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We fabricated superhydrophobic and multifunctional objects using a simple and inexpensive vat photopolymerization 3D printer with a 2-component ink and a 2-step process. 

We develop and analyse the HulC, an intuitive and general method for constructing confidence sets using the convex hull of estimates constructed from subsets of the data. Unlike classical methods which are based on estimating the (limiting) distribution of an estimator, the HulC is often simpler to use and effectively bypasses this step. In comparison to the bootstrap, the HulC requires fewer regularity conditions and succeeds in many examples where the bootstrap provably fails. Unlike sub-sampling, the HulC does not require knowledge of the rate of convergence of the estimators on which it is based. The validity of the HulC requires knowledge of the (asymptotic) median bias of the estimators. We further analyse a variant of our basic method, called the Adaptive HulC, which is fully data-driven and estimates the median bias using sub-sampling. We discuss these methods in the context of several challenging inferential problems which arise in parametric, semi-parametric, and non-parametric inference. Although our focus is on validity under weak regularity conditions, we also provide some general results on the width of the HulC confidence sets, showing that in many cases the HulC confidence sets have near-optimal width. 

Abstract Color morphing refers to color change in response to an environmental stimulus. Photochromic materials allow color morphing in response to light, but almost all photochromic materials suffer from degradation when exposed to moist/humid environments or harsh chemical environments. One way of overcoming this challenge is by imparting chemical shielding to the color morphing materials via superomniphobicity. However, simultaneously imparting color morphing and superomniphobicity, both surface properties, requires a rational design. In this work, we systematically design color morphing surfaces with superomniphobicity through an appropriate combination of a photochromic dye, a low surface energy material, and a polymer in a suitable solvent (for one-pot synthesis), applied through spray coating (for the desired texture). We also investigate the influence of polymer polarity and material composition on color morphing kinetics and superomniphobicity. Our color morphing surfaces with effective chemical shielding can be designed with a wide variety of photochromic and thermochromic pigments and applied on a wide variety of substrates. We envision that such surfaces will have a wide range of applications including camouflage soldier fabrics/apparel for chem-bio warfare, color morphing soft robots, rewritable color patterns, optical data storage, and ophthalmic sun screening. 

In this project, it was shown by simulation that the permittivity of a material could be enhanced by embedding metal inclusions in the host material. Later, a series of systematic experiments were carried out with free space material measurement equipment/system to demonstrate the enhancement of permittivity with circular metal patches printed periodically on a host material to validate the simulation results. 

A Natural Language Interface (NLI) enables the use of human languages to interact with computer systems, including smart phones and robots. Compared to other types of interfaces, such as command line interfaces (CLIs) or graphical user interfaces (GUIs), NLIs stand to enable more people to have access to functionality behind databases or APIs as they only require knowledge of natural languages. Many NLI applications involve structured data for the domain (e.g., applications such as hotel booking, product search, and factual question answering.) Thus, to fully process user questions, in addition to natural language comprehension, understanding of structured data is also crucial for the model. In this paper, we study neural network methods for building Natural Language Interfaces (NLIs) with a focus on learning structure data representations that can generalize to novel data sources and schemata not seen at training time. Specifically, we review two tasks related to natural language interfaces: i) semantic parsing where we focus on text-to-SQL for database access, and ii) task-oriented dialog systems for API access. We survey representative methods for text-to-SQL and task-oriented dialog tasks, focusing on representing and incorporating structured data. Lastly, we present two of our original studies on structured data representation methods for NLIs to enable access to i) databases, and ii) visualization APIs. 

Developing a materials perspective of how to control the degradation and negative impact of complex metal oxides requires an integrated understanding of how these nanomaterials transform in the environment and interact with biological systems. Doping with aluminum is known to stabilize oxide materials, but has not been assessed cohesively from synthesis to environmental fate and biological impact. In the present study, the influence of aluminum doping on metal ion release from transition metal oxides was investigated by comparing aqueous transformations of lithium nickel cobalt aluminum oxide (LiNi0.82Co0.15Al0.03O2; NCA) and lithium nickel cobalt oxide (LiNi0.80Co0.20O2; NC) nanoparticles and by calculating the energetics of metal release using a density functional theory (DFT) and thermodynamics method. Two model environmental organisms were used to assess biological impact, and metal ion release was compared for NCA and NC nanoparticles incubated in their respective growth media: moderately hard reconstituted water (MHRW) for the freshwater invertebrate Daphnia magna (D. magna) and minimal growth medium for the Gram-negative bacterium Shewanella oneidensis MR-1 (S. oneidensis). The amount of metal ions released was reduced for NCA compared to NC in MHRW, which correlated to changes in the modeled energetics of release upon Al substitution in the lattice. In minimal medium, metal ion release was approximately an order of magnitude higher compared to MHRW and was similar to the stoichiometry of the bulk nanoparticles for both NCA and NC. Interpretation of the release profiles and modeling indicated that the increase in total metal ion release and the reduced influence of Al doping arises from lactate complexation of metal ions in solution. The relative biological impacts of NC and NCA exposure for both S. oneidensis and D. magna were consistent with the metal release trends observed for minimal medium and MHRW, respectively. Together, these results demonstrate how a combined experimental and computational approach provides valuable insight into the aqueous transformations and biological impacts of complex metal oxide nanoparticles. 

Abstract High entropy oxides (HEOs), based on the incorporation of multiple‐principal cations into the crystal lattice, offer the possibility to explore previously inaccessible oxide compositions and unconventional properties. Here it is demonstrated that despite the chemical complexity of HEOs external stimuli, such as epitaxial strain, can selectively stabilize certain magneto‐electronic states. Epitaxial (Co_0.2Cr_0.2Fe_0.2Mn_0.2Ni_0.2)₃O₄‐HEO thin films are grown in three different strain states: tensile, compressive, and relaxed. A unique coexistence of rocksalt and spinel‐HEO phases, which are fully coherent with no detectable chemical segregation, is revealed by transmission electron microscopy. This dual‐phase coexistence appears as a universal phenomenon in (Co_0.2Cr_0.2Fe_0.2Mn_0.2Ni_0.2)₃O₄epitaxial films. Prominent changes in the magnetic anisotropy and domain structure highlight the strain‐induced bidirectional control of magnetic properties in HEOs. When the films are relaxed, their magnetization behavior is isotropic, similar to that of bulk materials. However, under tensile strain, the hardness of the out‐of‐plane (OOP) axis increases significantly. On the other hand, compressive straining results in an easy OOP magnetization and a maze‐like magnetic domain structure, indicating the perpendicular magnetic anisotropy. Generally, this study emphasizes the adaptability of the high entropy design strategy, which, when combined with coherent strain engineering, opens additional prospects for fine‐tuning properties in oxides. 

For any successful business endeavor, recruitment of a required number of appropriately qualified employees in proper positions is a key requirement. For effective utilization of human resources, reorganization of such workforce assignment is also a task of utmost importance. This includes situations when the under-performing employees have to be substituted with fresh applicants. Generally, the number of candidates applying for a position is large, and hence, the task of identifying an optimal subset becomes critical. Moreover, a human resource manager would also like to make use of the opportunity of retirement of employees to improve manpower utilization. However, the constraints enforced by the security policies prohibit any arbitrary assignment of tasks to employees. Further, the new employees should have the capabilities required to handle the assigned tasks. In this article, we formalize this problem as the Optimal Recruitment Problem (ORP), wherein the goal is to select the minimum number of fresh employees from a set of candidates to fill the vacant positions created by the outgoing employees, while ensuring satisfiability of the specified security conditions. The model used for specification of authorization policies and constraints is Attribute-Based Access Control (ABAC), since it is considered to be the de facto next-generation framework for handling organizational security policies. We show that the ORP problem is NP-hard and propose a greedy heuristic for solving it. Extensive experimental evaluation shows both the effectiveness and efficiency of the proposed solution. 

Metal-matrix composites with active components have been investigated as a way to functionalize metals. As opposed to surface-mounted approaches, smart materials embedded in metals can be effectively shielded against the environment while providing in-situ sensing, health monitoring, actuation, or energy harvesting functions. Typical manufacturing approaches can be problematic, however, in that they may physically damage the smart material or degrade its electromechanical properties. For instance, non-resin-based embedment procedures such as powder metallurgy involve isostatic compression and diffusion bonding, leading to high process temperatures and breakdown of the electromechanical properties of the active component to be embedded. This paper presents the development and characterization of an aluminum-matrix composite embedded with piezoelectric polyvinylidene fluoride (PVDF) sensors using ultrasonic additive manufacturing (UAM). UAM incorporates the principles of solid-state, ultrasonic metal welding and subtractive processes to fabricate metal-matrices with seamlessly embedded smart materials and without thermal loading. As implemented in this study, the UAM process uses as-received, commercial Al 6061 tape foilstock and TE Connectivity PVDF film. In order to increase the mechanical coupling between the sensor and the metal-matrix without the aid of adhesives, the PVDF sensor is embedded with an empirically optimized pre-compression defined by the tape foils welded above the sensor. The specimen is characterized by tensile (d31 mode), bending (d31 mode), and compression tests (d33 mode) to evaluate its functional performance. Within the investigated load range, the specimen exhibits open-circuit sensitivities of 4.6 mV/N under uniaxial tension and 9.7 mV/N under compressive impulse tests with better than 95% linearity and frequency bandwidth of several kilohertz. The technology presented in this study could be applied for load and tactile sensing, impact detection and localization, thermal measurements, energy harvesting, and non-destructive testing applications.