Estimating sizes of Type 2 AGN narrow-line regions from multiple survey spectra -- a demonstration

In the Letter, an interesting method is proposed to estimate size of narrow emission lines regions (NLRs) of a Type-2 AGN SDSS J083823.91+490241.1 (=SDSS J0838) at a redshift of 0.101, by comparing spectroscopic properties through the SDSS fiber (MJD=51873) (diameter of 3 arcseconds) and through the eBOSS fiber (MJD=55277) (diameter of 2 arcseconds). After subtractions of pPXF method determined host galaxy contributions, the narrow emission lines of SDSS J0838 in the SDSS spectrum and in the eBOSS spectrum can be well measured by Gaussian functions, leading more than 90\% of [O~{\sc iii}] emissions to be covered by the eBOSS fiber with diameter of 2 arcseconds. Meanwhile, both none broad emission components and none-variabilities of ZTF 3years-long g/r-band light curves can be applied to confirm SDSS J0838 as a Type-2 AGN, indicating few orientation effects on the projected NLRs size in SDSS J0838. Therefore, upper limit about 1arcsecond (2250pc) of the NLRs size can be reasonably accepted in SDSS J0838. Combining with the intrinsic reddening corrected [O~{\sc iii}] line luminosity, the upper limit of NLRs size in SDSS J0838 well lies within the 99.9999\% confidence bands of the R-L empirical relation for NLRs in AGN.Comment: 6 pages, 5 figures, accepted to be published in MNRA

EACOFT: an energy-aware correlation filter for visual tracking.

Correlation filter based trackers attribute to its calculation in the frequency domain can efficiently locate targets in a relatively fast speed. This characteristic however also limits its generalization in some specific scenarios. The reasons that they still fail to achieve superior performance to state-of-the-art (SOTA) trackers are possibly due to two main aspects. The first is that while tracking the objects whose energy is lower than the background, the tracker may occur drift or even lose the target. The second is that the biased samples may be inevitably selected for model training, which can easily lead to inaccurate tracking. To tackle these shortcomings, a novel energy-aware correlation filter (EACOFT) based tracking method is proposed, in our approach the energy between the foreground and the background is adaptively balanced, which enables the target of interest always having a higher energy than its background. The samples’ qualities are also evaluated in real time, which ensures that the samples used for template training are always helpful with tracking. In addition, we also propose an optimal bottom-up and top-down combined strategy for template training, which plays an important role in improving both the effectiveness and robustness of tracking. As a result, our approach achieves a great improvement on the basis of the baseline tracker, especially under the background clutter and fast motion challenges. Extensive experiments over multiple tracking benchmarks demonstrate the superior performance of our proposed methodology in comparison to a number of the SOTA trackers

Autonomous Learning of Speaker Identity and WiFi Geofence From Noisy Sensor Data

A fundamental building block towards intelligent environments is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit unique vocal characteristics as people interact with one another in common spaces. However, manually enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. Instead, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation, e.g., sniffed wireless Media Access Control (MAC) addresses, can we learn to associate a specific identity with a particular voiceprint? To address this problem, this paper advocates an Internet of Things (IoT) solution and proposes to use co-located WiFi as supervisory weak labels to automatically bootstrap the labelling process. In particular, a novel cross-modality labelling algorithm is proposed that jointly optimises the clustering and association process, which solves the inherent mismatching issues arising from heterogeneous sensor data. At the same time, we further propose to reuse the labelled data to iteratively update wireless geofence models and curate device specific thresholds. Extensive experimental results from two different scenarios demonstrate that our proposed method is able to achieve 2-fold improvement in labelling compared with conventional methods and can achieve reliable speaker recognition in the wild

Structural analysis of a trimeric assembly of the mitochondrial dynamin-like GTPase Mgm1.

The fusion of inner mitochondrial membranes requires dynamin-like GTPases, Mgm1 in yeast and OPA1 in mammals, but how they mediate membrane fusion is poorly understood. Here, we determined the crystal structure of Saccharomyces cerevisiae short Mgm1 (s-Mgm1) in complex with GDP. It revealed an N-terminal GTPase (G) domain followed by two helix bundles (HB1 and HB2) and a unique C-terminal lipid-interacting stalk (LIS). Dimers can form through antiparallel HB interactions. Head-to-tail trimers are built by intermolecular interactions between the G domain and HB2-LIS. Biochemical and in vivo analyses support the idea that the assembly interfaces observed here are native and critical for Mgm1 function. We also found that s-Mgm1 interacts with negatively charged lipids via both the G domain and LIS. Based on these observations, we propose that membrane targeting via the G domain and LIS facilitates the in cis assembly of Mgm1, potentially generating a highly curved membrane tip to allow inner membrane fusion

Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources

Drude Conductivity of Dirac Fermions in Graphene

Electrons moving in graphene behave as massless Dirac fermions, and they exhibit fascinating low-frequency electrical transport phenomena. Their dynamic response, however, is little known at frequencies above one terahertz (THz). Such knowledge is important not only for a deeper understanding of the Dirac electron quantum transport, but also for graphene applications in ultrahigh speed THz electronics and IR optoelectronics. In this paper, we report the first measurement of high-frequency conductivity of graphene from THz to mid-IR at different carrier concentrations. The conductivity exhibits Drude-like frequency dependence and increases dramatically at THz frequencies, but its absolute strength is substantially lower than theoretical predictions. This anomalous reduction of free electron oscillator strength is corroborated by corresponding changes in graphene interband transitions, as required by the sum rule. Our surprising observation indicates that many-body effects and Dirac fermion-impurity interactions beyond current transport theories are important for Dirac fermion electrical response in graphene

Dynamic Transformation of Nano-MoS2 in a Soil-Plant System Empowers Its Multifunctionality on Soybean Growth

Molybdenum disulfide (nano-MoS2) nanomaterials have shown great potential for biomedical and catalytic applications due to their unique enzyme-mimicking properties. However, their potential agricultural applications have been largely unexplored. A key factor prior to the application of nano-MoS2 in agriculture is understanding its behavior in a complex soil-plant system, particularly in terms of its transformation. Here, we investigate the distribution and transformation of two types of nano-MoS2 (MoS2 nanoparticles and MoS2 nanosheets) in a soil-soybean system through a combination of synchrotron radiation-based X-ray absorption near-edge spectroscopy (XANES) and single-particle inductively coupled plasma mass spectrometry (SP-ICP-MS). We found that MoS2 nanoparticles (NPs) transform dynamically in soil and plant tissues, releasing molybdenum (Mo) and sulfur (S) that can be incorporated gradually into the key enzymes involved in nitrogen metabolism and the antioxidant system, while the rest remain intact and act as nanozymes. Notably, there is 247.9 mg/kg of organic Mo in the nodule, while there is only 49.9 mg/kg of MoS2 NPs. This study demonstrates that it is the transformation that leads to the multifunctionality of MoS2, which can improve the biological nitrogen fixation (BNF) and growth. Therefore, MoS2 NPs enable a 30% increase in yield compared to the traditional molybdenum fertilizer (Na2MoO4). Excessive transformation of MoS2 nanosheets (NS) leads to the overaccumulation of Mo and sulfate in the plant, which damages the nodule function and yield. The study highlights the importance of understanding the transformation of nanomaterials for agricultural applications in future studies.</p

Adverse drug events associated with linezolid administration: a real-world pharmacovigilance study from 2004 to 2023 using the FAERS database

Introduction: Linezolid is an oxazolidinone antibiotic that is active against drug-resistant Gram-positive bacteria and multidrug-resistant Mycobacterium tuberculosis. Real-world studies on the safety of linezolid in large populations are lacking. This study aimed to determine the adverse events associated with linezolid in real-world settings by analyzing data from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).Methods: We retrospectively extracted reports on adverse drug events (ADEs) from the FAERS database from the first quarter of 2004 to that of 2023. By using disproportionality analysis including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), along with the multi-item gamma Poisson shrinker (MGPS), we evaluated whether there was a significant association between linezolid and ADE. The time to onset of ADE was further analyzed in the general population and within each age, weight, reporting population, and weight subgroups.Results: A total of 11,176 reports of linezolid as the “primary suspected” drug and 263 significant adverse events of linezolid were identified, including some common adverse events such as thrombocytopenia (n = 1,139, ROR 21.98), anaemia (n = 704, ROR 7.39), and unexpected signals that were not listed on the drug label such as rhabdomyolysis (n = 90, ROR 4.33), and electrocardiogram QT prolonged (n = 73, ROR 4.07). Linezolid-induced adverse reactions involved 27 System Organ Class (SOC). Gender differences existed in ADE signals related to linezolid. The median onset time of all ADEs was 6 days, and most ADEs (n = 3,778) occurred within the first month of linezolid use but some may continue to occur even after a year of treatment (n = 46).Conclusion: This study reports the time to onset of adverse effects in detail at the levels of SOC and specific preferred term (PT). The results of our study provide valuable insights for optimizing the use of linezolid and reducing potential side effects, expected to facilitate the safe use of linezolid in clinical settings

Intraband Optical Transitions in Graphene

Abstract: We measured tunable interband and intraband transitions in graphene using infrared spectroscopy. Graphene electrons have strong intraband absorption at terahertz frequency range. The absorption spectra are described by a Drude-like frequency dependence

A Tunable Phonon-Exciton Fano System in Bilayer Graphene

Interference between different possible paths lies at the heart of quantum physics. Such interference between coupled discrete and continuum states of a system can profoundly change its interaction with light as seen in Fano resonance. Here we present a unique many-body Fano system composed of a discrete phonon vibration and continuous electron-hole pair transitions in bilayer graphene. Mediated by the electron-phonon interactions, the excited state is described by new quanta of elementary excitations of hybrid phonon-exciton nature. Infrared absorption of the hybrid states exhibit characteristic Fano lineshapes with parameters renormalized by many-body interactions. Remarkably, the Fano resonance in bilayer graphene is continuously tunable through electrical gating. Further control of the phonon-exciton coupling may be achieved with an optical field exploiting the excited state infrared activity. This tunable phonon-exciton system also offers the intriguing possibility of a 'phonon laser' with stimulated phonon amplification generated by population inversion of band-edge electrons.Comment: 21 pages, 3 figure