Learning Deep Morphological Networks with Neural Architecture Search

Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. The majority of non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for a variety of image processing problems. We investigate the utility of integrating these operations in an end-to-end deep learning framework in this paper. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification and edge detection.Comment: 19 page

Ambient Pressure, Low-Temperature Synthesis and Characterization of Colloidal InN Nanocrystals

Highly soluble, non-aggregated colloidal wurtzite InN nanocrystals were obtained through an ambient pressure, low-temperature method followed by post-synthesis treatment with nitric acid.Engineering and Applied Science

Discrepancy Between Social Status and Implicit Self-Esteem Prompts Preference For Counterfeit Luxury

The current research explores how perceived social status and implicit self-esteem influence counterfeit luxury consumption. Results of two studies showed a novel effect that a discrepancy between social status and implicit self-esteem led to higher preference for counterfeit luxury products

Graph Learning of Multifaceted Motivations for Online Engagement Prediction in Counter-party Social Networks

Social media has emerged as an essential venue to invigorate online political engagement. However, political engagement is multifaceted and impacted by both individuals\u27 self-motivation and social influence from peers and remains challenging to model in a counter-party network. Therefore, we propose a counter-party graph representation learning model to study individuals\u27 intrinsic and extrinsic motivations for online political engagement. Firstly, we capture users\u27 intrinsic political interests providing self-motivation from a user-topic network. Then, we encode how users cast influence on others from the inner-/counter-party through a user-user network. With the learned embedding of intrinsic and extrinsic motivations, we model the interactions between these two facets and utilize the dependency by deep sequential model decoding. Finally, extensive experiments using Twitter data related to the 2020 U.S. presidential election and the 2019 HK protests validate the model\u27s predictive power. This study has implications for online political engagement, political participation, and political polarization

Algebraic properties of an integrable t-J model with impurities

We investigate the algebraic structure of a recently proposed integrable $t-J$ model with impurities. Three forms of the Bethe ansatz equations are presented corresponding to the three choices for the grading. We prove that the Bethe ansatz states are highest weight vectors of the underlying $gl(2|1)$ supersymmetry algebra. By acting with the $gl(2|1)$ generators we construct a complete set of states for the model.Comment: 20 pages, LaTe

Outcome of Respiratory Syncytial Virus Lower Respiratory Tract Disease in Hematopoietic Cell Transplant Recipients Receiving Aerosolized Ribavirin: Significance of Stem Cell Source and Oxygen Requirement

AbstractRespiratory syncytial virus (RSV) infection is an important complication after hematopoietic cell transplantation (HCT), and RSV lower respiratory tract disease (LRD) results in substantial early mortality and late airflow obstruction among survivors. Factors associated with poor outcome are unknown. We evaluated the effect of transplant and treatment factors on overall survival, mortality from respiratory failure, and pulmonary function among 82 HCT recipients who had RSV LRD between 1990 and 2011. All patients received aerosolized ribavirin. In multivariable analyses, only the use of marrow or cord blood as graft source (adjusted hazard ratio [aHR], 4.1; 95% confidence interval [CI], 1.8 to 9.0; P < .001) and oxygen requirement (aHR, 3.3; 95% CI, 1.5 to 6.7; P = .003) remained independently associated with overall mortality and death due to respiratory failure (aHR, 4.7; 95% CI, 1.8 to 13; P = .002 and aHR, 5.4; 95% CI, 1.8 to 16; P = .002, respectively). Antibody-based treatments, including intravenous immunoglobulin and palivizumab, were not independently associated with improved outcome and did not alter the associations of the graft source and oxygen requirements in statistical models. In conclusion, use of peripheral blood stem cells as graft source and lack of oxygen requirement at diagnosis appear to be important factors associated with improved survival of HCT recipients with RSV LRD. These results may explain differences in outcomes reported from RSV infection over time and may guide the design of future interventional trials

Forecasting Cosmological Constraints from Redshift Surveys

Observations of redshift-space distortions in spectroscopic galaxy surveys offer an attractive method for observing the build-up of cosmological structure, which depends both on the expansion rate of the Universe and our theory of gravity. In this paper we present a formalism for forecasting the constraints on the growth of structure which would arise in an idealized survey. This Fisher matrix based formalism can be used to study the power and aid in the design of future surveys.Comment: 7 pages, 5 figures, minor revisions to match version accepted by MNRA

Complex I deficiency due to selective loss of Ndufs4 in the mouse heart results in severe hypertrophic cardiomyopathy.

Mitochondrial complex I, the primary entry point for electrons into the mitochondrial respiratory chain, is both critical for aerobic respiration and a major source of reactive oxygen species. In the heart, chronic dysfunction driving cardiomyopathy is frequently associated with decreased complex I activity, from both genetic and environmental causes. To examine the functional relationship between complex I disruption and cardiac dysfunction we used an established mouse model of mild and chronic complex I inhibition through heart-specific Ndufs4 gene ablation. Heart-specific Ndufs4-null mice had a decrease of ∼ 50% in complex I activity within the heart, and developed severe hypertrophic cardiomyopathy as assessed by magnetic resonance imaging. The decrease in complex I activity, and associated cardiac dysfunction, occurred absent an increase in mitochondrial hydrogen peroxide levels in vivo, accumulation of markers of oxidative damage, induction of apoptosis, or tissue fibrosis. Taken together, these results indicate that diminished complex I activity in the heart alone is sufficient to drive hypertrophic cardiomyopathy independently of alterations in levels of mitochondrial hydrogen peroxide or oxidative damage

Combinations of β-lactam or aminoglycoside antibiotics with plectasin are synergistic against methicillin-sensitive and methicillin-resistant Staphylococcus aureus.

Bacterial infections remain the leading killer worldwide which is worsened by the continuous emergence of antibiotic resistance. In particular, methicillin-sensitive (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) are prevalent and the latter can be difficult to treat. The traditional strategy of novel therapeutic drug development inevitably leads to emergence of resistant strains, rendering the new drugs ineffective. Therefore, rejuvenating the therapeutic potentials of existing antibiotics offers an attractive novel strategy. Plectasin, a defensin antimicrobial peptide, potentiates the activities of other antibiotics such as β-lactams, aminoglycosides and glycopeptides against MSSA and MRSA. We performed in vitro and in vivo investigations to test against genetically diverse clinical isolates of MSSA (n = 101) and MRSA (n = 115). Minimum inhibitory concentrations (MIC) were determined by the broth microdilution method. The effects of combining plectasin with β-lactams, aminoglycosides and glycopeptides were examined using the chequerboard method and time kill curves. A murine neutropenic thigh model and a murine peritoneal infection model were used to test the effect of combination in vivo. Determined by factional inhibitory concentration index (FICI), plectasin in combination with aminoglycosides (gentamicin, neomycin or amikacin) displayed synergistic effects in 76-78% of MSSA and MRSA. A similar synergistic response was observed when plectasin was combined with β-lactams (penicillin, amoxicillin or flucloxacillin) in 87-89% of MSSA and MRSA. Interestingly, no such interaction was observed when plectasin was paired with vancomycin. Time kill analysis also demonstrated significant synergistic activities when plectasin was combined with amoxicillin, gentamicin or neomycin. In the murine models, plectasin at doses as low as 8 mg/kg augmented the activities of amoxicillin and gentamicin in successful treatment of MSSA and MRSA infections. We demonstrated that plectasin strongly rejuvenates the therapeutic potencies of existing antibiotics in vitro and in vivo. This is a novel strategy that can have major clinical implications in our fight against bacterial infections