Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Review to Know How Interventions Realized with Immersive Virtual Reality-Based Serious Games for Individuals with Autism are Evaluated

Today, the growing diffusion of Information Communication Technology and the concomitant system cost reductions encouraged, even more, the exploitation of innovative solutions, especially in the field of interventions for autism. The need for efficient and effective interventions for autism is becoming more pressing for several reasons ranging from the dramatic increase in its prevalence to the high direct and indirect costs of its care over a lifetime. Among all Information Communication Technology-based interventions for autism, those realized with Immersive Virtual Reality based Serious Games appear to be the most promising. However, even if an intervention is considered promising for a clinical population does not mean that it is also efficient and effective. In fact, to be efficient and effective, an intervention must be evidence-based. Unfortunately, nowadays, there is a lack of evaluation protocols that allow for avoiding methodological biases thus providing interventions evidence-based. Consequently, clinicians are reluctant to use these interventions in clinical practice. This awareness led to the emergence of the research question of this systematic review: how interventions realized with Immersive Virtual Reality based Serious Games for individuals with autism are evaluated? The present systematic review answered this research question showing consistent methodological issues related to how these kinds of interventions are evaluated and laying the foundation for a reflection on the need to create specific evaluation protocols to overcome these issues and promoting thus the blossoming of rigorous studies on this topic.
A Review of Stability in Topic Modeling: Metrics for Assessing and Techniques for Improving Stability

Topic modeling includes a variety of machine learning techniques for identifying latent themes in a corpus of documents. Generating an exact solution (i.e., finding global optimum) is often computationally intractable. Various optimization techniques (e.g., Variational Bayes or Gibbs Sampling) are employed to generate topic solutions approximately by finding local optima. Such an approximation often begins with a random initialization, which leads to different results with different initializations. The term “stability” refers to a topic model’s ability to produce solutions that are partially or completely identical across multiple runs with different random initializations. Although a variety of work has been done analyzing, measuring, or improving stability, no single paper has provided a thorough review of different stability metrics nor of various techniques that improved the stability of a topic model. This paper fills that gap and provides a systematic review of different approaches to measure stability and of various techniques that are intended to improve stability. It also describes differences and similarities between stability measures and other metrics (e.g., generality, coherence). Finally, the paper discusses the importance of analyzing both stability and quality metrics to assess and to compare topic models.
Methodologies for Selection of Optimal Sites for Renewable Energy Under a Diverse Set of Constraints and Objectives

In this paper, we present methodologies for optimal selection for renewable energy sites under a different set of constraints and objectives. We consider two different models for the site-selection problem - coarse-grained and fine-grained, and analyze them to find solutions. We consider multiple different ways to measure the benefits of setting up a site. We provide approximation algorithms with a guaranteed performance bound for two different benefit metrics with the coarse-grained model. For the fine-grained model, we provide a technique utilizing Integer Linear Program to find the optimal solution. We present the results of our extensive experimentation with synthetic data generated from sparsely available real data from solar farms in Arizona.
M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework

Users in recommender systems exhibit multi-behavior in multiple business scenarios on real-world e-commerce platforms. A crucial challenge in such systems is to make recommendations for each business scenario at the same time. On top of this, multiple predictions (e.g., Click Through Rate and Conversion Rate) need to be made simultaneously in order to improve the platform revenue. Research focus on making recommendations for several business scenarios is in the field of Multi-Scenario Recommendation (MSR), and Multi-Task Recommendation (MTR) mainly attempts to solve the possible problems in collaboratively executing different recommendation tasks. However, existing researchers have paid attention to either MSR or MTR, ignoring the integration of MSR and MTR that faces the issue of conflict between scenarios and tasks. To address the above issue, we propose a Meta-based Multi-scenario Multi-task RECommendation framework (M3REC) to serve multiple tasks in multiple business scenarios by a unified model. However, integrating MSR and MTR in a proper manner is non-trivial due to: 1) Unified representation problem: Users’ and items’ representation behave Non-i.i. d in different scenarios and tasks which takes inconsistency into recommendations. 2) Synchronous optimization problem: Tasks distribution varies in different scenarios, and a unified optimization method is needed to optimize multi-tasks in multi-scenarios. Thus, to unified represent users and items, we design a Meta-Item-Embedding Generator (MIEG) and a User-Preference Transformer (UPT). The MIEG module can generate initialized item embedding using item features through meta-learning technology, and the UPT module can transfer user preferences in other scenarios. Besides, the M3REC framework uses a specifically designed backbone network together with a task-specific aggregate gate to promote all tasks to achieve the purpose of optimizing multiple tasks in multiple business scenarios within one model. Experiments on two public datasets have shown that M3REC outperforms those compared MSR and MTR state-of-the-art methods.
Contextual Multi-Armed Bandit for Email Layout Recommendation

We present the use of a contextual multi-armed bandit approach to improve the personalization of marketing emails sent to Wayfair’s customers. Emails are a critical outreach tool as they economically unlock a significant amount of revenue. We describe how we formulated our problem of selecting the optimal personalized email layout to use as a contextual multi-armed bandit problem. We also explain how we approximated a solution with an Epsilon-greedy strategy. We detail the thorough evaluations we ran, including offline experiments, an off-policy evaluation, and an online A/B test. Our results demonstrate that our approach is able to select personalized email layouts that lead to significant gains in topline business metrics including engagement and conversion rates.
Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study

As personalization has great potential to improve mobile health apps, analyzing the effect of different recommender algorithms in the health domain is still in its infancy. As such, this paper investigates whether more accurate recommendations from a content-based recommender or more diverse recommendations from a user-based collaborative filtering recommender will lead to more motivation to move. An eight-week longitudinal between-subject user study is being conducted with an Android app in which participants receive personalized recommendations for physical activities and tips to reduce sedentary behavior. The objective manipulation check confirmed that the group with collaborative filtering received significantly more diverse recommendations. The subjective manipulation check showed that the content-based group assigned more positive feedback for perceived accuracy and star rating to the recommendations they chose and executed. However, perceived diversity and inspiringness was significantly higher in the content-based group, suggesting that users might experience the recommendations differently. Lastly, momentary motivation for the executed activities and tips was significantly higher in the content-based group. As such, the preliminary results of this longitudinal study suggest that more accurate and less diverse recommendations have better effects on motivating users to move more.
The Eleventh International Workshop on News Recommendation and Analytics (INRA’23)

Artificial Intelligence is transforming the news eco-system at a rapid pace. Large Language Models have emerged and facilitate producing content in larger quantities and with less skill or technical oversight. At the same time, media organizations struggle to maintain public trust as misinformation and disinformation continue to spread. The 11th International Workshop on News Recommendation and Analytics (INRA) serves as a venue for exchanging ideas, discussing recent developments, and important issues concerning news. We welcome contributions as scientific articles, demonstrations, and innovative ideas or citicism. Our goal is to bring together both academia and practitioners to address vital challenges facing the media world. The workshop gives attendees the chance to learn about ongoing research, discuss technical as well as ethical aspects of personalization, and contemplate about how technology, in particular Artificial Intelligence, will affect the way humans engage with news. Topics of interest include Large Language Models, advances in news personalization, mis- and disinformation, and user experience.
Nonlinear Bandits Exploration for Recommendations

The paradigm of framing recommendations as (sequential) decision-making processes has gained significant interest. To achieve long-term user satisfaction, these interactive systems need to strike a balance between exploitation (recommending high-reward items) and exploration (exploring uncertain regions for potentially better items). Classical bandit algorithms like Upper-Confidence-Bound and Thompson Sampling, and their contextual extensions with linear payoffs have exhibited strong theoretical guarantees and empirical success in managing the exploration-exploitation trade-off. Building efficient exploration-based systems for deep neural network powered real-world, large-scale industrial recommender systems remains under studied. In addition, these systems are often multi-stage, multi-objective and response time sensitive. In this talk, we share our experience in addressing these challenges in building exploration based industrial recommender systems. Specifically, we adopt the Neural Linear Bandit algorithm, which effectively combines the representation power of deep neural networks, with the simplicity of linear bandits to incorporate exploration in DNN based recommender systems. We introduce exploration capability to both the nomination and ranking stage of the industrial recommender system. In the context of the ranking stage, we delve into the extension of this algorithm to accommodate the multi-task setup, enabling exploration in systems with multiple objectives. Moving on to the nomination stage, we will address the development of efficient bandit algorithms tailored to factorized bi-linear models. These algorithms play a crucial role in facilitating maximum inner product search, which is commonly employed in large-scale retrieval systems. We validate our algorithms and present findings from real-world live experiments.
Open quantum system approach to the gravitational decoherence of spin-
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 particles

This paper investigates the decoherence effect resulting from the interaction of squeezed gravitational waves with a system of massive particles in spatial superposition. This paper investigates the decoherence effect resulting from the interaction of squeezed gravitational waves with a system of massive particles in spatial superposition. We first employ the open quantum system approach to obtain the established decoherence in a spatial superposition of massive objects induced by squeezed gravitational waves. Subsequently, we focus on the spin-1/2 particle system, and our analysis reveals that the decoherence rate depends on both the squeezing strength and the squeezing angle of the gravitational waves. Our results demonstrate that squeezed gravitational waves with squeezing strengths of $r_p\geq1.2$ and a squeezing angle of $\varphi_p=\pi/2$ can induce a 1 % decoherence within 1 s free falling of a cloud of spin-1/2 particles. This investigation sheds light on the relationship between squeezed gravitational waves and the coherence of spatial superposition states in systems of massive particles and their spin. The dependence of decoherence on squeezing strength and, in the case of spin-$1/2$ particles, on the squeezing angle paves the way for further exploration and understanding of the quantum-gravity connection. We suggest that such an experimental setup could also be employed to eventually investigate the level of squeezing effect (and hence quantum-related properties) of gravitational waves produced in the early universe from inflation.
Structure-based discovery of CFTR potentiators and inhibitors

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, while its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify novel CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered novel mid-nanomolar potentiators as well as inhibitors that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.
Interlayer-spin-interaction-driven Sliding Ferroelectricity in a van der Waals Magnetic Heterobilayer

Sliding ferroelectricity is widely existed in van der Waals (vdW) two-dimensional (2D) multilayers, exhibiting great potential on low-dissipation non-volatile memories. However, in a vdW heterostructure, interlayer sliding usually fails to reverse or distinctly change the electric polarization, which makes the electrical control difficult in practice. Here we propose that in a vdW magnetic system, the interlayer spin interaction could provide an extra degree-of-freedom to remarkably tune the electric polarization. Combining tight-binding model analysis and first-principles calculations, we show that in the CrI3/MnSe2 and other vdW magnetic heterobilayers, the switching of the interlayer magnetic order can greatly change, even reverse the off-plane electronic polarization. Furthermore, interlayer sliding causes a non-volatile switching of the magnetic order and, thus, reverses the electric polarization, suggesting a non-volatile magnetoelectric coupling effect. These findings will significantly advances the development of 2D ferroelectrics and multiferroics for spintronic applications.
Electrical Stimulation for Treatment of Dysphagia Post Head Neck Cancer: A Systematic Review and Meta-Analysis

Abstract Introduction Dysphagia induced by radiotherapy in the head and neck region comprises a challenging scenario and sometimes difficult rehabilitation due to the severity of the adverse effects. Some resources such as electrical stimulation have emerged as an alternative to complement the therapeutic process, but there is still no consensus on its use. Objective The purpose of the present study was to evaluate, through a meta-analysis, the effect of electrical stimulation on the rehabilitation of dysphagia generated after head and neck cancer treatment. Data Synthesis Four randomized controlled trials with a total of 146 participants were included. The age of the participants was 58.37 ± 1.8 years old and there was a predominance of males. The time to start the intervention ranged from 50.96 ± 40.12 months after cancer treatment. The intervention showed great heterogeneity regarding the positioning of the electrodes, parameters, duration of the stimulus, number of sessions, and intensity. No difference was identified in the following aspects: oral transit time, hyoid elevation, penetration and/or aspiration after electrostimulation. The quality of the evidence ranged from very low to moderate and high risk of bias. Conclusion : In this meta-analysis, we found weak evidence for small and moderate swallowing benefits in patients after radiotherapy for head and neck cancer in short-term clinical trials.
Demo: the Triangolazioni Mobile Guide for Exploring the Interconnections between Science, Art and Territory

Most Cultural Heritage sites are immersed in parallel and interlaced stories about art, history, and science, which are typically presented as isolated narratives, providing a partial view of the richness of such places. To address this issue, the Triangolazioni project of the University of Torino has developed a mobile app that enables the customer to gain an overall view of these stories, and their interconnections, by hopping from one to the other, depending on her/his information interests. This demo paper presents the Triangolazioni mobile guide, which supports the thematic exploration of Points of Interest through the navigation, and the exploration of narratives based on their thematic similarity as well as on the Points of Interest they talk about.
Poster - Sim2Learn: Simulation of an Electron Microscope to facilitate Mental to Conceptual Model convergence

Scientific instruments such as electron microscopes allow rapid development in many fields of science but are difficult to fully exploit. Even for experienced users, their internal state (shape of the electron beam), represented in conceptual models, remains fairly inaccessible. This prevents the operator from quickly understanding the complete causal link between the observed phenomenon and the resulting image. This paper investigates how a simulation and visualization application of such an instrument, may help users bridge the gap between their mental and the conceptual models of the instrument by displaying an approximation of its internal state in real time. This is supported by a study with 10 microscopists who had to perform a series of plausible tasks on the interface and answer questions. The study shows that users have a better understanding of the instrument’s inner state while using the user interface. These results will help software designers develop the next generation of scientific instrument tools.
Some notes concerning a generalized KMM-type optimization method for density ratio estimation

In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The associated codes can be found at https://github.com/CDAlecsa/Generalized-KMM.
Postnatal intestinal epithelial maturation by LSD1 controls the small intestinal immune cell composition independently from the microbiota

Postnatal development of the gastrointestinal tract involves the establishment of the commensal microbiota, maturation of the intestinal epithelium, and the acquisition of immune tolerance via a balanced immune cell composition. While studies have uncovered an interplay between the commensal microbiota and immune system development, less is known about the role of the maturing epithelium. Here, we comprehensively show that intestinal-epithelial intrinsic expression of lysine-specific demethylase 1A (LSD1) is necessary for the postnatal maturation of intestinal epithelium as well as maintaining this developed epithelial state in adulthood. Although the stool microbiome was altered in animals with an intestinal-epithelial specific deletion of Lsd1, by depleting the microbial component using antibiotics, we found that the cellular state and number of certain immune cell types were dependent on maturation of the epithelium. We found plasma cells, innate lymphoid cells (ILCs), and a specific myeloid population to be depending on epithelial LSD1 expression. We propose that LSD1 controls the expression of epithelial-derived chemokines, such as Cxcl16, and this is a mode of action for this epithelial-immune cell interplay. For example, we show that LSD1-mediated epithelial-intrinsic CXCL16 controls the number of local ILC2s but not ILC3s. Together, our findings suggest that the maturing epithelium plays a dominant role in regulating the local immune cell composition, thereby contributing to gut homeostasis.
Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach

With the rapid development of enterprise Learning Management Systems (LMS), more and more companies are trying to build enterprise training and course learning platforms for promoting the career development of employees. Indeed, through course learning, many employees have the opportunity to improve their knowledge and skills. For these systems, a major issue is how to recommend learning plans, i.e., a set of courses arranged in the order they should be learned, that can help employees improve their work performance. Existing studies mainly focus on recommending courses that users are most likely to click on by capturing their learning preferences. However, the learning preference of employees may not be the right fit for their career development, and thus it may not necessarily mean their work performance can be improved accordingly. Furthermore, how to capture the mutual correlation and sequential effects between courses, and ensure the rationality of the generated results, is also a major challenge. To this end, in this paper, we propose the Generative Learning plAn recommenDation (GLAD) framework, which can generate personalized learning plans for employees to help them improve their work performance. Specifically, we first design a performance predictor and a rationality discriminator, which have the same transformer-based model architecture, but with totally different parameters and functionalities. In particular, the performance predictor is trained for predicting the work performance of employees based on their work profiles and historical learning records, while the rationality discriminator aims to evaluate the rationality of the generated results. Then, we design a learning plan generator based on the gated transformer and the cross-attention mechanism for learning plan generation. We calculate the weighted sum of the output from the performance predictor and the rationality discriminator as the reward, and we use Self-Critical Sequence Training (SCST) based policy gradient methods to train the generator following the Generative Adversarial Network (GAN) paradigm. Finally, extensive experiments on real-world data clearly validate the effectiveness of our GLAD framework compared with state-of-the-art baseline methods and reveal some interesting findings for talent management.