The New Scientist Book Club read Silvia Park's near-future sci-fi novel Luminous in May, and had lots of good things to say (along with a few complaints) The New Scientist Book Club read Silvia Park's Luminous in May The New Scientist Book Club had quite a change of science-fictional pace in May, moving from the wilds of space in our April read, Kim Stanley Robinson's, to a much closer-to-home future in Silvia Park's . Like another of our reads this year, Sierra Greer's, this imagines a world where robots are integrated into society - and explores how we might deal with this on many different levels: emotionally, spiritually, practically, sexually. Set in a reunified Korea, it's a compelling blend of three storylines: a police procedural, in which detective Jun is out to discover what might have become of a robot girl who has gone missing; a ragtag bunch of kids on an adventure, in which Ruijie and her schoolmates find an abandoned robot boy in a scrapyard; and a tale of a dysfunctional family. Jun and his younger sister Morgan grew up with a third sibling, a robot who disappeared when they were young, fracturing their family. Author Silvia Park: 'No one is your enemy, not even death' Silvia Park, author of the May read for the New Scientist Book Club, 'Luminous' on emotional artificial intelligence, our inevitable love for robots and coping with grief.

Let (X,dX) and (Y,dY) two nonempty compact Polish spaces, µ 2M +1 (X), 2M +1 (Y) two probability measures on these spaces and c: X Y! R+ a nonnegative and continuous function. As X and Y are compact, r(µ,) is tight, then Prokhorov's theorem applies and the closure of r(µ,) is sequentially compact. Let us now show that r(µ,) is closed. Indeed, Let ( n)n 0 a sequence of r(µ,) converging towards . In addition as ( n)n 0 live in the simplex r, we can also extract a sub-sequence, such that n! 2 r.

For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?

Many researchers have sought ways of model compression to reduce the size of a deep neural network (DNN) with minimal performance degradation in order to use DNNs in embedded systems. Among the model compression methods, a method called knowledge transfer is to train a student network with a stronger teacher network. In this paper, we propose a novel knowledge transfer method which uses convolutional operations to paraphrase teacher's knowledge and to translate it for the student. This is done by two convolutional modules, which are called a paraphraser and a translator. The paraphraser is trained in an unsupervised manner to extract the teacher factors which are defined as paraphrased information of the teacher network. The translator located at the student network extracts the student factors and helps to translate the teacher factors by mimicking them. We observed that our student network trained with the proposed factor transfer method outperforms the ones trained with conventional knowledge transfer methods.

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For the rest 10k iterations, we further finetune the shared volume and the RGB branch.

Prominent methods (e.g., Tacotron 2)usuallyfirst generate mel-spectrogram from text, and then synthesize speech from themel-spectrogram using vocoder such as WaveNet. Compared with traditionalconcatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech isusually not robust (i.e., some words are skipped or repeated) and lack of con-trollability (voice speed or prosody control).

For our synthetic datasets, we conduct two additional rounds of instruction evolution to increase complexity. As shown in Figure 3, the scores of instructions across the three datasets consistently increase.