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              <text>This paper focuses on the effect of the Phase One Agreement, particularly purchasing commitment, on the US agricultural product price. By first identifying the treatment group based on the agreement and setting January 2020 as the treatment time, I conducted a difference-in-difference estimation with time effects. The results show a limited effect on the agricultural product price. The heterogeneity test shows the differences across product groups. Dairy products experienced a significant decline in prices, whereas prices for grains and meat remained largely unchanged </text>
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              <text>Aesthetics in video games, defined as “the desirable emotional responses evoked in the player” in the Mechanics Dynamics and Aesthetics (MDA) framework, can be key to a video game’s success. According to the MDA framework, aesthetics and user engagement are completely dependent on the mechanics of the game and cannot otherwise be influenced. However, if games are seen as an aesthetic artefact, a video game’s form and style should be entertaining on their own, and therefore its art style should influence aesthetics and user engagement. A small metroidvania-style game, Sandcat, was developed in three art styles – digital, pixel and pencil – to test this, and 15 participants played the game in all styles and completed a survey on their experiences. The results showed that while game mechanics had the most influence over aesthetics, art styles still had a significant effect on aesthetics and contributed to a game feeling more ‘usable’ for user engagement. Despite limitations with stretching and blurry assets with the pencil and pixel styles, pencil was the most preferred style (with descriptions of “whimsical” and even more “alive” than other art styles), closely followed by pixel (often described as “nostalgic”), and only one participant preferred digital. Beyond preferences, 80% of participants claimed they would spend more on a game which had their chosen art style, which may imply further commercial implications for developers. Overall, although not as strong as game mechanics, art styles still have a noteworthy influence on video game aesthetics.</text>
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                <text>Politics &amp; International Studies </text>
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                <text>Harriet Myfanwy Charles</text>
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              <text>The poster is about investigating  the convergence of neural network in two-timescale regime.</text>
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              <text>Two-timescale,&#13;
Neural network,&#13;
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                <text>Dr. Ranko Lazic</text>
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              <text>Within philosophical aesthetics, the question of what makes an object aesthetically valuable remains a perennial one. One influential response is that of aesthetic hedonism, which reduces an object’s aesthetic value to its capacity to afford pleasure. This paper challenges the hedonic view by drawing upon an underexplored aesthetic phenomenon: aesthetic akrasia, which presents a disjunction between one’s value judgement and one’s derivation of pleasure. Situating aesthetic akrasia against the premises of hedonism reveals that hedonism not only fails to account for aesthetic akrasia, but also misconstrues the nature of the relation between aesthetic judging and liking. In developing this account, this paper demonstrates how the premises of hedonism necessitate that liking unconditionally follows from judging aesthetic value - a claim that contradicts aesthetic reality. In light of this, the paper concludes that hedonism is increasingly untenable as a theory of aesthetic value.</text>
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              <text>Aesthetics, Philosophy of Art, Hedonism, Akrasia, Aesthetic Value</text>
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                <text>Aesthetic Hedonism and The Problem of Akratic Art</text>
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                <text>Cyrus Khor </text>
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                <text>Eileen John</text>
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                <text>Is aesthetic value tied to hedonic value?</text>
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              <text> Within a biological system, proteins are essential; they are the building blocks of life. Their functions range from performing as antibodies to catalysing reactions as enzymes; so, proteins have an extremely varied and extensive use through a variety of industries. With mutations, their stability and functionality can be modified, with even single-point mutations having a strong effect. Therefore, being able to predict the effect in stability changes of these mutations is essential, but often computationally costly and weak when encountering unseen data. With the rise in popularity of protein language models (PLMs), including ESM, there have been improvements, but these models still encounter the same difficulties. This study explores the effect of ensemble learning on improving protein stability predictions. This is done by combining two base models: a convolutional neural network (CNN) using wild-type and mutant sequences, and a neural network trained on frozen ESM embeddings. They each form two base-learners, where each is trained on half the base training data. To combine their predictions, intermediate meta-learners for each model type and a final meta-learner are used. Results show that ensemble learning improves predictions over base models, showing improvements in all performance metrics used. Notably, even the weaker CNN-based learners contributed to improved ensemble performance, reinforcing the benefit of this type of architecture. Although it is necessary to further optimise hyperparameters and include stronger base models in the future, these results indicate that ensemble learning can lead to more accurate protein stability prediction with minimal additional computational cost.</text>
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              <text>protein stability predictions, mutation effect prediction, ensemble learning</text>
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                <text>Computer Science </text>
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                <text>2025</text>
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                <text>Predicting Protein Stability of Single-Point  Mutations using Ensemble Learning and ESM  Embeddings</text>
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                <text>Victoria Krushovska</text>
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