Interesting Task Research Articles

Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction

Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to characterize the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here, we address these fundamental questions by applying machine learning methods and functional magnetic resonance imaging (fMRI) to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from processes other than deception. Specifically, we demonstrate that discriminant validity is compromised by the predictor's ability to predict behavior in a control task that does not involve deception. Second, we show that the presence of confounding signals need not be fatal and that the validity of the neural predictor can be improved by removing confounding signals while retaining those associated with the task of interest. To this end, we develop a "dual-goal tuning" approach in which, beyond the typical goal of predicting the behavior of interest, the predictor also incorporates a second compulsory goal that enforces chance performance in the control task. Together, these findings provide a firmer scientific foundation for understanding the neural basis of a neglected class of behavior, and they suggest an approach for improving validity of neural predictors.

A blockchain consensus mechanism for real-time regulation of renewable energy power systems

With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance. Here, we present a consensus mechanism that enables real-time security control of systems, called Proof of Task. Instead of solving meaningless hash puzzles in Proof of Work, Proof of Task addresses problems closely related to the stable operation and control performance of these systems. With the proposed verification mechanism, Proof of Task significantly enhances the real-time performance of blockchain while mines its computational resources for tasks of interest. To demonstrate the effectiveness and necessity of Proof of Task, it is deployed across three renewable energy power systems. The results show that Proof of Task markedly fortifies the security and computing capability of these systems, ensuring their reliable and stable operation. This work highlights the promise of blockchain to facilitate security control and trusted computing of large-scale, complex-dynamic systems.

Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, finance, healthcare and climatology. Analyzing the underlying structures, i.e. , the causal relations, could be extremely valuable for various applications. Recently, causal discovery from temporal data has been considered as an interesting yet critical task and attracted much research attention. According to the nature and structure of temporal data, existing causal discovery works can be divided into two highly correlated categories i.e. , multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the multivariate time series causal discovery but ignore the second category. In this paper, we specify the similarity between the two categories and provide an overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.

Hardness of Approximate Diameter: Now for Undirected Graphs

Approximating the graph diameter is a basic task of both theoretical and practical interest. A simple folklore algorithm can output a 2-approximation to the diameter in linear time by running BFS from an arbitrary vertex. It has been open whether a better approximation is possible in near-linear time. A series of papers on fine-grained complexity have led to strong hardness results for diameter in directed graphs, culminating in a recent tradeoff curve independently discovered by [Li, STOC’21] and [Dalirrooyfard and Wein, STOC’21], showing that under the Strong Exponential Time Hypothesis (SETH), for any integer k ≥ 2 and δ > 0, a \(2-\frac{1}{k}-\delta \) approximation for diameter in directed m -edge graphs requires m 1 + 1/( k − 1) − o (1) time. In particular, the simple linear time 2-approximation algorithm is optimal for directed graphs. In this paper we prove that the same tradeoff lower bound curve is possible for undirected graphs as well, extending results of [Roditty and Vassilevska W., STOC’13], [Li’20] and [Bonnet, ICALP’21] who proved the first few cases of the curve, k = 2, 3 and 4, respectively. Our result shows in particular that the simple linear time 2-approximation algorithm is also optimal for undirected graphs. To obtain our result, we extract the core ideas in known reductions and introduce a unification and generalization that could be useful for proving SETH-based hardness for other problems in undirected graphs related to distance computation.

Effect of Gamification Combined with Mobile Learning on Chinese Language Speaking Skills of Second-Year Foreign Students at Guangdong University of Foreign Studies

Background and Aims: In the era of the mobile internet, transforming language learning content into interesting game tasks could stimulate students' interest in learning and improve engagement and learning outcomes. The study aimed to evaluate the impact of gamification combined with a mobile learning approach on students' Chinese language speaking skills and satisfaction. The significance of the study was to innovate teaching methods and improve oral expression. Students were guided to participate in interactive learning by designing gamified learning tasks with clear objectives. Methodology: This study utilized a pre-experimental design featuring a one-group pretest-posttest model. The sample of this study was 30 second-year foreign students studying at Guangdong University of Foreign Studies derived from a cluster random sampling method. The research tools were (1) Course lesson plan, (2) Chinese language speaking skill test, and (3) Student satisfaction questionnaire. Conduct pre-test and post-test on the sample using the Chinese speaking proficiency test, and analyze the mean, standard deviation, paired sample t-test, and one sample t-test of the data using statistics. Result: After using gamification combined with mobile learning, students' Chinese language speaking skills improved by 13.75%, surpassing the predetermined criteria of 70% and the difference was statistically significant at 0.05. Based on the scores from the statistical student satisfaction questionnaire, the overall results of Gamification combined with mobile learning by students are at a high level with (M=4.02, S.D. = 0.77) Conclusion: The study confirms that learning management using gamification combined with mobile learning significantly enhances Chinese language speaking skills it stimulates students' interest in learning, develops their independent learning ability, boosts their motivation and initiative, and caters to their individual learning needs. This is a new trend in future education.

A Machine Learning Model Based on Thyroid US Radiomics to Discriminate Between Benign and Malignant Nodules.

Background/Objectives: Thyroid nodules are a very common finding, mostly benign but sometimes malignant, and thus require accurate diagnosis. Ultrasound and fine needle biopsy are the most widely used and reliable diagnostic methods to date, but they are sometimes limited in addressing benign from malignant nodules, mainly with regard to ultrasound, by the operator's experience. Radiomics, quantitative feature extraction from medical images and machine learning offer promising avenues to improve diagnosis. The aim of this work was to develop a machine learning model based on thyroid ultrasound images to classify nodules into benign and malignant classes. Methods: For this purpose, images of ultrasonography from 142 subjects were collected. Among these subjects, 40 patients (28.2%) belonged to the class "malignant" and 102 patients (71.8%) belonged to the class "benign", according to histological diagnosis from fine-needle aspiration. This image set was used for the training, cross-validation and internal testing of three different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic feature could capture the disease heterogeneity among the two groups. Three models consisting of four ensembles of machine learning classifiers (random forests, support vector machines and k-nearest neighbor classifiers) were developed for the binary classification task of interest. The best performing model was then externally tested on a cohort of 21 new patients. Results: The best model (ensemble of random forest) showed Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) (%) of 85 (majority vote), 83.7 ** (mean) [80.2-87.2], accuracy (%) of 83, 81.2 ** [77.1-85.2], sensitivity (%) of 70, 67.5 ** [64.3-70.7], specificity (%) of 88, 86.5 ** [82-91], positive predictive value (PPV) (%) of 70, 66.5 ** [57.9-75.1] and negative predictive value (NPV) (%) of 88, 87.1 ** [85.5-88.8] (* p < 0.05, ** p < 0.005) in the internal test cohort. It achieved an accuracy of 90.5%, a sensitivity of 100%, a specificity of 86.7%, a PPV of 75% and an NPV of 100% in the external testing cohort. Conclusions: The model constituted of four ensembles of random forest classifiers could identify all the malignant nodes and the consistent majority of benign in the external testing cohort.

Criticality-enhanced precision in phase thermometry

Temperature estimation of interacting quantum many-body systems is both a challenging task and topic of interest in quantum metrology, given that critical behavior at phase transitions can boost the metrological sensitivity. Here we study noninvasive quantum thermometry of a finite, two-dimensional Ising spin lattice based on measuring the dephasing dynamics of a spin probe coupled to the lattice. We demonstrate a strong critical enhancement of the achievable precision in terms of the quantum Fisher information, which depends on the coupling range and the interrogation time. Our numerical simulations are compared to instructive analytic results for the critical scaling of the sensitivity in the Curie-Weiss model of a fully connected lattice and to the mean-field description in the thermodynamic limit, both of which fail to describe the critical spin fluctuations on the lattice the spin probe is sensitive to. Phase metrology could thus help to investigate the critical behavior of finite many-body systems beyond the validity of mean-field models. Published by the American Physical Society 2024

Universality laws for Gaussian mixtures in generalized linear models*

Abstract Let ( x i , y i ) i = 1 , … , n denote independent samples from a general mixture distribution ∑ c ∈ C ρ c P c x , and consider the hypothesis class of generalized linear models y ^ = F ( Θ ⊤ x ) . In this study, we investigate the asymptotic joint statistics of a family of generalized linear estimators ( Θ 1 , … , Θ M ) obtained either from (a) minimizing an empirical risk R ^ n ( Θ ; X , y ) or (b) sampling from the associated Gibbs measure exp ⁡ ( − β n R ^ n ( Θ ; X , y ) ) . Our main contribution is to characterize under which conditions the asymptotic joint statistics of this family depends (in a weak sense) only on the means and covariances of the class conditional features distribution P c x . In particular, this allows us to prove the universality of different quantities of interest, such as the training and generalization errors, redeeming a recent line of work in high-dimensional statistics working under the Gaussian mixture hypothesis. Finally, we discuss the applications of our results in different machine learning tasks of interest, such as ensembling and uncertainty quantification.

Simulation modeling of artillery operations in computer games: approach based on Markov processes

The object of this research is an approach to simulating the combat operation of artillery in computer games based on Markov processes. In modern computer games, an important role is played by the realism and plausibility of combat simulation. One of the most difficult and at the same time most interesting tasks is the modeling of artillery operations, where it is necessary to take into account numerous factors affecting the effectiveness of combat work. The research was aimed at improving the methods and models of controlling the combat work of artillery under the conditions for the firing position change and the presence of external disturbances. The use of stochastic models allows more accurate modeling of the behavior of artillery units, taking into account the random nature of many parameters, such as projectile speed, reload time, and the probability of detection by enemy forces. The proposed approach includes the development of a simulation model that allows determining optimal strategies to achieve maximum effectiveness of combat work. The model is based on Markov processes, which allows taking into account possible system states and probable transitions between them. This allows not only to simulate combat operations, but also to predict the results depending on different scenarios. The results of the study show that the use of Markov processes in the simulation of combat operations can significantly increase the realism and efficiency of artillery operations in computer games. This opens up new opportunities for game developers to create more immersive and authentic gaming experiences. The proposed model can be used as a basis for further research and improvement of combat simulation methods in computer games. It can also be used in military simulators and simulators where realistic combat conditions must be taken into account.

Tubulin Homologs in Bacteria and Archaea

While cytoskeletal proteins have long been considered to be present only in eukaryotes, but not in prokaryotes, homologs of the major cytoskeletal proteins, including tubulin, have been discovered in bacteria and archaea in the last 30 years. The properties of tubulin homologs, as well as of the cytoskeleton-like structures they form in prokaryotic cells, vary and differ significantly from the relevant properties of eukaryotic tubulins. The comparison of prokaryotic tubulin homologs with each other seems therefore to be an interesting task and thus is the goal of the current review. We consider such tubulin homologs found in bacteria and archaea as FtsZ, TubZ, PhuZ, BtubA/BtubB, CetZ, etc. The ability of various tubulin homologs to act as targets for pharmaceuticals, similar to the FtsZ protein, which is already a target for promising antibiotics, is also discussed.

평면도형의 이동에서 모양과 방향의 변화에 대한 학생의 이해

This study analyzes how the shape and direction of figures are addressed in relation to transformations (translation, reflection, and rotation) in elementary school textbooks from the 7th curriculum to the 2015 revised curriculum, and investigates 5th-grade students' understanding of shape and direction through a test. The results revealed that students interpreted the meaning of shape inconsistently, varying their understanding based on the given context. In tasks requiring students to identify whether the shape or direction of a figure remained unchanged after reflection or rotation, their responses were influenced by the symmetry of each figure. Based on these results, this study discusses the issues inherent in the linguistic expressions and activities related to shape and direction in the textbooks. The conclusion suggests that, for 3rd and 4th graders, the focus should be on observing various combinations of translation, reflection, and rotation, and engaging in art-related activities or interesting problem-solving tasks. Activities that involve identifying whether shapes are the same should be more thoroughly addressed in the 5th and 6th grades, alongside the concept of congruence.

THE IMAGE OF KAZAKH ZOO-PHRASEOLOGISMS IN TRANSLATION (on the basis of novel “The Nomads” by I. Esenberlin in Kazakh, Russian and English languages)

The article considers the translation problems of zoo-phraseologisms of Kazakh language. The translation problem is an actual and complex theme and translation of phraseological units is an interesting task which needs special creativity. The main objective of a translator is to find phraseologism in a sentence and distinguish its ethocultural peculiarities and translation of such a phraseological unit keeping their national features is a rather complicated problem. That is why the actuality and the importance of the given articlegroup with these problems. Phraseological units of ethnocultural character does not replicate in the second language, and cannot be found in dictionaries. Therefore the scientific novelty of the article is the identification and analysis of translation methods of phraseological units of ethnocultural character which describe the culture, lifestyle and worldview of definite people. The article aims to make definite conclusions according to the results of research work by analyzing the methods of translating phraseological units appropriate to the definite nation, defining the failures and successes of translation.The translation methods of Kazakh phraseologisms into Russian and English languages are compared and analysed on the basis of the trilogy “The Nomads” by I. Essenberlin. Using the phraseologisms with zoo-component as the main material, the ways of translation (comparative, descriptive methods) were analysed. The meaning of them can be different while translating into other language, and so translators should try to keep the meaning of the word combination correctly or find the right analogue to make the reader understand the translated phrasal combination. Thepractical significance of the research with scientific value can be used at the courses of Lexicology, Phraseology, The theory and practice of translation, and as the lexical material in subjects as Linguocultorology and Linguistic analysis of literary texts and to compile dictionaries in higher education institutes.

The Moderating Role of Interest in the Relationship between Perceived Task Difficulty and Invested Mental Effort

Including motivational variables such as interest in the cognitive load framework is an ongoing process. Of particular interest is the question of how motivational variables influence the investment of mental effort. In this study, we investigated how topic interest affects the investment of mental effort in simple tasks. A total of 1543 students’ judgments regarding invested mental effort, perceived task difficulty, and topic interest for 32 tasks of a chemistry test were analyzed at the task level based on item response theory parameters. Additionally, objective task difficulty was calculated. The Rasch parameters were used for correlation and moderated regression analyses. The results indicated that when perceived task difficulty was low, students invested more mental effort in solving tasks of low topic interest compared to tasks of high topic interest. With increasing perceived task difficulty, the amount of invested mental effort rose for tasks of low as well as high topic interest. However, the difference between tasks of low and high topic interest in the amount of invested mental effort decreased as perceived task difficulty increased and even vanished when perceived task difficulty roughly corresponded to students’ performance capability. These results are in line with flow theory and the expectancy-value-cost model of motivation. When solving tasks that match their performance capability, students can experience a flow situation. However, when solving rather easy tasks of low interest, students can experience motivational costs in terms of additional effort, such as an increased need for motivational self-regulation. The results of this study provide a basis for systematically investigating and better understanding the relationship between interest, task difficulty, invested mental effort, flow experience, and emotional costs.

Further results on enumeration of perfect matchings of Cartesian product graphs

Abstract Counting perfect matchings is an interesting and challenging combinatorial task. It has important applications in statistical physics and chemistry. As the general problem is #P-complete, it is usually tackled by randomized heuristics and approximation schemes. Let G G and H H be two graphs. Denote by G × H G\times H the Cartesian product of graphs G G and H H . The number of perfect matchings of some types of Cartesian product G × H G\times H is investigated by the Pfaffian method, where G ≅ P 2 G\cong {P}_{2} , P 3 {P}_{3} , P 4 {P}_{4} or C 4 {C}_{4} , and H ≅ T H\cong T or H H is a non-bipartite graph with a unique cycle. In this article, we construct a Pfaffian orientation of the graph G × P 2 G\times {P}_{2} , where G G is a non-bipartite graph with k k odd cycles. We obtain the counting formula on the number of perfect matchings of G × P 2 G\times {P}_{2} .

Extremal Trees for Logarithmic VDB Topological Indices

Vertex-degree-based (VDB) topological indices have been applied in the study of molecular structures and chemical properties. At present, the exponential VDB index has been studied extensively. Naturally, we began to consider the logarithmic VDB index lnTf. In this paper, we first discuss the necessity of a logarithmic VDB index, and then present sufficient conditions so that Pn and Sn are the only trees with the smallest and greatest values of lnTf(T). As applications, the minimal and maximal trees of some logarithmic VDB indices are determined. Through our work, we found that the logarithmic VDB index lnTf has excellent discriminability, but the relevant results are not completely opposite to the exponential VDB index. The study of logarithmic VDB indices is an interesting but difficult task that requires further resolution.

Designing a utility evaluation framework for synthetic health data

ObjectivesSynthetic data (SD) promises to unlock health data for training, research, and innovation. However, where utility evaluation is performed, it is applied ad-hoc for a single task of interest. We produce an initial design for a robust benchmark across a range of tasks. ApproachWe undertook several projects as a prototyping experiment to gather requirements. These projects replicate previous studies performed on the Medical Information Mart for Intensive Care — a dataset used in more than 4,000 studies. We refine definitions, identify personas, draft a user statement, and collect requirements. ResultsDefinitions: We define utility as an extrinsic measure of SD on a larger system, most often through comparison to system performance on real data. This contrasts with fidelity, which measures the accuracy of SD through direct comparison to real data. Personas: Data custodian, User of SD, SD researcher. User statement: As a technical stakeholder, I need a reliable way to measure the utility of datasets and a benchmark to compare generation techniques. Requirements: SD researchers can focus on generation not evaluation; Supports comparison and leaderboards; Based on relevant and applications; Comprehensive across study types and applications; Future proof for population research requiring linking. ConclusionWe propose the following design: Data pipelines follow an extract-generate-evaluate workflow. Study types include cross-sectional and longitudinal. Applications include predictive modelling and clinical research. This results in a comprehensive utility benchmarking suite that complements current frameworks for fidelity and privacy of SD.

Педагогические условия развития творческого потенциала обучающихся на уроках литературы в основной школе

This article considers the possibilities of revealing the creative potential of students by creating special pedagogical conditions through the emotional impact on adolescents by means of fine arts with the help of performing a number of interesting, modern, unusual and unfamiliar tasks when studying a work of fiction. In the literature lessons, it is proposed to use forms of work aimed at improving the written language of students, developing their creative potential by activating imagination. The author presents the results of experimental work and draws conclusions about the feasibility of using the proposed pedagogical conditions in teaching practice. Fragments of creative works produced by students following the methods described in the article are provided, vividly demonstrating the outcomes of their application. The novelty of the research lies in its comprehensive approach to developing the creative potential of primary school students.

Predicting reaction rate constants of organic compounds with oxidants in the atmospheric aqueous-phase through multi-task learning

The atmospheric aqueous-phase chemistry has received increasing attention in the last decades for its non-negligible environmental significance. Yet, the insufficient experimental data on oxidative reaction rate constants (kaq) obstructs the further analysis and modeling of this system. Predictive models based on machine learning (ML) algorithms have shown potential as an effective estimation tool, however, they are restricted to the lack of training data as well. To overcome this data limitation, we developed multi-task (MT) models that could exploit the common knowledge from reactions in gas- and aqueous-phases simultaneously. Toward kaq of organic compounds with hydroxyl radical (OH), nitrate radical (NO3), and ozone (O3), the MT models showed a notably better predictive ability compared to benchmark models, while obtaining wide applicability on compounds from different chemical classes. By interpreting the models using Shapley additive explanations (SHAP), we evidenced that the MT models utilized the common knowledge in both phases and correctly identified the reaction mechanisms. This study aims to provide new insight into the estimation of necessary kinetic parameters in atmospheric aqueous-phase chemistry, as well as a reference to ML research for other predictive tasks of atmospheric interest.

IMPLEMENTATION OF STATISTICAL METHODS IN SOCIOLOGICAL RESEARCH USING INFORMATION TECHNOLOGY

Differential characteristics of survey are: the number of respondents, fairness and objectivity of received information; the purpose of the survey. The probabilistic mechanism of selection and special mathematical procedures, that provide the best objectivity, are rightful. Application of Likert scaling methods, factor analysis at determining the peculiarities of prospective students’ choice of higher educational establishments are studied in this article. The questionnaire was made according to received information. Respondent had to express the importance of each item on the six-point scale from “absolutely not important” to “very important”. Research objective was the description of the methodology, which allowsto consider the peculiarities of prospective students’ choice of higher educational establishments and analyzing actions of prospective students on the basis of beforehand included criteria. Research included preparatory, operational and resulting stages. Components that influence prospective student’s choice of higher educational establishment were found out at the first stage by means of factor analysis. In addition to these components as a result of factor analysis procedure it is now possible to develop a variety of research hypotheses. Their proof or refutation is an interesting application task that can be solved on closer detailed analysis of already gained statistic data or the pursuance of additional research. The feasibility of batch processing in SPSS was used to speed up the analysis process.

Age Group Discrimination via Free Handwriting Indicators.

Ageing is a physiological phenomenon associated with cognitive and functional decline which, in the long term, could hamper the execution of daily life activities and threaten both social and independent life. The onset of chronic diseases can intensify this process, increasing the risk of hospitalisation and admission to long term care. This represents a significant burden on public health and reduces the quality of life of those affected. Early detection of unhealthy decline is therefore key, but the similarity to normal ageing hinders its prompt screening. This study presents a first step towards the early screening of unhealthy ageing, based on an innovative instrumented ink pen to ecologically assess handwriting performance in different age groups: 40-59 (Group 1), 60-69 (Group 2) and 70+ (Group 3) years old. Raw handwriting data were collected from 60 healthy subjects and used to extract fourteen indicators related to gesture and tremor. The indicators were then used to discriminate between subjects of different age groups in three binary classification tasks, using a selection of machine learning algorithms. This approach produced remarkable results, particularly in the task of greatest interest, identifying subjects at the very beginning of the ageing process (Group 2) from elderly subjects (Group 3), achieving an accuracy of 97.5%, an F1 score of 97.44% and a ROC-AUC of 95%. Explainability of the model, facilitated by the analysis of the Shapley values of the learned indicators, revealed age-dependent sensitivity of handwriting and tremor-related indicators. The proposed method represents a promising solution for the early detection of abnormal signs of ageing, and is designed for the remote, non-invasive, unsupervised home monitoring, to improve the care of older adults.