Stable Embedding Research Articles

Protective Effect of IgY Embedded in W/O/W Emulsion on LPS Enteritis-Induced Colonic Injury in Mice.

Chicken yolk immunoglobulin (IgY), an immunologically active component, is used as an alternative to antibiotics for the treatment of enteritis. In this study, IgY was embedded in a W/O/W emulsion to overcome the digestive barrier and to investigate the protective effect of IgY against LPS-induced enteritis in mice. Four different hydrophilic emulsifiers (T80, PC, SC, and WPI) were selected to prepare separate W/O/W emulsions for encapsulating IgY. The results showed that the IgY-embedded double emulsion in the WPI group was the most effective. IgY embedded in the W/O/W emulsion could reduce the damage of LPS to the mouse intestine and prevent LPS-induced intestinal mucosal damage in mice. It increased the number of cup cells, promoted the expression of Muc2, and increased the mRNA expression levels of KLF3, TFF3, Itln1, and Ang4 (p < 0.05). It also enhanced the antioxidant capacity of the colon tissue, reduced the level of inflammatory factors in the colon tissue, and protected the integrity of the colon tissue. Stable embedding of IgY could be achieved using the W/O/W emulsion. In addition, the IgY-embedded W/O/W emulsion can be used as a dietary supplement to protect against LPS-induced enteritis in mice.

A New Paradigm for Dealing With Manifold Structures in Visual Inertial Odometry by Using Stable Embedding

A New Paradigm for Dealing With Manifold Structures in Visual Inertial Odometry by Using Stable Embedding

Tensorially absorbing inclusions of C*-algebras

Abstract When $\mathcal {D}$ is strongly self-absorbing, we say an inclusion $B \subseteq A$ of C*-algebras is $\mathcal {D}$ -stable if it is isomorphic to the inclusion $B \otimes \mathcal {D} \subseteq A \otimes \mathcal {D}$ . We give ultrapower characterizations and show that if a unital inclusion is $\mathcal {D}$ -stable, then $\mathcal {D}$ -stability can be exhibited for countably many intermediate C*-algebras concurrently. We show that such unital embeddings between unital $\mathcal {D}$ -stable C*-algebras are point-norm dense in the set of all unital embeddings, and that every unital embedding between $\mathcal {D}$ -stable C*-algebras is approximately unitarily equivalent to a $\mathcal {D}$ -stable embedding. Examples are provided.

Nitrogen-doped carbon with antimony nanoparticles as a stable anode for potassium-ion batteries

Potassium-ion batteries are considered promising alternatives to lithium-ion batteries (LIBs) because they have a lower redox K+/K potential, a lower Stokes radius, abundant raw materials, and economic benefits. However, K has a larger ionic radius than Li, which may limit the diffusion of K ions into electrodes and cause poor electrode rate capability and cycling stability. In this study, N-doped C and Sb (NC@Sb) composites were synthesised using a facile and cost-effective method of carbonisation and ball milling. The NC@Sb composites had an interconnected and highly porous structure, which was formed by the release of gases such as CO, H2O, CO2, and NH3 during carbonisation. Additionally, NC@Sb is a promising anode owing to the synergistic effects of the high specific capacity of Sb and the good cycling stability of NC. In particular, the NC@Sb composite that contained 10 wt% Sb (NC@Sb10) exhibited a high reversible capacity of 264 mAh g−1 after 100 cycles at a specific current of 200 mA g−1. Furthermore, the NC@Sb10 electrode maintained 85% of its initial capacity at a high specific current of 1 A g−1 even after 500 discharge–charge cycles. The excellent electrochemical performance of the NC@Sb composite is mainly attributed to the stable embedding of the Sb nanoparticles in the porous and electrically conductive NC matrix, which can buffer Sb stress and provide a pathway for the penetration of the electrolyte and K ions.

The stable embedding tower and operadic structures on configuration spaces

The stable embedding tower and operadic structures on configuration spaces

Deep global semantic structure-preserving hashing via corrective triplet loss for remote sensing image retrieval

With the explosive increase of remote sensing data, how to search for remote sensing data quickly and accurately in a vast dataset is an incredibly critical matter for research subjects. The deep hashing method has become the dominant method for remote sensing image retrieval because of its low-cost storage and high-speed retrieval. However, for the reason of the limitation of fixed convolutional kernels, deep hashing frameworks based on Convolutional Neural Networks (CNNs) fail to obtain the global semantic information well, which leads to the generation of suboptimal solutions. Furthermore, existing hashing methods commonly employ the random sampling strategy or hardest sample mining to build training batches, resulting in bad local minima. To remedy these problems, a novel Deep Global Semantic Structure-preserving Hashing framework via corrective triplet loss (DGSSH) is proposed for remote sensing image retrieval to learn a discriminative and stable embedding space, achieving intra-class confusion and inter-class diversity. Specifically speaking, the feature extraction module based on Swim Transformer architecture is developed to capture global semantic information and multiscale features from remote sensing images. Based on a distribution matching constraint, the corrective triplet loss for deep hashing schemes is designed to reduce the distribution shift caused by the random selection or hardest sample mining. Meanwhile, to reduce the time overhead of the model, the asymmetric learning strategy is employed to perform effective compact representation learning. Numerous experiments have been carried out on three publicly available benchmarks, which indicates that the proposed DGSSH framework could achieve optimal performance for remote sensing image retrieval applications. The source code of our DGSSH framework is hosted at https://github.com/QinLab-WFU/DGSSH.git.

Explainable, Stable, and Scalable Network Embedding Algorithms for Unsupervised Learning of Graph Representations

Explainable, Stable, and Scalable Network Embedding Algorithms for Unsupervised Learning of Graph Representations

First principles study on magnetic anisotropy of 5d transition metal doped graphdiyne

The exceptional porous architecture of graphdiyne (GDY) renders it a potential candidate for magnetic storage media. This paper delves into the magnetic properties of GDY doped with 5d transition metal (TM) atoms via first-principles calculations. Our results divulge the stable embedding of these TM atoms within the triangular cavities of GDY, yielding a significant magneto-crystal anisotropy energy. In particular, Ta@GDY exhibits a remarkable magneto-crystal anisotropy energy value of 11.72 meV. By introducing TM atoms at the top, one could significantly change the magneto-crystal anisotropy energy value of the system, subsequently flipping the easy magnetization axis. The MAE values of Os-@GDY and Re-@GDY are −21.60 meV and −41.68 meV, which are expanded by a factor of 4 and 6 compared to those before the introduction of the top atom. Furthermore, we observed that the magneto-crystal anisotropy energy value of Ta@GDY is modulated by strain. Our research uncovers GDY as a promising substrate for two-dimensional magnetic materials that could be exploited in forthcoming magnetic memory devices.

Construction of a stable biochar-supported amorphous aluminum solid acid catalyst with Brønsted–Lewis dual acid sites for efficient conversion of cellulose

The development of sustainable catalysts for the efficient conversion of biomass to desirable chemicals is significant and challenging. Herein, a stable biochar (BC)-supported amorphous aluminum solid acid catalyst with Brønsted–Lewis dual acid sites was constructed through one-step calcination of a mechanical activation (MA)-treated precursor (starch, urea, and Al(NO3)3). The as-prepared N-doped BC (N-BC)-supported Al composite (MA-Al/N-BC) was used for the selective catalytic conversion of cellulose to produce levulinic acid (LA). MA treatment promoted uniform dispersion and stable embedding of Al-based components in the N-BC support with nitrogen- and oxygen-containing functional groups. This process provided the MA-Al/N-BC catalyst with Brønsted–Lewis dual acid sites and improved its stability and recoverability. When the MA-Al/N-BC catalyst was used under optimal reaction conditions (180 °C, 4 h), it achieved a cellulose conversion rate of 93.1% and a LA yield of 70.1%. Additionally, it also showed high activity for catalytic conversion of other carbohydrates. The results of this study offer a promising solution for the production of sustainable biomass-derived chemicals through the use of stable and eco-friendly catalysts.

Zero-Shot SAR Target Recognition Based on Classification Assistance

As one of main active learning methods, zero-shot target recognition with synthetic aperture radar (SAR) data has received considerable attention in recent years. Its goal is to distinguish new targets from the known classes without requiring additional training data. Existing zero-shot learning (ZSL) methods perform well on optical targets, but they fail to recognize zero-shot SAR targets. Due to the strong similarity of SAR targets between different classes, the ZSL task usually suffers from the distribution concentration problem. To tackle this problem, a Dual Branch Auto-Encoder (DBAE) network is proposed in this letter. DBAE effectively alleviates the distribution concentration problem by adding a classification assistance net. Its dual branch structure is specially designed for further improving the intra-class similarity and inter-class dissimilarity of SAR targets in the embedding space. By training with the defined hybrid loss function, DBAE automatically builds a stable embedding space. Extensive experiments on the public data set of Moving and Stationary Target Acquisition and Recognition (MSTAR) show that DBAE is of rational design and provides better or comparable ZSL recognition results.

Unscented Kalman filter with stable embedding for simple, accurate, and computationally efficient state estimation of systems on manifolds in Euclidean space

AbstractThis article proposes a simple, accurate, and computationally efficient method to apply the ordinary unscented Kalman filter (UKF) developed in Euclidean space to systems whose dynamics evolve on manifolds. We use the mathematical theory called stable embedding to make a variant of UKF that keeps state estimates in close proximity to the manifold while exhibiting excellent estimation performance. We confirm the performance of our devised filter by applying it to the satellite system model and comparing the performance with other UKFs devised specifically for systems on manifolds. Our devised filter has a low estimation error, keeps the state estimates in close proximity to the manifold as expected, and consumes a minor amount of computation time. Also our devised filter is simple and easy to use because our filter directly employs the off‐the‐shelf standard UKF devised in Euclidean space without any particular manifold‐structure‐preserving discretization method or coordinate transformation.

Normalization of technology for social contact in a Norwegian care facility during COVID-19

BackgroundThe COVID-19 pandemic has seen unprecedented growth in the use of interactive technologies in care facilities for social contact between residents and their close contacts due to the need for social distancing. As the pandemic is transitioning into a new phase, there is a need to critically examine the new practices associated with technology usage.ObjectiveOur analysis is based on a case study of how a care facility in western Norway adopted a novel technology called KOMP. We empirically investigate the stability of practices with KOMP for maintaining social communication between residents and their relatives and consider whether these practices are likely to last beyond the pandemic. We draw on normalization process theory (NPT) to interpret our findings and critically examine how stable embedding of new technologies for social communication occurs under extraordinary circumstances.MethodsWe conducted a case study based on participant observation and interviews, and the data were analyzed through inductive thematic analysis. Participants are health care professionals from a public care facility in western Norway.ResultsFour major themes emerged from the data. The first revolved around the pressing need for communications between residents and relatives with a suitable tool. Second, staff showed engagement through motivation to learn and adapt the technology in their practices. A third theme centered on how staff and the organization could work effectively to embed KOMP in daily practice. Our fourth theme suggested that the professionals continuously assessed their own use of the technology.ConclusionFrom the perspective of NPT, practices with KOMP have been partially embedded by developing a shared understanding, engaging through cognitive participation, working collectively with staff and the organization, and reflexively monitoring the benefits of using KOMP. However, staff engagement with the technology was continuously threatened by factors related to diverging staff preferences, the burden of facilitating KOMP for residents with impaired cognitive and physical abilities, issues of privacy and ethics, and the technical skills of the residents’ relatives. Our analysis suggests that caring practices via KOMP have become relatively stable despite barriers to engagement and are therefore likely to persist beyond the pandemic.

A simple preparation method for CLEM using pre-embedding immunohistochemistry with a novel fluorescent probe and stable embedding resin.

Correlative light and electron microscopy (CLEM) is an excellent approach for examining the cellular localization of biomolecules. Here, we developed a simple method for CLEM by combining pre-embedding immunohistochemistry with a novel fluorescent probe, namely Fluolid NS Orange, and an embedding resin called 'Durcupan™'. Specimens were embedded in Durcupan™ or LR White after immunolabeling and post-fixation using glutaraldehyde and osmium tetroxide. Next, ultrathin sections were prepared on a finder grid with navigation markers. The section of the specimen embedded in Durcupan™ was found to be more stable against electron beam irradiation than specimens embedded in LR White. A fluorescence light microscopy image and a transmission electron microscopy (TEM) image, at wide-field, and low magnification, were independently obtained with the same ultrathin section. Using the three corners between finder grid bars as landmarks, fluorescence light microscopy images were superimposed with wide-field, low-magnification TEM images to identify the region of interest, which was subsequently enlarged to ascertain cellular structures localized beneath fluorescent signals. However, the enlarged TEM images appeared blurred, and fluorescence signals had a hazy appearance. To resolve this, the enlarged TEM images were replaced by high-resolution TEM images focused directly on the region of interest, thereby facilitating the collection of high-resolution CLEM images. The simple sample processing method for CLEM using osmium-resistant Fluolid NS Orange and electron beam damage-resistant Durcupan™ allowed the determination of the precise localization of fluorescence signals at subcellular levels.

Tracking Controller Design for Satellite Attitude Under Unknown Constant Disturbance Using Stable Embedding

We propose a tracking control law for the fully actuated rigid body system in the presence of any unknown constant disturbance by employing quaternions with the stable embedding technique and Lyapunov stability theory. The stable embedding technique extends the attitude dynamics from the set of unit quaternions to the set of quaternions, which is a Euclidean space, such that the set of unit quaternions is an invariant set of the extended dynamics. Such a stable extension of the system dynamics to a Euclidean space allows us to employ well studied Lyapunov techniques in Euclidean spaces such as LaSalle–Yoshizawa’s theorem. A robust tracking control law is proposed for the attitude dynamics subject to unknown constant disturbance and the convergence properties of the tracking control law is rigorously proven. It is demonstrated with the help of numerical simulations that the proposed control law has a remarkable performance even in some challenging situations.

Effect of Low-Carbohydrate Diet on Metabolism in Type 2 Diabetes Patients with the Aid of Intelligent Administration of Insulin-Loaded Nanoparticle

Nanoparticles play a major role in drug delivery. We investigated the effects of the intelligent administration of insulin-loaded nanoparticles (ILNP) when combined with a low-carbohydrate diet (LCD) on the metabolism of patients with type 2 diabetes. ILNP and smart vesicle polymers were developed, and their properties were studied in vitro. Further clinical trials were performed, during which body mass index (BMI), fasting blood glucose (FBG) levels, and glycated hemoglobin (HbA1c) levels were compared between type 2 diabetes patients on LCDs those on normal diets. The results demonstrated that ILNP resisted protease degradation due to steric hindrance, and remained relatively stable at a pH range of 5.0 to 7.4. The nanoparticle enteric-coated capsules resisted the gastric juice acidity (pH = 2.5) and ensured the stable embedding of the insulin. The insulin was then released at a slightly higher pH (pH = 6.6), which mimicked the small intestine. Smart vesicle polymers further embedded the insulin and glucose oxidase simultaneously in nano polymer compounds, which allowed for a dose-dependent response to the concentration of glucose. Thus, the insulin was not released in a low-concentration glucose solution, but rather in a high-concentration glucose solution. Based on these results, we concluded that the clinical trial results showed that the intelligent administration of ILNP combined with a LCD reduced BMI, FBG, and HbA1c levels in patients with type 2 diabetes.

Hardware-Compliant Compressive Image Sensor Architecture Based on Random Modulations and Permutations for Embedded Inference

This work presents a compact CMOS Image Sensor (CIS) architecture enabling embedded object recognition facilitated by a dedicated end-of-column Compressive Sensing (CS), reducing on-chip memory needs. Our sensing scheme is based on a combination of random modulations and permutations leading to an implementation with very limited hardware impacts. It is designed to meet both theoretical ( i.e. , stable embedding, measurements incoherence) and practical requirements ( i.e. , silicon footprint, power consumption). The only additional hardware compared to a standard CIS architecture using first order incremental Sigma-Delta ( $\Sigma \Delta $ ) Analog to Digital Converter (ADC) are a pseudo-random data mixing circuit, an in- $\Sigma \Delta ~\pm 1$ modulator and a small Digital Signal Processor (DSP). On the algorithmic side, three variants are presented to perform the inference on compressed measurements with a tunable complexity ( i.e. , one-vs.-all SVM, hierarchical SVM and small ANN with 1-D max-pooling). An object recognition accuracy of $\simeq 98.8$ % is reached on the COIL database (COIL, 100 classes) using our dedicated Neural Network classifier. We stress that the signal-independent dimensionality reduction performed by our dedicated CS scheme (1/480 in $480 \times 640$ VGA resolution case) allows to dramatically reduce memory requirements mainly related to the remotely learned coefficients used for the inference stage.

SURREAL: Subgraph Robust Representation Learning

The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhoods. However, many existing network representation learning methods generate embeddings that are still not effective enough, or lead to unstable representations due to random processes (e.g., random walks to generate context) and thus, cannot generalize to multi-graph problems. In this paper, we propose SURREAL, a novel, stable graph embedding algorithmic framework that leverages “spatio-electric” (SE) subgraphs: it learns graph representations using the analogy of graphs with electrical circuits. It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes that may incidentally connect a pair of nodes in a graph. Further, it exploits the strength of weak ties and meta-data that have been neglected by baselines. The experiments show that SURREAL outperforms state-of-the-art techniques by up to 37% (6% on average) on different multi-label classification problems. Further, in contrast to baseline methods, SURREAL, being deterministic, is stable and thus can generalize to single and multi-graph tasks.

Velocity coupling of grid cell modules enables stable embedding of a low dimensional variable in a high dimensional neural attractor.

Grid cells in the medial entorhinal cortex (MEC) encode position using a distributed representation across multiple neural populations (modules), each possessing a distinct spatial scale. The modular structure of the representation confers the grid cell neural code with large capacity. Yet, the modularity poses significant challenges for the neural circuitry that maintains the representation, and updates it based on self motion. Small incompatible drifts in different modules, driven by noise, can rapidly lead to large, abrupt shifts in the represented position, resulting in catastrophic readout errors. Here, we propose a theoretical model of coupled modules. The coupling suppresses incompatible drifts, allowing for a stable embedding of a two-dimensional variable (position) in a higher dimensional neural attractor, while preserving the large capacity. We propose that coupling of this type may be implemented by recurrent synaptic connectivity within the MEC with a relatively simple and biologically plausible structure.

On a Holomorphic Family of Stein Manifolds with Strongly Pseudoconvex Boundaries

We study the stable embedding problem for a CR family of 3-dimensional strongly pseudoconvex CR manifolds with each fiber bounding a stein manifold.

The Adaptive Multi-Level Phase Coding Method in Audio Steganography

Audio steganography allows and inspires many researchers to design methods for secure communication. Based on the evaluation on the existing methods, it was found that most methods focused on one or two requirements while disregarding others, causing imbalanced performance. Moreover, most methods lack adaptivity and dynamic allocation. Therefore, in this research, a method called Adaptive Multi-level Phase Coding (AMPC) was proposed to optimize the above issues. The reverse logic of the main tradeoffs was used to empirically design several embedding levels that that simultaneously attained good performance for all aspects as much as possible. Then, an adaptive component was added by selecting the embedding level that provided the best performance for each embedding process. Moreover, the error spreading factor was introduced to achieve a fair payload distribution. The performance balance objective requires a new formulation that will enable the accurate selection of the degree of modification, multiple-bit embedding per modification, and reduced retrieval errors. As a result, the interval centering quantization (ICQ) was formulated and implemented in the proposed method. The experimental results show that AMPC successfully fulfilled the research objectives. Also, AMPC surpassed other phase coding methods in all aspects while time-domain methods achieved the highest transparency and capacity with the lowest robustness. Moreover, experiments show that the implementation of adaptive multi-level concept is able to improve the existing method's performance significantly. In summary, AMPC was able to achieve a stable embedding rate of 33 Kbps at 35 dB of SNR, which is higher than the recorded embedding rate of other phase coding methods.