Quantum State Tomography Inspired by Language Modeling

Quantum state tomography is an elementary tool to fully characterize an unknown quantum state. As the quantum hardware scales up in size, the standard quantum state tomography becomes increasingly challenging due to its exponentially growing complexity. In this work, we propose a scalable solution by considering state tomography as a language modeling task, where the unknown quantum state is treated as an unknown language, the correlation of the quantum state is interpreted as the semantic information specific to this language, and the measurement outcomes are simply the text instances generated from the language. Based on a customized transformer model from language modeling, we demonstrate that our method can accurately reconstruct prototypical pure and mixed quantum states using less samples than state-of-the-art methods. More importantly, our method can reconstruct a class of similar states simultaneously, in comparison with the existing neural network methods that need to train a model for each unknown state

Information Asymmetry and Accounting Conservatism under IFRS Adoption

LaFond and Watts (2008) provide evidence that information asymmetry might be a determinant of accounting conservatism. One implication of their paper is that regulators trying to reduce information asymmetry by lowering the level of accounting conservatism might be wrong. However, there is a trend in moving away from conservative accounting. The typical example is IFRS adoption. Therefore, this paper studies information asymmetry and accounting conservatism under IFRS adoption. The results show that the level of accounting conservatism decreases after mandatory IFRS adoption, but the adoption of IFRS is likely to weaken the relationship between information asymmetry and accounting conservatism. Moreover, this paper investigates how the change of accounting conservatism under IFRS is related to the change in information environment. The finding shows that accounting conservatism increases information environment, supporting the idea that, by providing comparatively credible information, conservative accounting is beneficial to the information environment

An Empirical Model for Estimating Soil Thermal Diffusivity from Texture, Bulk Density, and Degree of Saturation

Soil thermal diffusivity κ is an essential parameter for studying surface and subsurface heat transfer and temperature changes. It is well understood that κ mainly varies with soil texture, water content θ, and bulk density ρb, but few models are available to accurately quantify the relationship. In this study, an empirical model is developed for estimating κ from soil particle size distribution, ρb, and degree of water saturation Sr. The model parameters are determined by fitting the proposed equations to heat-pulse κdata for eight soils covering wide ranges of texture, ρb, and Sr. Independent evaluations with published κdata show that the new model describes the κ(Sr) relationship accurately, with root-mean-square errors less than 0.75 × 10−7 m2 s−1. The proposed κ(Sr) model also describes the responses of κ to ρb changes accurately in both laboratory and field conditions. The new model is also used successfully for predicting near-surface soil temperature dynamics using the harmonic method. The results suggest that this model provides useful estimates of κ from Sr, ρb, and soil texture

How to Face COVID-19 in Ophthalmology Practice

Background: The novel coronavirus pneumonia has attracted considerable attention from the international community. With the spread of outbreaks around the world, the WHO characterized COVID-19 as a pandemic. Methods: Relevant studies in PubMed were searched from January 1, 2020 to April 12, 2020, using the following search strategy: (“novel coronavirus pneumonia†OR “severe acute respiratory syndrome coronavirus 2†OR “coronavirus disease 2019†OR “COVID-19†OR “novel coronavirus pneumoniaâ€) AND (“ophthalmology†OR “ophthalmologist†OR “eye†OR “conjunctiva†OR “conjunctivitis†OR “corneal†OR “keratitisâ€). Results: SARS-CoV-2 can spread through aerosol and is detected in tears of patients with COVID-19 infection. Notably, some infected patients had conjunctivitis, and conjunctivitis was the first symptom in some patients later diagnosed to have COVID-19 infection. This would increase the risk for ophthalmologists through inpatient consultations or regular clinical practice. When dealing with seemingly regular ophthalmic patients, the vigilance of ophthalmologists and associated staff tends to be reduced. Conclusion: Ophthalmologists must continuously update their knowledge regarding COVID-19 and take effective measures to prevent COVID-19 transmission

An Improved Thermo-TDR Technique for Monitoring Soil Thermal Properties, Water Content, Bulk Density, and Porosity

The thermo-time domain reflectometry (thermo-TDR) technique is valuable for monitoring in situ soil water content (θ), thermal properties, bulk density (ρb), porosity (n), and air-filled porosity (na) in the vadose zone. However, the previous thermo-TDR sensor has several weaknesses, including limited precision of TDR waveforms due to the short probe length, small measurement volume, and thermal property estimation errors resulting from finite probe properties not accounted for by the heat pulse method. We have developed a new thermo-TDR sensor design for monitoring θ, thermal properties, ρb, n, and na. The new sensor has a robust heater probe (outer diameter of 2.38 mm and length of 70 mm) and a 10-mm spacing between the heater and sensing probes, which provides a sensing volume three times larger than that of the previous sensor. The identical cylindrical perfect conductors and the tangent line–second-order bounded mean oscillation theories were applied to analyze the raw data. Laboratory tests showed that θ values determined with the new sensor had a RMSE of 0.014 m3 m−3 compared with 0.016 to 0.026 m3 m−3 with the previous sensor. Soil thermal property estimates with the new sensor agreed well with modeled values. Soil ρb, n, and na derived from θ and thermal properties were consistent with those derived from gravimetric measurements. Thus, the new thermo-TDR sensor provides more accurate θ, thermal properties, ρb, n, and na values than the previous sensor

Fertilization and Cytogenetic Examination of Interspecific Reciprocal Hybridization between the Scallops, Chlamys farreri and Mimachlamys nobilis

Crossbreeding is a powerful tool for improving productivity and profitability in aquaculture. We conducted a pilot study of an artificial cross between two important cultivated scallops in China, Chlamys farreri and Mimachlamys nobilis, to test the feasibility of interspecific hybridization. Reciprocal hybridization experiments were performed using a single-pair mating strategy (M. nobilis ♀ × C. farreri ♂ and C. farreri ♀ × M. nobilis ♂). The fertilization of each pair was tracked using fluorescence staining of the gametes, and the chromosomes of the F1 hybrid larvae were examined via conventional karyotyping and genomic in situ hybridization (GISH). We observed moderate fertilization success in both interspecific crosses, although the overall fertilization was generally less rapid than that of intraspecific crosses. Conventional karyotyping showed that 70.4% of the viable F1 larvae in M. nobilis ♀ × C. farreri ♂ and 55.4% in C. farreri ♀ × M. nobilis ♂ comprised hybrid karyotypes (2n = 35 = 6m+5sm+11st+13t), and the results were further confirmed by GISH. Interestingly, we detected a few F1 from the M. nobilis ♀ × C. farreri ♂ cross that appeared to have developed gynogenetically. In addition, chromosome fragmentations, aneuploids and allopolyploids were observed in some F1 individuals. Our study presents evidence that the artificial cross between M. nobilis and C. farreri is experimentally possible. Further investigations of the potential heterosis of the viable F1 offspring at various developmental stages should be conducted to obtain a comprehensive evaluation of the feasibility of crossbreeding between these two scallop species

Stability analysis of load frequency control for power systems with interval time-varying delays

This study investigates the stability problem of load frequency control (LFC) for power systems with interval time-varying delays. The two categories of time delays, the lower bound being zero and non-zero, are considered. The systems can be described as time delay systems of load disturbances. First, an augmented Lyapunov–Krasovskii functional (LKF) is constructed. Some delay-dependent nonintegral terms and single integral terms are additionally introduced to make full use of the information on the system state variables and the time-varying delays. Second, to overcome the problem of nonlinear inequalities caused by the augmented LKF, the nonlinear inequalities are converted into linear matrix inequalities (LMIs) by applying the new negative definite inequality equivalence transformation lemma, which can be solved easily by the MATLAB LMI toolbox. A new stability criterion is presented by applying the Lyapunov stability theory. The stability criterion is less conservative than some existing literature studies, which further improves the stability margin for the power systems based on LFC. Finally, some numerical examples are given to show the effectiveness of the proposed method and the superiority of the results

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis

BackgroundApoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).MethodsThe tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated.ResultsSix biomarkers including CASP1, TNFSF10, CASP4, CASP5, IFI16, and ATF3 were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values > 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators.ConclusionThe six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection

The Anti-Sigma Factor MucA of Pseudomonas aeruginosa: Dramatic Differences of a mucA22 vs. a ΔmucA Mutant in Anaerobic Acidified Nitrite Sensitivity of Planktonic and Biofilm Bacteria in vitro and During Chronic Murine Lung Infection

Mucoid mucA22 Pseudomonas aeruginosa (PA) is an opportunistic lung pathogen of cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients that is highly sensitive to acidified nitrite (A-NO2-). In this study, we first screened PA mutant strains for sensitivity or resistance to 20 mM A-NO2- under anaerobic conditions that represent the chronic stages of the aforementioned diseases. Mutants found to be sensitive to A-NO2- included PA0964 (pmpR, PQS biosynthesis), PA4455 (probable ABC transporter permease), katA (major catalase, KatA) and rhlR (quorum sensing regulator). In contrast, mutants lacking PA0450 (a putative phosphate transporter) and PA1505 (moaA2) were A-NO2- resistant. However, we were puzzled when we discovered that mucA22 mutant bacteria, a frequently isolated mucA allele in CF and to a lesser extent COPD, were more sensitive to A-NO2- than a truncated ΔmucA deletion (Δ157–194) mutant in planktonic and biofilm culture, as well as during a chronic murine lung infection. Subsequent transcriptional profiling of anaerobic, A-NO2--treated bacteria revealed restoration of near wild-type transcript levels of protective NO2- and nitric oxide (NO) reductase (nirS and norCB, respectively) in the ΔmucA mutant in contrast to extremely low levels in the A-NO2--sensitive mucA22 mutant. Proteins that were S-nitrosylated by NO derived from A-NO2- reduction in the sensitive mucA22 strain were those involved in anaerobic respiration (NirQ, NirS), pyruvate fermentation (UspK), global gene regulation (Vfr), the TCA cycle (succinate dehydrogenase, SdhB) and several double mutants were even more sensitive to A-NO2-. Bioinformatic-based data point to future studies designed to elucidate potential cellular binding partners for MucA and MucA22. Given that A-NO2- is a potentially viable treatment strategy to combat PA and other infections, this study offers novel developments as to how clinicians might better treat problematic PA infections in COPD and CF airway diseases