Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrathin 2D-oxides: A perspective on fabrication, structure, defect, transport, electron, and phonon properties

In the field of atomically thin 2D materials, oxides are relatively unexplored in spite of the large number of layered oxide structures amenable to exfoliation. There is an increasing interest in ultra-thin film oxide nanostructures from applied points of view. In this perspective paper, recent progress in understanding the fundamental properties of 2D oxides is discussed. Two families of 2D oxides are considered: (1) van der Waals bonded layered materials in which the transition metal is in its highest valence state (represented by V$_2$O$_5$ and MoO$_3$) and (2) layered materials with ionic bonding between positive alkali cation layers and negatively charged transition metal oxide layers (LiCoO$_2$). The chemical exfoliation process and its combinaton with mechanical exfoliation are presented for the latter. Structural phase stability of the resulting nanoflakes, the role of cation size and the importance of defects in oxides are discussed. Effects of two-dimensionality on phonons, electronic band structures and electronic screening are placed in the context of what is known on other 2D materials, such as transition metal dichalcogenides. Electronic structure is discussed at the level of many-body-perturbation theory using the quasiparticle self-consistent $GW$ method, the accuracy of which is critically evaluated including effects of electron-hole interactions on screening and electron-phonon coupling. The predicted occurence of a two-dimensional electron gas on Li covered surfaces of LiCoO$_2$ and its relation to topological aspects of the band structure and bonding is presented as an example of the essential role of the surface in ultrathin materials. Finally, some case studies of the electronic transport and the use of these oxides in nanoscale field effect transistors are presented.
Persistent Homology Analysis for Dense QCD Effective Model with Heavy Quarks

The isospin chemical potential region is known as the sign-problem-free region of quantum chromodynamics (QCD). In this paper, we introduce the isospin chemical potential to the three-dimensional three-state Potts model to mimic dense QCD; e.g., the QCD effective model with heavy quarks at finite density. We call it the QCD-like Potts model. The QCD-like Potts model does not have a sign problem, but we expect it to share some properties with QCD. Since we can obtain the non-approximated Potts spin configuration at finite isospin chemical potential, where the simple Metropolis algorithm can work, we perform the persistent homology analysis toward exploring the dense spatial structure of QCD. We show that the averaged birth-death ratio has the same information with the Polyakov loop, but the maximum birth-death ratio has additional information near the phase transition where the birth-death ratio means the ratio of the creation time of a hole and its vanishing time based on the persistent homology.
Strong Detectability and Observers for Linear Time Varying Systems

This work presents a notion of strong detectability for linear time varying systems affected by unknown inputs. It is shown that this notion is equivalent to detectability of an auxiliary system without unknown inputs. This allows a straightforward observer design for dependable state estimation in the presence of unknown inputs. The design reduces to a deterministic Kalman filter design problem, where the observer gains can be obtained from the solution of a differential Riccati equation. The efficacy of the proposed approach is demonstrated by means of a numerical example.
A Conceptual Reference Model for Human as a Service Provider in Cyber Physical Systems

In Cyber Physical Systems humans are often kept in the loop as operators and/or service users. Yet in many cases, humans and machines collaborate and provide services to each other. Research on service models and service composition for CPS exist; however, humans as service providers have not been adequately considered as part of the CPS service composition model. We provide a classification of human-as-a-service in CPS, and we propose a Service Oriented Architecture (SOA) ontology model for the CPS environment as part of the Everything-as-a-Service paradigm. The model considers human characteristics and their dynamics, as a service provider or collaborator with the machine. As the ontology model is an enabler for engineering a self-adaptive CPS with human-machine collaboration as service providers, we describe how a commonly used self-adaptive reference model can be refined to benefit from the vision. We evaluate the ontological contribution against criteria that relates to accuracy, completeness, adaptability, clarity, and consistency. We demonstrate the feasibility of our conceptual reference model using a use case from the medical domain and we show how human-machine service provision is possible.
ON NON-COMPACT p-ADIC DEFINABLE GROUPS

Abstract In [16], Peterzil and Steinhorn proved that if a group G definable in an o-minimal structure is not definably compact, then G contains a definable torsion-free subgroup of dimension 1. We prove here a p-adic analogue of the Peterzil–Steinhorn theorem, in the special case of abelian groups. Let G be an abelian group definable in a p-adically closed field M. If G is not definably compact then there is a definable subgroup H of dimension 1 which is not definably compact. In a future paper we will generalize this to non-abelian G.
PET Chemistry: The Driving Force in Molecular Imaging

Molecular Imaging with PET - Open Questions? - Fluorine-18 Labeling Methods: Features and Possibilities of Basic Reactions. - Fluorine-18 Labeling of Small Molecules: The Use of 18F-Labeled Aryl Fluorides Derived from No-Carrier-Added [18F]Fluoride as Labeling Precursors. - Fluorine-18 Labeling of Peptides and Proteins. - [18F]Fluoropyridines: From Conventional Radiotracers to the Labeling of Macromolecules Such as Proteins and Oligonucleotides. - Production of Non-standard PET Radionuclides and the Application of Radiopharmaceuticals Labeled with these Nuclides. - Carbon-11 Labeling Chemistry Based upon [11C]Methyl Iodide. - 68Ga-PET Radiopharmacy: A Generator-Based Alternative to 18F-Radiopharmacy. - Microwaving in F-18 Chemistry: Quirks and Tweaks. - Micro-reactors for PET Tracer Labeling. - Synthesis Modules and Automation in F-18 Labeling. - Pharmacological Prerequisites for PET Ligands and Practical Issues in Preclinical PET Research. - Positron Emission Tomography Imaging as a Key Enabling Technology in Drug Development.
IMF-induced intrinsic uncertainties on measuring galaxy distances based on the number of giant stars: the case of the ultradiffuse galaxy NGC 1052-DF2

The surface brightness fluctuation (SBF) technique is one of the distance measurement methods that has been applied on the low surface brightness galaxy NGC 1052-DF2 yielding a distance of about 20 Mpc implying it to be a dark matter deficient galaxy. We assume the number of giant stars above a given luminosity threshold to represent the SBF magnitude. The SBF magnitude depends on the distance, but this is degenerate with the star formation history (SFH). Using a stellar population synthesis model we calculate the number of giant stars for stellar populations with different galaxy-wide stellar initial mass functions (gwIMFs), ages, metallicities and SFHs. If the gwIMF is the invariant canonical IMF, the 1σ (3σ) uncertainty in colour allows a distance as low as 12 Mpc (8 Mpc). If instead the true underlying gwIMF is the integrated galaxy-wide IMF (IGIMF) then overestimating distances for low-mass galaxies would be a natural result, allowing NGC 1052-DF2 to have a distance of 11 Mpc within the 1σ colour uncertainty. Finally, we show that our main conclusion on the existence of a bias in the SBF distance estimation is not much affected by changing the luminosity lower limit for counting giant stars.
A multi-layer network model to assess school opening policies during the COVID-19 vaccination campaign

We propose a multi-layer network model for the spread of COVID-19 that accounts for interactions within the family, between schoolmates, and casual contacts in the population. We utilize the proposed model-calibrated on epidemiological and demographic data-to investigate current questions concerning the implementation of non-pharmaceutical interventions (NPIs) during the vaccination campaign. Specifically, we consider scenarios in which the most fragile population has already received the vaccine, and we focus our analysis on the role of schools as drivers of the contagions and on the implementation of targeted intervention policies oriented to children and their families. We perform our analysis by means of a campaign of Monte Carlo simulations. Our findings suggest that, in a phase with NPIs enacted but in-person education, children play a key role in the spreading of COVID-19. Interestingly, we show that children's testing might be an important tool to flatten the epidemic curve, in particular when combined with enacting temporary online education for classes in which infected students are detected. Finally, we test a vaccination strategy that prioritizes the members of large families and we demonstrate its good performance. We believe that our modeling framework and our findings could be of help for public health authorities for planning their current and future interventions, as well as to increase preparedness for future epidemic outbreaks.
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload

The use of deep learning models for forecasting the resource consumption patterns of SQL queries have recently been a popular area of study. While these models have demonstrated promising accuracy, training them over large scale industry workloads are expensive. Space inefficiencies of encoding techniques over large numbers of queries and excessive padding used to enforce shape consistency across diverse query plans implies 1) longer model training time and 2) the need for expensive, scaled up infrastructure to support batched training. In turn, we developed Prestroid, a tree convolution based data science pipeline that accurately predicts resource consumption patterns of query traces, but at a much lower cost. We evaluated our pipeline over 19K Presto OLAP queries, on a data lake of more than 20PB of data from Grab. Experimental results imply that our pipeline outperforms benchmarks on predictive accuracy, contributing to more precise resource prediction for large-scale workloads, yet also reduces per-batch memory footprint by 13.5x and per-epoch training time by 3.45x. We demonstrate direct cost savings of up to 13.2x for large batched model training over Microsoft Azure VMs.
The numerical solutions of linear semidiscrete evolution problems on the half‐line using the Unified Transform Method

We discuss a semidiscrete analogue of the Unified Transform Method (UTM), introduced by A. S. Fokas, to solve initial‐boundary‐value problems for linear evolution partial differential equations of constant coefficients. The semidiscrete method is applied to various spacial discretizations of several first‐ and second‐order linear equations on the half‐line x≥0 , producing the exact solution for the semidiscrete problem, given appropriate initial and boundary data. We additionally show how the UTM treats derivative boundary conditions and ghost points introduced by the choice of discretization stencil and propose the notion of “natural” discretizations. We consider the continuum limit of the semidiscrete solutions and provide several numerical examples.
MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstruction

Capturing scenes with a high dynamic range is crucial to reproducing images that appear similar to those seen by the human visual system. Despite progress in developing data-driven deep learning approaches for converting low dynamic range images to high dynamic range images, existing approaches are limited by the assumption that all conversions are governed by the same nonlinear mapping. To address this problem, we propose"Model-Agnostic Meta-Learning for HDR Image Reconstruction"(MetaHDR), which applies meta-learning to the LDR-to-HDR conversion problem using existing HDR datasets. Our key novelty is the reinterpretation of LDR-to-HDR conversion scenes as independently sampled tasks from a common LDR-to-HDR conversion task distribution. Naturally, we use a meta-learning framework that learns a set of meta-parameters which capture the common structure consistent across all LDR-to-HDR conversion tasks. Finally, we perform experimentation with MetaHDR to demonstrate its capacity to tackle challenging LDR-to-HDR image conversions. Code and pretrained models are available at https://github.com/edwin-pan/MetaHDR.
Fcc → Bcc Phase Transition Kinetics in an Immiscible Binary System: Atomistic Evidence of the Twinning Mechanism of Transformation

Extensive atomistic simulations based on the quasiparticle (QA) approach are performed to determine the momentous aspects of the displacive fcc/bcc phase transformation in a binary system. We demonstrate that the QA is able to predict the major structural characteristics of fcc/bcc phase transformations, including the growth of a bcc nuclei in a fcc matrix, and eventually the formation of an internally twinned structure consisting in two variants with Kurdjumov-Sachs orientation relationship. At atomic level, we determine the defect structure of twinning boundaries and fcc/bcc interfaces, and identify the main mechanism for their propagation. In details, it is shown that twin boundaries are propagated by the propagation of screw dislocations in fcc along the α direction, while the propagation of fcc screw dislocations along coherent terrace edges is the pivotal vector of the fcc/bcc transformation. The simulation results are compared with our TEM and HRTEM observations of Fe-rich bcc twinned particle embedded in the fcc Cu-rich matrix in the Cu-Fe-Co system.
The interaction of forward guidance in a two-country new Keynesian model

Using the method of Haberis and Lipińska (2020), this paper explores the effect of forward guidance (FG) in a two-country New Keynesian (NK) economy under the zero lower bound (ZLB). We simulate the effect of different length of FG or the zero interest rate policy under the circumstance of the global liquidity trap. We show that the size of the intertemporal elasticity of substitution plays an important role in determining the beggarthy-neighbor effect or the prosper-thy-neighbor effect of home FG policy on the foreign economy. And in the former case, by targeting a minimum welfare loss of the individual country alone but not global welfare loss, two central banks can perform interesting FG bargaining in which they cooperatively adopt the same length of FG or strategically deviate from cooperation. JEL codes: E52; E58; F41
Diversity Regularized Interests Modeling for Recommender Systems

With the rapid development of E-commerce and the increase in the quantity of items, users are presented with more items hence their interests broaden. It is increasingly difficult to model user intentions with traditional methods, which model the user's preference for an item by combining a single user vector and an item vector. Recently, some methods are proposed to generate multiple user interest vectors and achieve better performance compared to traditional methods. However, empirical studies demonstrate that vectors generated from these multi-interests methods are sometimes homogeneous, which may lead to sub-optimal performance. In this paper, we propose a novel method of Diversity Regularized Interests Modeling (DRIM) for Recommender Systems. We apply a capsule network in a multi-interest extractor to generate multiple user interest vectors. Each interest of the user should have a certain degree of distinction, thus we introduce three strategies as the diversity regularized separator to separate multiple user interest vectors. Experimental results on public and industrial data sets demonstrate the ability of the model to capture different interests of a user and the superior performance of the proposed approach.
Induced side-branching in smooth and faceted dendrites: theory and phase-field simulations

The present work is devoted to the phenomenon of induced side branching stemming from the disruption of free dendrite growth. We postulate that the secondary branching instability can be triggered by the departure of the morphology of the dendrite from its steady state shape. Thence, the instability results from the thermodynamic trade-off between non monotonic variations of interface temperature, surface energy, kinetic anisotropy and interface velocity within the Gibbs–Thomson equation. For the purposes of illustration, the toy model of capillary anisotropy modulation is prospected both analytically and numerically by means of phase-field simulations. It is evidenced that side branching can befall both smooth and faceted dendrites, at a normal angle from the front tip which is specific to the nature of the capillary anisotropy shift applied. This article is part of the theme issue ‘Transport phenomena in complex systems (part 2)’.
Autonomous flight through cluttered outdoor environments using a memoryless planner

This paper introduces a collision avoidance system for navigating a multicopter in cluttered outdoor environments based on the recent memory-less motion planner, rectangular pyramid partitioning using integrated depth sensors (RAP-PIDS). The RAPPIDS motion planner generates collision-free flight trajectories at high speed with low computational cost using only the latest depth image. In this work we extend it to improve the performance of the planner by taking the following issues into account. (a) Changes in the dynamic characteristics of the multicopter that occur during flight, such as changes in motor input/output characteristics due to battery voltage drop. (b) The noise of the flight sensor, which can cause unwanted control input components. (c) Planner utility function which may not be suitable for the cluttered environment. Therefore, in this paper we introduce solutions to each of the above problems and propose a system for the successful operation of the RAPPIDS planner in an outdoor cluttered flight environment. At the end of the paper, we validate the proposed method's effectiveness by presenting the flight experiment results in a forest environment. A video can be found at www.youtube. com/watch? v=3av5xEuKg2w&ab_channel=HiPeRLab
Distributed coordinated path following using guiding vector fields

It is essential in many applications to impose a scalable coordinated motion control on a large group of mobile robots, which is efficient in tasks requiring repetitive execution, such as environmental monitoring. In this paper, we design a guiding vector field to guide multiple robots to follow possibly different desired paths while coordinating their motions. The vector field uses a path parameter as a virtual coordinate that is communicated among neighboring robots. Then, the virtual coordinate is utilized to control the relative parametric displacement between robots along the paths. This enables us to design a saturated control algorithm for a Dubins-car-like model. The algorithm is distributed, scalable, and applicable for any smooth paths in an n-dimensional configuration space, and global convergence is guaranteed. Simulations with up to fifty robots and outdoor experiments with fixed-wing aircraft validate the theoretical results.
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shape generation. In this work, we turn the discrete point sets into smooth surfaces by introducing the well-known implicit moving least-squares (IMLS) surface formulation, which naturally defines locally implicit functions on point sets. We incorporate IMLS surface generation into deep neural networks for inheriting both the flexibility of point sets and the high quality of implicit surfaces. Our IMLSNet predicts an octree structure as a scaffold for generating MLS points where needed and characterizes shape geometry with learned local priors. Furthermore, our implicit function evaluation is independent of the neural network once the MLS points are predicted, thus enabling fast runtime evaluation. Our experiments on 3D object reconstruction demonstrate that IMLSNets outperform state-of-the-art learning-based methods in terms of reconstruction quality and computational efficiency. Extensive ablation tests also validate our network design and loss functions.
Anomaly detection using principles of human perception

In the ﬁelds of statistics and unsupervised machine learning a fundamental and well-studied problem is anomaly detection. Anomalies are diﬃcult to deﬁne, yet many algorithms have been proposed. Underlying the approaches is the nebulous understanding that anomalies are rare, unusual or inconsistent with the majority of data. The present work provides a philosophical treatise to clearly deﬁne anomalies and develops an algorithm for their eﬃcient detection with minimal user intervention. Inspired by the Gestalt School of Psychology and the Helmholtz principle of human perception, anomalies are assumed to be observations that are unexpected to occur with respect to certain groupings made by the majority of the data. Under appropriate random variable modelling anomalies are directly found in a set of data by a uniform and independent random assumption of the distribution of constituent elements of the observations, with anomalies corresponding to those observations where the expectation of the number of occurrences of the elements in a given view is < 1. Starting from fundamental principles of human perception an unsupervised anomaly detection algorithm is developed that is simple, real-time and parameter-free. Experiments suggest it as a competing choice for univariate data with promising results on the detection of global anomalies in multivariate data.
FoamFactor: Hydrogel-Foam Composite with Tunable Stiffness and Compressibility

This paper presents FoamFactor, a novel material with tunable stiffness and compressibility between hydration states, and a tailored pipeline to design and fabricate artifacts consisting of it. This technique compounds hydrogel with open-cell foams via additive manufacturing to produce a water-responsive composite material. Enabled by the large volumetric changes of hydrogel dispersions, the material is soft and compressible when dehydrated and becomes stiffer and rather incompressible when hydrated. Leveraging this material property transition, we explore its design space in various aspects pertaining the transition of hydration states, including multi-functional shoes, amphibious cars, mechanical transmission systems, and self-deploying robotic grippers.
Theory of scanning tunneling spectroscopy

A new three‐dimensional tunneling theory is introduced for interpreting scanning tunneling spectroscopy (STS) images. By expanding the asymptotic wave function of the acting atom in terms of complete sets of eigenfunctions in spherical coordinates and parabolic coordinates, a derivative rule is derived, which can be applied to interpret scanning tunneling microscopy and STS images immediately. The relation between the observed dynamic conductance and the density of states of the sample is shown in conjunction with a linear bias‐distortion correction.
Analytical investigations for maximum power tracking of PV supplied IDB converter

Analytical investigations and development of the computer program for the maximum power tracking of the photovoltaic system supplied through an interleaved dual boost (IDB) converter is presented. Mathematical models are developed using a state-space average technique. A closed form solution is used to compute the photovoltaic array maximum power point. The tracking responses of the system operating at the solar array maximum power point are evaluated.