Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct imaging of local atomic structures in zeolite using optimum bright-field scanning transmission electron microscopy

Zeolites are used in industries as catalysts, ion exchangers, and molecular sieves because of their unique porous atomic structures. However, direct observation of zeolitic local atomic structures via electron microscopy is difficult owing to low electron irradiation resistance. Subsequently, their fundamental structure-property relationships remain unclear. A low-electron-dose imaging technique, optimum bright-field scanning transmission electron microscopy (OBF STEM), has recently been developed. It reconstructs images with a high signal-to-noise ratio and a dose efficiency approximately two orders of magnitude higher than that of conventional methods. Here, we performed low-dose atomic-resolution OBF STEM observations of two types of zeolite, effectively visualizing all atomic sites in their frameworks. In addition, we visualized the complex local atomic structure of the twin boundaries in a faujasite (FAU)–type zeolite and Na+ ions with low occupancy in eight-membered rings in a Na-Linde Type A (LTA) zeolite. The results of this study facilitate the characterization of local atomic structures in many electron beam–sensitive materials.
A discrete probabilistic model for analyzing pairwise comparison matrices

Abstract The pairwise comparison matrix is often used for the estimation of the priorities in the analytic hierarchy process. In this paper, we propose an estimation method based on the discrete probabilistic expression of each choice. Moreover, we show numerical examples to compare our method with commonly used ones. As a result, it is shown that, using a robust divergence measure for the estimation, the proposed method can extract the priorities more stably even if some outlying observations are included.
Subspace filters and polar bases for spaces of symmetric multilinear functions

Abstract Let V be a finite dimensional complex vector space, and for each positive integer n let denote the vector space whose elements are the complex valued symmetric n-linear functions on (n-copies). If , that is, f is a linear functional on V, then we define the symmetric n-linear function by for all . It is well known that the elements , known as polar elements, span . A basis for consisting entirely of polar elements is a polar basis. We present a surprisingly simple recursive algorithm for the generation of polar bases. Explicit stand alone formulas for polar bases are also presented. One such explicit basis is so natural that it should probably be called the canonical polar basis for . We introduce the notion of subspace filter and show that each subspace filter of order n provides polar bases for all of the spaces simultaneously, and we extend this result to the entire symmetric algebra .
Axenfeld‐Rieger syndrome

Axenfeld‐Rieger syndrome (ARS) is a clinically and genetically heterogeneous group of developmental disorders affecting primarily the anterior segment of the eye, often leading to secondary glaucoma. Patients with ARS may also present with systemic changes, including dental defects, mild craniofacial dysmorphism, and umbilical anomalies. ARS is inherited in an autosomal‐dominant fashion; the underlying defect in 40% of patients is mutations in PITX2 or FOXC1. Here, an overview of the clinical spectrum of ARS is provided. As well, the known underlying genetic defects, clinical diagnostic possibilities, genetic counseling and treatments of ARS are discussed in detail.
The applicability of human mobility scaling laws on animals—A Herring Gull case study

With the development of sensors, recording and availability of high-resolution movement data from animals and humans, two disciplines have rapidly developed: human mobility and movement ecology. Addressing methodological gaps between these two mobility fields could improve the understanding of movement processes and has been defined as the Integrated Science of Movement. We apply well-known human mobility metrics and data processing methods to Global Positioning System (GPS) tracking data of European Herring Gulls (Larus argentatus) to test the usefulness of these methods for explaining animal mobility behavior. We use stop detection, spatial aggregation, and for the first time on animal movement data, two approaches to temporal aggregation (Next Time-Bin and Next Place). We also calculate from this data a set of movement statistics (visitation frequency, distinct locations over time, and radius of gyration). Furthermore, we analyze and compare the gull and human data from the perspective of scaling laws commonly used for human mobility. The results confirm those of previous studies and indicate differences in movement parameters between the breeding season and other parts of the year. This paper also shows that methods used in human mobility analysis have the potential to improve our understanding of animal behavior.
Infantile Prognosis of Neonates Conceived by Assisted Reproductive Technology: A Prospective Study

Background: Neonates of infertile couples are golden babies, and taking care of them is of paramount importance. However, it is not yet clear if the prognosis is different between these neonates and those from fertile couples. Objectives: The present study aimed to assess neonates conceived by assisted reproductive technology (ART). Methods: In this cohort study, 165 newborns conceived by ART and 165 naturally conceived newborns were included. The prospective study was conducted from April 2020 to October 2021. A neonatologist examined all newborns after birth, and outcomes were followed up over one year and compared with neonates of fertile couples using appropriate statistical tests. Results: The preterm neonates (60% vs. 38%) were higher in ART cases (P < 0.001) than in the other cases. Also, the mean gravid and parity were lower, but the nulliparity was higher in the ART group (P < 0.001) than in the other cases. Moreover, multiple pregnancies (45% vs. 10%; P < 0. 001) and the C/S rate (91% vs. 67%) were higher in ART cases (P < 0.001) than in the other cases. Similarly, the preeclampsia rate (16 % vs. 6%; P = 0.004) and the number of females were higher in the ART group (P = 0.035) than in the other groups. However, birth weight (P = 0.002) and the Apgar were significantly lower (P = 0.002; P = 0.012) in the ART group than in the other groups. IUGR was significantly higher (17% vs. 7%) (P = 0.006), while NEC and RDS were more common in the ART group (P < 0.001) than in the other groups. In addition, more extended hospital stay (P < 0.001) and more common re-admission and weight < the 5th percentile after one year were observed in the ART group (P = 0.021) than in the other groups. Conclusions: According to the findings, pregnancy after ART has more side effects before and after birth and in infancy, not only because of multistation but also due to manipulations in ART.
The Search for GTO: Determining Optimal Poker Strategy Using Linear Programming

This project applies techniques from game theory and linear programming to find the optimal strategies of two variants of poker. A set of optimal poker strategies describe a Nash equilibrium, where no player can improve their outcome by changing their own strategy, given the strategies of their opponent(s). We first consider Kuhn Poker as a simple application of our methodology. We then turn our attention to 2-7 Draw Poker, a modern variant onto which little previous research is focused. However, the techniques that we use are incapable of solving large, full-scale variants of poker such as 2-7 Draw. Therefore, we utilize several abstractions techniques to render a computationally-feasible LP that retains the underlying spirit of the game. We use the Gambit software package to build and solve LPs whose solutions are the optimal strategies for each game.
Photonic spin Hall effect of monolayer black phosphorus in the Terahertz region

Abstract As a two-dimensional (2D) material, black phosphorus (BP) has attracted significant attention owing to exotic physical properties such as low-energy band gap, high carrier mobility, and strong in-plane anisotropy. The striking in-plane anisotropy is a promising candidate for novel light-matter interaction. Here, we investigate the photonic spin Hall effect (PSHE) on a monolayer of BP. Due to the in-plane anisotropic property of BP, the PSHE is accompanied with Goos-Hänchen and Imbert-Fedorov effects, resulting in an asymmetric spin splitting. The asymmetric spin splitting can be flexibly tuned by the angle between the incident plane and the armchair crystalline direction of BP and by the carrier density via a bias voltage. The centroid displacements of two opposite spin components of the reflected beam along directions parallel and perpendicular to the incident plane can be considered as four independent channels for information processing. The potential application in barcode-encryption is proposed and discussed. These findings provide a deeper insight into the spin-orbit interaction in 2D material and thereby facilitate the development of optoelectronic devices in the Terahertz region.
Wave Manipulation by Topology Optimization

Sound and light propagate as waves and are scattered, reflected and change direction when encountering other media and obstacles. By optimizing the spatial placement and distribution of the media, which the waves encounter, one can obtain useful and interesting effects. This thesis describes how topology optimization can be used to design structures for manipulation of the electromagnetic and acoustic waves. The wave problems considered here fall within three classes. The first class concerns the design of cloaks, which when wrapped around an object will render the object undetectable for an outside observer. In the study the material layout of cloaks are restricted to isotropic materials readily available in nature. For fully enclosed, all-dielectric cloaks the cloaking is shown to be nearly perfect for a few discrete angles of incidences in a limited frequency range. The working principle for the cloak is to delay the waves in regions of higher permittivity than the background and subsequently phase match them to the waves outside. Directional acoustic cloaks can also be designed using the topology optimization method. Aluminum cylinders constitutes the design and their placement and size is optimized such that their combined scattering pattern cancel the scattering from a big cylinder. If only the backscattering in a limited angular range needs to be eliminated the electromagnetic cloak design simplifies to surprisingly simple annular Bragg-like grating structures with layer dimensions that depend on the obstacle radius. The second class concerns the optimization of grating couplers for efficient inand out-coupling of electromagnetic surface waves at a metal-dielectric interface. Results indicate that efficiencies beyond 68% are possible for slanted grove-based gratings. The third class concerns the design of planar Fresnel zone plate lenses for focusing electromagnetic waves. The topology optimized zone plates improve the focusing performance compared to results known from the literature.
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidents

Traffic accident anticipation is a vital function of Automated Driving Systems (ADSs) for providing a safety-guaranteed driving experience. An accident anticipation model aims to predict accidents promptly and accurately before they occur. Existing Artificial Intelligence (AI) models of accident anticipation lack a human-interpretable explanation of their decision-making. Although these models perform well, they remain a black-box to the ADS users, thus difficult to get their trust. To this end, this paper presents a Gated Recurrent Unit (GRU) network that learns spatio-temporal relational features for the early anticipation of traffic accidents from dashcam video data. A post-hoc attention mechanism named Grad-CAM is integrated into the network to generate saliency maps as the visual explanation of the accident anticipation decision. An eye tracker captures human eye fixation points for generating human attention maps. The explainability of network-generated saliency maps is evaluated in comparison to human attention maps. Qualitative and quantitative results on a public crash dataset confirm that the proposed explainable network can anticipate an accident on average 4.57 seconds before it occurs, with 94.02% average precision. In further, various post-hoc attention-based XAI methods are evaluated and compared. It confirms that the Grad-CAM chosen by this study can generate high-quality, human-interpretable saliency maps (with 1.23 Normalized Scanpath Saliency) for explaining the crash anticipation decision. Importantly, results confirm that the proposed AI model, with a human-inspired design, can outperform humans in the accident anticipation.
Inertial migration of a spherical particle in laminar square channel flows from low to high Reynolds numbers

The lateral migration properties of a rigid spherical particle suspended in a pressure-driven flow through channels with square cross-sections were investigated numerically, in the range of Reynolds numbers ( $Re$ ) from 20 to 1000. The flow field around the particle was computed by the immersed boundary method to calculate the lateral forces exerted on the particle and its trajectories, starting from various initial positions. The numerical simulation showed that eight equilibrium positions of the particle are present at the centres of the channel faces and near the corners of the channel cross-section. The equilibrium positions at the centres of the channel faces are always stable, whereas the equilibrium positions at the corners are unstable until $Re$ exceeds a certain critical value, $Re_{c}$ . At $Re\approx Re_{c}$ , additional equilibrium positions appear on a heteroclinic orbit that joins the channel face and corner equilibrium positions, and the lateral forces along the heteroclinic orbit are very small. As $Re$ increases, the channel face equilibrium positions are shifted towards the channel wall at first, and then shifted away from the channel wall. The channel corner equilibrium positions exhibit a monotonic shift towards the channel corner with increasing $Re$ . Migration behaviours of the particle in the cross-section are also predicted for various values of $Re$ . These numerical results account for the experimental observations of particle distributions in the cross-section of micro and millimetre scale channels, including the characteristic alignment and focusing of the particles, the absence of the corner equilibrium positions at low $Re$ and the progressive shift of the equilibrium positions with $Re$ .
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks

Solving long-horizon sequential decision making tasks in environments with sparse rewards is a longstanding problem in reinforcement learning (RL) research. Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction. Despite the success of recent works in dealing with inherent nonstationarity and sample complexity, it remains difficult to generalize to unseen environments and to transfer different layers of the policy to other agents. In this paper, we propose a novel HRL architecture, Hierarchical Decompositional Reinforcement Learning (HiDe), which allows decomposition of the hierarchical layers into independent subtasks, yet allows for joint training of all layers in end-to-end manner. The main insight is to combine a control policy on a lower level with an image-based planning policy on a higher level. We evaluate our method on various complex continuous control tasks, demonstrating that generalization across environments and transfer of higher level policies, such as from a simple ball to a complex humanoid, can be achieved. See videos this https URL.
A framework for online investment decisions

ABSTRACT The artificial segmentation of the investment management process into silos of human operators can restrict silos from collectively and adaptively pursuing a unified investment goal. In this article, we argue that the investment process can be accelerated and be made more cohesive by replacing batch processing for component tasks of the investment process with online processing. We propose an integrated and online framework for investment workflows, where components produce outputs that are automatically and sequentially updated as new data arrives. The workflow can be further enhanced to refine signal generation and asset class evolution and definitions. Our results demonstrate that we use this framework in conjunction with resampling methods to optimise component decisions with direct reference to investment objectives while making clear the extent of backtest overfitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.
Magnetic Equivalent Circuit Model Considering Overhang Structure of a Surface-Mounted Permanent-Magnet Motor

An overhang structure is used to enhance the air-gap flux density and increase the torque density of a motor. In the preliminary design stage, consideration of the overhang effect is important for a precise prediction of the performance of a motor with overhang and for the selection of the proper overhang length. The 3-D finite-element method (FEM) is essential when analyzing a motor with an overhang structure due to the asymmetry in the axial direction caused by overhang. However, it is computationally expensive and time-consuming, especially during the initial design stage. In this paper, we propose a magnetic equivalent circuit model, which considers the overhang structure of a surface-mounted permanent-magnet motor. With the proposed analytical method, the computational time is significantly reduced in the preliminary design stage. The proposed analytical model is verified with a 3-D FEM analysis.
Cloak: Transitioning States on Legacy Blockchains Using Secure and Publicly Verifiable Off-Chain Multi-Party Computation

In recent years, the confidentiality of smart contracts has become a fundamental requirement for practical applications. While many efforts have been made to develop architectural capabilities for enforcing confidential smart contracts, a few works arise to extend confidential smart contracts to Multi-Party Computation (MPC), i.e., multiple parties jointly evaluate a transaction off-chain and commit the outputs on-chain without revealing their secret inputs/outputs to each other. However, existing solutions lack public verifiability and require O(n) transactions to enable negotiation or resist adversaries, thus suffering from inefficiency and compromised security. In this paper, we propose Cloak, a framework for enabling Multi-Party Transaction (MPT) on existing blockchains. An MPT refers to transitioning blockchain states by an publicly verifiable off-chain MPC. We identify and handle the challenges of securing MPT by harmonizing TEE and blockchain. Consequently, Cloak secures the off-chain nondeterministic negotiation process (a party joins an MPT without knowing identities or the total number of parties until the MPT proposal settles), achieves public verifiability (the public can validate that the MPT correctly handles the secret inputs/outputs from multiple parties and reads/writes states on-chain), and resists Byzantine adversaries. According to our proof, Cloak achieves better security with only 2 transactions, superior to previous works that achieve compromised security at O(n) transactions cost. By evaluating examples and real-world MPTs, the gas cost of Cloak reduces by 32.4% on average.
Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases not just limited to the specific ones, such as crack and pothole. IOPLIN can be iteratively trained with only the image label via using Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplishes this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle any resolution of image and sufficiently utilize image information particularly for the high-resolution ones. Moreover, it can roughly localize the pavement distress without using any prior localization information in training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consists of 60059 high-resolution pavement images, which are acquired from different areas at different time. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classificaiton approaches in automatic pavement disease detection.
Exploring generative atomic models in cryo-EM reconstruction

Cryo-EM reconstruction algorithms seek to determine a molecule's 3D density map from a series of noisy, unlabeled 2D projection images captured with an electron microscope. Although reconstruction algorithms typically model the 3D volume as a generic function parameterized as a voxel array or neural network, the underlying atomic structure of the protein of interest places well-defined physical constraints on the reconstructed structure. In this work, we exploit prior information provided by an atomic model to reconstruct distributions of 3D structures from a cryo-EM dataset. We propose Cryofold, a generative model for a continuous distribution of 3D volumes based on a coarse-grained model of the protein's atomic structure, with radial basis functions used to model atom locations and their physics-based constraints. Although the reconstruction objective is highly non-convex when formulated in terms of atomic coordinates (similar to the protein folding problem), we show that gradient descent-based methods can reconstruct a continuous distribution of atomic structures when initialized from a structure within the underlying distribution. This approach is a promising direction for integrating biophysical simulation, learned neural models, and experimental data for 3D protein structure determination.