![]() ![]() Only other tip I could provide is to maybe get a 6GB variant of the 1060 video card or an AMD RX580 or something. You can stick with an 8GB stick, but just get one that's like 2666 or even upwards around 3000 MHz if possible. You could attempt to swap the RAM out for something better like a Corsair, Mushkin, or G.Skill for example. CPU is alright, the video card is a low-mid tier, so it should at least do ok with some low settings and maybe a couple medium. I can't help too much due to the fact that this game lacks some key optimization it could use, but likely will never get. The average is around 2800 MHz for DDR4 memory, with that creeping towards 3200 MHz from what I've seen lately. I think the lowest I've seen is 2133 MHz, and that's just on the very low-end of the spectrum. TwinShadow 【天狐】님이 먼저 게시:For a DDR4 RAM module, that is a very low-spec speed for that type of RAM. ![]()
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![]() ![]() Using machine learning algorithms, these behavioral variables served to predict Big Five personality on domain and facet level. More specifically, we quantified music preferences at fine granularity via technical audio features from Spotify and via lyrical attributes obtained through natural language processing. We provided a holistic account of individual differences in music listening by integrating everyday preferences for various musical attributes with habitual listening behaviors. The present study (N = 330) proposes a personality computing approach to fill these gaps with new insights from ecologically valid music listening records from smartphones. While there is initial evidence relating self-reported musical style preferences to the Big Five dimensions, little is known about day-to-day music listening behavior and the intrinsic attributes of music that give rise to personality patterns. Psychologists have long theorized that people use music to create auditory environments matching their personality traits. Importantly, our work contrasts a recent self-report-based meta-analysis, which suggested that personality traits play only a small role in musical preferences rather, we show with big data and advanced machine learning methods that personality is indeed important and warrants continued rigorous investigation. Findings from machine learning showed that the Big Five personality traits are predicted by musical preferences and habitual listening behaviors with moderate to high accuracy. ![]() Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. ![]() Here we overcome limitations of prior research by leveraging ecologically valid streaming data: 17.6 million songs and over 662,000 hr of music listened to by 5,808 Spotify users spanning a 3-month period. Advances in digital technology have put music libraries at people’s fingertips, giving them immediate access to more music than ever before. ![]() |
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