Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analyses of Sperm DNA Methylomes Among Three Commercial Pig Breeds Reveal Vital Hypomethylated Regions Associated With Spermatogenesis and Embryonic Development

Identifying epigenetic changes is essential for an in-depth understanding of phenotypic diversity and pigs as the human medical model for anatomizing complex diseases. Abnormal sperm DNA methylation can lead to male infertility, fetal development failure, and affect the phenotypic traits of offspring. However, the whole genome epigenome map in pig sperm is lacking to date. In this study, we profiled methylation levels of cytosine in three commercial pig breeds, Landrace, Duroc, and Large White using whole-genome bisulfite sequencing (WGBS). The results showed that the correlation of methylation levels between Landrace and Large White pigs was higher. We found that 1,040–1,666 breed-specific hypomethylated regions (HMRs) were associated with embryonic developmental and economically complex traits for each breed. By integrating reduced representation bisulfite sequencing (RRBS) public data of pig testis, 1743 conservated HMRs between sperm and testis were defined, which may play a role in spermatogenesis. In addition, we found that the DNA methylation patterns of human and pig sperm showed high similarity by integrating public data from WGBS and chromatin immunoprecipitation sequencing (ChIP-seq) in other mammals, such as human and mouse. We identified 2,733 conserved HMRs between human and pig involved in organ development and brain-related traits, such as NLGN1 (neuroligin 1) containing a conserved-HMR between human and pig. Our results revealed the similarities and diversity of sperm methylation patterns among three commercial pig breeds and between human and pig. These findings are beneficial for elucidating the mechanism of male fertility, and the changes in commercial traits that undergo strong selection.
Analysis and Prediction of Legal Judgements in the Slovak Criminal Proceedings

: This paper uses machine learning to analyze criminal judgements in the Slovak republic to determine their adequacy and set a baseline for predicting their outcomes. First, we summarize past and recent advancements in predicting verdicts and other attributes of legal text written in different languages. We then demonstrate data preparation of all publicly available Slovak judgements, extraction of their verdicts and separation into main parts using a Slovak words inﬂexion dictionary called Tvaroslovník. Later we use this data to classify the judgements into acquittal or conviction using several known machine learning methods ranging from simple statistical methods such as SVM and random forests to deep learning networks based on convolution to recurrence and their combinations. We evaluate their efﬁciency, analyze and identify signiﬁcant highly correlated terms with each result class, and offer a hypothesis as to why these terms are correlated with these results. We have found that a sequential input of word2vec embeddings combined with convolution-based deep learning methods produces the best results, achieving over 99% accuracy.
Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream Classification

: Graphs are one of the most ubiquitous kinds of data. However, data analysis methods have been developed primarily for numerical data, and to make use of them, graphs need to be represented as elements of some Euclidean space. An increasingly popular way of representing them in this way are graph neural networks (GNNs). Because data analysis applications typically require identical results for isomorphic graphs, the representations learned by GNNs also need to be invariant with respect to graph isomorphism. That motivated recent research into the possibilities of recognizing non-isomorphic pairs of graphs by GNNs, primarily based on the Weisfeiler-Lehman (WL) isomorphism test. This paper reports the results of a ﬁrst experimental comparison of four variants of two important GNNs based on the WL test from the point of view of graph representation for down-stream classiﬁcation by means of a support vector machins (SVM). Those methods are compared not only with each other, but also with a recent generalization of the WL sub-tree kernel. For all GNN variants, two different representations are included in the comparison. The comparison revealed that the four considered representations of the same kind of GNN never signiﬁcantly differ. On the other hand, there was always a statistically signiﬁcant difference be-tween representations originating from different kinds of GNNs, as well as between any representation originating from any of the considered GNNs and the representation originating from the generalized WL kernel.
Simulation of Non-pharmaceutical Interventions in an Agent based Epidemic Model

: The standard SEIR equation-based models represent the state-of-the-art approach in epidemiological modelling. Their drawbacks include unrealistic infection-related contact estimates and difﬁculties in modelling non-pharmaceutical interventions, such as contact reductions or partial closures. In this paper, we present our agent-based model that addresses the above-mentioned issues. It works with a population of individuals (agents) and their contacts are modelled as a multi-graph social network according to real data based on a Czech county. Custom algorithmic procedures simulating testing, quarantine and partial closures of various contact types are implemented. The model can serve as
Deep Autoencoders for Nonlinear Physics-Constrained Data-Driven Computational Framework with Application to Biological Tissue Modeling

Physics-constrained data-driven computing is an emerging paradigm that directly integrates material database into physical simulations of complex materials, bypassing the construction of classical constitutive models. However, most of the developed data-driven computing approaches are based on simplistic distance minimization and thus suffer from dealing with high-dimensional applications and lack generalization ability. This study proposes a deep learning enhanced data-driven computing framework to address these fundamental issues for nonlinear materials modeling. To this end, an au-toencoder, a special multi-layer neural network architecture, is introduced to learn the underlying low-dimensional embedding representation of the material database. Incorporating the ofﬂine trained autoencoder and the discovered embedding space in online data-driven computation enables to search for the optimal material state from database in low-dimensional embedding space, enhancing the robustness and predictability of data-driven computing on limited material data. To enhance stability and convergence of data-driven computing, a convexity-preserving interpolation scheme is introduced for constructing the material state on the low-dimensional embedding space given by autoencoders. The effectiveness and enhanced generalization performance of the proposed approach are examined by modeling biological tissue with experimental data.
Physics Informed Deep Learning for Well Test Analysis

Well Test Analysis is a section of reservoir engineering that best describes the reservoir characteristics with principles of fluid flow in porous media using pressure transient analysis. The transient pressure distribution for fluid flowing through the wellbore, across the porous reservoir model, at a constant terminal flow rate can be determined by solving the partial differential equationdiffusivity equation, along with the set of boundary conditions that define the reservoir model. Since the diffusivity equation has a non-linear quadratic term, it is either solved analytically by ignoring the quadratic term and thus compromising the model accuracy or solved using numerical approaches that is complex and time-consuming. This study provides an alternative and simpler approach to determine the pressure distribution using the Neural Networks method. This method could be applied to any type of reservoir that has a defined diffusivity equation and boundary conditions to predict the pressure distribution with good accuracy. To validate this approach and demonstrate the accuracy of the neural network with a greater level of confidence, for the purpose of this study, we have chosen to validate against analytical solution as it could be applied to all types of reservoir models in generic form. Typical neural network-based approaches, however, were not yielding good results for Well Test Analysis as it needed bulk data since they typically ignored physical insights from the scientific system under consideration. In this paper, this problem is resolved by Physics informed neural networks that are trained to solve supervised learning tasks while honoring any given physics law.
Prototypes of Productivity Tools for the Jadescript Programming Language

Jadescript is an agent-oriented programming language built on top of JADE. So far, the focus of the development of the language was on design choices, on syntax refinements, and on the introduction of expressions and constructs for agent-related abstractions and tasks. In this paper, a proposal to achieve the crucial goal of making Jadescript suitable for professional use is presented. The success of Jadescript, as a solid language to build real-world agent-based software systems, is necessarily related to its effective integration with mainstream development tools. In this paper, some of the productivity tools developed to integrate Jadescript with a mainstream development environment are presented as a way to promote the successful adoption of the language towards the community of JADE users.
Investigating Adjustable Social Autonomy in Human Robot Interaction

More and more often, Human Robot Interaction(HRI) applications require the design of robotics systems whose decision process implies the capability to evaluate not only the physical environment, but especially the mental states and the features of its human interlocutor, in order to adapt their social autonomy every time humans require the robot’s help. Robots will be really cooperative and effective when they will expose the capability to consider not only the goals or interests explicitly required by humans, but also those one that are not declared and to provide help that go beyond the literal task execution. In order to improve the quality of this kind of smart help, a robot has to operate a meta-evaluation of its own predictive skills to build a model of the interlocutor and of her/his goals. The robot’s capability to self-trust its skills to interpret the interlocutor and the context, is a fundamental requirement for producing smart and effective decisions towards humans. In this work we propose a simulated experiment, designed with the goal to test a cognitive architecture for trustworthy human robot collaboration. The experiment has been designed in order to demonstrate how the robot’s capability to learn its own level of self-trust on its predictive abilities in perceiving the user and building a model of her/him, allows it to establish a trustworthy collaboration and to maintain an high level of user’s satisfaction, with respect to the robot’s performance, also when these abilities progressively degrade.
Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus

The data-driven discrete exterior calculus (DDEC) structure provides a novel machine learning architecture for discovering structure-preserving models which govern data, allowing for example machine learning of reduced order models for complex continuum scale physical systems. In this work, we present a Greedy Fiedler Spectral (GFS) partitioning method to obtain a chain complex structure to support DDEC models, incorporating synthetic data obtained from high-ﬁdelity solutions to partial differential equations. We provide justi-ﬁcation for the effectiveness of the resulting chain complex and demonstrate its DDEC model trained for Darcy ﬂow on a heterogeneous domain
Learning Dynamical Systems across Environments

Learning the behavior of natural phenomena automatically from the data has gained much traction these last years. However, in most real world scenarios, the environment in which the data samples are acquired is varying and may not be the same for each data sample. This is due to different circum-stances e.g. acquisition in different spatial locations, or simply experimental settings which slightly differ. This severely hinders the training process, and makes the standard learning framework inapplicable. In this work, we propose a novel framework for modeling physical systems in this context, where we are able to leverage the data across different environments in order to learn the underlying dynamical systems, ensuring generalization without compromising the model’s expressiveness and predictive performance. We instantiate our framework on two different families of dynamical systems, proving that our approach yields superior results over the classical learning approach as well as against competitive baselines. Finally, we also show that we are also able to accelerate and improve the learning for environments that have never been seen before.
CHOLINESTERASE IMMOBLIZATION FOR LARGE-SCALE PRODUCTION OF HIGH SENSITIVE TEST CARDS FOR RAPID DETECTION OF ORGANOPHOSPHORUS AND CARBAMATE PESTICIDES IN AGRICULTURAL PRODUCTS

A single step test card for field screening of pesticide residues in vegetables based on simultaneous inhibition and reaction of cholinesterase was previously proposed. Here, favorable conditions to immobilize cholinesterase on filter paper for bulk production of this test card were developed. An amount of 28 mL of 6 U/mL cholinesterase solution was sprayed onto a filter paper sheet (210 mm × 297 mm) with speed of 1.5 mL/s. After incubation at -20 ◦C for 60 min, enzyme coated-papers were dried at 60 ◦C for 30 minutes and stacked on indicator and substratecoated papers. The stack was cut into 10 mm × 10 mm-sized pieces for depositing on metallised polyester film sheets. Then another metallised film which holed 5 mm diameter holes was placed on the paper pieces, and 15 mm × 15 mm reaction windows were made by heat pressing. The developed test card’s detect limit by naked eyes ranged between 0.0001 and 0.1 ppm of organophosphorus pesticides. This high sensitive test card provides a useful tool for detection of toxic organophosphorus and carbamate compounds in agricultural products, food and environment with cost-effective, minimized reagent consumption, and simple operation.
Estimation of Proportions of SARS-CoV-2 Variants in a Mixed Sequencing Sample

Estimation of proportions of SARS-CoV-2 genome variants (e.g. variant B.1.1.7 originating from Britain, variant B.1.351 originating from South-Africa) in a population is currently done by sequencing individual samples, which demands individual laboratory processing of each sample. This labor can be significantly reduced by mixing several samples together and processing them in one batch. Our project aims to estimate the proportion of samples with given variants from such mixtures using probabilistic modeling. Copyright © 2021 for this paper by its authors.
Video Scene Location Recognition with Neural Networks

This paper provides an insight into the possibility of scene recognition from a video sequence with a small set of repeated shooting locations (such as in television series) using artificial neural networks. The basic idea of the presented approach is to select a set of frames from each scene, transform them by a pre-trained singleimage pre-processing convolutional network, and classify the scene location with subsequent layers of the neural network. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. We have investigated different neural network layers to combine individual frames, particularly AveragePooling, MaxPooling, Product, Flatten, LSTM, and Bidirectional LSTM layers. We have observed that only some of the approaches are suitable for the task at hand.
Towards Reliable Network Entity Tracking Using Behavioural Bag of Words Representation

: In this paper, we study the problem of entity identiﬁcation and tracking in the domain of network security. The ability to uniquely identify and track network entities in time is essential for network behavioural analytics. Our approach leverages the Bag of Words (BoW) representation, enabling us to build representations from many different features from multiple data sources. However, normalisation methods traditionally used with BoW in other application domains (e.g. tf-idf, stop words) do not work well with network data as they are not designed to capture behavioral patterns. Some features, such as common networks servers (e.g. google.com, update.microsoft.com), or executed binaries (e.g. web browsers), are often too frequent but still valuable behavioural indicators. In order to capture important long-term patterns in entities’ behaviour, we introduce time-aware normalisation of the BoW representations. We compare different representations for device tracking on real network telemetry. Our results show that using multiple data sources signiﬁcantly improves entity tracking, especially when combined with proposed time-aware normalisation.
2-adic Fuzzy Partitions and Multi-Scale Representation of Time Series

: We are focused on a new method of time series analysis that is based on the extraction of representative keypoints. We use the multi-scale theory based on the use of non-traditional kernels derived from the theory of F-transforms. The sequence of kernels corresponds to what is called as 2-adic fuzzy partitions. This leads to simpli-ﬁed algorithms and comparable efﬁciency in the selection of keypoints. We reduce the number of representative key-points and enhance robustness of their selection. We also propose a new keypoint descriptor and test it on matching ﬁnancial time series with high volatility.
Tackling Generation of Combat Encounters in Role-playing Digital Games

: Procedural content generation (PCG) has been used in digital games since the early 1980s. Here we focus on a new problem of generating personalized combat encounters in role playing video games (RPG). A game should provide a player with combat encounters of adequate difﬁculties, which ideally should be matching the player’s performance in order for a game to provide adequate challenge to the player. In this paper, we describe our own reinforcement learning algorithm that estimates difﬁculties of combat encounters during game runtime, which can be them used to ﬁnd next suitable combat encounter of desired difﬁculty in a stochastic hill-climbing manner. After a player ﬁnishes the encounter, its result is propagated through the matrix to update the estimations of not only the presented combat encounter, but also similar ones. To test our solution, we conducted a preliminary study with human players on a simpliﬁed RPG game we have developed. The data collected suggests our algorithm can adapt the matrix to the player performance fast from little amounts of data, even though not precisely.
Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data

Deep learning (DL) techniques have been successful in many applications, with the most impressive results achieved on problems where the dimension of the underlying data or problem domain is large. In this paper, we describe recent results on both scalar-and Hilbert-valued function approximation via Deep Neural Networks (DNN). This problem arises in many engineering problems, in particular those involving the solution of parametric Partial Differential Equations (PDEs). Such problems are challenging, since point-wise samples are expensive to acquire, and the function is usually high-dimensional. First, we consider a DNN architecture and training procedure for which the resulting DNN is guaranteed to perform as well as current best-in-class schemes for holo-morphic, scalar-or Hilbert-valued functions based on polynomial approximations. This result demonstrates the efﬁcacy of DL for this problem, and makes explicit the effect of all sources of error, including discretization error of the underlying Hilbert space and measurement error. Second, we provide several numerical results illustrating the performance of DNNs on both real-valued functions and solutions of parametric PDEs. These results suggest that better approximations can be achieved through careful tuning of the DNN architecture and training algorithm.
A Brief Introduction Into Activation-Based Conditional Inference

Activation-based conditional inference integrates several aspects of human reasoning into formal conditional reasoning, such as focusing, forgetting, and remembering, by combining conditional reasoning and the cognitive architecture ACT-R . The idea is to select a reasonable subset of a conditional belief base before drawing inferences. The selection is based on an activation function which assigns to the conditionals in the belief base a degree of activation based on the conditional’s relevance for the current query and its usage history.
Better discrimination of the concomitant peri-ankle fractures in the spiral tibial shaft fractures by thin-slice axial and three-dimensional CT

Abstract The objective of this study was to examine the morphologic features of spiral tibial shaft as well as concomitant fibular and peri-ankle fractures on multidetector high-resolution CT and to speculate about the mechanisms underlying these combined fractures. This is a retrospective cohort study. A total of 197 tibial shaft fractures underwent multidetector high-resolution CT before intramedullary nailing. The presence and location of peri-ankle fractures were recorded using thin-slice axial CT. Tibial shaft fractures were classified as spiral or non-spiral. The morphologies of spiral tibial fractures and concomitant lateral malleolar fractures were delineated using three-dimensional CT. Seventy-five spiral and 122 non-spiral fractures were identified. Peri-ankle fractures excluding lateral malleolar fractures were found in 77.3% of spiral fractures and 18.9% of non-spiral fractures. The most frequent location of peri-ankle fractures in the spiral group was the posterior malleolus, followed by the anterolateral distal tibia, while the medial malleolus was the most frequent site in the non-spiral group. Of 75 spiral fractures, 72 showed a fracture morphology attributed to external rotation force. There were 13 lateral malleolar fractures that were defined as within 6 cm from the distal end of the fibula. No lateral malleolar fractures showed the typical morphology of isolated supination/external rotation-type ankle injuries. The displaced syndesmotic injuries commonly coexisting in pronation/external rotation-type ankle injuries were not observed. Most spiral tibial shaft fractures were caused by external rotation force. However, the morphology of concomitant peri-ankle fractures was inconsistent with typical mechanisms of isolated external rotation ankle injuries.