Robusni algoritam praćenja mjerenjem smjera pomoću strukturiranog potpunog Kalmanovog filtra zasnovanog na metodi najmanjih kvadrata

A nonlinear approach called the robust structured total least squares kalman filter (RSTLS-KF) algorithm is proposed for solving tracking inaccuracy caused by outliers in bearings-only multi-station passive tracking. In that regard, the robust extremal function is introduced to the weighted structured total least squares (WSTLS) location criterion, and then the improved Danish equivalent weight function is built on the basis, which can identify outliers automatically and reduce the weight of the polluted data. Finally, the observation equation is linearized according to the RSTLS location result with the structured total least norm (STLN) solution. Hence location and velocity of the target can be given by the Kalman filter. Simulation results show that tracking performance of the RSTLS-KF is comparable or better than that of conventional algorithms. Furthermore, when outliers appear, the RSTLS-KF is accurate and robust, whereas the conventional algorithms become distort seriously.U ovome radu predložen je nelinearni pristup za rješavanje netočnosti uzrokovanih netipčnim vrijednostima kod praćenja mjerenjem smjera pasivnim senzorima s više stanica. Pristup je zasnovan na robusnom strukturiranom potpunom Kalmanovom filtru zasnovanom na metodi najmanjih kvadrata. Pomoću predložene metode moguće je estimirati položaj i brzinu praćenog objekta. Simulacijski rezultati pokazuju da je učinkovitost predloženog algoritma jednaka ili bolja od konvencionalnih algoritama. Nadalje, u prisustvu netipčnih vrijednosti mjerenja, predloženi algoritam zadržava točnost i robusnost, dok konvencionalni algoritmi pokazuju pogreške u estimaciji

Methylation protects microRNAs from an AGO1- associated activity that uridylates 5′ RNA fragments generated by AGO1 cleavage

In plants, methylation catalyzed by HEN1 (small RNA methyl transferase) prevents microRNAs (miRNAs) from degradation triggered by uridylation. Howmethylation antagonizes uridylation of miRNAs in vivo is not well understood. In addition, 5′ RNA fragments (5′ fragments) produced by miRNA-mediated RNA cleavage can be uridylated in plants and animals. However, the biological significance of this modification is unknown, and enzymes uridylating 5′ fragments remain to be identified. Here, we report that in Arabidopsis, HEN1 suppressor 1 (HESO1, a miRNA nucleotidyl transferase) uridylates 5′ fragments to trigger their degradation.We also show that Argonaute 1 (AGO1), the effector protein of miRNAs, interacts with HESO1 through its Piwi/Argonaute/Zwille and PIWI domains, which bind the 3′ end of miRNA and cleave the target mRNAs, respectively. Furthermore, HESO1 is able to uridylate AGO1-bound miRNAs in vitro. miRNA uridylation in vivo requires a functional AGO1 in hen1, in which miRNA methylation is impaired, demonstrating that HESO1 can recognize its substrates in the AGO1 complex. On the basis of these results, we propose that methylation is required to protect miRNAs from AGO1-associated HESO1 activity that normally uridylates 5′ fragments

Methylation protects microRNAs from an AGO1- associated activity that uridylates 5′ RNA fragments generated by AGO1 cleavage

In plants, methylation catalyzed by HEN1 (small RNA methyl transferase) prevents microRNAs (miRNAs) from degradation triggered by uridylation. Howmethylation antagonizes uridylation of miRNAs in vivo is not well understood. In addition, 5′ RNA fragments (5′ fragments) produced by miRNA-mediated RNA cleavage can be uridylated in plants and animals. However, the biological significance of this modification is unknown, and enzymes uridylating 5′ fragments remain to be identified. Here, we report that in Arabidopsis, HEN1 suppressor 1 (HESO1, a miRNA nucleotidyl transferase) uridylates 5′ fragments to trigger their degradation.We also show that Argonaute 1 (AGO1), the effector protein of miRNAs, interacts with HESO1 through its Piwi/Argonaute/Zwille and PIWI domains, which bind the 3′ end of miRNA and cleave the target mRNAs, respectively. Furthermore, HESO1 is able to uridylate AGO1-bound miRNAs in vitro. miRNA uridylation in vivo requires a functional AGO1 in hen1, in which miRNA methylation is impaired, demonstrating that HESO1 can recognize its substrates in the AGO1 complex. On the basis of these results, we propose that methylation is required to protect miRNAs from AGO1-associated HESO1 activity that normally uridylates 5′ fragments

PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars

We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |{\Delta}z| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown to decreases from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML .Comment: 10 pages, 8 figures, accepted for publication in MNRA

Experimental and theoretical analysis of microstructural evolution and deformation behaviors of CuW composites during equal channel angular pressing

CuW composites were synthesized using an equal channel angular pressing (ECAP) technique. Microstructural evolution during sintering process was investigated using both optical microscopy and transmission electron microscopy (TEM), and their deformation mechanisms were studied using finite element analysis (FEA). Results showed severe plastic deformation of the CuW composites and effective refinement of W grains after the ECAP process. TEM observation revealed that the ECAP process resulted in lamellar bands with high densities dislocations inside the composites. Effects of extrusion temperature and extrusion angles on stress-strain relationship and sizes of deformation zones after the ECAP process were investigated both theoretically and experimentally. When the extrusion angle was 90°, a maximum equivalent stress of ~1001 MPa was obtained when the extrusion test was done at room temperature of 22 °C, and this value was lower than compression strength of the CuW composites (1105.43 MPa). The maximum equivalent strains were varied between 0.5 and 0.7. However, when the extrusion temperature was increased to 550 °C and further to 900 °C, the maximum equivalent stresses were decreased sharply, with readings of 311 MPa and 68 MPa, respectively. When the extrusion angle was increased to 135°, the maximum equivalent stresses were found to be 716.9 MPa, 208 MPa, and 32 MPa for the samples extruded at temperatures of 22 °C, 550 °C and 900 °C, respectively. Simultaneously, the maximum equivalent strains were decreased to 0.2–0.4. Furthermore, results showed that the maximum equivalent stress was located on the sample's external surface and the stress values were gradually decreased from the surface to the center of samples, and the magnitudes of plastic deformation zones at the surface were much larger than those at the central part of the sintered samples. FEA simulation results were in good agreements with experimentally measured ones

One Transformer Can Understand Both 2D & 3D Molecular Data

Unlike vision and language data which usually has a unique format, molecules can naturally be characterized using different chemical formulations. One can view a molecule as a 2D graph or define it as a collection of atoms located in a 3D space. For molecular representation learning, most previous works designed neural networks only for a particular data format, making the learned models likely to fail for other data formats. We believe a general-purpose neural network model for chemistry should be able to handle molecular tasks across data modalities. To achieve this goal, in this work, we develop a novel Transformer-based Molecular model called Transformer-M, which can take molecular data of 2D or 3D formats as input and generate meaningful semantic representations. Using the standard Transformer as the backbone architecture, Transformer-M develops two separated channels to encode 2D and 3D structural information and incorporate them with the atom features in the network modules. When the input data is in a particular format, the corresponding channel will be activated, and the other will be disabled. By training on 2D and 3D molecular data with properly designed supervised signals, Transformer-M automatically learns to leverage knowledge from different data modalities and correctly capture the representations. We conducted extensive experiments for Transformer-M. All empirical results show that Transformer-M can simultaneously achieve strong performance on 2D and 3D tasks, suggesting its broad applicability. The code and models will be made publicly available at https://github.com/lsj2408/Transformer-M.Comment: 20 pages; ICLR 2023, Camera Ready Version; Code: https://github.com/lsj2408/Transformer-

catena-Poly[[(5-phenyl-2,2′-bipyridine-κ2 N,N′)copper(I)]-μ-thio­cyanido-κ2 N:S]

The title compound, [Cu(NCS)(C16H12N2)]n, was synthesised under hydro­thermal conditions. The CuI ion shows distorted tetra­hedral geometry being coordinated by two N atoms from a 5-phenyl-2,2′-bipyridine ligand and by the N and S atoms from two different thio­cyanate anions. The CuI ions are bridged by thio­cyanide groups, forming a one-dimensional coordination polymer along the b axis. The crystal packing is through van der Waals contacts and C—H⋯π inter­actions

Efficient Opinion Summarization on Comments with Online-LDA

Customer reviews and comments on web pages are important information n our daily life. For example, we prefer to choose a hotel with positive comments rom previous customers. As the huge amounts of such information demonstrate the haracteristics of big data, it places heavy burdens on the assimilation of the customercontributed pinions. To overcoming this problem, we study an efficient opinion ummarization approach for a set of massive user reviews and comments associated ith an online resource, to summarize the opinions into two categories, i.e., positive nd negative. In this paper, we proposed a framework including: (1) overcoming the ig data problem of online comments using the efficient online-LDA approach; (2) electing meaningful topics from the imbalanced data; (3) summarizing the opinion f comments with high precision and recall. This framework is different from much f the previous work in that the topics are pre-defined and selected the topics for etter opinion summarization. To evaluate the proposed framework, we perform the xperiments on a dataset of hotel reviews for the variety of topics contained. The esults show that our framework can gain a significant performance improvement on pinion summarization