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Twitter’s Audio Cards Bring More Music To Your Social Feed

When Apple unveiled its M1 Ultra chip on the “Peek Performance” occasion earlier this month, there was little doubt that it was a monster. In head-to-head competitions with other CPUs, together with its personal M1, M1 Pro, and M1 Max, the M1 Ultra was merely in a class by itself, with scores that put machines costing twice as a lot to shame. On the graphics facet, nonetheless, things aren’t so reduce-and-dried. For the most graphics-intensive needs, like 3D rendering and advanced image processing, M1 Ultra has a 64-core GPU – 8x the dimensions of M1 – delivering quicker efficiency than even the best-end Pc GPU available whereas using 200 fewer watts of energy. Benchmarks tests showed Nvidia’s $1,499 graphics card handily trouncing the $3,999 M1 Ultra in all sorts of duties. It didn’t take long to fully debunk that claim. But as Obi-wan Kenobi would say, what Apple informed us was true, from a sure viewpoint.
Apple’s chart and the literal interpretation of its quote show GPU efficiency versus energy, which is the place the M1 chips excel. Within the chart, Apple cuts the RTX 3090 off at about 320 watts, which severely limits its potential. The M1 Ultra has a max power consumption of 215W versus the RTX 3090’s 350 watts. For most avid gamers, nonetheless, power consumption isn’t a concern. Now there’s the RTX 3090 Ti, which costs as a lot as an M1 Studio and promises to beat the M1 Ultra even harder. Most notably, it could draw a mind-boggling 450 watts of power, greater than twice that of the M1 Ultra. If energy efficiency issues to you, the M1 Ultra is king. We haven’t seen benchmarks yet, however they’re going to eclipse the RTX 3090, which already handily beat the M1 Ultra at full energy. The comparisons to the RTX 3090 Ti are going to be extremely lopsided. I don’t know if there’s any graphics card that may really compete in opposition to Nvidia’s newest behemoth, however it’s actually not anything Apple makes. Apple is prioritizing power consumption and will continue to launch extra environment friendly chips while Nvidia is prioritizing efficiency and will keep pushing the envelope there. Nvidia and Apple are enjoying at completely different ends of the pool with their respective high-end chips. Could Apple make a discrete graphics card that rivals the RTX 3090? Which will change with the Apple silicon-primarily based Mac Pro, however till that day arrives, the comparisons aren’t worth the time they take to argue, even if Apple did bring them on itself. Maybe. But till that day arrives, Nvidia’s latest flagship card goes to run circles round Apple’s best processors. Don’t take it personally, it’s just not a good combat. Now, Intel’s new Arc GPUs, that’s one other story.
N ( ⋅ ), which is broadly adopted to model long-time period dependencies in a sequence. Since words in a tweet fluctuate of their contribution to the tweet’s overall semantic that means, the eye mechanism is adopted to aggregate phrase hidden representations right into a tweet vector. POSTSUPERSCRIPT are learnable parameters. POSTSUPERSCRIPT, generating a forward and a backward sequence. Word-Level Encoder. The word-level encoder concatenates temporally adjacent tweets into a protracted sequence of phrases. POSTSUBSCRIPT are learnable parameters. K is the entire phrase count in the temporally concatenated tweets. N ( ⋅ ) concerning its specific length. To avoid the undesirable bias incorporated in feature engineering, the profile-property sub-community makes use of profile properties that might be immediately retrieved from the Twitter API. A bidirectional RNN with attention is adopted to encode the concatenated sequence. There are 15 true-or-false property items in complete. We use 1 for true and 0 for false. “profile uses background image”.
’s on-line actions. Each action kind can be encoded with a character. By identifying the group of accounts that share the longest frequent substring, a set of bot accounts are obtained. Alhosseini et al. (Ali Alhosseini et al., 2019): Alhosseini et al. Twitter bots. It makes use of following information and consumer options to learn representations and classify Twitter customers. Botometer (Davis et al., 2016): Botometer is a publicly accessible service that leverages multiple thousand features to categorise an account. POSTSUBSCRIPT: The proposed representation studying framework SATAR is firstly trained with self-supervised person classification duties based on their follower depend, then the final softmax layer is reinitialized and educated on the duty of bot detection. POSTSUBSCRIPT: The proposed representation studying framework SATAR is firstly trained utilizing self-supervised users, then the ultimate softmax layer is reinitialized and nice-tuning is performed on the whole framework using the training set of bot detection. Evaluation Metrics. We undertake Accuracy, F1-rating and MCC (Matthews, 1975) as analysis metrics of various bot detection methods.
Such a partition is shared across all experiments in Section 5.2, Section 5.3 and Section 5.4. We choose these three benchmarks out of quite a few bot detection datasets due to their bigger measurement, assortment time span and superior annotation quality. Lee et al. (Lee et al., 2011): Lee et al. Figure 2. Train SATAR and two competitive baselines on one area of TwiBot-20 and test on the opposite three domains. Twitter consumer features. e.g. the longevity of the account. Yang et al. (Yang et al., 2020): Yang et al. Kudugunta et al. (Kudugunta and Ferrara, 2018): Kudugunta et al. Wei et al. (Wei and Nguyen, 2019): Wei et al. BiLSTM to encode tweets. A completely linked softmax layer is adopted for binary classification. 107 options from an user’s tweet. Miller et al. (Miller et al., 2014): Miller et al. Property info. Bot customers are conceived as abnormal outliers. Modified stream clustering algorithm is adopted to identify Twitter bots. Cresci et al. (Cresci et al., 2016): Cresci et al.
Across the entire Twittersphere, it is reported that bot accounts for 9% to 15% of whole energetic users (Yardi et al., 2010). Since bots jeopardize consumer experience in Twitter and will even induce undesirable social effects, many research efforts have been devoted to Twitter bot detection. The first work to detect automated accounts in social media dates again to 2010 (Yardi et al., 2010). Early studies conducted function engineering. Adopted conventional classification algorithms. Wei et al. (Wei and Nguyen, 2019) adopted long brief-term memory to extract semantics data from tweets. Three categories of options were thought-about: (1) user property options (D’Andrea et al., 2015); (2) options derived from tweets (Miller et al., 2014); and (3) options extracted from neighborhood information (Yang et al., 2013). Later, researchers began to suggest neural community based bot detection frameworks. Kudugunta et al. (Kudugunta and Ferrara, 2018) proposed a method that mixed feature engineering and neural community fashions.