with dynamic ACR.
An error estimate for the Gauss-Jacobi-Lobatto quadrature rule

An error estimate for the Gauss-Lobatto quadrature formula for integration over the interval [−1, 1], relative to the Jacobi weight function w(t) = (1 − t)(1 + t) , α, β > −1, is obtained. This estimate holds true for functions belonging to some Sobolev-type subspaces of the weighted space L wα,β ([−1, 1]).
Trireme: Exploring Hierarchical Multi-Level Parallelism for Domain Specific Hardware Acceleration

The design of heterogeneous systems that include domain specific accelerators is a challenging and time-consuming process. While taking into account area constraints, designers must decide which parts of an application to accelerate in hardware and which to leave in software. Moreover, applications in domains such as Extended Reality (XR) offer opportunities for various forms of parallel execution, including loop level, task level and pipeline parallelism. To assist the design process and expose every possible level of parallelism, we present Trireme, a fully automated tool-chain that explores multiple levels of parallelism and produces domain specific accelerator designs and configurations that maximize performance, given an area budget. Experiments on demanding benchmarks from the XR domain revealed a speedup of up to 20x, as well as a speedup of up to 37x for smaller applications, compared to software-only implementations.
Equivalent Distance Geometry Error for Molecular Conformation Comparison

Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design. However, existing loss functions in these models either cost overmuch time or fail to guarantee the equivalence during optimization, which means treating different items unfairly, resulting in poor local geometry in generated conformation. So, we propose Equivalent Distance Geometry Error (EDGE) to calculate the differential discrepancy between conformations where the essential factors of three kinds in conformation geometry (i.e. bond lengths, bond angles and dihedral angles) are equivalently optimized with certain weights. And in the improved version of our method, the optimization features minimizing linear transformations of atom-pair distances within 3-hop. Extensive experiments show that, compared with existing loss functions, EDGE performs effectively and efficiently in two tasks under the same backbones.
Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild

Since Bitcoin appeared in 2009, over 6,000 different cryptocurrency projects have followed. The cryptocurrency world may be the only technology where a massive number of competitors offer similar services yet claim unique benefits, including scalability, fast transactions, and security. But are these projects really offering unique features and significant enhancements over their competitors? To answer this question, we conducted a large-scale empirical analysis of code maintenance activities, originality and security across 592 crypto projects. We found that about half of these projects have not been updated for the last six months; over two years, about three-quarters of them disappeared, or were reported as scams or inactive. We also investigated whether 11 security vulnerabilities patched in Bitcoin were also patched in other projects. We found that about 80% of 510 C-language-based cryptocurrency projects have at least one unpatched vulnerability, and the mean time taken to fix the vulnerability is 237.8 days. Among those 510 altcoins, we found that at least 157 altcoins are likely to have been forked from Bitcoin, about a third of them containing only slight changes from the Bitcoin version from which they were forked. As case studies, we did a deep dive into 20 altcoins (e.g., Litecoin, FujiCoin, and Feathercoin) similar to the version of Bitcoin used for the fork. About half of them did not make any technically meaningful change - failing to comply with the promises (e.g., about using Proof of Stake) made in their whitepapers.
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning

We present a two-step hybrid reinforcement learning (RL) policy that is designed to generate interpretable and robust hierarchical policies on the RL problem with graph-based input. Unlike prior deep reinforcement learning policies parameterized by an end-to-end black-box graph neural network, our approach disentangles the decision-making process into two steps. The first step is a simplified classification problem that maps the graph input to an action group where all actions share a similar semantic meaning. The second step implements a sophisticated rule-miner that conducts explicit one-hop reasoning over the graph and identifies decisive edges in the graph input without the necessity of heavy domain knowledge. This two-step hybrid policy presents human-friendly interpretations and achieves better performance in terms of generalization and robustness. Extensive experimental studies on four levels of complex text-based games have demonstrated the superiority of the proposed method compared to the state-of-the-art.