Miracle-dz/UniFAR-FTUs huggingface dataset:
Excess shear force exerted on an oscillating plate due to a nearby particle

In the present paper we theoretically study the shear force exerted on an infinite horizontal plane undergoing fast lateral oscillations in presence of a rigid particle suspended in the viscous liquid above the plate. The study is largely motivated by Quartz Crystal Microbalance (QCM-D) technique which relies on analyzing response (complex impedance) of fast oscillating (in MHz range) quartz crystal disk in the liquid medium due to small substances adsorbed at its surface. In fact, small substances suspended in the liquid medium in the vicinity of the oscillating crystal may also contribute to impedance, as they modify the local shear force the suspending liquid exerts on the quartz crystal. For a dilute suspension the contributions of individual particles are additive and, therefore, our analysis is restricted to the excess shear force due a single spherical particle located at arbitrary distance above the plane. Three distinct cases are considered: (i) limiting case of high solid inertia, whereas the heavy particle can be considered as stationary; (ii) a freely suspended particle of arbitrary mass, undergoing fluid-mediated time-periodic rotation and translation and (iii) an adsorbed particle moving with the plate as a whole without rotation. For small-amplitude plane oscillations the unsteady Stokes flow equations apply. We construct the series solution of these equations using the method of reflections, whereas its terms are written explicitly. Due to the exponential decay of the flow away from the oscillating plate, the truncated series containing only few low-order terms shows an excellent agreement with the rigorous numerical results for a wide range of particle sizes and separation distances. The present results support the notion that the hydrodynamic contribution of the suspended small substances to the measured impedance is non-negligible or even dominant.
GAN-MC: a Variance Reduction Tool for Derivatives Pricing

We propose a parameter-free model for estimating the price or valuation of financial derivatives like options, forwards and futures using non-supervised learning networks and Monte Carlo. Although some arbitrage-based pricing formula performs greatly on derivatives pricing like Black-Scholes on option pricing, generative model-based Monte Carlo estimation(GAN-MC) will be more accurate and holds more generalizability when lack of training samples on derivatives, underlying asset's price dynamics are unknown or the no-arbitrage conditions can not be solved analytically. We analyze the variance reduction feature of our model and to validate the potential value of the pricing model, we collect real world market derivatives data and show that our model outperforms other arbitrage-based pricing models and non-parametric machine learning models. For comparison, we estimate the price of derivatives using Black-Scholes model, ordinary least squares, radial basis function networks, multilayer perception regression, projection pursuit regression and Monte Carlo only models.
The Hitchiker's Guide to Successful Living Lab Operations

Living labs have been established across different countries to evaluate how the interaction between humans and buildings can be optimized to improve comfort, health, and energy savings. However, existing living labs can be too project-specific, not scalable, and inflexible for comparison against other labs. Furthermore, the lack of transparency in its software infrastructure inhibits opportunities for critique and reuse, reducing the platform's overall potential. In the face of climate change and global energy shortage, we envision the future of living labs to be open source and scalable to support the integration of different IoTs, subjective measures, human-building interactions, security, and privacy contexts. In this work, we share our living lab software stack and present our experience developing a platform that supports qualitative and quantitative experiments from the ground up. We propose the first open-source interoperable living lab platform for multidisciplinary smart environment research.
VIBRATION ANALYSIS OF TWO BUILDINGS LINKED BY MAXWELL MODEL-DEFINED FLUID DAMPERS

Fluid dampers that operate on the principle of fluid flow through orifices specially shaped and defined by the Maxwell model have found more and more applications in vibration mitigation of buildings and structures. This paper thus presents an accurate and effective procedure for determining dynamic characteristics and seismic response of adjacent buildings linked by fluid dampers. Dynamic characteristics of damper-linked adjacent buildings are obtained by solving the eigenvalue problem for real non-symmetric matrices. Random seismic response of adjacent buildings linked by dampers is determined by a combination of the state-space method and the pseudo-excitation method. Based on a derived formula, a computer program is written and extensive parametric studies are performed to assess the effectiveness of the fluid damper and to identify beneficial damper relaxation time and damping coefficient at zero frequency. It is shown that using the Maxwell model-defined fluid dampers of proper parameters to link adjacent buildings can increase the modal damping ratios and reduce the seismic responses of adjacent buildings significantly. It is also shown that the behaviour of the adjacent buildings linked by the Maxwell model-defined fluid dampers could be the same as that connected by the Voigt model-defined viscoelastic dampers.
Investigating the transition form factors of $\Lambda_b\to\Lambda_c(2625)$ and $\Xi_b\to\Xi_c(2815)$ and the corresponding weak decays with support from baryon spectroscopy

We calculate the form factors of the $\Lambda_b\to\Lambda_c(2625)$ and $\Xi_b\to\Xi_c(2815)$ transitions, and additionally evaluate the corresponding semileptonic decays and the color-allowed two-body nonleptonic decays. In order to obtain the concerned form factors, we use the three-body light-front quark model with the support from baryon spectroscopy. In this work, as important physical inputs, the spatial wave functions of concerned baryons are obtained by the Gaussian expansion method with a semirelativistic potential model. For the semileptonic processes, the branching ratios of the electron and muon channels can reach up to the order of magnitude of $1\%$, where our result $\mathcal{B}(\Lambda_b^0\to\Lambda_c^+(2625)\mu^-\nu_{\mu})=(1.641\pm0.113)\%$ is consistent with the current experimental data. As for the nonleptonic processes, the decays to $\pi^-$, $\rho^-$, and $D_s^{(*)-}$ final states have considerable widths. These discussed decay modes could be accessible at the LHCb experiment.
Numerical simulation on a HPLWR fuel assembly flow with one revolution of wrapped wire spacers

Abstract Three dimensional computational-fluid-dynamics simulations are performed for the fluid flow within a 40 rod fuel bundle in a square arrangement with a central moderator channel. To ensure spacing between the rods, the design of the bundle uses thin wires wrapped counter-clockwise around each rod. This geometry is presently investigated in the framework of the European High-Performance Light-Water Reactor (HPLWR), which operates at supercritical pressure of 25 MPa. A section with one revolution located in the evaporator region of the HPLWR core is investigated using hydraulic (to ensure fully developed flow inlet boundary conditions and reference for heated cases) and thermal-hydraulic boundary conditions. The geometry of wrapped wires gives rise to additional mixing and a circulating or ‘sweeping’ flow near the outer and inner regions of the fuel element next to the wall of the so called fuel assembly and moderator box. Some interesting flow features associated with the complex three-dimensional flow with significant transverse velocity components are visualized as the first evaluated result of this diversified investigation.
Shape-Guided Diffusion with Inside-Outside Attention

When manipulating an object, existing text-to-image diffusion models often ignore the shape of the object and generate content that is incorrectly scaled, cut off, or replaced with background content. We propose a training-free method, Shape-Guided Diffusion, that modifies pretrained diffusion models to be sensitive to shape input specified by a user or automatically inferred from text. We use a novel Inside-Outside Attention mechanism during the inversion and generation process to apply this shape constraint to the cross- and self-attention maps. Our mechanism designates which spatial region is the object (inside) vs. background (outside) then associates edits specified by text prompts to the correct region. We demonstrate the efficacy of our method on the shape-guided editing task, where the model must replace an object according to a text prompt and object mask. We curate a new ShapePrompts benchmark derived from MS-COCO and achieve SOTA results in shape faithfulness without a degradation in text alignment or image realism according to both automatic metrics and annotator ratings. Our data and code will be made available at https://shape-guided-diffusion.github. io.
Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images

Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retinal edema lesions joint segmentation from OCT images and enabling the segmentation results more reliable. In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment. Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed. Meanwhile, to make the segmentation results more reliable, a novel uncertainty segmentation head based on the subjective logical evidential theory is introduced to generate the final segmentation results with a corresponding overall uncertainty evaluation score map. We conduct comprehensive experiments on the public database of AI-Challenge 2018 for retinal edema lesions segmentation, and the results show that our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches. The code will be released on: https://github.com/LooKing9218/ReliableRESeg.
Stability and Sample Complexity of Divergence Regularized Optimal Transport

We study stability and sample complexity properties of divergence regularized optimal transport (DOT). First, we obtain quantitative stability results for optimizers of DOT measured in Wasserstein distance, which are applicable to a wide class of divergences and simultaneously improve known results for entropic optimal transport. Second, we study the case of sample complexity, where the DOT problem is approximated using empirical measures of the marginals. We show that divergence regularization can improve the corresponding convergence rate compared to unregularized optimal transport. To this end, we prove upper bounds which exploit both the regularity of cost function and divergence functional, as well as the intrinsic dimension of the marginals. Along the way, we establish regularity properties of dual optimizers of DOT, as well as general limit theorems for empirical measures with suitable classes of test functions.
Axion minivoids and implications for direct detection

In the scenario in which QCD axion dark matter is produced after inflation, the Universe is populated by large inhomogeneities on very small scales. Eventually, these fluctuations will collapse gravitationally to form dense axion miniclusters that trap up to $\sim$75% of the dark matter within asteroid-mass clumps. Axion miniclusters are physically tiny however, so haloscope experiments searching for axions directly on Earth are much more likely to be probing ``minivoids'' -- the space in between miniclusters. This scenario seems like it ought to spell doom for haloscopes, but while these minivoids might be underdense, they are not totally devoid of axions. Using Schr\"odinger-Poisson and N-body simulations to evolve from realistic initial field configurations, we quantify the extent to which the local ambient dark matter density is suppressed in the post-inflationary scenario. We find that a typical experimental measurement will sample an axion density that is only around 10% of the expected galactic dark matter density. Our results also have implications for experimental campaigns lasting longer than a few years, as well as broadband haloscopes that have sensitivity to transient signatures. We show that for a $\mathcal{O}$(year)-long integration times, the measured dark matter density should be expected to vary by 20--30%.
A universal formula for the $x-y$ swap in topological recursion

We prove a recent conjecture of Borot et al. that a particular universal closed algebraic formula recovers the correlation differentials of topological recursion after the swap of $x$ and $y$ in the input data. We also show that this universal formula can be drastically simplified (as it was already done by Hock). As an application of this general $x-y$ swap result, we prove an explicit closed formula for the topological recursion differentials for the case of any spectral curve with unramified $y$ and arbitrary rational $x$.
Numerical simulations of inflationary dynamics: slow roll and beyond

Numerical simulations of the inflationary dynamics are presented here for a single canonical scalar field minimally coupled to gravity. We spell out the basic equations governing the inflationary dynamics in terms of cosmic time $t$ and define a set of dimensionless variables convenient for numerical analysis. We then provide a link to our simple numerical Python code on GitHub that can be used to simulate the background dynamics as well as the evolution of linear perturbations during inflation. The code computes both scalar and tensor power spectra for a given inflaton potential $V(\phi)$. We discuss a concrete algorithm to use the code for various purposes, especially for computing the enhanced scalar power spectrum in the context of Primordial Black Holes and scalar-induced Gravitational Waves. We also compare the efficiency of different variables used in the literature to compute the scalar fluctuations. We intend to extend the framework to simulate the dynamics of a number of different quantities, including the computation of scalar-induced second-order tensor power spectrum in the near future.
Customer Scheduling with Incomplete Information

A stochastic scheduling model with linear waiting costs and unknown routing probabilities is considered. Using a Bayesian approach and methods of Bayesian dynamic programming, we investigate the finite-horizon stochastic scheduling problem with incomplete information. In particular, we study an equivalent nonstationary bandit model and show the monotonicity of the total expected reward and of the Gittins index. We derive the monotonicity and well-known structural properties of the (greatest) maximizers, the so-called stay-on-a-winnerproperty and the stopping-property. The monotonicity results are based on a special partial ordering on .
At the threshold of distributed Kerr-Lens mode-locking in a Cr:ZnS waveguide laser

We demonstrate feasibility of spatiotemporal mode-locking in a mid-IR Cr:ZnS waveguide laser based on the nonlinear spatial mode coupling. The experiment shows efficient power scaling with the excitation of intra-mode beatings, causing a pronounced Q-switching which could cause a nonlinear mode-coupling. We suggest that a particularly high nonlinearity in Cr:ZnS combined with multimode waveguide leads to a soft aperture induced by a pump beam. The latter provides an effective spatial mode control in a nonlinear multimode waveguide and, thereby, opening the way to the birth of a spatiotemporal dissipative soliton, or light bullet, formation. Thus, forming the basis for the future distributed Kerr-Lens Mode Locking in the energy-scalable solid-state waveguide (or yet unrealized fiber) laser.
Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning

Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a reduced cost. Given the sheer magnitude of the collected data and the significant challenges offered by the presence of highly similar genomic structure across species, there is a need for robust, scalable analysis platforms to extract actionable knowledge such as the presence of potentially zoonotic pathogens. The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis. In this work, we propose MG2Vec, a deep learning-based solution that uses the transformer network as its backbone, to learn robust features from raw metagenome sequences for downstream biomedical tasks such as targeted and generalized pathogen detection. Extensive experiments on four increasingly challenging, yet realistic diagnostic settings, show that the proposed approach can help detect pathogens from uncurated, real-world clinical samples with minimal human supervision in the form of labels. Further, we demonstrate that the learned representations can generalize to completely unrelated pathogens across diseases and species for large-scale metagenome analysis. We provide a comprehensive evaluation of a novel representation learning framework for metagenome-based disease diagnostics with deep learning and provide a way forward for extracting and using robust vector representations from low-cost next generation sequencing to develop generalizable diagnostic tools.
Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer

A passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW images, PMMW hidden object detection based on deep learning usually suffers from low accuracy and low classification confidence. To tackle the above problems, this article proposes a task-aligned detection transformer network, named PMMW-DETR. In the first stage, a denoising coarse-to-fine transformer (DCFT) backbone is designed to extract long- and short-range features in the different scales. In the second stage, we propose the query selection module to introduce learned spatial features into the network as prior knowledge, which enhances the semantic perception capability of the network. In the third stage, aiming to improve the classification performance, we perform a task-aligned dual-head block that decouples the classification and regression tasks, and enhances the interaction between the two heads. Based on our self-developed PMMW security screening dataset, experimental results, including comparison with state-of-the-art (SOTA) methods and ablation study, demonstrate that the PMMW-DETR obtains higher accuracy and classification confidence than previous works, and exhibits robustness to the PMMW images of low quality.
Rethinking Two Consensuses of the Transferability in Deep Learning

Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is named knowledge transferability. A commonly used paradigm for DTL is firstly learning general knowledge (pre-training) and then reusing (fine-tuning) them for a specific target task. There are two consensuses of transferability of pre-trained DNNs: (1) a larger domain gap between pre-training and downstream data brings lower transferability; (2) the transferability gradually decreases from lower layers (near input) to higher layers (near output). However, these consensuses were basically drawn from the experiments based on natural images, which limits their scope of application. This work aims to study and complement them from a broader perspective by proposing a method to measure the transferability of pre-trained DNN parameters. Our experiments on twelve diverse image classification datasets get similar conclusions to the previous consensuses. More importantly, two new findings are presented, i.e., (1) in addition to the domain gap, a larger data amount and huge dataset diversity of downstream target task also prohibit the transferability; (2) although the lower layers learn basic image features, they are usually not the most transferable layers due to their domain sensitivity.
Photometry of the Four Anti-Galactocentric Old Open Clusters: Czernik 30, Berkeley 34, Berkeley 75, and Berkeley 76

We present a BVI photometric study of four old open clusters (OCs) in the the Milky Way, Czernik 30, Berkeley 34, Berkeley 75, and Berkeley 76 using the observation data obtained with the Small and Moderate Aperture Research Telescope System 1.0 m telescope at the Cerro Tololo Inter-American Observatory, Chile. These four OCs are located at the anti-Galactocentric direction and in the Galactic plane. We determine the fundamental physical parameters for the four OCs, such as age, metallicity, distance modulus, and color excess, using red clump and PARSEC isochrone fitting methods after finding center and size of the four OCs. These four old OCs are 2–3 Gyr old and 6–8 kpc away from the Sun. The metallicity ([Fe/H]) values of the four OCs are between −0.6 and 0.0 dex. We combine data for these four OCs with those for old OCs from five literatures resulting in 236 objects to investigate Galactic radial-metallicity distribution. The gradient of a single linear fit for this Galactocentric [Fe/H] distribution is −0.052 ± 0.004 dex kpc−1. If we assume the existence of a discontinuity in this radial-metallicity distribution, the gradient at Galactocentric radius <12 kpc is −0.070 ± 0.006 dex kpc−1, while that at the outer part is −0.016 ± 0.010 which is flatter than that of the inner part. Although there are not many sample clusters at the outer part, the broken linear fit seems to better follow the observation data.
The characteristic time scale of cultural evolution

Abstract Numerous researchers from various disciplines have explored commonalities and divergences in the evolution of complex social formations. Here, we explore whether there is a “characteristic” time course for the evolution of social complexity in a handful of different geographic areas. Data from the Seshat: Global History Databank is shifted so that the overlapping time series can be fitted to a single logistic regression model for all 23 geographic areas under consideration. The resulting regression shows convincing out-of-sample predictions, and its period of extensive growth in social complexity can be identified via bootstrapping as a time interval of roughly 2,500 years. To analyze the endogenous growth of social complexity, each time series is restricted to a central time interval without major disruptions in cultural or institutional continuity, and both approaches result in a similar logistic regression curve. Our results suggest that these different areas have indeed experienced a similar course in the their evolution of social complexity, but that this is a lengthy process involving both internal developments and external influences.