Miracle-dz/UniFAR-FTUs huggingface dataset:
Description and classification of soil structure using distance transform data

Classification of soil based on structure is useful for conveying information about physical properties and soil processes. The distance transform is an image analysis technique suitable for quantifying soil structure. An analysis of distance transform data, in the form of cumulative area distribution curves for previously published images of soil structures of various types, is presented. The images were used to derive a quantitative classification of structure using maximum distance of solid from a macropore (Dmax, measured), the distance from macropore space containing 50% of the solid area (k, derived by fitting a sigmoidal function to the cumulative area distribution curve), the total interface length between pore and solid per area of sample (IA, measured), the porosity or the proportion of pores per area of sample (PA, measured) and the pore distribution characteristic (n, derived by fitting a sigmoidal function to the cumulative area distribution curve) which is related to the number, continuity and distribution of pores. The influence of image resolution was investigated, and within limits found to be fairly small. The final classification of soil structure was based on the hypothesized relations between the descriptors and structure‐forming processes.
Effect of flexoelectricity and order electricity on defect cores in nematic droplets

Macroscopic polarization, induced in a strongly deformed nematic director field, is shown to strongly affect the stability of defect structures. We present a numerical study which addresses the role of flexoelectricity and order electricity in confined nematic liquid crystals, more specifically in nematic droplets with planar and homeotropic surface anchoring. Electrostatic potential induced by flexo- and order electricity are calculated in the droplets taking into the account full anisotropy of the nematic director profile. Potentials as high as 1 V are found in 200 nm droplets with flexoelectric constant e = 40 pC m−1. Spatial profiles of contributions to the total free energy are calculated to identify flexo- and order elasticity relevant regions. For different flexoelectric coupling strengths and different anchoring strengths, changes in the core of the defects are examined. Their stability is discussed. Finally, flexo- and order electricity driven transitions between point defect, ring defect and axial structure are demonstrated in homeotropic droplets.
Identifying vital nodes by Achlioptas process

The vital nodes are the ones that play an important role in the organization of network structure or the dynamical behaviors of networked systems. Previous studies usually applied the node centralities to quantify the importance of nodes. Realizing that the percolation clusters are dominated by local connections in the subcritical phase and by global connections in the supercritical phase, in this paper we propose a new method to identify the vital nodes via a competitive percolation process that is based on an Achlioptas process. Compared with the existing node centrality indices, the new method performs overall better in identifying the vital nodes that maintain network connectivity and facilitate network synchronization when considering different network structure characteristics, such as link density, degree distribution, assortativity, and clustering. We also find that our method is more tolerant of noisy data and missing data. More importantly, compared with the unique ranking list of nodes given by most centrality methods, the randomness of the percolation process expands the possibility space of the optimal solutions, which is of great significance in practical applications.
Kinetic Ratchet Effect as a Non-equilibrium Design Principle for Selective Channels

In living cells, ion channels passively allow for ions to flow through as the concentration gradient relax to thermal equilibrium. Most ion channels are selective, only allowing one type of ion to go through while blocking another. One salient example is KcsA, which allows for larger K ions through but blocks the smaller Na ions. This counter-intuitive selectivity has been explained by two distinct theories that both focus on equilibrium properties: particle-channel affinity and particlesolvent affinity. However, ion channels operate far from equilibrium. By constructing minimal kinetic models of channels, we discover a ubiquitous kinetic ratchet effect as a non-equilibrium mechanism to explain such selectivity. We find that a multi-site channel kinetically couples the competing flows of two types of particles, where one particle’s flow could suppress or even invert the flow of another type. At the inversion point (transition between the ratchet and dud modes), the channel achieves infinite selectivity. We have applied our theory to obtain general design principles of artificial selective channels.
Constructible hypersheaves via exit paths

The goal of this article is to extend a theorem of Lurie \[ \mathsf{Sh}_A (X) = \mathsf{Fun}(\mathsf{Exit}_A (X), \mathsf{S}) \] representing constructible sheaves with values in $ \mathsf{S} $, the $ \infty $-category of spaces, on a stratified space $ X $ with poset of strata $ A $, as functors from the exit paths $ \infty $-category $ \mathsf{Exit}_A (X) $ to $ \mathsf{S} $. Lurie's representation theorem works provided $ A $ satisfy the ascending chain condition. This typically rules out infinite dimensional examples of stratified space. Building on it and with the help of a stratified homotopy invariance theorem from Haine, we show that when $ X $ is a nice enough $ A $-stratified space and when $ A $ is itself stratified $ A_{\leq 0} \subset A_{\leq 1} \subset \cdots \subset A $ by posets satisfying the ascending chain condition, \[ \mathsf{Hyp}_A (X) = \mathsf{Fun}(\mathsf{Exit}_A(X), \mathsf{S}) \] the $ \infty $-category of $ A $-constructible hypersheaves on $ X $ is represented by functors from the exit paths $ \infty $-category of $ X $. There are two types of nice stratified spaces on which this extended representation theorem applies: conically stratified spaces and spaces that are sequential colimits of conically stratified spaces. Examples of application include the metric and the topological exponentials of a Fr\'echet manifold, locally countable simplicial complexes and more generally, locally countable cylindrically normal CW-complexes.
Classification with abstention but without disparities

Classification with abstention has gained a lot of attention in recent years as it allows to incorporate human decision-makers in the process. Yet, abstention can potentially amplify disparities and lead to discriminatory predictions. The goal of this work is to build a general purpose classification algorithm, which is able to abstain from prediction, while avoiding disparate impact. We formalize this problem as risk minimization under fairness and abstention constraints for which we derive the form of the optimal classifier. Building on this result, we propose a post-processing classification algorithm, which is able to modify any off-the-shelf score-based classifier using only unlabeled sample. We establish finite sample risk, fairness, and abstention guarantees for the proposed algorithm. In particular, it is shown that fairness and abstention constraints can be achieved independently from the initial classifier as long as sufficiently many unlabeled data is available. The risk guarantee is established in terms of the quality of the initial classifier. Our post-processing scheme reduces to a sparse linear program allowing for an efficient implementation, which we provide. Finally, we validate our method empirically showing that moderate abstention rates allow to bypass the risk-fairness trade-off.
DECOMPOSITION METHODS FOR GLOBAL SOLUTION OF MIXED-INTEGER LINEAR PROGRAMS

This paper introduces two decomposition-based methods for two-block mixed-integer linear programs (MILPs), which aim to take advantage of separable structures of the original problem by solving a sequence of lower-dimensional MILPs. The ﬁrst method is based on the (cid:96) 1 -augmented Lagrangian method (ALM), and the second one is based on a modiﬁed alternating direction method of multipliers (ADMM). In the presence of certain block-angular structures, both methods create parallel subproblems in one block of variables, and add nonconvex cuts to update the other block; they converge to globally optimal solutions of the original MILP under proper conditions. Numerical experiments on three classes of MILPs demonstrate the advantages of the proposed methods on structured problems over the state-of-the-art MILP solvers.
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case

Large datasets' availability is catalyzing a rapid expansion of deep learning in general and computer vision in particular. At the same time, in many domains, a sufficient amount of training data is lacking, which may become an obstacle to the practical application of computer vision techniques. This paper challenges small and imbalanced datasets based on the example of a plant phenomics domain. We introduce an image augmentation framework, which enables us to extremely enlarge the number of training samples while providing the data for such tasks as object detection, semantic segmentation, instance segmentation, object counting, image denoising, and classification. We prove that our augmentation method increases model performance when only a few training samples are available. In our experiment, we use the DeepLabV3 model on semantic segmentation tasks with Arabidopsis and Nicotiana tabacum image dataset. The obtained result shows a 9% relative increase in model performance compared to the basic image augmentation techniques.
On the Minimal Error of Empirical Risk Minimization

We study the minimal error of the Empirical Risk Minimization (ERM) procedure in the task of regression, both in the random and the fixed design settings. Our sharp lower bounds shed light on the possibility (or impossibility) of adapting to simplicity of the model generating the data. In the fixed design setting, we show that the error is governed by the global complexity of the entire class. In contrast, in random design, ERM may only adapt to simpler models if the local neighborhoods around the regression function are nearly as complex as the class itself, a somewhat counter-intuitive conclusion. We provide sharp lower bounds for performance of ERM for both Donsker and non-Donsker classes. We also discuss our results through the lens of recent studies on interpolation in overparameterized models.
4D Panoptic LiDAR Segmentation

Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points. To this end, we present an approach and a point-centric evaluation metric. Our approach determines a semantic class for every point while modeling object instances as probability distributions in the 4D spatio-temporal domain. We process multiple point clouds in parallel and resolve point-to-instance associations, effectively alleviating the need for explicit temporal data association. Inspired by recent advances in benchmarking of multi-object tracking, we propose to adopt a new evaluation metric that separates the semantic and point-to-instance association aspects of the task. With this work, we aim at paving the road for future developments of temporal LiDAR panoptic perception.
vrCAPTCHA: Exploring CAPTCHA Designs in Virtual Reality

With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) designs for VR devices. In this paper, we first present four traditional two-dimensional (2D) CAPTCHAs (i.e., text-based, image-rotated, image-puzzled, and image-selected CAPTCHAs) in VR. Then, based on the three-dimensional (3D) interaction characteristics of VR devices, we propose two vrCAPTCHA design prototypes (i.e., task-driven and bodily motion-based CAPTCHAs). We conducted a user study with six participants for exploring the feasibility of our two vrCAPTCHAs and traditional CAPTCHAs in VR. We believe that our two vrCAPTCHAs can be an inspiration for the further design of CAPTCHAs in VR.
Multichannel LSTM-CNN for Telugu Technical Domain Identification

With the instantaneous growth of text information, retrieving domain-oriented information from the text data has a broad range of applications in Information Retrieval and Natural language Processing. Thematic keywords give a compressed representation of the text. Usually, Domain Identification plays a significant role in Machine Translation, Text Summarization, Question Answering, Information Extraction, and Sentiment Analysis. In this paper, we proposed the Multichannel LSTM-CNN methodology for Technical Domain Identification for Telugu. This architecture was used and evaluated in the context of the ICON shared task TechDOfication 2020 (task h), and our system got 69.9% of the F1 score on the test dataset and 90.01% on the validation set.
Towards Optimized Distributed Multi-Robot Printing: An Algorithmic Approach

This paper presents a distributed multi-robot printing method which utilizes an optimization approach to decompose and allocate a printing task to a group of mobile robots. The motivation for this problem is to minimize the printing time of the robots by using an appropriate task decomposition algorithm. We present one such algorithm which decomposes an image into rasterized geodesic cells before allocating them to the robots for printing. In addition to this, we also present the design of a numerically controlled holonomic robot capable of spraying ink on smooth surfaces. Further, we use this robot to experimentally verify the results of this paper.
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization

Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems. The theory behind such empirical observations, however, is still largely unknown. This paper studies this fundamental problem through investigating the nonconvex rectangular matrix factorization problem, which has infinitely many global minima due to rotation and scaling invariance. Hence, gradient descent (GD) can converge to any optimum, depending on the initialization. In contrast, we show that a perturbed form of GD with an arbitrary initialization converges to a global optimum that is uniquely determined by the injected noise. Our result implies that the noise imposes implicit bias towards certain optima. Numerical experiments are provided to support our theory.
The Talbot effect as the fundamental solution to the free Schrödinger equation

The Talbot effect is usually modeled using the Helmholtz equation, but its main experimental features are captured by the solution to the free Schr\"odinger equation with the Dirac comb as initial datum. This simplified description is a consequence of the paraxial approximation in geometric optics. However, it is a heuristic approximation that is not mathematically well justified, so K. I. Oskolkov raised the problem of"mathematizing"it. We show that it holds exactly in the sense of distributions.
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.
Quantifying Confounding Bias in Generative Art: A Case Study

In recent years, AI generated art has become very popular. From generating art works in the style of famous artists like Paul Cezanne and Claude Monet to simulating styles of art movements like Ukiyo-e, a variety of creative applications have been explored using AI. Looking from an art historical perspective, these applications raise some ethical questions. Can AI model artists’ styles without stereotyping them? Does AI do justice to the socio-cultural nuances of art movements? In this work, we take a first step towards analyzing these issues. Leveraging directed acyclic graphs to represent potential process of art creation, we propose a simple metric to quantify confounding bias due to the lack of modeling the influence of art movements in learning artists’ styles. As a case study, we consider the popular cycleGAN model and analyze confounding bias across various genres. The proposed metric is more effective than state-of-the-art outlier detection method in understanding the influence of art movements in artworks. We hope our work will elucidate important shortcomings of computationally modeling artists’ styles and trigger discussions related to accountability of AI generated art.
A Review of Generalizability and Transportability

When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This article presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, and the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 10 is March 2023. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Limit theorems for discounted convergent perpetuities

Let ( ξ 1 , η 1 ) , ( ξ 2 , η 2 ) , . be independent identically distributed R 2 -valued random vec-tors. We prove a strong law of large numbers, a functional central limit theorem and a law of the iterated logarithm for the convergent perpetuities (cid:80) k ≥ 0 b ξ 1 + . + ξ k η k +1 as b → 1 − . Under the standard actuarial interpretation, these results correspond to the situation when the actuarial market is close to the customer-friendly scenario of no risk.
High electrocatalytic activity of PtRu nanoparticles supported on starch-functionalized multi-walled carbon nanotubes for ethanol oxidation

The high-performance PtRu electrocatalysts on ethylenediamine modified cross-linked starch-functionalized multi-walled carbon nanotubes have been synthesized by the polyol process. The method was simple and can be carried out at room temperature without the use of expensive chemicals or corrosive acids, thus preserving the integrity and the electronic structure of the nanotubes. Transmission electron microscopy revealed that the PtRu is well distributed with no formation of aggregates. Cyclic voltammetry and chronoamperometry studies indicate the material had a higher electrochemically active surface area, electrocatalytic activity and stability for the electro-oxidation of ethanol compared to that of PtRu electrocatalysts supported on conventional acid-treated multi-walled carbon nanotubes and on XC-72 carbon black.
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

This article presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the article presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.60% or better is achievable.
Charmonium and charmoniumlike states at the BESIII experiment

Abstract Charmonium is a bound state of a charmed quark and a charmed antiquark, and a charmoniumlike state is a resonant structure that contains a charmed quark and antiquark pair but has properties that are incompatible with a conventional charmonium state. While operating at center-of-mass energies from 2 to 5 GeV, the BESIII experiment can access a wide mass range of charmonium and charmoniumlike states, and has contributed significantly in this field. We review BESIII results involving conventional charmonium states, including the first observation of the M1 transition ψ(2S) → γηc(2S) and the discovery of the ψ2(3823) state; and report on studies of charmoniumlike states, including the discoveries of the Zc(3900) and Zc(4020) tetraquark candidates, the resolution of the fine structure of the Y(4260) state, the discovery of the new production process e+e− → γX(3872) and the uncovering of strong evidence for the commonality among the X(3872), Y(4260) and Zc(3900) states. The prospects for further research at BESIII and proposed future facilities are also presented.