Bill and Hillary Clinton declare themselves ABOVE THE LAW as they defy Epstein subpoena with astonishing letter slamming Trump's'cruel agenda' Vicious whispers expose the uncomfortable truth about Tulsi Gabbard... and what it means for her future Lawyer shows why he's most forgiving husband in America after glamorous teacher wife, 25, cheated on him with boy, 17, in marital home Iran's Islamic rulers are teetering on collapse. America must give them a final shove. Here's what I am advising Trump's team: MARK DUBOWITZ Waitress wearing a crash helmet'who started fatal Swiss resort inferno' was among the 40 to die in the tragedy Restaurant server sparks outrage after'infuriating' move on customer's bill: '15% wasn't good enough, apparently' Jolene needs surgery to cure her crippling disease. Doctors say they're too busy... but approved euthanasia in ONE HOUR How much do you know about history's most infamous serial killer? Take our Jack the Ripper quiz in this week's The Crime Desk newsletter But now this tiny detail in this disgraceful new picture has made me realise she may be the most deluded woman in the world.

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.

With the rapid advancement of large language models like Gemini, GPT, and others, bridging the gap between the human brain and language processing has become an important area of focus. To address this challenge, researchers have developed various models to decode EEG signals into text. However, these models still face significant performance limitations. To overcome these shortcomings, we propose a new model, R1 Translator, which aims to improve the performance of EEG-to-text decoding. The R1 Translator model combines a bidirectional LSTM encoder with a pretrained transformer-based decoder, utilizing EEG features to produce high-quality text outputs. The model processes EEG embeddings through the LSTM to capture sequential dependencies, which are then fed into the transformer decoder for effective text generation. The R1 Translator excels in ROUGE metrics, outperforming both T5 (previous research) and Brain Translator. Specifically, R1 achieves a ROUGE-1 score of 38.00% (P), which is up to 9% higher than T5 (34.89%) and 3% better than Brain (35.69%). It also leads in ROUGE-L, with a F1 score of 32.51%, outperforming T5 by 3% (29.67%) and Brain by 2% (30.38%). In terms of CER, R1 achieves a CER of 0.5795, which is 2% lower than T5 (0.5917) and 4% lower than Brain (0.6001). Additionally, R1 performs better in WER with a score of 0.7280, outperforming T5 by 4.3% (0.7610) and Brain by 3.6% (0.7553). Code is available at https://github.com/Mmurrad/EEG-To-text.

Finetuning pre-trained language models with small amounts of data is a commonly-used method to create translators for ultra-low resource languages such as endangered Indigenous languages. However, previous works have reported substantially different performances with translators created using similar methodology and data. In this work we systematically explored possible causes of the performance difference, aiming to determine whether it was a product of different cleaning procedures, limitations of the pre-trained models, the size of the base model, or the size of the training dataset, studying both directions of translation. Our studies, using two Brazilian Indigenous languages, related but with significant structural linguistic characteristics, indicated none or very limited influence from those training factors, suggesting differences between languages may play a significant role in the ability to produce translators by fine-tuning pre-trained models.

Existing research suggests that machine translations of literary texts remain unsatisfactory. Such quality assessment often relies on automated metrics and subjective human ratings, with little attention to the stylistic features of machine translation. Empirical evidence is also scant on whether the advent of AI will transform the literary translation landscape, with implications for other critical domains for translation such as creative industries more broadly. This pioneering study investigates the stylistic features of AI translations, specifically examining GPT -4's performance against human translations in a Chinese online literature task. Our computational stylometry analysis reveals that GPT -4 translations closely mirror human translations in lexical, syntactic and content features. As such, AI translations can in fact replicate the'human touch' in literary translation style. The study provides critical insights into the implications of AI on literary translation in the posthuman, where the line between machine and human translations may become increasingly blurry.

Building on the Extended Mind (EM) theory and radical enactivism, this article suggests an alternative to representation-based models of the mind. We lay out a novel ABC framework of the translating mind, in which translation is not the manipulation of static interlingual correspondences but an enacted activity, dynamically integrating affective, behavioral, and cognitive (ABC) processes. Drawing on Predictive Processing and (En)Active Inference, we argue that the translator's mind emerges, rather than being merely extended, through loops of brain-body-environment interactions. This non-representational account reframes translation as skillful participation in sociocultural practice, where meaning is co-created in real time through embodied interaction with texts, tools, and contexts.

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.

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

Neural networks are capable of translating between languages--in some cases even between two languages where there is little or no access to parallel translations, in what is known as Unsupervised Machine Translation (UMT).