Momentum distribution and contacts of one-dimensional spinless Fermi gases with an attractive p-wave interaction

We present a rigorous study of momentum distribution and p-wave contacts of one dimensional (1D) spinless Fermi gases with an attractive p-wave interaction. Using the Bethe wave function, we analytically calculate the large-momentum tail of momentum distribution of the model. We show that the leading ($\sim 1/p^{2}$) and sub-leading terms ($\sim 1/p^{4}$) of the large-momentum tail are determined by two contacts $C_2$ and $C_4$, which we show, by explicit calculation, are related to the short-distance behaviour of the two-body correlation function and its derivatives. We show as one increases the 1D scattering length, the contact $C_2$ increases monotonically from zero while $C_4$ exhibits a peak for finite scattering length. In addition, we obtain analytic expressions for p-wave contacts at finite temperature from the thermodynamic Bethe ansatz equations in both weakly and strongly attractive regimes.Comment: 19 pages,2 figure

Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly

Continental-scale impacts of intra-seasonal rainfall variability on simulated ecosystem responses in Africa

Climate change is expected to modify intraseasonal rainfall variability, arising from shifts in rainfall frequency, intensity and seasonality. These intra-seasonal changes are likely to have important ecological impacts on terrestrial ecosystems. Yet, quantifying these impacts across biomes and large climate gradients is largely missing. This gap hinders our ability to better predict ecosystem services and their responses to climate change, especially for arid and semi-arid ecosystems. Here we use a synthetic weather generator and an independently validated vegetation dynamic model (SEIB-Dynamic Global Vegetation Model, DGVM) to virtually conduct a series of "rainfall manipulation experiments" to study how changes in the intra-seasonal rainfall variability affect continent-scale ecosystem responses across Africa. We generate different rainfall scenarios with fixed total annual rainfall but shifts in (i) frequency vs. intensity, (ii) rainy season length vs. frequency, (iii) intensity vs. rainy season length. These scenarios are fed into SEIB-DGVM to investigate changes in biome distributions and ecosystem productivity. We find a loss of ecosystem productivity with increased rainfall frequency and decreased intensity at very low rainfall regimes ( 1800mm year-11) where radiation limitation prevents further productivity gains. This result reconciles seemingly contradictory findings in previous field studies on the impact of rainfall frequency/intensity on ecosystem productivity. We also find that changes in rainy season length can yield more dramatic ecosystem responses compared with similar percentage changes in rainfall frequency or intensity, with the largest impacts in semi-arid woodlands. This study demonstrates that intra-seasonal rainfall characteristics play a significant role in influencing ecosystem function and structure through controls on ecohydrological processes. Our results suggest that shifts in rainfall seasonality have potentially large impacts on terrestrial ecosystems, and these understudied impacts should be explicitly examined in future studies of climate impacts

Service differentiation in OFDM-Based IEEE 802.16 networks

IEEE 802.16 network is widely viewed as a strong candidate solution for broadband wireless access systems. Various flexible mechanisms related to QoS provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the standards. Among the mechanisms, bandwidth request scheme can be used to indicate and request bandwidth demands to the base station for different services. Due to the diverse QoS requirements of the applications, service differentiation (SD) is desirable for the bandwidth request scheme. In this paper, we propose several SD approaches. The approaches are based on the contention-based bandwidth request scheme and achieved by the means of assigning different channel access parameters and/or bandwidth allocation priorities to different services. Additionally, we propose effective analytical model to study the impacts of the SD approaches, which can be used for the configuration and optimization of the SD services. It is observed from simulations that the analytical model has high accuracy. Service can be efficiently differentiated with initial backoff window in terms of throughput and channel access delay. Moreover, the service differentiation can be improved if combined with the bandwidth allocation priority approach without adverse impacts on the overall system throughput

Per- and polyfluoroalkyl substances (PFAS) exposure and thyroid cancer risk

BACKGROUND: Although per- and polyfluoroalkyl substances (PFAS) exposure is a potential contributor to the increasing thyroid cancer trend, limited studies have investigated the association between PFAS exposure and thyroid cancer in human populations. We therefore investigated associations between plasma PFAS levels and thyroid cancer diagnosis using a nested case-control study of patients with thyroid cancer with plasma samples collected at/before cancer diagnosis. METHODS: 88 patients with thyroid cancer using diagnosis codes and 88 healthy (non-cancer) controls pair-matched on sex, age (±5 years), race/ethnicity, body mass index, smoking status, and year of sample collection were identified in the BioMe population (a medical record-linked biobank at the Icahn School of Medicine at Mount Sinai in New York); 74 patients had papillary thyroid cancer. Eight plasma PFAS were measured using untargeted analysis with liquid chromatography-high resolution mass spectrometry and suspect screening. Associations between individual PFAS levels and thyroid cancer were evaluated using unconditional logistic regression models to estimate adjusted odds ratios (OR adj) and 95% confidence intervals (CI). FINDINGS: There was a 56% increased rate of thyroid cancer diagnosis per doubling of linear perfluorooctanesulfonic acid (n-PFOS) intensity (OR adj, 1.56, 95% CI: 1.17-2.15, P = 0.004); results were similar when including patients with papillary thyroid cancer only (OR adj, 1.56, 95% CI: 1.13-2.21, P = 0.009). This positive association remained in subset analysis investigating exposure timing including 31 thyroid cancer cases diagnosed ≥1 year after plasma sample collection (OR adj, 2.67, 95% CI: 1.59-4.88, P < 0.001). INTERPRETATION: This study reports associations between exposure to PFAS and increased rate of (papillary) thyroid cancer. Thyroid cancer risk from PFAS exposure is a global concern given the prevalence of PFAS exposure. Individual PFAS studied here are a small proportion of the total number of PFAS supporting additional large-scale prospective studies investigating thyroid cancer risk associated with exposure to PFAS chemicals. FUNDING: National Institutes of Health grants and The Andrea and Charles Bronfman Philanthropies

Human Leukocyte Antigen Profile Predicts Severity of Autoimmune Liver Disease in Children of European Ancestry

Background and Aims Genetic predisposition to autoimmune hepatitis (AIH) in adults is associated with possession of human leukocyte antigen (HLA) class I (A*01, B*08) and class II (DRB1*03, ‐04, ‐07, or ‐13) alleles, depending on geographic region. Juvenile autoimmune liver disease (AILD) comprises AIH‐1, AIH‐2, and autoimmune sclerosing cholangitis (ASC), which are phenotypically different from their adult counterparts. We aimed to define the relationship between HLA profile and disease course, severity, and outcome in juvenile AILD. Approach and Results We studied 236 children of European ancestry (152 female [64%], median age 11.15 years, range 0.8‐17), including 100 with AIH‐1, 59 with AIH‐2, and 77 with ASC. The follow‐up period was from 1977 to June 2019 (median 14.5 years). Class I and II HLA genotyping was performed using PCR/sequence‐specific primers. HLA B*08, ‐DRB1*03, and the A1‐B8‐DR3 haplotype impart predisposition to all three forms of AILD. Homozygosity for DRB1*03 represented the strongest risk factor (8.8). HLA DRB1*04, which independently confers susceptibility to AIH in adults, was infrequent in AIH‐1 and ASC, suggesting protection; and DRB1*15 (DR15) was protective against all forms of AILD. Distinct HLA class II alleles predispose to the different subgroups of juvenile AILD: DRB1*03 to AIH‐1, DRB1*13 to ASC, and DRB1*07 to AIH‐2. Possession of homozygous DRB1*03 or of DRB1*13 is associated with fibrosis at disease onset, and possession of these two genes in addition to DRB1*07 is associated with a more severe disease in all three subgroups. Conclusions Unique HLA profiles are seen in each subgroup of juvenile AILD. HLA genotype might be useful in predicting responsiveness to immunosuppressive treatment and course

Hybrid weakness and continuous flowering caused by compound expression of FTLs in Chrysanthemum morifolium × Leucanthemum paludosum intergeneric hybridization

Hybridization is an important evolutionary mechanism ubiquitous to plants. Previous studies have shown that hybrid polyploidization of cultivated chrysanthemum, ‘Zhongshanzigui’, and Leucanthemum paludosum exhibit spring-flowering traits. This study explores the function of the LpFTLs gene via the phenotype of A. thaliana after heterologous transformation of the LpFTLs gene, and analyzes the mechanism ofthe continuous flowering phenotype and heterosis of hybrid offspring. The results suggest that the flowering phenotype of hybrid offspring in spring may be related to the expression of the LpFTLs gene. Ectopic expression of Leucanthemum paludosumLpFTLs in Arabidopsis thaliana resulted in earlier flowering, indicating that the LpFTLs gene also affects the flowering time in L. paludosum. Compound expression of FTLs in C. morifolium × L. paludosum intergeneric hybridization directly leads to serious heterosis in the hybrid offspring. Moreover, continuous flowering appears to be accompanied by hybrid weakness under the balance of vegetative and reproductive growth. Therefore, in future studies on chrysanthemum breeding, a suitable balance point must be established to ensure the target flowering time under normal growth

Evidence Network Inference Recognition Method Based on Cloud Model

Uncertainty is widely present in target recognition, and it is particularly important to express and reason the uncertainty. Based on the advantage of the evidence network in uncertainty processing, this paper presents an evidence network reasoning recognition method based on a cloud fuzzy belief. In this method, a hierarchical structure model of an evidence network is constructed; the MIC (maximum information coefficient) method is used to measure the degree of correlation between nodes and determine the existence of edges, and the belief of corresponding attributes is generated based on the cloud model. In addition, the method of information entropy is used to determine the conditional reliability table of non-root nodes, and the target recognition under uncertain conditions is realized afterwards by evidence network reasoning. The simulation results show that the proposed method can deal with the random uncertainty and cognitive uncertainty simultaneously, overcoming the problem that the traditional method has where it cannot carry out hierarchical recognition, and it can effectively use sensor information and expert knowledge to realize the deep cognition of the target intention