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We present a solution to image-based cell counting with dot annotations for both 2D and 3D cases. Current approaches have two major limitations: 1) inability to provide precise locations when cells overlap; and 2) reliance on costly labeled data. To address these two issues, we first adopt the inverse distance kernel, which yields separable density maps for better localization. Second, we take advantage of unlabeled data by self-supervised learning with focal consistency loss, which we propose for our pixel-wise task. These two contributions complement each other. Together, our framework compares favorably against stateof- the-art methods, including methods using full annotations on 2D and 3D benchmarks, while significantly reducing the amount of labeled data needed for training. In addition, we provide a tool to expedite the labeling process for dot annotations. Finally, we make the source code and labeling tool publicly available. 

Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and human behavior. To enhance analysis and comprehension of brain activity, Graph Neural Networks (GNNs) have been widely applied to the analysis of functional connectivities (FC) derived from fMRI data, due to their ability to capture the synergistic interactions among brain regions. However, in the human brain, performing complex tasks typically involves the activation of certain pathways, which could be represented as paths across graphs. As such, conventional GNNs struggle to learn from these pathways due to the long-range dependencies of multiple pathways. To address these challenges, we introduce a novel framework BrainMAP to learn multiple pathways in brain networks. BrainMAP leverages sequential models to identify long-range correlations among sequentialized brain regions and incorporates an aggregation module based on Mixture of Experts (MoE) to learn from multiple pathways. Our comprehensive experiments highlight BrainMAP's superior performance. Furthermore, our framework enables explanatory analyses of crucial brain regions involved in tasks. 

In this article, we aim to provide a general and complete understanding of semi-supervised (SS) causal inference for treatment effects. Specifically, we consider two such estimands: (a) the average treatment effect and (b) the quantile treatment effect, as prototype cases, in an SS setting, characterized by two available data sets: (i) a labeled data set of size n, providing observations for a response and a set of high dimensional covariates, as well as a binary treatment indicator; and (ii) an unlabeled data set of size N, much larger than n, but without the response observed. Using these two data sets, we develop a family of SS estimators which are ensured to be: (1) more robust and (2) more efficient than their supervised counterparts based on the labeled data set only. Beyond the 'standard' double robustness results (in terms of consistency) that can be achieved by supervised methods as well, we further establish root-n consistency and asymptotic normality of our SS estimators whenever the propensity score in the model is correctly specified, without requiring specific forms of the nuisance functions involved. Such an improvement of robustness arises from the use of the massive unlabeled data, so it is generally not attainable in a purely supervised setting. In addition, our estimators are shown to be semi-parametrically efficient as long as all the nuisance functions are correctly specified. Moreover, as an illustration of the nuisance estimators, we consider inverse-probability-weighting type kernel smoothing estimators involving unknown covariate transformation mechanisms, and establish in high dimensional scenarios novel results on their uniform convergence rates, which should be of independent interest. Numerical results on both simulated and real data validate the advantage of our methods over their supervised counterparts with respect to both robustness and efficiency. 

We consider quantile estimation in a semi-supervised setting, characterized by two available data sets: (i) a small or moderate sized labeled data set containing observations for a response and a set of possibly high dimensional covariates, and (ii) a much larger unlabeled data set where only the covariates are observed. We propose a family of semi-supervised estimators for the response quantile(s) based on the two data sets, to improve the estimation accuracy compared to the supervised estimator, i.e., the sample quantile from the labeled data. These estimators use a flexible imputation strategy applied to the estimating equation along with a debiasing step that allows for full robustness against misspecification of the imputation model. Further, a one-step update strategy is adopted to enable easy implementation of our method and handle the complexity from the non-linear nature of the quantile estimating equation. Under mild assumptions, our estimators are fully robust to the choice of the nuisance imputation model, in the sense of always maintaining root-n consistency and asymptotic normality, while having improved efficiency relative to the supervised estimator. They also attain semi-parametric optimality if the relation between the response and the covariates is correctly specified via the imputation model. As an illustration of estimating the nuisance imputation function, we consider kernel smoothing type estimators on lower dimensional and possibly estimated transformations of the high dimensional covariates, and we establish novel results on their uniform convergence rates in high dimensions, involving responses indexed by a function class and usage of dimension reduction techniques. These results may be of independent interest. Numerical results on both simulated and real data confirm our semi-supervised approach's improved performance, in terms of both estimation and inference. 

Abstract Conjugated ladder polymers (cLPs) represent an intriguing class of macromolecules, characterized by their multi‐stranded structure, with continuous fused π‐conjugated rings forming the backbone. Isotope substitution, such as deuteration and carbon‐13 labeling, offers unique approaches to address the significant challenges associated with elucidating the structure and solution phase dynamics of these polymers. For instance, selective deuteration can highlight parts of the polymer by controlling the scattering length density of specific molecular sections, thereby enhancing the contrast for neutron scattering experiments. In this context, deuteration of side‐chains in cLPs represents a promising approach to uncover the elusive polymer physics properties of their backbone. The synthesis of two distinct types of cLPs with perdeuterated side‐chains are reported here. During the synthesis,¹³C isotope labeling was also employed to verify the low levels of defects in the synthesized polymers. Demonstrating these synthetic successes lays the foundation for rigorous characterization of the defects, conformation, and dynamics of cLPs. 

Abstract Skin-like field-effect transistors are key elements of bio-integrated devices for future user-interactive electronic-skin applications. Despite recent rapid developments in skin-like stretchable transistors, imparting self-healing ability while maintaining necessary electrical performance to these transistors remains a challenge. Herein, we describe a stretchable polymer transistor capable of autonomous self-healing. The active material consists of a blend of an electrically insulating supramolecular polymer with either semiconducting polymers or vapor-deposited metal nanoclusters. A key feature is to employ the same supramolecular self-healing polymer matrix for all active layers, i.e., conductor/semiconductor/dielectric layers, in the skin-like transistor. This provides adhesion and intimate contact between layers, which facilitates effective charge injection and transport under strain after self-healing. Finally, we fabricate skin-like self-healing circuits, including NAND and NOR gates and inverters, both of which are critical components of arithmetic logic units. This work greatly advances practical self-healing skin electronics. 

The solubilization of conjugated polymers can be carefully quantified using static light scattering. Our findings reveal that the architecture of sidechains and backbones significantly influences polymer's conformation and aggregation. 

This work demonstrates a simple method to prepare hierarchically porous materials. The introduction of macropores in mesoporous matrix enables its improved sorbent performance against pollutants for water remediation.