Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Metody przeszukiwania przestrzeni planów realizacji zapytań za pomocą algorytmu IWO

The considered issue is a continuation of research concerning the application of the IWO algorithm for realization of the important task from the domain of distributed databases – predetermination of the progress of distributed data merging process. The paper includes modification proposal of method for exploration of the search space that forms a crucial part of the IWO algorithm.Prezentowane zagadnienie stanowi kontynuację badań poświęconych zastosowaniu algorytmu IWO do realizacji zadania istotnego dla dziedziny rozproszonych baz danych – określenia planu przebiegu procesu scalania danych rozproszonych. W niniejszym opracowaniu zaproponowano modyfikację ważnej części algorytmu IWO, jaką jest metoda penetracji przestrzeni poszukiwań

Zastosowanie algorytmu IWO do planowania procesu scalania danych rozproszonych

The considered issue is a continuation of research concerning the application of the evolutionary algorithm for realization of the important task from the domain of distributed databases – predetermination of the progress of distributed data merging process. Many common features of the IWO method and the evolutionary algorithm as well as the differences between them were mentioned in the paper along with results of comparative experimentsPrezentowane zagadnienie stanowi kontynuację badań poświęconych zastosowaniu algorytmu ewolucyjnego do realizacji zadania istotnego dla dziedziny rozproszonych baz danych – określenia planu przebiegu procesu scalania danych rozproszonych. Wskazano zarówno na cechy wspólne, jak i różnice między algorytmami IWO i ewolucyjnym, zamieszczono rezultaty eksperymentów porównawczych

Singing Voice Detection: A Survey

Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection

The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems

This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature