But dodging is just a subgoal and margin is not all that relevant. The actual goal is to defeat the player, if dodging by a slim margin happens to be the best strategy then that should not be penalized.
Every nice welfare state has a lot of unemployed people.
France has a permanent unemployed class, with perpetually high unemployment as a result of their labor laws and generous welfare state. Those people are not starving to death.
Clearly UBI is in fact not necessary at all, so say the best countries in the world, from Denmark to Canada.
German unemployment benefits are conditional on showing evidence that you're seeking jobs. So it's still founded on the assumption that everyone able should be working.
Copy on write filesystems can probably be optimized for SMR by using TRIM commands to punch holes and rewrite the content sequentially in a new zone. Afaik both zfs and btrfs have plans to do this.
That way they can be useful for more than archival.
I was thinking that F2FS might be a good filesystem as a base on which you'd use an object storage abstraction layer (like ceph)...
However since I initially saw the news of these drives a few days ago Samsung also axed some Linux devs, which gives me pause and makes me reconsider the long term viability of this filesystem...
A full blown filesystem is overkill for an object store. You could use something like libzbc ( https://github.com/hgst/libzbc ) to write directly to the SMR drives on the block level.
I believe Ceph now has abstracted the drives away through BlueStore, which simply puts a large RocksDB database on the drive, bypassing most of the functionality a filesystem offers. It should be much easier to make an SMR compatible version of the LSM-tree backend of RocksDB, than writing a full-blown file system.
Still seems random access might be an issue. But would love to see how eg nilfs2 (maybe on top of software raid) benchmarks against zfs on these big drives.
Unlike the other processes mentioned thought is computation and it is perfectly possible to run computation on wetware or on an emulator and get the same results.
That seems generous, I think? They restricted the input data to visual acuity at the level of a fly. But it doesn't look like the neurology actually influenced the design much:
> This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: “can we model a fly brain that can identify individuals?”
> We implemented a virtual fly visual system using standard deep learning libraries (Keras). Our implementation uses approximately 25,000 artificial neurons, whereas Drosophila have approximately 60,000 neurons in each visual hemisphere [16]. We purposefully did not model neurons that are structurally suggestive to respond to movement, and therefore we were specifically limited to ‘modular’ neurons (with 1 neuron/column) throughout the medulla. The connections between neuronal types were extracted from published connectomes [17]. We imposed artificial hierarchy on our model eliminating self-connections between neuron ‘subtypes’ (i.e. no connections between L1 and L1, or L1 and L2), and while we allowed initial layers to feed into multiple downstream layers, we eliminated ‘upstream’ connections. The final lobula-like artificial neurons were modelled after Wu et al. [15], where the layers were ordered according to their axon penetration deeper into the system. Our ability to model Drosophila’s visual system is further limited to the connectivity, ignoring the sign (excitatory or inhibitory), as well as the neurons’ intrinsic membrane properties. The ability to create more biologically realistic simulations will increase once these properties are discovered and integrated into the connectome. The model is illustrated in Fig 2B, beside the biological inspiration (Fig 2C). S1 Table depicts a complete connection map and hierarchy, and S2 Table shows comparative performance of this model on a traditional image-classification dataset. Additional details are provided in S1 Methods.
