Charles Ojiambo Mutoka, 72, with portraits of his son Oscar, who he learned was killed in August, during a press conference where relatives of conscripts demanded urgent government action to repatriate their kin, in Nairobi on Jan. 27 | AFP-JIJI Nairobi - The scars on Victor's forearm remind him constantly of the day a Ukrainian drone attacked him after he was forcibly conscripted, like hundreds of young Kenyans, into the Russian military. It was a war that had nothing to do with him and which he was exceptionally lucky to survive. Four Kenyans -- Victor, Mark, Erik and Moses -- recounted the web of deception that took them to the killing fields of Ukraine. Their names have been changed for fear of reprisals. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

On that basis, we hypothesize that these two problems stem from the conflict between the KL regularization inELBo andthefunction definition oftheprior distribution. Assuch, wepropose a novel regularization to substitute the KL regularization in ELBo for VAEs, which isbased on the density gapbetween the aggregated posterior distribution and the prior distribution.

We call that σ -algebra history before n . Identification tasks We focus on best arm identification (BAI).

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)? [Y es] We release the code and the models If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [Y es] We included the instructions given to participants in appendix F. In this appendix, we describe the neural network architecture used for our agents.Figure 2: Transformer encoder (left) used in both policy proposal network (center) and value network (right). Our model architecture is shown in Figure 2. It is essentially identical to the architecture in [11], except that it replaces the specialized graph-convolution-based encoder with a much simpler transformer encoder, removes all dropout layers, and uses separate policy and value networks. Aside from the encoder, the other aspects of the architecture are the same, notably the LSTM policy decoder, which decodes orders through sequential attention over each successive location in the encoder output to produce an action. The input to our new encoder is also identical to that of [11], consisting of the same representation of the current board state, previous board state, and a recent order embedding. Rather than processing various parts of this input in two parallel trunks before combining them into a shared encoder trunk, we take the simpler approach of concatenating all features together at the start, resulting in 146 feature channels across each of 81 board locations (75 region + 6 coasts). We pass this through a linear layer, add pointwise a learnable per-position per-channel bias, and then pass this to a standard transformer encoder architecture.