Constructing the Burrows-Wheeler transform (BWT) for long strings poses significant challenges regarding construction time and memory usage. We use a prefix of the suffix array to partition a long string into shorter substrings, thereby enabling the use of multi-string BWT construction algorithms to process these partitions fast. We provide an implementation, partDNA, for DNA sequences. Through comparison with state-of-the-art BWT construction algorithms, we show that partDNA with IBB offers a novel trade-off for construction time and memory usage for BWT construction on real genome datasets. Beyond this, the proposed partitioning strategy is applicable to strings of any alphabet.
We investigate a signed version of the Hammersley process, a discrete process on words related to a property of integer sequences called heapability (Byers et al., ANALCO 2011). The specific version that we investigate corresponds to a version of this property for signed sequences. We give a characterization of the words that can appear as images the signed Hammersley process. In particular we show that the language of such words is the intersection of two deterministic one-counter languages.
We propose Cdbgtricks, a new method for updating a compacted de Bruijn graph when adding novel sequences, such as full genomes. Our method indexes the graph, enabling to identify in constant time the location (unitig and offset) of any k -mer. The update operation that we propose also updates the index. Our results show that Cdbgtricks is faster than Bifrost and GGCAT. We benefit from the index of the graph to provide new functionalities, such as reporting the subgraph that share a desired percentage of k -mers with a query sequence with the ability to query a set of reads. The open-source Cdbgtricks software is available at . ### Competing Interest Statement The authors have declared no competing interest.
Programmable unitary converters are powerful tools for realizing unitary transformations, essential in the fields of quantum computing, machine learning, and optical communication. The precision of these unitary transformations is crucial for maintaining high fidelity in such applications. However, various physical artifacts can impair the accuracy of these transformations. A commonly employed approach utilizes the system's gradient to restore accuracy. Although this gradient can indeed be physically measured using external equipment, it leads to a rather bulky optical system. Alternatively, some studies propose approximating the gradient by finite difference, which demands precise parameter control and measurement. However, such precision leaves this approach vulnerable to noise. In this study, we propose a gradient measurement method that is resistant to noise and does not require any supplementary equipment. Our numerical analysis demonstrates that our method exhibits orders of magnitude higher tolerance to noise than the prior approach, thus considerably reducing the system requirements.
This study addresses the issues of geographical distance and security vulnerability in conventional PONs during broadcast downlink transmission. We experimentally demonstrated that IQ-imbalanced and phase-encrypted 4ASK downlink transmission between two ONU groups, separated by a distance of 10 km, can achieve highly secure point-to-multipoint communication.
A thermal RC-network is presented for compact modelling of self-heating in a Si photonic ring modulator. The model captures the non-linear thermo-optical dynamics at low frequencies (<1 GHz), and accurately reproduces nanosecond-scale variations in optical transmission as observed in experiments.
We investigate the theoretical properties of a novel all-optical switch design consisting of a saturable absorber in an add-drop ring resonator. Using a modified transfer matrix method we demonstrate nonvolatile switching, where the routing persists even after the control signal is deactivated.
Photonic hardware represents a promising alternative to speed-up Neural Network (NN) computations, outperforming electronic counterparts in terms of speed, energy consumption and computing density. In this paper we exploit a Photonic-Aware Neural Network (PANN) architecture with unipolar and bipolar weight implementations, considering ReLU and photonic sigmoid as candidate activation functions to solve a heartbeat sound classification task. Results indicate that increasing the bitwidths during quantization improves the F1-score. The use of bipolar implementation for weight choice demonstrates better performance. ReLU is identified as a better nonlinearity. Finally, a multi-resolution scenario in the bipolar photonic-sigmoid experiment is evaluated, revealing that incorporating multi-resolution does not enhance the model's generalization ability if the bitwidth for the first layer remains fixed. However, the importance of the highest bitwidth at the NN inputs is highlighted.
We introduce the concept of GTE-assisted double-pass MZI and its operation using photon number states, based on quantum-optical formalism. We also theoretically show impressive interferometric fringes for different combinations of N-photon states as circuit inputs.
