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For taxonomic classification, we are asked to index the genomes in a phylogenetic tree such that later, given a DNA read, we can quickly choose a small subtree likely to contain the genome from which that read was drawn. Although popular classifiers such as Kraken use $k$-mers, recent research indicates that using maximal exact matches (MEMs) can lead to better classifications. For example, we can build an augmented FM-index over the the genomes in the tree concatenated in left-to-right order; for each MEM in a read, find the interval in the suffix array containing the starting positions of that MEM's occurrences in those genomes; find the minimum and maximum values stored in that interval; take the lowest common ancestor (LCA) of the genomes containing the characters at those positions. This solution is practical, however, only when the total size of the genomes in the tree is fairly small. In this paper we consider applying the same solution to three lossily compressed representations of the genomes' concatenation: a KATKA kernel, which discards characters that are not in the first or last occurrence of any $k_{\max}$-tuple, for a parameter $k_{\max}$; a minimizer digest; a KATKA kernel of a minimizer digest. With a test dataset and these three representations of it, simulated reads and various parameter settings, we checked how many reads' longest MEMs occurred only in the sequences from which those reads were generated ("true positive" reads). For some parameter settings we achieved significant compression while only slightly decreasing the true-positive rate.

Rising urban populations have led to a surge in vehicle use and made traffic monitoring and management indispensable. Acoustic traffic monitoring (ATM) offers a cost-effective and efficient alternative to more computationally expensive methods of monitoring traffic such as those involving computer vision technologies. In this paper, we present MVD and MVDA: two open datasets for the development of acoustic traffic monitoring and vehicle-type classification algorithms, which contain audio recordings of moving vehicles. The dataset contain four classes- Trucks, Cars, Motorbikes, and a No-vehicle class. Additionally, we propose a novel and efficient way to accurately classify these acoustic signals using cepstrum and spectrum based local and global audio features, and a multi-input neural network. Experimental results show that our methodology improves upon the established baselines of previous works and achieves an accuracy of 91.98% and 96.66% on MVD and MVDA Datasets, respectively. Finally, the proposed model was deployed through an Android application to make it accessible for testing and demonstrate its efficacy.

One of the main tasks of Natural Language Processing (NLP), is Named Entity Recognition (NER). It is used in many applications and also can be used as an intermediate step for other tasks. We present ANER, a web-based named entity recognizer for the Arabic, and Arabizi languages. The model is built upon BERT, which is a transformer-based encoder. It can recognize 50 different entity classes, covering various fields. We trained our model on the WikiFANE\_Gold dataset which consists of Wikipedia articles. We achieved an F1 score of 88.7\%, which beats CAMeL Tools' F1 score of 83\% on the ANERcorp dataset, which has only 4 classes. We also got an F1 score of 77.7\% on the NewsFANE\_Gold dataset which contains out-of-domain data from News articles. The system is deployed on a user-friendly web interface that accepts users' inputs in Arabic, or Arabizi. It allows users to explore the entities in the text by highlighting them. It can also direct users to get information about entities through Wikipedia directly. We added the ability to do NER using our model, or CAMeL Tools' model through our website. ANER is publicly accessible at \url{http://www.aner.online}. We also deployed our model on HuggingFace at https://huggingface.co/boda/ANER, to allow developers to test and use it.

Core-collapse supernovae (CCSNe) are potentially capable of producing large quantities of dust, with strong evidence that ejecta dust masses can grow significantly over extended periods of time. Red-blue asymmetries in the broad emission lines of CCSNe can be modelled using the Monte Carlo radiative transfer code DAMOCLES, to determine ejecta dust masses. To facilitate easier use of DAMOCLES, we present a Tkinter graphical user interface (GUI) running DAMOCLES. The GUI was tested by high school students as part of the Original Research By Young Twinkle Students (ORBYTS) programme, who used it to measure the dust masses formed at two epochs in two Type IIP CCSNe: SN 2012aw and iPTF14hls, demonstrating that a wide range of people can contribute significantly to scientific advancement. Bayesian methods were used to quantify uncertainties on our model parameters. From the presence of a red scattering wing in the day 1863 H$\alpha$ profile of SN 2012aw, we were able to constrain the dust composition to large (radius $>0.1 \mu$m) silicate grains, with a dust mass of $6.0^{+21.9}_{-3.6}\times10^{-4} M_\odot$. From the day 1158 H$\alpha$ profile of SN 2012aw, we found a dust mass of $3.0^{+14}_{-2.5}\times10^{-4}$ M$_\odot$. For iPTF14hls, we found a day 1170 dust mass of 8.1 $^{+81}_{-7.6}\times10^{-5}$ M$_{\odot}$ for a dust composition consisting of 50% amorphous carbon and 50% astronomical silicate. At 1000 days post explosion, SN 2012aw and iPTF14hls have formed less dust than SN 1987A, suggesting that SN 1987A could form larger dust masses than other Type IIP's., Comment: Accepted by MNRAS on 28/11/2022, 10 pages, 6 figures. Author accepted manuscript

In this work, we investigated the effect of pitch distance on the statistical variation and morphology of extrusion in Cu TSVs and the underlying mechanisms. Extrusion statistics were obtained from TSV samples with two different pitch distances. A notable increase in the magnitude of extrusion was observed in vias with smaller pitch, yet the extrusion spread was largely unaffected. The morphologies of the extruded vias were characterized and categorized, and finite element analysis was carried out to study the effect of pitch distance on stress and deformation. The results suggested that the overlapping of stress fields from neighboring vias resulted in larger stress in the small-pitch vias, which subsequently led to higher extrusion. The morphologies observed in the extruded vias were related to the operation of different deformation mechanisms under the combined effect of stress and microstructure. The statistical spread of via extrusion, which was similar in both groups of vias, was related to the stochastic nature of the via microstructure. By using a thin cap layer of Ta to suppress the vacancy sources at the via top surface, the adverse effect of the pitch distance was minimized and a pronounced reduction of extrusion was achieved in vias of both pitch distances.

A novel moving-block technique is presented for parameter-identification tasks concerning the aeroelastic modes of rotary wings, using a least-squares solver in the time domain to estimate dampings and eigenvector components. The least-squares moving-block (LSMB) technique is shown to yield superior estimates to those of the previous moving-block technique for four different analytical test cases. An additional advantage of the LSMB approach is its ability to estimate eigenvector components with good accuracy; this feature is useful in assessing the coupling of the different modes. The method can also be used to perform periodic modal identification, and is applicable to experimental data for the extraction of modal information.