Miracle-dz/UniFAR-FTUs huggingface dataset:
GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution

Motivation: A large number of methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. These methods cannot be directly applied to single-cell data, since the temporal order of the cells is unknown. One way to address this challenge is to ﬁrst use pseudotime methods to order the cells, and then apply standard clustering techniques. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and/or over-conﬁdent gene clusters. Results: The proposed method, GPseudoClust, is a novel approach that both clusters genes for pseudotemporally ordered data and quantiﬁes the uncertainty in cluster allocations arising from the uncertainty in the pseudotime ordering. GPseudoClust combines a recent method for pseudo-time inference with nonparametric Bayesian clustering methods, eﬃcient MCMC sampling, and novel subsampling strategies. For branching data, GPseudoClust identiﬁes diﬀerences in dynamic patterns for diﬀerent branches. In an application to stimulated dendritic cells, we show that it categorises genes in a way consistent with known biological function. Furthermore, it integrates data from diﬀerent cell lines, batches or experimental protocols in a principled way. Availability: An implementation is available on GitHub: https://github.com/magStra/nonparametricSummaryPSM and https://github.com/magStra/GPseudoClust.
Mild Physical Activity Does Not Improve Spatial Learning in a Virtual Environment

It is well-established that physical exercise in humans improves cognitive functions, such as executive functions, pattern separation, and working memory. It is yet unknown, however, whether spatial learning, long known to be affected by exercise in rodents, is also affected in humans. In order to address this question, we recruited 20 healthy young male adults (18–30 years old) divided into exercise and control groups (n = 10 in each group). The exercise group performed three sessions per week of mild-intensity aerobic exercise for 12 weeks, while the control group was instructed not to engage in any physical activity. Both groups performed maximal oxygen uptake (VO2max) tests to assess their cardiovascular fitness at baseline and every 4 weeks through the 12 weeks of the training program. The effects of mild aerobic exercise were tested on performance in two different virtual reality (VR)-based spatial learning tasks: (1) virtual Morris water maze (VMWM) and (2) virtual Radial arm water maze (VRAWM). Subjects were tested in both tasks at baseline prior to the training program and at the end of 12 weeks training program. While the mild-intensity aerobic exercise did not affect subjects' VO2max parameters, mean time to anaerobic threshold increased for the exercise group compared with control. No effect was observed, however, on performance in the VMWM or VRAWM between the two groups. Based on these results, we suggest that mild-intensity aerobic exercise does not improve spatial learning and memory in young, healthy adults.
Mood Responses Associated With COVID-19 Restrictions

The COVID-19 pandemic resulted in more than half the world’s population being placed in lockdown to stem the spread of the virus. The severe restrictions imposed in many nations had the potential to significantly influence the physical and psychological well-being of those affected. The aim of the current study was to investigate mood responses during the period of restrictions from March to June, 2020. Mood responses of 1,062 participants (386 male, 676 female) were collected using the Brunel Mood Scale, hosted on the In The Mood website www.moodprofiling. com. The mean pattern of mood responses reflected an inverse iceberg profile, characterized by significantly elevated scores for tension, depression, anger, fatigue, and confusion, and below average scores for vigor; a profile associated with increased risk of mental health issues. Females reported more negative mood scores than males. Participants in the ≤25 age group reported the most negative profiles whereas those in the ≥56 age group reported the least negative profiles. Mood differences related to education status were also evident. Finally, mood scores fluctuated over time, with profiles being most negative during April and June. Overall, results confirmed significant mood disturbance during the period of COVID-19 restrictions, representing increased risk of psychopathology.
A Review of Methods to Determine Viability, Vitality, and Metabolic Rates in Microbiology

Viability and metabolic assays are commonly used as proxies to assess the overall metabolism of microorganisms. The variety of these assays combined with little information provided by some assay kits or online protocols often leads to mistakes or poor interpretation of the results. In addition, the use of some of these assays is restricted to simple systems (mostly pure cultures), and care must be taken in their application to environmental samples. In this review, the necessary data are compiled to understand the reactions or measurements performed in many of the assays commonly used in various aspects of microbiology. Also, their relationships to each other, as metabolism links many of these assays, resulting in correlations between measured values and parameters, are discussed. Finally, the limitations of these assays are discussed.
Dynamic Analysis and Circuit Realization of a Novel No-Equilibrium 5D Memristive Hyperchaotic System with Hidden Extreme Multistability

In this paper, a novel no-equilibrium 5D memristive hyperchaotic system is proposed, which is achieved by introducing an ideal flux-controlled memristor model and two constant terms into an improved 4D self-excited hyperchaotic system. The system parameters-dependent and memristor initial conditions-dependent dynamical characteristics of the proposed memristive hyperchaotic system are investigated in terms of phase portrait, Lyapunov exponent spectrum, bifurcation diagram, Poincaré map, and time series. Then, the hidden dynamic attractors such as periodic, quasiperiodic, chaotic, and hyperchaotic attractors are found under the variation of its system parameters. Meanwhile, the most striking phenomena of hidden extreme multistability, transient hyperchaotic behavior, and offset boosting control are revealed for appropriate sets of the memristor and other initial conditions. Finally, a hardware electronic circuit is designed, and the experimental results are well consistent with the numerical simulations, which demonstrate the feasibility of this novel 5D memristive hyperchaotic system.
Injecting Designers' Knowledge in Conversational Neural Network Systems

Sequence-to-sequence neural networks are redesigning dialog managers for Conversational AI in industries. However, industrial applications impose two important constraints: training data are often scarce and the behavior of dialog managers should be strictly controlled and certiﬁed. In this paper, we pro-pose the Conversational Logic Injected Neural Network (CLINN). This novel network merges dialog managers “programmed” using logical rules and a Sequence-to-Sequence Neural Network. We experimented with the Restaurant topic of the MultiWOZ dataset. Results show that injected rules are effective when training data set are scarce as well as when more data are available. 3
A Longitudinal Study on Student Persistence in Programming Self-assessments

Self-assessment is an educational practice that helps students evaluate their own learning by distributed practices. This evaluation potentially has an eﬀect on student’s self-eﬃcacy and therefore can inﬂuence their choice of activities and the likelihood of their success. The variation in the self-assessing behavior of students over the course of learning is often less explored. For instance, a student’s short-term behavior may not necessarily infer how they will behave in the long-term. It is unclear how such development in their self-assessing behavior is related to academic performance and the corresponding self-assessment strategies. This longitudinal study aims to ﬁll the gap by examining a self-assessment platform used in an introductory programming class from three diﬀerent semesters. We analyzed the activity logs and modeled students’ short-term and long-term study persistence on the platform using a probabilistic mixture model. The results suggest that short-term persistence was not re-lated to short-term performance. However, the performance in the ﬁnal exam was associated with earlier persistence patterns. A further analysis showed that low-performing students who maintained the self-assessment pattern improved in exams. Nevertheless, this longitudinal study contributes empirical evidence to the understanding of the development of self-assessment behavior in relation to academic performance.
Semisupervised Community Preserving Network Embedding with Pairwise Constraints

Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints (must-link and cannot-link) information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones.
Knowledge Distillation Techniques for Biomedical Named Entity Recognition

The limited amount of annotated biomedical literature and its peculiar characteristics make biomedical named entity recognition more challenging than standard named entity recognition. The multi-task learning approach overcomes these limitations by training diﬀerent related tasks simultaneously. It learns common features among diﬀerent tasks by sharing some layers of the neural network architecture. For this reason, the multi-task model attains more generalization properties than a single task learning. The generalization of the multi-task model can be utilized to enhance other models’ results. In particular, knowledge distillation techniques make this possible in which one model supervises, through its learned generalization, another model during the training. This research analyzes the knowledge distillation approach and shows that a simple deep learning model performance can be leveraged through distilling the multi-task model’s generalization. Results show that our approach outperformed compared with the multi-task model and single task model. This demonstrates that our model learns more diverse features using the knowledge distillation approach. We also found our approach statistically better than multi-task model and single task model.
On the Use of an Improved Artificial Fish Swarm Algorithm-Backpropagation Neural Network for Predicting Dam Deformation Behavior

Dam behavior is difficult to predict due to its complexity. At the same time, dam deformation behavior is vital to dam systems. Developing a precise prediction model of dam deformation from prototype data is still challenging but determinant in the structural safety assessment. In this paper, an artificial neural network (ANN), trained by the improved artificial fish swarm algorithm (IAFSA) and backpropagation algorithm (BP), is proposed for predicting the dam deformation. Initially, crossover operator is embedded into AFSA, which aims to enhance the performance. In light of the influence mechanism of many factors on dam deformation behavior, the hybrid (IAFSA and BP) model uses statistical input to obtain the optimal connection weights and threshold values of the neural network. The hybrid model integrates IAFSA’s strong global searching ability and BP’s strong local search ability. To avoid overfitting the training set data, a validation set is adopted to check the generalization capability. Subsequently, the obtained optimal parameters are applied to predict the dam deformation behavior. The hybrid model’s preciseness is verified against the radial displacements of a pendulum in a concrete arch dam and simulations of four models: statistical model, BP-ANN optimized by genetic algorithm (GA), particle swarm optimization (PSO), and AFSA. Results demonstrate that the proposed model outperforms other models and may provide alarms for safety control.
A Data-Driven Approach to Automatically Assessing Concept-Level CS Competencies Based on Student Programs

The rapid increase in demand for CS education has given rise to increased efforts to develop data-driven tools to support adaptive CS education. Automated assessment and personalized feedback are among the most important tools for facilitating effective learning experiences for novice students. An important first step in providing effective feedback tailored to individual students is assessing their areas of strength and weaknesses with regard to core CS concepts such as loops and conditionals. In this work, we propose a hypothesis-driven analytics approach to assessing students’ competencies of core CS concepts at a fine-grained level. We first label programs obtained from middle grades students’ interactions with a game-based CS learning environment featuring block-based programming, based on a rubric that was designed to assess students' competency in core CS concepts from their submitted programs. Then, we train a variety of regression models including linear, ridge, lasso, and support vector regression models, as well as Gaussian process regression models to infer students’ scores for each of the identified CS concepts. The evaluation results suggest that Gaussian process regression models often outperform other baseline models for predicting student competencies of core CS concepts with respect to mean squared error and adjusted coefficient of determination. Our approach shows significant potential to provide students with detailed, personalized feedback based on their inferred CS competency levels.
A Recommender System for Healthy and Personalized Recipes Recommendations

Unhealthy eating behavior is a serious public health issue with massive repercussions on an individual’s health. One potential solution to this problem is to help people change their eating behavior by developing systems able to recommend healthy recipes that can influence eating behavior. One challenge for such systems is to deliver healthy recommendations that take into account users’ needs and preferences, while also informing users about the healthiness of the recommended recipes. In this paper, we investigate whether introducing a healthy bias in a recipe recommendation algorithm, and displaying a healthy tag on recipe cards would have an influence on people’s decision making. To that end, we build three different recipes recommender systems: one that recommends recipes matching users’ preferences, another one that only recommends healthy recipes, and a third one that recommends recipes that are both healthy and match users’ preferences. We evaluate these three systems through a user study in which we asked participants online to select from a list of recipes the ones they like the most.
The Influence of Advertising Appeals on Consumers' Willingness to Participate in Sustainable Tourism Consumption

Sustainable consumption has attracted much attention from the tourism industry. However, from the perspective of academic research, the psychological mechanism of collaborative consumption has not been thoroughly understood as the main form of sustainable consumption. This study explores the impact of advertising appeals on consumers’ willingness to participate in collaborative consumption. Through two psychological experiments, it is believed that, relative to the rational appeal, the emotional appeal will positively affect consumers’ participation willingness to collaborative consumption, and psychological ownership mediates this relationship. Furthermore, the form of information presentation moderates the influence of advertising appeals on psychological ownership.
Machine Learning and Disk-based Methods for Qualitative Verification of Markov Decision Processes

State explosion is a well-known challenge of model checking. In this paper, we propose several approaches to improve the standard techniques for verifying the qualitative reachability properties of Markov decision processes. For the ﬁrst approach, we use two heuristics to reduce the total number of iterations of the standard iterative methods. The second approach uses the secondary hard disks for storing the information of a model and uses the main memory for a back-ward technique on the standard methods. The third approach uses a machine learning technique to classify the state space of a model to the related classes. While this approach does not need any memory overhead, its running time is much less than the running time of the standard approaches and can be used to cope with the state explosion problem.
Incorporating REDCap for Data Collection of K-12 CS Education Research

Over the course of the last two years, the resource center for the computer science educational community at cse-dresearch.org has been collecting evaluation instruments for measuring the impact of CS education primarily in primary and secondary schools. The CSEdResearch.org Resource Center provides over 100 surveys and protocols for use in re-searching and evaluating computer science education. Work has begun to expand the capability of the site so that researchers and evaluators can distribute the surveys directly on the site. This enhancement is a packaged plug-in called Research Electronic Data Capture (REDCap), a product of Vanderbilt University built under support from the National Institutes for Health (NIH) [2][3]. In this extended abstract, we present a summary of REDCap, the beneﬁts it can provide to our community, and how we intend to vet the tool before releasing it to the public.
The Procedures of Logistic Transport Systems Simulation into the Petri Nets Environment

The article presents the methodology of the logistic system simulation in the Petri Nets environment on the example of agro-products delivery from Ukraine to the Netherlands, as well as a set of case-studies that can be used as didactic means for course "The modeling of transport processes" to teach the future transport industry specialists. The content, features and capabilities of the Petri Nets in the logistics systems simulation have been analyzed. A structural logistic system model of agro-products delivery, which takes into account the operation sequence, the combination peculiarities and the use resources in each subsystem, has been constructed into the Petri Nets. A time criterion has been proposed to determine the effective option for agro-products delivery. According to the results of the logistics system simulation in the Petri Nets environment, a statistical analysis of the parameters has been carried out. It has been made a choice of a suitable option of the delivery scheme allowing for the time spent and the magnitude of the possible costs associated with the use of certain transport modes. It has been made conclusion about feasibility of creating the case-study set, which is based on real facts and being solved into the Petri Nets environment, in order to organize the future transport industry specialists' study and research activities.
Development of Software for Developing Information Systems Design Skills Considering Issues for Visually Impaired People Inclusion

The article is devoted to the problem of development information systems design skills considering issues for visually impaired people inclusion. It is established that the known training tools for information systems design are based on the principle of visualization. This makes it impossible to study the design of information systems for visually impaired people and requires the search for new means of education for the design of information systems. The information system has been developed for improving the design skills of the information systems for people with visual impairments.
Future Fetch - Towards a Ticket-based Data Access from Secondary Storage in Database Systems

When accessing data from a database, a database management system has to accomplish many tasks. Checking the conformity of the query, generating a reasonably good query plan and executing it in a concurrent system are just a few of them. During query execution, there are some points at which the main execution thread must stop and wait for data. This synchronous waiting can have a major impact on the overall query performance. In this paper we introduce the idea of a ticket-based fetch, which supports the database management system by waiting for data asynchronously. This can improve the query-execution performance in database systems.
Sense Tree: Discovery of New Word Senses with Graph-based Scoring

Language is dynamic and constantly evolving: both the usage context and the meaning of words change over time. Identifying words that acquired new meanings and the point in time at which new word senses emerged is elementary for word sense disambiguation and entity linking in historical texts. For example, cloud once stood mostly for the weather phenomenon and only recently gained the new sense of cloud computing . We propose a clustering-based approach that computes sense trees , showing how meanings of words change over time. The produced results are easy to interpret and explain using a drill-down mechanism. We evaluate our approach qualitatively on the Corpus of Historic American English (COHA), which spans two hundred years.
Visualizing the Behaviour of CBR Agents in a FPS Scenario

The analysis and visualization of agent behavior enables a developer to identify unexpected or faulty behaviors and can show room of improvement. Therefore, visualization tools can be helpful to analyze behaviors during or after simulations. This paper presents a visualization tool VISAB developed for analyzing and visualizing the movement and actions of CBR agents in a ﬁrst-person scenario. We describe the settings of the scenario and in more detail the visualization possibilities to get a better understanding of agent behavior during game-play.