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Hidden-charm strong decays of the spin-2 partner $X_2(4014)$ of the charmonium-like state $X(3872)$ have been studied in the framework of the covariant confined quark model. The exotic state $X_2$ has been interpreted as a four-quark state with molecular-type interpolating current. We have considered the decay widths of $X_2$ on the level of two-petal quark loops. The partial widths of the strong decays $X_2\!\to\!\omega \, J/\Psi$ and $X_2\!\to\!\rho^0 \, J/\Psi$ have been calculated and the related branching ratio has been analyzed.

Data reuse is fundamental for reducing the data integration effort required to build data supporting new applications, especially in data scarcity contexts. However, data reuse requires to deal with data heterogeneity, which is always present in data coming from different sources. Such heterogeneity appears at different levels, like the language used by the data, the structure of the information it represents, and the data types and formats adopted by the datasets. Despite the valuable insights gained by reusing data across contexts, dealing with data heterogeneity is still a high price to pay. Additionally, data reuse is hampered by the lack of data distribution infrastructures supporting the production and distribution of quality and interoperable data. These issues affecting data reuse are amplified considering cross-country data reuse, where geographical and cultural differences are more pronounced. In this paper, we propose LiveData, a cross-country data distribution network handling high quality and diversity-aware data. LiveData is composed by different nodes having an architecture providing components for the generation and distribution of a new type of data, where heterogeneity is transformed into information diversity and considered as a feature, explicitly defined and used to satisfy the data users purposes. This paper presents the specification of the LiveData network, by defining the architecture and the type of data handled by its nodes. This specification is currently being used to implement a concrete use case for data reuse and integration between the University of Trento (Italy) and the National University of Mongolia., Comment: Accepted to MMT-2024 Mongolian conference and ICTfocus journal (https://ictfocus.org/)

This study investigates the forecasting accuracy of human experts versus Large Language Models (LLMs) in the retail sector, particularly during standard and promotional sales periods. Utilizing a controlled experimental setup with 123 human forecasters and five LLMs, including ChatGPT4, ChatGPT3.5, Bard, Bing, and Llama2, we evaluated forecasting precision through Mean Absolute Percentage Error. Our analysis centered on the effect of the following factors on forecasters performance: the supporting statistical model (baseline and advanced), whether the product was on promotion, and the nature of external impact. The findings indicate that LLMs do not consistently outperform humans in forecasting accuracy and that advanced statistical forecasting models do not uniformly enhance the performance of either human forecasters or LLMs. Both human and LLM forecasters exhibited increased forecasting errors, particularly during promotional periods and under the influence of positive external impacts. Our findings call for careful consideration when integrating LLMs into practical forecasting processes.

This study analyzes the possible relationship between personality traits, in terms of Big Five (extraversion, agreeableness, responsibility, emotional stability and openness to experience), and social interactions mediated by digital platforms in different socioeconomic and cultural contexts. We considered data from a questionnaire and the experience of using a chatbot, as a mean of requesting and offering help, with students from 4 universities: University of Trento (Italy), the National University of Mongolia, the School of Economics of London (United Kingdom) and the Universidad Cat\'olica Nuestra Se\~nora de la Asunci\'on (Paraguay). The main findings confirm that personality traits may influence social interactions and active participation in groups. Therefore, they should be taken into account to enrich the recommendation of matching algorithms between people who ask for help and people who could respond not only on the basis of their knowledge and skills., Comment: 5 pages

The European Training Foundation (ETF) and the UNICEF Europe and Central Asia Regional Office (ECARO) conducted polls and focus groups to better understand the needs, views, sentiments, and experiences of young people, mainly between the ages of 14 and 34, in relation to career guidance. The geographical focus of the research was on select countries in the Europe and Central Asia region, specifically covering Albania, Bosnia and Herzegovina, Greece, Kosovo, Kyrgyzstan, Montenegro, North Macedonia, Romania, Serbia, Ukraine, and Uzbekistan. The research was co-designed and conducted in close collaboration, and with the full participation, of young people. The initiative's main objective has been to generate new evidence on career guidance demand and expectations among the young generations in these 11 countries and beyond. Fit-for-purpose career guidance should ideally be: a systematic combination of structured career education programs (as part of curricula throughout formal education, to be able to reach whole generations), quality online self-learning and self-help opportunities; and person-centered career guidance service offered outside of school, both face-to-face and online.

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080$\times$1080 and 1280$\times$1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640$\times$640 and 1280$\times$1280, respectively. The dataset will be available on GitHub with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications., Comment: CVPR Workshops 2023

Strong decays of the charmonium-like state $Y(4320)$ have been studied in the framework of the covariant confined quark model. The resonance $Y$ has been interpreted as a four-quark state of the molecular type. We evaluate the hidden-charm decay width of $Y$ to a vector and a scalar, with the latter decaying subsequently to a pair of charged pseudoscalar states. The strong decay modes $Y \!\to\! \pi^{+}\pi^{-}J/\Psi$ and $Y \!\to\! K^{+}K^{-} J/\Psi$ have been studied by involving the scalar resonance state $f_0(980)$. We have calculated the fractal widths of the related strong decays and the branching ratio ${\cal B}(Y \!\to\! K^{+}K^{-} J/\Psi)$ / ${\cal B}(Y \!\to\! \pi^{+}\pi^{-}J/\Psi)$, recently determined by the BESIII collaboration. The estimated branching ratio and calculated fractal strong decay widths are in good agreement with the latest experimental data, favoring the molecular representation of the $Y(4320)$ state.

Smartphones enable understanding human behavior with activity recognition to support people's daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings, making detecting simple activities less meaningful for context-aware applications. Hence, the understanding of (i) how multimodal smartphone sensors and machine learning models could be used to detect complex daily activities that can better inform about people's daily lives and (ii) how models generalize to unseen countries, is limited. We analyzed in-the-wild smartphone data and over 216K self-reports from 637 college students in five countries (Italy, Mongolia, UK, Denmark, Paraguay). Then, we defined a 12-class complex daily activity recognition task and evaluated the performance with different approaches. We found that even though the generic multi-country approach provided an AUROC of 0.70, the country-specific approach performed better with AUROC scores in [0.79-0.89]. We believe that research along the lines of diversity awareness is fundamental for advancing human behavior understanding through smartphones and machine learning, for more real-world utility across countries., Comment: ACM CHI 2023

Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. In contrast, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78-0.98 for two-class (negative vs. positive valence) and 0.76-0.94 for three-class (negative vs. neutral vs. positive valence) inference. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical similarity of countries might impact mood inference., Comment: ACM IMWUT 2022, To be presented at ACM Ubicomp 2023

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets., Comment: The 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper describes a method to enrich lexical resources with content relating to linguistic diversity, based on knowledge from the field of lexical typology. We capture the phenomenon of diversity through the notions of lexical gap and language-specific word and use a systematic method to infer gaps semi-automatically on a large scale. As a first result obtained for the domain of kinship terminology, known to be very diverse throughout the world, we publish a lexico-semantic resource consisting of 198 domain concepts, 1,911 words, and 37,370 gaps covering 699 languages. We see potential in the use of resources such as ours for the improvement of a variety of cross-lingual NLP tasks, which we demonstrate through a downstream application for the evaluation of machine translation systems., Comment: LREC 2022

We have studied the dominant radiative transitions of the charmonium $S$-and $P$-wave states within the covariant confined quark model. The gauge invariant leading-order transition amplitudes have been expressed by using either the conventional Lorentz structures, or the helicity amplitudes, where it was effective. The renormalization couplings of the charmonium states have been strictly fixed by the compositeness conditions that excludes the constituent degrees of freedom from the space of physical states. We use the basic model parameters for the constituent c-quark mass $m_c=1.80$ GeV and the global infrared cutoff $\lambda=0.181$ GeV. We additionally introduce only one adjustable parameter $\varrho>0$ common for the charmonium states $\eta_c({}^1\!S_0)$, $J/\psi({}^3\!S_1)$, $\chi_{c0}({^{3}}\!P_{0})$, $\chi_{c1}({^{3}}\!P_{1})$, $h_c({^{1}}\!P_{1})$, and $\chi_{c2}({^{3}}\!P_{2})$ to describe the quark distribution inside the hadron. This parameter describes the ratio between the charmonium size and its physical mass. The optimal value $\varrho=0.485$ has been fixed by fitting the latest data for the partial widths of the one-photon radiative decays of the triplet $\chi_{cJ}({^{3}}\!P_{J}), J=\{0,1,2\}$. Then, we calculate corresponding fractional widths for states $J/\psi({}^3\!S_1)$ and $h_c({^{1}}\!P_{1})$. Estimated results are in good agreement with the latest data. By using the fraction data from PDG2020 and our estimated partial decay width for $h_c({^{1}}\!P_{1})$ we recalculate the theoretical full width $\Gamma^{\rm theor}_{h_c} \simeq (0.57 \pm 0.12)$ MeV in comparison with latest data $\Gamma^{\rm exp}_{h_c} \simeq (0.7\pm 0.4)$ MeV. We also repeated our calculations by gradually decreasing the global cutoff parameter and revealed that the results do not change for any $\lambda<0.181$ GeV up to the deconfinement limit., Comment: 18 pages, 7 figures