On the grammaticalization of nominalization marker =ay in Kavalan and Amis: a contrastive study

In light of a functional perspective on nominalization, this paper investigates the nominalization marker =ay in two Formosan languages Kavalan and Amis. In addition to denoting persons/things or events, the marker also indicates perfectivity or emphasis. The latter non-referring functions of =ay are argued to derive from its grammatical status as an epistemic modality marker conveying a speaker's strong commitment to a proposition, which presumably has been grammaticalized from its referring functions. Several types of evidence are presented to support the hypothesized grammaticalization path, including synchronic-intralingual, synchronic-interlingual, historical, and typological

InGVIO: A Consistent Invariant Filter for Fast and High-Accuracy GNSS-Visual-Inertial Odometry

Combining Global Navigation Satellite System (GNSS) with visual and inertial sensors can give smooth pose estimation without drifting in geographical coordinates. The fusion system gradually degrades to Visual-Inertial Odometry (VIO) with the number of satellites decreasing, which guarantees robust global navigation in GNSS unfriendly environments. In this letter, we propose an open-sourced invariant filter-based platform, InGVIO, to tightly fuse monocular/stereo visual-inertial measurements, along with raw data from GNSS, i.e. pseudo ranges and Doppler shifts. InGVIO gives highly competitive results in terms of accuracy and computational load compared to current graph-based and `naive' EKF-based algorithms. Thanks to our proposed key-frame marginalization strategies, the baseline for triangulation is large although only a few cloned poses are kept. Besides, landmarks are anchored to a single cloned pose to fit the nonlinear log-error form of the invariant filter while achieving decoupled propagation with IMU states. Moreover, we exploit the infinitesimal symmetries of the system, which gives equivalent results for the pattern of degenerate motions and the structure of unobservable subspaces compared to our previous work using observability analysis. We show that the properly-chosen invariant error captures such symmetries and has intrinsic consistency properties. InGVIO is tested on both open datasets and our proposed fixed-wing datasets with variable levels of difficulty. The latter, to the best of our knowledge, are the first datasets open-sourced to the community on a fixed-wing aircraft with raw GNSS.Comment: 8 pages, 8 figures; manuscript will be submitted to IEEE RA-L for possible publicatio

Cooperative Spin Amplification

Quantum amplification is recognized as a key resource for precision measurements. However, most conventional paradigms employ an ensemble of independent particles that usually limit the performance of quantum amplification in gain, spectral linewidth, etc. Here we demonstrate a new signal amplification using cooperative 129Xe nuclear spins embedded within a feedback circuit, where the noble-gas spin coherence time is enhanced by at least one order of magnitude. Using such a technique, magnetic field can be substantially pre-enhanced by more than three orders and is in situ readout with an embedded 87Rb magnetometer. We realize an ultrahigh magnetic sensitivity of 4.0 fT/Hz$^{1/2}$ that surpasses the photon-shot noise and even below the spin-projection noise of the embedded atomic magnetometer, allowing for exciting applications including searches for dark matter with sensitivity well beyond supernova constraints. Our findings extend the physics of quantum amplification to cooperative spin systems and can be generalized to a wide variety of existing sensors, enabling a new class of cooperative quantum sensors.Comment: 7 pages, 4 figure

Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge

Uplift modeling is a fundamental component of marketing effect modeling, which is commonly employed to evaluate the effects of treatments on outcomes. Through uplift modeling, we can identify the treatment with the greatest benefit. On the other side, we can identify clients who are likely to make favorable decisions in response to a certain treatment. In the past, uplift modeling approaches relied heavily on the difference-in-difference (DID) architecture, paired with a machine learning model as the estimation learner, while neglecting the link and confidential information between features. We proposed a framework based on graph neural networks that combine causal knowledge with an estimate of uplift value. Firstly, we presented a causal representation technique based on CATE (conditional average treatment effect) estimation and adjacency matrix structure learning. Secondly, we suggested a more scalable uplift modeling framework based on graph convolution networks for combining causal knowledge. Our findings demonstrate that this method works effectively for predicting uplift values, with small errors in typical simulated data, and its effectiveness has been verified in actual industry marketing data.Comment: 6 pages, 6 figure

A single-step preparation of carbohydrate functionalized monoliths for separation and trapping of polar compounds

A single-step copolymerization strategy was developed for the preparation of carbohydrate (glucose and maltose) functionalized monoliths using click reaction. Firstly, novel carbohydrate-functionalized methacrylate monomers were synthesized through Cu(I)-catalyzed 1,3-dipolar cycloaddition (alkyne-azide reaction) of terminal alkyne with azide of carbohydrate derivatives. The corresponding carbohydrate functionalized monolithic columns were then prepared through a single-step in-situ copolymerization. The physicochemical properties and performance of the fabricated monolithic columns were evaluated using scanning electron microscopy, Fourier-transform infrared spectroscopy, and nano-liquid chromatography. For the optimized monolithic column, satisfactory column permeability and good separation performance were demonstrated for polar compounds including nucleoside, phenolic compounds and benzoic acid derivatives. The monolithic column is also highly useful for selective and efficient enrichment of glycopeptides from human IgG tryptic digests. This study not only provided a novel hydrophilic column for separation and selective trapping of polar compounds, but also proposed a facile and efficient approach for preparing carbohydrate functionalized monoliths

ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning

Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. The significant challenges for deep learning-based image fusion algorithms are the lack of a definitive ground truth as well as the corresponding distance measurement, with current manually given loss functions constrain the flexibility of model and generalizability for unified fusion tasks. To overcome these limitations, we introduce a unified image fusion framework based on meta-learning, named ReFusion, which provides a learning paradigm that obtains the optimal fusion loss for various fusion tasks based on reconstructing the source images. Compared to existing methods, ReFusion employs a parameterized loss function, dynamically adjusted by the training framework according to the specific scenario and task. ReFusion is constituted by three components: a fusion module, a loss proposal module, and a source reconstruction module. To ensure the fusion module maximally preserves the information from the source images, enabling the reconstruction of the source images from the fused image, we adopt a meta-learning strategy to train the loss proposal module using reconstruction loss. The update of the fusion module relies on the fusion loss proposed by the loss proposal module. The alternating updates of the three modules mutually facilitate each other, aiming to propose an appropriate fusion loss for different tasks and yield satisfactory fusion results. Extensive experiments demonstrate that ReFusion is capable of adapting to various tasks, including infrared-visible, medical, multi-focus, and multi-exposure image fusion. The code will be released

SAPPHIRE: Search for exotic parity-violation interactions with quantum spin amplifiers

Quantum sensing provides sensitive tabletop tools to search for exotic spin-dependent interactions beyond the Standard Model, which has attracted great attention in theories and experiments. Here we develop a technique based on quantum Spin Amplifier for Particle PHysIcs REsearch (SAPPHIRE) to resonantly search for exotic interactions, specifically parity-odd spin-spin interactions. The present technique effectively amplifies the pseudomagnetic field generated by exotic interactions by a factor of about 200 while being insensitive to spurious external magnetic fields. Our studies, using such a quantum amplification technique, open the doors to exploring the parity-violation interactions mediated by Z' bosons in the challenging parameter space (force range between 3 mm and 0.1 km) and set the most stringent constraints on Z'-mediated electron-neutron couplings, significantly improving previous limits by up to five orders of magnitude. Moreover, our bounds on Z'-mediated couplings between nucleons reaches into a hitherto unexplored parameter space (force range below 1 m), complementing the existing astrophysical and laboratory studies.Comment: 8 pages, 5 figure

Maternal High-Fat Diet Promotes the Development and Progression of Prostate Cancer in Transgenic Adenocarcinoma Mouse Prostate Offspring

Background/Aims: We aim to investigate the impact of maternal high fat diet (HFD) on the development and progression of prostate cancer (PCa) in transgenic adenocarcinoma mouse prostate (TRAMP) offspring. Methods: The TRAMP model was used, and divided into maternal HFD group and normal diet (ND) group in the present study. Each group contained 36 TRAMP mice. Serum levels of leptin, adiponectin, interleukin (IL) -1α, IL-1β, IL-6, tumor necrosis factor-α and monocyte chemotactic protein-1 were measured by the 20th, 24th and 28th week old through ProcartaPlex Multiplex Immunoassay. Body fat ratio was measured by MiniQMR. Tumor formation rate was measured through hematoxylin and eosin (H&E) staining, and mortality rate was measured meantime. Western blot was applied to determine the levels of Protein Kinase B (Akt) and Phosphatase and tensin homolog (PTEN). Results: The mortality rate of maternal HFD group was significantly higher than that of ND group (P = 0.046). The tumor formation rate was significantly higher in maternal HFD group than in ND group only in 20th week subgroup (P = 0.040). A significant increase of leptin was seen in maternal HFD 20th and 24th week subgroups (P = 0.001 and < 0.001, respectively) and a decrease of adiponectin was seen in maternal HFD 20th and 28th week subgroups (P =0.006 and < 0.001, respectively). Besides, an activated phos-Akt (P-Akt) and deactivated PTEN were observed in maternal HFD group. Conclusions: Maternal HFD could increase the standard serum leptin level, inhibit the expression of PTEN protein, promote P-Akt protein expression, activate the PI3K/Akt pathway, and ultimately promote the development and progression of PCa in TRAMP offspring

Orthogonal printing of uniform nanocomposite monolayer and oriented organic semiconductor crystals for high-performance nano-crystal floating gate memory

Inkjet printing is of great interest in the preparation of optoelectronic and microelectronic devices due to its low cost, low process temperature, versatile material compatibility, and ability to precisely manufacture multi-layer devices on demand. However, interlayer solvent erosion is a typical problem that limits the printing of organic semiconductor devices with multi-layer structures. In this study, we proposed a solution to address this erosion problem by designing polystyrene-block-poly(4-vinyl pyridine)-grafted Au nanoparticles (Au@PS-b-P4VP NPs). With a colloidal ink containing the Au@PS-b-P4VP NPs, we obtained a uniform monolayer of Au nano-crystal floating gates (NCFGs) embedded in the PS-b-P4VP tunneling dielectric (TD) layer using direct-ink-writing (DIW). Significantly, PS-b-P4VP has high erosion resistance against the semiconductor ink solvent, which enables multi-layer printing. An active layer of semiconductor crystals with high crystallinity and well-orientation was obtained by DIW. Moreover, we developed a strategy to improve the quality of the TD/semiconductor interface by introducing a polystyrene intermediate layer. We show that the NCFG memory devices exhibit a low threshold voltage (100 cycles), and long-term retention (>10 years). This study provides universal guidance for printing functional coatings and multi-layer devices