An exomoon survey of 70 cool giant exoplanets and the new candidate Kepler-1708 b-i

Exomoons represent a crucial missing puzzle piece in our efforts to understand extrasolar planetary systems. To address this deficiency, we here describe an exomoon survey of 70 cool, giant transiting exoplanet candidates found by Kepler. We identify only one exhibiting a moon-like signal that passes a battery of vetting tests: Kepler-1708 b. We show that Kepler-1708 b is a statistically validated Jupiter-sized planet orbiting a Sun-like quiescent star at 1.6 au. The signal of the exomoon candidate, Kepler-1708 b-i, is a 4.8σ effect and is persistent across different instrumental detrending methods, with a 1% false-positive probability via injection–recovery. Kepler-1708 b-i is ~2.6 Earth radii and is located in an approximately coplanar orbit at ~12 planetary radii from its ~1.6 au Jupiter-sized host. Future observations will be necessary to validate or reject the candidate.

Distribution characteristics of SARS-CoV-2 IgM and IgG in false-positive results detected by Chemiluminescent immunoassay

There have been several false-positive results in the antibody detection of the COVID-19. This study aims to analyze the distribution characteristics of SARS-CoV-2 IgM and IgG in false-positive results detected using chemiluminescent immunoassay. The characteristics of the false-positive results in SARS-CoV-2 IgM and IgG testing were retrospectively analyzed. The dynamic changes in the results of SARS-CoV-2 IgM and IgG antibodies were observed. The false-positive proportion of the single SARS-CoV-2 IgM positive results was 95.88%, which was significantly higher than those of the single SARS-CoV-2 IgG positive results (67.50%) (P < 0.001) and SARS-CoV-2 IgM & IgG positive results (29.55%) (P < 0.001). The S/CO of the SARS-CoV-2 IgM and IgG in false-positive results ranged from 1.0 to 50.0. The false-positive probability of SARS-CoV-2 IgM in the S/CO range (1.0 ~ 3.0) was 91.73% (77/84), and the probability of false-positive of SARS-CoV-2 IgG in the S/CO range (1.0 ~ 2.0) was 85.71% (24/28). Dynamic monitoring showed that the S/CO values of IgM in false-positive results decreased or remained unchanged, whereas the S/CO values of IgG in false-positive results only decreased. The possibility of false-positive of the single SARS-CoV-2 IgM positive and single SARS-CoV-2 IgG positive results was high. As the value of S/CO decreased, the probability of false-positive consequently increased, especially among the single SARS-CoV-2 IgM positive results.

BionetBF: A Novel Bloom Filter for Faster Membership Identification of Paired Biological Network Data

Biological network represents the interaction or relationship between the biological entities such as proteins and genes of a biological process. A biological network with thousands of millions of vertices makes its processing complex and challenging. In this article, we have proposed a novel Bloom Filter for biological networks, called BionetBF, to provide fast membership identification of the biological network edges or paired biological data. BionetBF is capable of executing millions of operations within a second on datasets having millions of paired biological data while occupying tiny amount of main memory. We have conducted rigorous experiments to prove the performance of BionetBF with large datasets. The experiment is conducted using 12 generated datasets and three biological network datasets. BionetBF demonstrates higher performance while maintaining a 0.001 false positive probability. BionetBF is also compared with other filters: Cuckoo Filter and Libbloom, where BionetBF proves its supremacy by exhibiting higher performance with a smaller sized memory compared with large sized filters of Cuckoo Filter and Libbloom.

Designing Cuckoo Based Pending Interest Table for CCN Networks

<p class="JESTECAbstract">Content Centric Networking (CCN) is a modern architecture that got wide attention in the current researches as a substitutional for the current IP-based architecture. Many studies have been investigated on this novel architecture but only little of them focused on Pending Interest Table (PIT) which is very important component in every CCN router. PIT has fundamental role in packet processing in both upstream process (Interest packets) and downstream process (Data packets). PIT must be fast enough in order to not become an obstruction in the packet processing and also it must be big enough to save a lot of incoming information. In this paper, we suggest a new PIT design and implementation named CF-PIT for CCN router. Our PIT design depends on modifying and utilizing an approximate data structure called Cuckoo filter (CF). Cuckoo filter has ideal characteristics like: high insertion/query/deletion performance, acceptable storage demands and false positive probability which make it with our modification convenient for PIT implementation. The experimental results showed that our CF-PIT design has high performance in different side of views which make it very suitable to be implemented on CCN routers.</p>

Preregistration is important, but not enough: Many statistical analyses can inflate the risk of false-positives

Even with a small number of variables researchers can test many possible models of their data thus increasing the risk of false-positive results. Using combinatorics, we show that one key independent variable and three covariates can generate 95 possible models, while six covariates can generate over 2.3 million models. Such large model sets nearly guarantee false-positive results. Using simulation, we show that preregistering a single analysis with a key independent variable heavily reduces the risk of false-positives. However, even so, many models produce false-positive results with a much higher probability than the expected 5%. The worst-case scenario are models with interactions between binary dummy coded variables and omitted main effects. Such models can generate false-positive results up to 34.5% of the time. While preregistration is a crucial step towards reducing false-positive results, researchers need to carefully consider what analyses they plan and we provide recommendations for what analyses to avoid. Our findings also suggest that interpreting p-values in exploratory analyses might be meaningless considering the high false-positive probability.

Re-analysis of the 267 GHz ALMA observations of Venus

Context. ALMA observations of Venus at 267 GHz that show the apparent presence of phosphine (PH3) in its atmosphere have been presented in the literature. Phosphine currently has no evident production routes on the planet’s surface or in its atmosphere. Aims. The aim of this work is to assess the statistical reliability of the line detection via independent re-analysis of the ALMA data. Methods. The ALMA data were reduced the same way as in the published study, following the provided scripts. First, the spectral analysis presented in the study was reproduced and assessed. Subsequently, the spectrum, including its dependence on selected ALMA baselines, was statistically evaluated. Results. We find that the 12th-order polynomial fit to the spectral passband utilised in the published study leads to spurious results. Following their recipe, five other > 10σ lines can be produced in absorption or emission within 60 km s−1 from the PH3 1−0 transition frequency by suppressing the surrounding noise. Our independent analysis shows a feature near the PH3 frequency at a ∼2σ level, below the common threshold for statistical significance. Since the spectral data have a non-Gaussian distribution, we consider a feature at such level as statistically unreliable, which cannot be linked to a false positive probability. Conclusions. We find that the published 267 GHz ALMA data provide no statistical evidence for phosphine in the atmosphere of Venus.

Planetary candidates transiting cool dwarf stars from campaigns 12 to 15 of K2

ABSTRACT We analysed the photometry of 20 038 cool stars from campaigns 12, 13, 14, and 15 of the K2 mission in order to detect, characterize, and validate new planetary candidates transiting low-mass stars. We present a catalogue of 25 new periodic transit-like signals in 22 stars, of which we computed the parameters of the stellar host for 19 stars and the planetary parameters for 21 signals. We acquired speckle and AO images, and also inspected archival Pan-STARRS1 images and Gaia DR2 to discard the presence of close stellar companions and to check possible transit dilutions due to nearby stars. False positive probability (FPP) was computed for 22 signals, obtaining FPP &lt; $1{{\ \rm per\ cent}}$ for 17. We consider 12 of them as statistically validated planets. One signal is a false positive and the remaining 12 signals are considered as planet candidates. 20 signals have an orbital period of P$_{\rm orb} \lt 10\,\mathrm{ d}$, 2 have $10\, \mathrm{ d} \lt $ P$_{\rm orb} \lt 20\, \mathrm{ d}$, and 3 have P$_{\rm orb} \gt 20\, \mathrm{ d}$. Regarding radii, 11 candidates and validated planets have computed radius R &lt; 2R⊕, 9 have 2R⊕ &lt; R &lt; 4R⊕, and 1 has R &gt; 4R⊕. Two validated planets and two candidates are located in moderately bright stars ($\rm \mathit{ m}_{kep}\lt 13$) and two validated planets and three candidates have derived orbital radius within the habitable zone according to optimistic models. Of special interest is the validated warm super-Earth K2-323 b (EPIC 248616368 b) with T$_{\rm eq} = 318^{+24}_{-43} \, \mathrm{ K}$, S$_{\rm p} = 1.7\pm 0.2 \, \mathrm{ S}_{\oplus }$, and R$_{\rm p} = 2.1\pm 0.1 \, \mathrm{ R}_{\oplus }$, located in an m$\rm _{kep}$ = 14.13 star.

Transit least-squares survey

The Sun-like star Kepler-160 (KOI-456) has been known to host two transiting planets, Kepler-160 b and c, of which planet c shows substantial transit-timing variations (TTVs). We studied the transit photometry and the TTVs of this system in our search for a suspected third planet. We used the archival Kepler photometry of Kepler-160 to search for additional transiting planets using a combination of our Wōtan detrending algorithm and our transit least-squares detection algorithm. We also used the Mercury N-body gravity code to study the orbital dynamics of the system in trying to explain the observed TTVs of planet c. First, we recovered the known transit series of planets Kepler-160 b and c. Then we found a new transiting candidate with a radius of 1.91−0.14+0.17 Earth radii (R⊕), an orbital period of 378.417−0.025+0.028 d, and Earth-like insolation. The vespa software predicts that this signal has an astrophysical false-positive probability of FPP3 = 1.8 × 10−3 when the multiplicity of the system is taken into account. Kepler vetting diagnostics yield a multiple event statistic of MES = 10.7, which corresponds to an ~85% reliability against false alarms due to instrumental artifacts such as rolling bands. We are also able to explain the observed TTVs of planet c with the presence of a previously unknown planet. The period and mass of this new planet, however, do not match the period and mass of the new transit candidate. Our Markov chain Monte Carlo simulations of the TTVs of Kepler-160 c can be conclusively explained by a new nontransiting planet with a mass between about 1 and 100 Earth masses and an orbital period between about 7 and 50 d. We conclude that Kepler-160 has at least three planets, one of which is the nontransiting planet Kepler-160 d. The expected stellar radial velocity amplitude caused by this new planet ranges between about 1 and 20 m s−1. We also find the super-Earth-sized transiting planet candidate KOI-456.04 in the habitable zone of this system, which could be the fourth planet.

Midas: Microcluster-Based Detector of Anomalies in Edge Streams

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose Midas, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. Midas has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 108–505 times faster than state-of-the-art approaches; (c) it provides 46%-52% higher accuracy (in terms of AUC) than state-of-the-art approaches.

Performance Analysis of Addressing Mechanisms in Inter-Operable IoT Device with Low-Power Wake-Up Radio

Internet of Things (IoT) technology is rapidly expanding the use of its application, from individuals to industries. Owing to this, the number of IoT devices has been exponentially increasing. Considering the massive number of the devices, overall energy consumption is becoming more serious. From this point of view, attaching low-power wake-up radio (WUR) to the devices can be one of the candidate solutions to deal with this problem. With WUR, IoT devices can go to sleep until WUR receives a wake-up signal, which enables a significant reduction of its power consumption. Meanwhile, one concern for WUR operation is the addressing mechanism, since operational efficiency of the wake-up feature can significantly vary depending on the addressing mechanism. We therefore introduce addressing mechanisms for IoT devices equipped with WUR and analyze their performances, such as elapsed time to wake up, false positive probability and power/energy consumption, to provide appropriate addressing mechanisms over practical environments for IoT devices with WUR.