Multi-Modal Machine Learning for Assessing Gaming Skills in Online Streaming: A Case Study with CS:GO

Online streaming is an emerging market that address much attention. Assessing gaming skills from videos is an important task for streaming service providers to discover talented gamers. Service providers require the information to offer customized recommendation and service promotion to their customers. Meanwhile, this is also an important multi-modal machine learning tasks since online streaming combines vision, audio and text modalities. In this study we begin by identifying flaws in the dataset and proceed to clean it manually. Then we propose several variants of latest end-to-end models to learn joint representation of multiple modalities. Through our extensive experimentation, we demonstrate the efficacy of our proposals. Moreover, we identify that our proposed models is prone to identifying users instead of learning meaningful representations. We purpose future work to address the issue in the end

Análisis de la gobernanza fronteriza entre los EE. UU. y México desde la perspectiva de la seguridad nacional

Desde la antigüedad hasta el presente, la seguridad fronteriza ha jugado un papel vital en la seguridad nacional, porque la frontera es el límite de un país, conectada a los dos países y también bloqueada a los dos países. Los Estados Unidos y México tienen una frontera de más de tres mil kilómetros, y los problemas que enfrentan también son infinitos, como la droga, las migraciones ilegales y la industrialización fronteriza en México, que también ha afectado seriamente tanto la seguridad fronteriza como la nacional de los dos países. Ellos dos han adoptado muchas políticas para el control fronterizo, pero no hay mucho efecto, por lo que estos problemas han coexistido hasta ahora. En este trabajo se analizan los principales problemas fronterizos entre los EE. UU. y México con el motivo de encontrar sus efectos en la seguridad nacional, estudiando sus historias, características y tendencias para buscar mejores soluciones

Semantic reconstruction of continuous language from MEG signals

Decoding language from neural signals holds considerable theoretical and practical importance. Previous research has indicated the feasibility of decoding text or speech from invasive neural signals. However, when using non-invasive neural signals, significant challenges are encountered due to their low quality. In this study, we proposed a data-driven approach for decoding semantic of language from Magnetoencephalography (MEG) signals recorded while subjects were listening to continuous speech. First, a multi-subject decoding model was trained using contrastive learning to reconstruct continuous word embeddings from MEG data. Subsequently, a beam search algorithm was adopted to generate text sequences based on the reconstructed word embeddings. Given a candidate sentence in the beam, a language model was used to predict the subsequent words. The word embeddings of the subsequent words were correlated with the reconstructed word embedding. These correlations were then used as a measure of the probability for the next word. The results showed that the proposed continuous word embedding model can effectively leverage both subject-specific and subject-shared information. Additionally, the decoded text exhibited significant similarity to the target text, with an average BERTScore of 0.816, a score comparable to that in the previous fMRI study

Grand Canonical Monte Carlo Simulations of Ethanol Conversion to Propylene Over Zeolite Catalysts

The transformation of ethanol to propylene (ETP) was investigated over H-ZSM-5 (40) and H-LEV (40) catalysts. For H-ZSM-5 (40), the propylene yield kept constant at about 20.0% during 8 h. For H-LEV (40), higher initial propylene yield reached 34.0%. However, there is almost no propylene obtained over H-LEV (40) catalyst after 2 h. H-ZSM-5 (40) catalyst exhibited higher stability than H-LEV (40). The lower stability of H-LEV (40) is probably due to coke deposition. The reactant and products adsorption performances in the ethanol conversion reaction over H-ZSM-5 (40) and H-LEV (40) catalysts were studied by Monte Carlo simulations. Results show that the higher adsorption amount of ethanol, ethylene and propylene in H-LEV (40) led to the more difficult desorption of products and higher content of coke deposition

Highly reversible transition metal migration in superstructure-free Li-rich oxide boosting voltage stability and redox symmetry

The further practical applications of Li-rich layered oxides are impeded by voltage decay and redox asymmetry, which are closely related to the structural degradation involving irreversible transition metal migration. It has been demonstrated that the superstructure ordering in O2-type materials can effectively suppress voltage decay and redox asymmetry. Herein, we elucidate that the absence of this superstructure ordering arrangement in a Ru-based O2-type oxide can still facilitate the highly reversible transition metal migration. We certify that Ru in superstructure-free O2-type structure can unlock a quite different migration path from Mn in mostly studied cases. The highly reversible migration of Ru helps the cathode maintain the structural robustness, thus realizing terrific capacity retention with neglectable voltage decay and inhibited oxygen redox asymmetry. We untie the knot that the absence of superstructure ordering fails to enable a high-performance Li-rich layered oxide cathode material with suppressed voltage decay and redox asymmetry