How two-dimensional brick layer J-aggregates differ from linear ones: Excitonic properties and line broadening mechanisms

We study the excitonic coupling and homogeneous spectral line width of brick layer J-aggregate films. We begin by analysing the structural information revealed by the two-exciton states probed in two-dimensional spectra. Our first main result is that the relation between the excitonic couplings and the spectral shift in a two-dimensional structure is different (larger shift for the same nearest neighbour coupling) from that in a one-dimensional structure, which leads to an estimation of dipolar coupling in two-dimensional lattices. We next investigate the mechanisms of homogeneous broadening - population relaxation and pure dephasing - and evaluate their relative importance in linear and two-dimensional aggregates. Our second main result is that pure dephasing dominates the line width in two-dimensional systems up to a crossover temperature, which explains the linear temperature dependence of the homogeneous line width. This is directly related to the decreased density of states at the band edge when compared with linear aggregates, thus reducing the contribution of population relaxation to dephasing. Pump-probe experiments are suggested to directly measure the lifetime of the bright state and can therefore support the proposed model

Fasting Insulin Level Is Positively Associated With Incidence of Hypertension Among American Young Adults

OBJECTIVE Although hyperinsulinemia, a surrogate of insulin resistance, may play a role in the pathogenesis of hypertension (HTN), the longitudinal association between fasting insulin level and HTN development is still controversial. We examined the relation between fasting insulin and incidence of HTN in a large prospective cohort. RESEARCH DESIGN AND METHODS A prospective cohort of 3,413 Americans, aged 18?30 years, without HTN in 1985 (baseline) were enrolled. Six follow-ups were conducted in 1987, 1990, 1992, 1995, 2000, and 2005. Fasting insulin and glucose levels were assessed by a radioimmunoassay and hexokinase method, respectively. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% CIs of incident HTN (defined as the initiation of antihypertensive medication, systolic blood pressure ?140 mmHg, or diastolic blood pressure ?90 mmHg). RESULTS During the 20-year follow-up, 796 incident cases were identified. After adjustment for potential confounders, participants in the highest quartile of insulin levels had a significantly higher incidence of HTN (HR 1.85 [95% CI 1.42?2.40]; Ptrend \u3c 0.001) compared with those in the lowest quartile. The positive association persisted in each sex/ethnicity/weight status subgroup. A similar dose-response relation was observed when insulin-to-glucose ratio or homeostatic model assessment of insulin resistance was used as exposure. CONCLUSIONS Fasting serum insulin levels or hyperinsulinemia in young adulthood was positively associated with incidence of HTN later in life for both men and women, African Americans and Caucasians, and those with normal weight and overweight. Our findings suggested that fasting insulin ascertainment may help clinicians identify those at high risk of HTN

CoAIcoder: Examining the Effectiveness of AI-assisted Collaborative Qualitative Analysis

While the domain of individual-level AI-assisted analysis has been extensively explored in previous studies, the field of AI-assisted collaborative qualitative analysis remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we introduce our collaborative qualitative coding tool, CoAIcoder, and designed the four different collaboration methods. We subsequently implemented a between-subject design involving 32 pairs of users who have undergone training in CQA across three commonly utilized phases under four methods. Our results suggest that CoAIcoder, which employs AI and a Shared Model, could potentially improve the efficiency of the coding process in CQA by fostering a quicker shared understanding and promoting early-stage discussions. However, this may come with the potential downside of reduced code diversity. We also underscored the existence of a trade-off between the level of independence and the coding outcome when humans collaborate during the early coding stages. Lastly, we identify design implications that could inspire and inform the future design of CQA systems

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

Background: Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits.Results: A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework.Conclusions: sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets

Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection

Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from non-informative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method.Comment: Camera-ready for 23, ACM M