Rich Space Research Articles

Relationally Responsive Expert Trustworthiness

ABSTRACT Social epistemologists often operationalize the task of indirectly assessing experts’ trustworthiness to identifying whose beliefs are more reliably true on matters in an area of expertise. Not only does this neglect the philosophically rich space between belief formation and testimonial utterances, it also reduces trustworthiness to reliability. In ethics of trust, by contrast, explicitly relational views of trust include things like good will and responsiveness. One might think that relational aspects can be safely set aside for social epistemology of trust in experts, that such considerations may be relevant for personal relationships but not for expert trustworthiness. Against these claims I argue for the social-epistemic relevance of relational aspects of trust in experts, and to that end I discuss three sorts of considerations – responsively positive, neutral, and negative factors – that can make a difference for expert trustworthiness.

Distinguishing foreground and background alignment for unsupervised domain adaptative semantic segmentation

Unsupervised domain adaptive semantic segmentation uses the knowledge learned from the labeled source domain dataset to guide the segmentation of the target domain. However, this domain migration method will cause a large inter-domain difference due to the different feature distributions between the source domain and the target domain. We use the self-supervised learning method to generate pseudo labels for the target domain, so that the corresponding pixels are directly aligned with the source domain according to the segmentation loss. Through observation, it is found that the spatial distribution of the background class in the source domain and the target domain has a small difference, and the appearance of the same class of the foreground class will also be quite different. We use the method of distinguishing alignment between foreground and background classes. We understand that acquiring the rich space and channel information on the feature map during the convolution process is essential for fine-grained semantic segmentation. Therefore, in order to obtain the dependency relationship between the channels of the feature map and the spatial position information, we use a channel and spatial parallel attention module. This module enables the network to select and amplify valuable space and channel information from the global information and suppress useless information. In addition, we introduce focal loss to solve the problem of class imbalance in the data set. Experiments show that our method achieves better segmentation performance in unsupervised domain adaptive semantic segmentation. • We use self-supervised learning to generate pseudo-labels for the target domain. • We distinguish and align the foreground and background classes. • We use parallel attention module to capture the space and channel information. • We add focal loss to the overall loss to reduce the impact of class imbalance.

Hypergraph Cuts with General Splitting Functions

The minimum $s$-$t$ cut problem in graphs is one of the most fundamental problems in combinatorial optimization, and graph cuts underlie algorithms throughout discrete mathematics, theoretical computer science, operations research, and data science. While graphs are a standard model for pairwise relationships, hypergraphs provide the flexibility to model multiway relationships and are now a standard model for complex data and systems. However, when generalizing from graphs to hypergraphs, the notion of a “cut hyperedge” is less clear, as a hyperedge's nodes can be split in several ways. Here, we develop a framework for hypergraph cuts by considering the problem of separating two terminal nodes in a hypergraph in a way that minimizes a sum of penalties at split hyperedges. In our setup, different ways of splitting the same hyperedge have different penalties, and the penalty is encoded by what we call a splitting function. Our framework opens a rich space on the foundations of hypergraph cuts. We first identify a natural class of cardinality-based hyperedge splitting functions that depend only on the number of nodes on each side of the split. In this case, we show that the general hypergraph $s$-$t$ cut problem can be reduced to a tractable graph $s$-$t$ cut problem if and only if the splitting functions are submodular. We also identify a wide regime of non-submodular splitting functions for which the problem is NP-hard. Finally, we outline several open questions on general hypergraph cut problems.

Operational Optimization to Maximize Dynamic Range in EXCLAIM Microwave Kinetic Inductance Detectors

Microwave Kinetic Inductance Detectors (MKIDs) are highly scalable detectors that have demonstrated nearly background-limited sensitivity in the far-infrared from high-altitude balloon-borne telescopes and space-like laboratory environments. In addition, the detectors have a rich design space with many optimizable parameters, allowing highly sensitive measurements over a wide dynamic range. For these reasons, MKIDs were chosen for the Experiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM), a balloon-borne telescope targeting nearly background-limited performance in a high-altitude atmospheric environment from 420-540 GHz. We describe MKID optimization in the specific context of EXCLAIM and provide general results that apply to broader applications. Extending the established approach of tone frequency tracking, we show that readout power optimization enables significant, further improvement in dynamic range.

Cation[formula omitted]anion bonding interactions in 1–Ethyl–3–Methylimidazolium based ionic liquids

The potential energy surfaces for the interactions between the [EMIM]+ cation (1–ethyl–3–methylimidazolium) with the series of acetate, bromide, and tetrafluoroborate anions are exhaustively explored under simulated annealing conditions. Rich structural spaces are obtained, with several ionic pairs having energies very close to the putative global minima. All protons in the bare cation show unusually high affinities for the anions, with the central C–H bond in the (C2H5) N▪CH▪N(CH3) fragment acting as the preferred site for explicit cation⋯anion bonding, which occurs mostly in the form of uncharacteristically strong charge assisted secondary hydrogen bonds or direct attack of the anion to the aromatic system, affording strongly bound ion pairs according to highly correlated DLPNO–CCSD(T) binding energies. Formal dissection of intermolecular bonding interactions reveals a wide range of strengths, affording a picture of cation⋯anion bonding with complex contributions from long range closed shell ionic character, with surprisingly high covalent contributions. Computed proton NMR chemical shifts are directly related to the strength of hydrogen bonds in the ion pairs. The structural variety, aided by the complex mixture of interactions, yields multiple states accessible at room temperature, which is a significant result that leads to a lack of structure in the liquid state and thus partially helps explaining, from a molecular perspective, the ionic liquids difficulties to crystallize.

An Algorithmic Approach for Developing Single-Walled Carbon Nanotube Optical Sensors Against Adulterants in Aquaculture

The nanoparticle corona phase (CP) offers a unique materials design space for constructs capable of molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs have the additional ability to transduce MR through its band gap photoluminescence (PL). DNA oligonucleotides are well-known to disperse SWCNTs through forming CPs and can be manufactured with molecular precision. Nevertheless, no generalized scheme exists for the de novo prediction of SWCNT MR based on these CPs due to their sequence-dependent three-dimensional complexity.In aquaculture, there exist a pressing need for rapid tests for the detection of commonly harmful adulterants such as metal ions and small molecule antibiotics. Through a screening process, we found that DNA-SWCNTs offer a rich design space for MR capable of differentiating divalent metal ions. We also created best practices for the study of colloidal SWCNT analyte responses involve mitigating the effects of ionic strength, dilution kinetics, laser power and analyte response kinetics.Next, we generated the largest DNA-SWCNT PL response library of 1408 elements against 4 adulterant targets. We leveraged machine learning (ML) techniques and used both local features (LFs) and high-level features (HLFs) of the DNA sequences were utilized as model inputs. Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of the 8 tested experimental conditions. Furthermore, models utilizing both LFs and HLFs show improvement over that with HLFs alone, demonstrating that DNA-SWCNT CP engineering is more complex than simply specifying general DNA molecular properties. Taken as a whole, we detail the feasibility and utility of a ML-guided approach for nanoparticle CP engineering with relatively few experiments within a high-dimensional design space.

OVIS: Open-Vocabulary Visual Instance Search via Visual-Semantic Aligned Representation Learning

We introduce the task of open-vocabulary visual instance search (OVIS). Given an arbitrary textual search query, Open-vocabulary Visual Instance Search (OVIS) aims to return a ranked list of visual instances, i.e., image patches, that satisfies the search intent from an image database. The term ``open vocabulary'' means that there are neither restrictions to the visual instance to be searched nor restrictions to the word that can be used to compose the textual search query. We propose to address such a search challenge via visual-semantic aligned representation learning (ViSA). ViSA leverages massive image-caption pairs as weak image-level (not instance-level) supervision to learn a rich cross-modal semantic space where the representations of visual instances (not images) and those of textual queries are aligned, thus allowing us to measure the similarities between any visual instance and an arbitrary textual query. To evaluate the performance of ViSA, we build two datasets named OVIS40 and OVIS1600 and also introduce a pipeline for error analysis. Through extensive experiments on the two datasets, we demonstrate ViSA's ability to search for visual instances in images not available during training given a wide range of textual queries including those composed of uncommon words. Experimental results show that ViSA achieves an mAP@50 of 27.8% on OVIS40 and achieves a recall@30 of 21.3% on OVIS1400 dataset under the most challenging settings.

Approaches for handling high-dimensional cluster expansions of ionic systems

Disordered multicomponent systems attract great interest due to their engineering design flexibility and subsequent rich space of properties. However, detailed characterization of the structure and atomic correlations remains challenging and hinders full navigation of these complex spaces. A lattice cluster expansion is one tool to obtain configurational and energetic resolution. While in theory a cluster expansion can be applied to any system of any dimensionality, the method has primarily been used in binary systems or ternary alloys. Here we apply cluster expansions in high-component ionic systems, setting up the largest cluster expansion ever attempted to our knowledge. In doing so, we address and discuss challenges specific to high-component ionic systems, namely charge state assignments, structural relaxations, and rank-deficient systems. We introduce practical procedures to make the fitting and analysis of complex systems tractable, providing guidance for future computational studies of disordered ionic systems.

Hard Ferromagnetism Down to the Thinnest Limit of Iron-Intercalated Tantalum Disulfide.

Two-dimensional (2D) magnetic crystals hold promise for miniaturized and ultralow power electronic devices that exploit spin manipulation. In these materials, large, controllable magnetocrystalline anisotropy (MCA) is a prerequisite for the stabilization and manipulation of long-range magnetic order. In known 2D magnetic crystals, relatively weak MCA typically results in soft ferromagnetism. Here, we demonstrate that ferromagnetic order persists down to the thinnest limit of FexTaS2 (Fe-intercalated bilayer 2H-TaS2) with giant coercivities up to 3 T. We prepare Fe-intercalated TaS2 by chemical intercalation of van der Waals-layered 2H-TaS2 crystals and perform variable-temperature transport, transmission electron microscopy, and confocal Raman spectroscopy measurements to shed new light on the coupled effects of dimensionality, degree of intercalation, and intercalant order/disorder on the hard ferromagnetic behavior of FexTaS2. More generally, we show that chemical intercalation gives access to a rich synthetic parameter space for low-dimensional magnets, in which magnetic properties can be tailored by the choice of the host material and intercalant identity/amount, in addition to the manifold distinctive degrees of freedom available in atomically thin, van der Waals crystals.

Exceptional Cycling Performance Enabled by Local Structural Rearrangements in Disordered Rocksalt Cathodes

AbstractThe capacity of lithium transition‐metal (TM) oxide cathodes is directly linked to the magnitude and accessibility of the redox reservoir associated with TM cations and/or oxygen anions, which traditionally decreases with cycling as a result of chemical, structural, or mechanical fatigue. Here, it is shown that a capacity increase over 125% can be achieved upon cycling of high‐energy Mn‐ and F‐rich cation‐disordered rocksalt oxyfluoride cathodes. This study reveals that in Li1.2Mn0.7Nb0.1O1.8F0.2, repeated Li extraction/reinsertion utilizing Mn3+/Mn4+ redox along with some degree of O‐redox participation leads to local structural rearrangements and formation of domains with off‐stoichiometry spinel‐like features. The effective integration of these local “structure‐domains” within the cubic disordered rocksalt framework promotes better Li diffusion and improves material utilization, consequently increased capacity upon cycling. This study provides important new insights into materials design strategies to further exploit the rich compositional and structural space of Mn chemistry for developing sustainable, high‐energy cathode materials.

Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation

Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies. The existing work focuses on image classification and object detection, whereas we provide the first study on semantic image segmentation and introduce two new approaches: SmartAugment and SmartSamplingAugment. SmartAugment uses Bayesian Optimization to search a rich space of augmentation strategies and achieves new state-of-the-art performance in all semantic segmentation tasks we consider. SmartSamplingAugment, a simple parameter-free approach with a fixed augmentation strategy, competes in performance with the existing resource-intensive approaches and outperforms cheap state-of-the-art data augmentation methods. Furthermore, we analyze the impact, interaction, and importance of data augmentation hyperparameters and perform ablation studies, which confirm our design choices behind SmartAugment and SmartSamplingAugment. Lastly, we will provide our source code for reproducibility and to facilitate further research.

Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network

Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of noise

An optical tweezer array of ground-state polar molecules

Fully internal and motional state controlled and individually manipulable polar molecules are desirable for many quantum science applications leveraging the rich state space and intrinsic interactions of molecules. While prior efforts at assembling molecules from their constituent atoms individually trapped in optical tweezers achieved such a goal for exactly one molecule (Zhang J T et al 2020 Phys. Rev. Lett. 124 253401; Cairncross W B et al 2021 Phys. Rev. Lett. 126 123402; He X et al 2020 Science 370 331–5), here we extend the technique to an array of five molecules, unlocking the ability to study molecular interactions. We detail the technical challenges and solutions inherent in scaling this system up. With parallel preparation and control of multiple molecules in hand, this platform now serves as a starting point to harness the vast resources and long-range dipolar interactions of molecules.

Manganese‐Catalyzed Anti‐Markovnikov Hydroarylation of Enamides: Modular Synthesis of Arylethylamines

Comprehensive SummaryThe arylethylamine framework is a prevalent motif in a great range of biologically important organic compounds. One intractable task for frontline drug discovery is to find structurally new medicine candidates instead of opioid, based on the construction of a rich chemical space of arylethylamine motif. In this report, a practical protocol for the synthesis of arylethylamine functionality common in pharmaceutical chemicals has been developed. It proceeds by Mn‐catalyzed anti‐Markovnikov hydroarylation of electron‐rich enamides under mild conditions without the use of ligands. In spite of mismatched electronic effects during the manganese‐mediated migratory insertion process, both terminal and internal enamides can be regioselectively hydroarylated with various aryl boronic acids in 15 min. Also, the successful hydroalkenylation of enamides with alkenyl boronic acids in air atmosphere affords an elegant route to synthetically useful β‐alkenylated amines in satisfactory yields. The synthetic robustness and practicality of the reaction reveal its simple operation, short reaction time, viability on a gram‐scale and its value in late‐stage modification of complex molecules.

Role of disorder in the synthesis of metastable zinc zirconium nitrides

In materials science, it is often assumed that ground-state crystal structures predicted by density functional theory are the easiest polymorphs to synthesize. Ternary nitride materials, with many possible metastable polymorphs, provide a rich materials space to study what influences thermodynamic stability and polymorph synthesizability. For example, ${\mathrm{ZnZrN}}_{2}$ is theoretically predicted at zero Kelvin to have an unusual layered ``wurtsalt'' ground-state crystal structure with compelling optoelectronic properties, but it is unknown whether this structure can be realized experimentally under practical synthesis conditions. Here, we use combinatorial sputtering to synthesize hundreds of ${\mathrm{Zn}}_{x}{\mathrm{Zr}}_{1\ensuremath{-}x}{\mathrm{N}}_{y}$ thin-film samples, and find metastable rocksalt-derived or boron-nitride-derived structures rather than the predicted wurtsalt structure. Using a statistical polymorph sampler approach, it is demonstrated that although rocksalt is the least stable polymorph at zero Kelvin, it becomes the most stable polymorph at high effective temperatures similar to those achieved using this sputter deposition method, and thus corroborates experimental results. Additional calculations show that this destabilization of the wurtsalt polymorph is due to configurational entropic and enthalpic effects, and that vibrational contributions are negligible. Specifically, rocksalt- and boron-nitride-derived structures become the most stable polymorphs in the presence of disorder because of higher tolerances to cation cross substitution and off stoichiometry than the wurtsalt structure. This understanding of the role of disorder tolerance in the synthesis of competing polymorphs can enable more accurate predictions of synthesizable crystal structures and their achievable material properties.

Urbanization hampers biological control of insect pests: A global meta-analysis

Biological control is a major ecosystem service provided by pest natural enemies, even in densely populated areas where the use of pesticides poses severe risks to human and environmental health. However, the impact of urbanization on this service and the abundance patterns of relevant functional groups of arthropods (herbivores, predators, and parasitoids) remain contested. Here, we synthesize current evidence through three hierarchical meta-analyses and show that advancing urbanization leads to outbreaks of sap-feeding insects, declining numbers of predators with low dispersal abilities, and weakened overall biological pest control delivered by arthropods. Our results suggest that sedentary predators may have the potential to effectively regulate sap-feeders, that are one of the most important pests in urban environments. A well-connected network of structurally diverse and rich green spaces with less intensive management practices is needed to promote natural plant protection in urban landscapes and sustainable cities.

Multipartite classically entangled scalar beams.

Classically entangled light is used to refer to a class of structured beams with space-polarization, polarization-time, and space-time non-separable states akin to entangled states, which enable novel quantum-analog methods and applications in structured light. Here, we argue that classical entanglement is also available for pure scalar beams with multiple non-separable spatial degrees of freedom (DoFs). We theoretically and experimentally demonstrate a class of scalar ray-wave structured light with multiple controllable local DoFs to emulate multipartite entangled states, including the Greenberger-Horne-Zeilinger states. Our work unveils a rich parameter space for high-dimensional and multi-DoF control of structured light to extend applications in classical-quantum regimes.

Ko Aoraki te Mauka: performing my hybrid identity

ABSTRACT Adopted at birth and brought up Pākehā, I discovered at the age of 30 that I am also Ngāi Tahu (Māori). I am hybrid. Hybridity is much discussed in Aotearoa/New Zealand; some envisage it as a productive space which can liberate the subject from not belonging, others argue that the hybrid can negotiate between races. Still, others feel an exhausting pull from one side to the other. As Ngāi Tahu, our iwi identity is already caught up with complicated issues of hybridity due to historical high rates of intermarriage with Pākehā. Using creative practice research and autoethnography I explored and expressed my new hybrid identity by filming myself speaking my pepeha, a traditional Māori introduction. As a filmmaker, I am accustomed to being behind the lens, in control, and to stand in front of it and speak Te Reo Māori was humbling and difficult. In this chapter I discuss how I worked with Māori knowledges in order to perform myself as an in-betweener and created a work that speaks to the ongoing losses of adoption and the complicated and rich space of hybridity.

Implications of coordination chemistry to cationic interactions in honeycomb layered nickel tellurates

Honeycomb layered tellurates represent a burgeoning class of multi-functional materials with fascinating crystal-structural versatility and a rich composition space. Despite their multifold capabilities, their compositional diversity remains underexplored due to complexities in experimental design and syntheses. Thus, in a bid to expand this frontier and derive relevant insights into allowed metastable compositions, we employ a density functional theory (DFT) approach to predict in silico the crystal structures of new honeycomb layered tellurates embodied by the composition, A2Ni2TeO6 (A = alkali, hydrogen or coinage-metal cations). Here, alkali-metal atoms with vastly larger radii than K (for instance, Rb and Cs) are found to engender a prismatic coordination with the oxygen atoms from the honeycomb slabs whilst coinage-metal atoms (such as Ag, Au and Cu) display a propensity for linear coordination. Further, H2Ni2TeO6 is found to also render a linear coordination wherein the hydrogen atom preferentially establishes a stronger coordination with one of the oxygen atoms to form hydroxyl groups. All A cations in the studied A2Ni2TeO6 compositions form a honeycomb lattice. Conclusions on the possibility of a monolayer-bilayer phase transition in coinage metal atom tellurates can be drawn by considering the implications of conformal symmetry of the cation honeycomb lattice and metallophilicity. This work not only propounds new honeycomb layered tellurate compositions but also provides novel insight into the rational design of multifunctional materials for applications ranging from energy storage, catalysis and optics to analogue condensed matter systems of gravity.

The impact of collarette region-based convolutional neural network for iris recognition

Iris recognition is a biometric technique that reliably and quickly recognizes a person by their iris based on unique biological characteristics. Iris has an exceptional structure and it provides very rich feature spaces as freckles, stripes, coronas, zigzag collarette area, etc. It has many features where its growing interest in biometric recognition lies. This paper proposes an improved iris recognition method for person identification based on Convolutional Neural Networks (CNN) with an improved recognition rate based on a contribution on zigzag collarette area - the area surrounding the pupil - recognition. Our work is in the field of biometrics especially iris recognition; the iris recognition rate using the full circle of the zigzag collarette was compared with the detection rate using the lower semicircle of the zigzag collarette. The classification of the collarette is based on the Alex-Net model to learn this feature, the use of the couple (collarette/CNN) allows for noiseless and more targeted characterization and also an automatic extraction of the lower semicircle of the collarette region, finally, the SVM training model is used for classification using grayscale eye image data taken from (CASIA-iris-V4) database. The experimental results show that our contribution proves to be the best accurate, because the CNN can effectively extract the image features with higher classification accuracy and because our new method, which uses the lower semicircle of the collarette region, achieved the highest recognition accuracy compared with the old methods that use the full circle of collarette region.