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Boolean matrix factorization (BMF) has been widely utilized in fields such as recommendation systems, graph learning, text mining, and -omics data analysis. Traditional BMF methods decompose a binary matrix into the Boolean product of two lower-rank Boolean matrices plus homoscedastic random errors. However, real-world binary data typically involves biases arising from heterogeneous row- and column-wise signal distributions. Such biases can lead to suboptimal fitting and unexplainable predictions if not accounted for. In this study, we reconceptualize the binary data generation as the Boolean sum of three components: a binary pattern matrix, a background bias matrix influenced by heterogeneous row or column distributions, and random flipping errors. We introduce a novel Disentangled Representation Learning for Binary matrices (DRLB) method, which employs a dual auto-encoder network to reveal the true patterns. DRLB can be seamlessly integrated with existing BMF techniques to facilitate bias-aware BMF. Our experiments with both synthetic and real-world datasets show that DRLB significantly enhances the precision of traditional BMF methods while offering high scalability. Moreover, the bias matrix detected by DRLB accurately reflects the inherent biases in synthetic data, and the patterns identified in the bias-corrected real-world data exhibit enhanced interpretability. 

Biofilms are clusters of microorganisms that form at various interfaces, including those between air and liquid or liquid and solid. Due to their roles in enhancing wastewater treatment processes, and their unfortunate propensity to cause persistent human infections through lowering antibiotic susceptibility, understanding and managing bacterial biofilms is of paramount importance. A pivotal stage in biofilm development is the initial bacterial attachment to these interfaces. However, the determinants of bacterial cell choice in colonizing an interface first and heterogeneity in bacterial adhesion remain elusive. Our research has unveiled variations in the buoyant density of free-swimming Staphylococcus aureus cells, irrespective of their growth phase. Cells with a low cell buoyant density, characterized by fewer cell contents, exhibited lower susceptibility to antibiotic treatments (100 μg/mL vancomycin) and favored biofilm formation at air–liquid interfaces. In contrast, cells with higher cell buoyant density, which have richer cell contents, were more vulnerable to antibiotics and predominantly formed biofilms on liquid–solid interfaces when contained upright. Cells with low cell buoyant density were not able to revert to a more antibiotic sensitive and high cell buoyant density phenotype. In essence, S. aureus cells with higher cell buoyant density may be more inclined to adhere to upright substrates. 

The overall level of negative affect (NeA) has been linked to impaired health. However, whether the diurnal timing of NeA matters and whether the NeA-health relationship is mediated by sleep quality remain unclear.

Using a longitudinal dataset (2006, 2009 and 2014 waves) consisting of 1959 participants, we examined the within-person impact of both bedtime NeA and non-bedtime NeA measured by Day Reconstruction Method (DRM) on subjective health measured by Visual Analogue Scale (VAS), and the mediating effect of sleep quality on the NeA-health relationships by fixed effect models.

Bedtime NeA predicted poorer health, while non-bedtime NeA was unrelated to health. The deleterious impact of bedtime NeA reduced and became non-significant after sleep quality was controlled for. Bedtime NeA also significantly predicted impaired sleep quality.

Bedtime NeA is a stronger predictor of poorer health than non-bedtime NeA, and the deleterious influence of bedtime NeA on health seems to operate through poor sleep quality. Therefore, interventions to reduce bedtime NeA could potentially improve subsequent sleep quality, thereby protecting people to some extent from impaired health status.

 

A key challenge facing the use of machine learning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has emerged, attempting to ameliorate biases while maintaining the predictive accuracy of ML algorithms. Almost all existing FAML algorithms define their optimization goals according to a selection task, meaning that ML outputs are assumed to be the final selection outcome. In practice, though, ML outputs are rarely used as-is. In personnel selection, for example, ML often serves a support role to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection task. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate. This paper, however, reveals a qualitative difference between the two in terms of fairness. Specifically, we demonstrate through conceptual development and mathematical analysis that miscategorizing a screening task as a selection one could not only degrade final selection quality but also result in fairness problems such as selection biases within the minority group. After validating our findings with experimental studies on simulated and real-world data, we discuss several business and policy implications, highlighting the need for firms and policymakers to properly categorize the task assigned to ML in assessing and correcting algorithmic biases.

 

Amidst the rapid expansion of the electric vehicle industry, the need for alternative battery technologies that balance economic viability with sustainability has never been more critical. Here, we report that common lithium salts of Li2CO3 and Li2SO4 are transformed into cathode active mass in Li-ion batteries by ball milling to form a composite with Cu2S. The optimal composite cathode comprising Li2CO3, Li2SO4, and Cu2S, with a practical active mass loading of 12.5-13.0 mg/cm2, demonstrates a reversible capacity of 247 mAh/g based on the total mass of Cu2S and the lithium salts, a specific energy of 716 Wh/kg, and a stable cycle life. This cathode chemistry rivals layered oxide cathodes of Li-ion batteries in energy density but at substantially reduced cost and ecological footprint. Mechanistic investigations reveal that in the composite Li2CO3 serves as the primary active mass, Li2SO4 enhances kinetic properties and reversibility, and Cu2S stabilizes the resulting anionic radicals for reversibility as a binding agent. Our findings pave the way for directly using precursor lithium salts as cathodes for Li-ion batteries to meet the ever-increasing market demands sustainably.

 

Solomon rings, upholding the symbol of wisdom with profound historical roots, were widely used as decorations in ancient architecture and clothing. However, it was only recently discovered that such topological structures can be formed by self-organization in biological/chemical molecules, liquid crystals, etc. Here, we report the observation of polar Solomon rings in a ferroelectric nanocrystal, which consist of two intertwined vortices and are mathematically equivalent to a$${4}_{1}^{2}$$$4_1^2$link in topology. By combining piezoresponse force microscopy observations and phase-field simulations, we demonstrate the reversible switching between polar Solomon rings and vertex textures by an electric field. The two types of topological polar textures exhibit distinct absorption of terahertz infrared waves, which can be exploited in infrared displays with a nanoscale resolution. Our study establishes, both experimentally and computationally, the existence and electrical manipulation of polar Solomon rings, a new form of topological polar structures that may provide a simple way for fast, robust, and high-resolution optoelectronic devices.

 

Over the past two decades, behavioral research in privacy has made considerable progress transitioning from acontextual studies to using contextualization as a powerful sensitizing device for illuminating the boundary conditions of privacy theories. Significant challenges and opportunities wait, however, on elevating and converging individually contextualized studies to a context-contingent theory that explicates the mechanisms through which contexts influence consumers’ privacy concerns and their behavioral reactions. This paper identifies the important barriers occasioned by this lack of context theorizing on the generalizability of privacy research findings and argues for accelerating the transition from the contextualization of individual research studies to an integrative understanding of context effects on privacy concerns. It also takes a first step toward this goal by providing a conceptual framework and the associated methodological instantiation for assessing how context-oriented nuances influence privacy concerns. Empirical evidence demonstrates the value of the framework as a diagnostic device guiding the selection of contextual contingencies in future research, so as to advance the pace of convergence toward context-contingent theories in information privacy. This paper was accepted by Anindya Ghose, information systems.