To get an equivalent network of perceptrons we replace all the NAND gates by perceptrons with two inputs, each with weight −2−2, and an overall bias of 33. Here's the resulting network. Note that I've moved the perceptron corresponding to the bottom right NAND gate a little, just...
GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities ParaNames: A Massively Multilingual Entity Name Corpus Towards a Multi...
(16.1), three steps are required to construct a NAND gate using an IMP gate. During the first cycle, M3 is initialized to the HRS (0) via a reset operation. During the second cycle, the first IMP operation is carried out with p and s and the result is s'. Lastly, the second IMP...
Artificial neural networks, central to deep learning, are powerful but energy-consuming and prone to overfitting. The authors propose a network design inspired by biological dendrites, which offers better robustness and efficiency, using fewer trainable parameters, thus enhancing precision and resilience ...
Here we provide such a machine code along with a programming framework by using a recurrent neural network—a reservoir computer—to decompile, code and compile analogue computations. By decompiling the reservoir’s internal representation and dynamics into an analytic basis of its inputs, we define...
state[0] = 1.0 # Initialize with the |0...0⟩ state def apply_gate(self, gate, target_qubits): gate_matrix = self._get_gate_matrix(gate) target_qubits = sorted(target_qubits, reverse=True) # Sort in descending order for target_qubit in target_qubits: gate_matrix = np.kron(np....
First, as a model of a learning molecular machine, we formulate a logic gate that can learn conditioned reflex and introduce the network of the logic gates. Then we derive a key principle for learning, called the flipping principle, by which we present a learning algorithm for the network ...
Note: BLSTM, bidirectional long short-term memory network; BGRU, bidirectional gate recurrent unit network. On the whole, CRISPR-ONT achieved the highest SCC, with increase by 2.6% on average compared with the second best DeepCas9 (Fig. 3). It is clear that CRISPR-ONT consistently outperformed...
Gate- and flux-tunable sin(2φ) Josephson element with planar-Ge junctions Hybrid superconductor-semiconductor circuits have recently garnered attention for various applications. Here, the authors use a semiconductor heterostructure with a high-mobility Ge channel to create a Josephson device, where the...
The multimode transistors can enable multimode neural networks: STP for artificial neural networks (ANNs), LTP for recurrent neural networks (RNNs) and LIF behaviour for spiking neural networks (SNNs). ANN With short gate pulses (2 V; duration, 20 ms; period, 40 ms), the increased...