CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning

The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they ignore the fact that people are easily influenced by the recent actions of other users in the contextual scenario, and applying evolution across all historical interactions dilutes the importance of recent ones, thus failing to model the evolution of dynamic interest accurately. To address this issue, we propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios by creating three representations for each user and item under different dynamics: static embedding, historical temporal states, and contextual temporal states. To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization. Within the PMTL paradigm, CPMR employs a shared-bottom network to conduct the evolution of temporal states across historical and contextual scenarios, as well as the fusion of them at the user-item level. In addition, CPMR incorporates one real tower for incremental predictions, and two pseudo towers dedicated to updating the respective temporal states based on new batches of interactions. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines and achieves significant gains on three of them. The code is available at: https://github.com/DiMarzioBian/CPMR.Comment: Accepted by CIKM 2023. Alias: "Modeling Context-Aware Temporal Dynamics via Pseudo-Multi-Task Learning

Identifying Keys to Success in Innovative Teaching: Student Engagement and Instructional Practices as Predictors of Student Learning in a Course Using a Team- Based Learning Approach

When implementing innovative teaching techniques, instructors often seek to gauge the success of their methods. Proposing one approach to assessing classroom innovation, this study examines the ability of students’ ratings of engagement and instructional practices to predict their learning in a cooperative (team-based) framework. After identifying the factor structures underlying measures of student engagement and instructional practices, these factors were used as predictors of self-reported student learning in a general chemistry course delivered using a team-based learning approach. Exploratory factor analyses showed a four- factor structure of engagement: teamwork involvement, investment in the learning process, feelings about team-based learning, level of academic challenge; and a three-factor structure of instructional practices: instructional guidance, fostering self-directed learning skills, and cognitive level. Multiple linear regression revealed that feelings about team-based learning and perceptions of instructional guidance had significant effects on learning, beyond other predictors, while controlling gender, GPA, class level, number of credit hours, whether students began college at their current institution, expected highest level of education, racial or ethnic identification, and parental level of education. These results yield insight into student perceptions about team-based learning, and how to measure learning in a team-based learning framework, with implications for how to evaluate innovative instructional methods

Effect of different types of fibers on the microstructure and the mechanical behavior of Ultra-High Performance Fiber-Reinforced Concretes

International audienceThis study investigates the effect of adding different types of fibers on the microstructure and the mechanical behavior of cementitious composites, in particular on UHPC. These fibers were distinguished mainly by their differing nature (steel, mineral and synthetic), their dimensions (macroscopic or microscopic), and their mechanical properties. The microstructure of the specimens was examined by using SEM observation and by measuring the porosity, the intrinsic permeability and the P-wave velocity. The mechanical behavior under loading has been studied using a uni-axial compression test which combines the gas permeability and the acoustic emission (AE) measurement. This work focuses on the cracking process under mechanical loading. The experimental results show that the fiber has a relatively slight influence on the compressive strength and elastic modulus of concrete, except for the steel fiber which improves the strength because of its intrinsic rigidity. However, The addition of fiber significantly reduces the lateral strain at peak loading and increases the threshold of initial cracking (σk-ci) and that of unstable cracking (σk-pi). Therefore, the fibers clearly restrain the cracking process in concrete under the mechanic loadin

Effects of thermal damage on physical properties and cracking behavior of ultrahigh-performance fiber-reinforced concrete

International audienceIn this work, we study the impact of thermal damage on the physical and mechanical properties of ultrahigh-performance fiber-reinforced concrete (UHPFRC), especially on their cracking process under compressive loading. Four mixtures of UHPFRC were prepared using identical composition but reinforced with different types of fibers: mineral fibers (Steel or Wollastonite) or organic fibers (PP or PVA) and compared with that without fibers (UHPC). To induce a thermal damage on UHPFRC, the samples were subjected to temperatures ranging from 150 to 400 °C. After each degradation stage, the gas permeability and the P-wave velocity were measured. The mechanical behavior under loading has been studied using a uniaxial compression test which combines the gas permeability and the acoustic emission measurement. The results show that the melting of organic fibers at approximately 180 °C builds a tunnel across the cement paste and increases brutally the gas permeability. At 400 °C treatment, a decrease of compression strength by 30 % and of Young modulus by approximately 60 % was observed. However, we can see that the thermal damage results a decrease in the threshold of initial cracking (rk-ci) and that of unstable cracking (rk-pi), and this can be explained by the initiation of new cracks and their coalescence

Retrieval-Augmented Classification with Decoupled Representation

Retrieval augmented methods have shown promising results in various classification tasks. However, existing methods focus on retrieving extra context to enrich the input, which is noise sensitive and non-expandable. In this paper, following this line, we propose a $k$-nearest-neighbor (KNN) -based method for retrieval augmented classifications, which interpolates the predicted label distribution with retrieved instances' label distributions. Different from the standard KNN process, we propose a decoupling mechanism as we find that shared representation for classification and retrieval hurts performance and leads to training instability. We evaluate our method on a wide range of classification datasets. Experimental results demonstrate the effectiveness and robustness of our proposed method. We also conduct extra experiments to analyze the contributions of different components in our model.\footnote{\url{https://github.com/xnliang98/knn-cls-w-decoupling}}Comment: preprin

DEWP: Deep Expansion Learning for Wind Power Forecasting

Wind is one kind of high-efficient, environmentally-friendly and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing dramatically in recent years, the amount of generated wind power can be directly or latently affected by multiple uncertain factors, such as wind speed, wind direction, temperatures, etc. More importantly, there exist very complicated dependencies of the generated power on the latent composition of these multiple time-evolving variables, which are always ignored by existing works and thus largely hinder the prediction performances. To this end, we propose DEWP, a novel Deep Expansion learning for Wind Power forecasting framework to carefully model the complicated dependencies with adequate expressiveness. DEWP starts with a stack-by-stack architecture, where each stack is composed of (i) a variable expansion block that makes use of convolutional layers to capture dependencies among multiple variables; (ii) a time expansion block that applies Fourier series and backcast/forecast mechanism to learn temporal dependencies in sequential patterns. These two tailored blocks expand raw inputs into different latent feature spaces which can model different levels of dependencies of time-evolving sequential data. Moreover, we propose an inference block corresponding for each stack, which applies multi-head self-attentions to acquire attentive features and maps expanded latent representations into generated wind power. In addition, to make DEWP more expressive in handling deep neural architectures, we adapt doubly residue learning to process stack-by-stack outputs. Finally, we present extensive experiments in the real-world wind power forecasting application on two datasets from two different turbines to demonstrate the effectiveness of our approach.Comment: Accepted by TKD

Advanced ODE Based Head Modelling for Chinese Marionette Art Preservation

Puppetry has been a popular art form for many centuries in different cultures, which becomes a valuable and fascinating heritage assert. Traditional Chinese marionette art with over 2,000 years history is one of the most representative forms offering a mixture of stage performance of singing, dancing, music, poem, opera, story narrative and action. Apart from a set of string rules which controls the dynamics, head carving skill is another important pillar in this art form. This paper addresses the heritage preservation of the marionette head carving by digitalizing the head models with a novel modelling technique using ordinary differential equations (ODEs). The technique has been specially tailored to suit the modelling complexity and the need of accurate description of shapes. It offers smoothly sewing ODE swept patches to represent the distinct features of a marionette head with sharp variance of local geometry. Such features otherwise are difficult to model and capture accurately, which may require a great effort and tedious hand-crafting of an experienced modeller, when using other representation forms like polygons

Development and application of the Chinese version of the adult strabismus quality of life questionnaire (AS-20): a cross-sectional study

Abstract Background Patients with strabismus experience visual dysfunction, self-image disorders, low self-esteem, and social and emotional barriers, which adversely influence their health-related quality of life (HRQoL). Currently no strabismus-specific questionnaire is available in China to identify patients’ quality of life and to evaluate the effectiveness of strabismus treatment. The aims of the present study were to validate the Chinese-language version of the Adult Strabismus Quality of Life Questionnaire (AS-20) and to evaluate the impacts of strabismus on the quality of life among Chinese strabismus patients. Methods Two hundred and fifty-five Chinese adults with strabismus, one hundred visually normal adults and one hundred patients with other eye diseases completed the Chinese version of AS-20. Psychometric properties of the Chinese AS-20 were examined by Cronbach’s α coefficient, test-retest and split-half reliability, and construct and criterion-related validity. Independent-samples t test and one-way ANOVA analyses were conducted to explore the impact of demographic factors and clinical characteristics on HRQoL in Chinese strabismic adults. Results The final AS-20 in Chinese (AS-C) included 18 items and two subscales: psychosocial (12 items) and function (6 items). The Cronbach’s α was 0.908 for overall scale, with 0.913 and 0.808 for \u27psychosocial’ and \u27function’ subscales respectively, indicating high internal consistency reliability. The mean of the overall AS-C score among strabismus patients was 62.80 ± 18.94, significantly lower than that in visually normal adults (t = -18.693, P \u3c 0.001), and in patients with other eye diseases (t = -5.512, P \u3c 0.001). Conclusions The AS-C is a culturally appropriate tool to evaluate the HRQoL in Chinese strabismus adults. The psychosocial health well-being and overall quality of life in strabismic patients should receive greater emphasis