Miracle-dz/UniFAR-FTUs huggingface dataset:
Phylogeny of the Diploid Species of Rubus (Rosaceae)

Rubus L. (Rosaceae, Rosoideae) contains around 700 species distributed on all continents except Antarctica, with the highest species diversity in temperate to subtropical regions of the northern hemisphere. The taxonomy of Rubus is challenging due to the frequency of polyploidy, hybridization and apomixis. Previous studies mostly sampled sparsely and used limited DNA sequence data. The evolutionary relationships between infrageneric taxa, therefore, remain to be further clarified. In the present study, genotyping by sequencing (GBS) reduced-representation genome sequencing data from 186 accessions representing 65 species, 1 subspecies and 17 varieties of Rubus, with emphasis on diploid species, were used to infer a phylogeny using maximum likelihood and maximum parsimony methods. The major results were as follows: (1) we confirmed or reconfirmed the polyphyly or paraphyly of some traditionally circumscribed subgenera, sections and subsections; (2) 19 well-supported clades, which differed from one another on molecular, morphological and geographical grounds, were identified for the species sampled; (3) characteristics such as plants with dense bristles or not, leaves leathery or papyraceous, number of carpels, instead of inflorescences paniculate or not, aggregate fruits and leaves abaxially tomentose or not, may be of some use in classifying taxa whose drupelets are united into a thimble-shaped aggregate fruit that falls in its entirety from the dry receptacle; and (4) a preliminary classification scheme of diploid species of Rubus is proposed based on our results combined with those from previous phylogenetic analyses.
Estimation Methods of Three Parameters Discrete Generalized Exponential Distribution

This article presents the estimation methods of three parameters Discrete Generalized Exponential distribution. The two-parameter generalized exponential distribution was introduced by Gupta and Kundu (1999). We present the three parameters discrete Generalized Exponential distribution is the sum of infinite probability function. Moment estimation, inverse integer moment estimation, moment generating function, maximum likelihood estimation and L-moment estimation are derived for this infinite probability function. The Generalized Exponential models are the reliability models can be used in the reliability engineering discipline. This paper focuses on the reliability analysis of the discrete Generalized Exponential distribution's to model in which some operational time has already been accumulated for the equipment of interest. This paper presents the relationship between shape parameter and other properties such as probability function, cumulative distribution function, reliability function, hazard function, cumulative hazard function, rth moment estimation, Inverse integer moment estimation, moment generating functions, maximum likelihood estimation and L-moment estimation are presented mathematically. The Discrete Generalized Exponential distribution will be suitable for modeling for the applications of mechanical or electrical components lying in the life testing experiment. Some works has already been done on Generalized Exponential distribution by Gupta and Kundu (2003). Debasis Kundu, Rameshwar D. Gupta and Anubhav Manglick (2005) presented the Discriminating between the log-normal and generalized exponential distributions. Gupta, R. D; Kundu, D (2001 b) also derived Generalized exponential distributions for different methods of estimation.
Numerical Investigation of Nonlinear Wave Effects Around Multiple Cylinders

The paper investigates nonlinear wave diffraction effects around a generic, 4 column, surface piercing structure. The surface elevation is computed at designated locations within the confines of the structure, and for a selected range of frequencies. Numerical results are presented, with and without the multicolumn structure, at three different "levels", the common denominator being that inviscid fluid is assumed: 1. As a "base case" linear diffraction results are obtained employing the linearised mean free surface boundary condition. 2. A higher order incident and diffracted wave is computed using a perturbation expansion for the free surface boundary condition to second order. The importance and relevance of the second order correction to the linear surface elevation is discussed in detail. 3. Finally, results obtained by fully nonlinear numerical wave tank simulations are presented. General confidence in computed results is an extremely important an delicate issue within nonlinear numerical hydrodynamics, and the more fundamental aspects related to numerical accuracy and robustness are discussed in some detail throughout the text. Provided the wave steepness stays below a certain threshold, it is demonstrated that for periodic waves, the linear and second order terms are the most important ones in order to obtain a reasonable estimate of the wave elevation.
Torus breakdown in the symmetry-reduced state space of the Kuramoto-Sivashinsky system

Systems such as fluid flows in channels and pipes or the complex Ginzburg-Landau system, defined over periodic domains, exhibit both continuous symmetries, translational and rotational, as well as discrete symmetries under spatial reflections or complex conjugation. The simplest, and very common symmetry of this type is the equivariance of the defining equations under the orthogonal group O(2). We formulate a novel symmetry-reduction scheme for such systems by combining the method of slices with invariant polynomial methods, and show how it works by applying it to the Kuramoto-Sivashinsky system in one spatial dimension. As an example, we track a relative periodic orbit through a sequence of bifurcation to the onset of chaos. Within the symmetry-reduced state space we are able to compute and visualize the unstable manifolds of relative periodic orbits, their torus bifurcations, a transition to chaos via torus breakdown, and heteroclinic connections between various relative periodic orbits. It would be very hard to carry through such analysis in the full state space, without a symmetry reduction such as the one we present here.
Optically controlling the competition between spin flips and intersite spin transfer in a Heusler half-metal on sub–100-fs time scales

The direct manipulation of spins via light may provide a path toward ultrafast energy-efficient devices. However, distinguishing the microscopic processes that can occur during ultrafast laser excitation in magnetic alloys is challenging. Here, we study the Heusler compound Co2MnGa, a material that exhibits very strong light-induced spin transfers across the entire M-edge. By combining the element specificity of extreme ultraviolet high-harmonic probes with time-dependent density functional theory, we disentangle the competition between three ultrafast light-induced processes that occur in Co2MnGa: same-site Co-Co spin transfer, intersite Co-Mn spin transfer, and ultrafast spin flips mediated by spin-orbit coupling. By measuring the dynamic magnetic asymmetry across the entire M-edges of the two magnetic sublattices involved, we uncover the relative dominance of these processes at different probe energy regions and times during the laser pulse. Our combined approach enables a comprehensive microscopic interpretation of laser-induced magnetization dynamics on time scales shorter than 100 femtoseconds.
Submodular Minimax Optimization: Finding Effective Sets

Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings. In this paper, we fill this gap by providing a characterization of submodular minimax optimization, the problem of finding a set (for either the min or the max player) that is effective against every possible response. We show when and under what conditions we can find such sets. We also demonstrate how minimax submodular optimization provides robust solutions for downstream machine learning applications such as (i) efficient prompt engineering for question answering, (ii) prompt engineering for dialog state tracking, (iii) identifying robust waiting locations for ride-sharing, (iv) ride-share difficulty kernelization, and (v) finding adversarial images. Our experiments demonstrate that our proposed algorithms consistently outperform other baselines.
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems.
Learning to Imagine: Visually-Augmented Natural Language Generation

People often imagine relevant scenes to aid in the writing process. In this work, we aim to utilize visual information for composition in the same manner as humans. We propose a method, LIVE, that makes pre-trained language models (PLMs) Learn to Imagine for Visually-augmented natural language gEneration. First, we imagine the scene based on the text: we use a diffusion model to synthesize high-quality images conditioned on the input texts. Second, we use CLIP to determine whether the text can evoke the imagination in a posterior way. Finally, our imagination is dynamic, and we conduct synthesis for each sentence rather than generate only one image for an entire paragraph. Technically, we propose a novel plug-and-play fusion layer to obtain visually-augmented representations for each text. Our vision-text fusion layer is compatible with Transformer-based architecture. We have conducted extensive experiments on four generation tasks using BART and T5, and the automatic results and human evaluation demonstrate the effectiveness of our proposed method. We will release the code, model, and data at the link: https://github.com/RUCAIBox/LIVE.
Exploiting Abstract Meaning Representation for Open-Domain Question Answering

The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems leverage Pretrained Language Models (PLMs) to model the relationship between questions and passages. However, the diversity in surface form expressions can hinder the model's ability to capture accurate correlations, especially within complex contexts. Therefore, we utilize Abstract Meaning Representation (AMR) graphs to assist the model in understanding complex semantic information. We introduce a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs. Results from Natural Questions (NQ) and TriviaQA (TQ) demonstrate that our GST method can significantly improve performance, resulting in up to 2.44/3.17 Exact Match score improvements on NQ/TQ respectively. Furthermore, our method enhances robustness and outperforms alternative Graph Neural Network (GNN) methods for integrating AMRs. To the best of our knowledge, we are the first to employ semantic graphs in ODQA.
Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures

This work presents an approach for automating the discretization and approximation procedures in constructing digital representations of composites from micro-CT images featuring intricate microstructures. The proposed method is guided by the Support Vector Machine (SVM) classification, offering an effective approach for discretizing microstructural images. An SVM soft margin training process is introduced as a classification of heterogeneous material points, and image segmentation is accomplished by identifying support vectors through a local regularized optimization problem. In addition, an Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for appropriate approximations of weak discontinuities across material interfaces. The proposed method modifies the smooth kernel functions with a regularized Heaviside function concerning the material interfaces to alleviate Gibb's oscillations. This IM-RKPM is formulated without introducing duplicated degrees of freedom associated with the interface nodes commonly needed in the conventional treatments of weak discontinuities in the meshfree methods. Moreover, IM-RKPM can be implemented with various domain integration techniques, such as Stabilized Conforming Nodal Integration (SCNI). The extension of the proposed method to 3-dimension is straightforward, and the effectiveness of the proposed method is validated through the image-based modeling of polymer-ceramic composite microstructures.
To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support

Optimizing the phrasing of argumentative text is crucial in higher education and professional development. However, assessing whether and how the different claims in a text should be revised is a hard task, especially for novice writers. In this work, we explore the main challenges to identifying argumentative claims in need of specific revisions. By learning from collaborative editing behaviors in online debates, we seek to capture implicit revision patterns in order to develop approaches aimed at guiding writers in how to further improve their arguments. We systematically compare the ability of common word embedding models to capture the differences between different versions of the same text, and we analyze their impact on various types of writing issues. To deal with the noisy nature of revision-based corpora, we propose a new sampling strategy based on revision distance. Opposed to approaches from prior work, such sampling can be done without employing additional annotations and judgments. Moreover, we provide evidence that using contextual information and domain knowledge can further improve prediction results. How useful a certain type of context is, depends on the issue the claim is suffering from, though.
Data Topology-Dependent Upper Bounds of Neural Network Widths

This paper investigates the relationship between the universal approximation property of deep neural networks and topological characteristics of datasets. Our primary contribution is to introduce data topology-dependent upper bounds on the network width. Specifically, we first show that a three-layer neural network, applying a ReLU activation function and max pooling, can be designed to approximate an indicator function over a compact set, one that is encompassed by a tight convex polytope. This is then extended to a simplicial complex, deriving width upper bounds based on its topological structure. Further, we calculate upper bounds in relation to the Betti numbers of select topological spaces. Finally, we prove the universal approximation property of three-layer ReLU networks using our topological approach. We also verify that gradient descent converges to the network structure proposed in our study.
Spawning Nodes Generate Deterministic Scale-Free Networks

In this paper we present a deterministic vertex spawning model that yields a scale-free network. The model specifies that a parent vertex produces a child vertex in a time interval approximately proportional to the current time and inversely proportional to the number of edges currently connected to the parent. Spawned offspring maintain an undirected edge with its parent. No information about the network as a whole is required to obtain scale-invariant behavior. Although the algorithm is deterministic, the number of nodes spawning in a small time interval quickly becomes randomized. We show theoretically and with simulations that such a spawned network will have a degree distribution obeying a power law with exponent 2.5. Simulations show that the distribution matches a Zipf distribution.
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation

k-Nearest neighbor machine translation (kNN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains. By using an upstream NMT model to traverse the downstream training corpus, it is equipped with a datastore containing vectorized key-value pairs, which are retrieved during inference to benefit translation.However, there often exists a significant gap between upstream and downstream domains, which hurts the datastore retrieval and the final translation quality.To deal with this issue, we propose a novel approach to boost the datastore retrieval of kNN-MT by reconstructing the original datastore.Concretely, we design a reviser to revise the key representations, making them better fit for the downstream domain. The reviser is trained using the collected semantically-related key-queries pairs, and optimized by two proposed losses: one is the key-queries semantic distance ensuring each revised key representation is semantically related to its corresponding queries, and the other is an L2-norm loss encouraging revised key representations to effectively retain the knowledge learned by the upstream NMT model. Extensive experiments on domain adaptation tasks demonstrate that our method can effectively boost the datastore retrieval and translation quality of kNN-MT.Our code is available at https://github.com/DeepLearnXMU/Revised-knn-mt.
Indexes of vector fields for mixed functions

A mixed function is a real analytic map $f\colon \mathbb{C}^n \to \mathbb{C}$ in the complex variables $z_1,\dots,z_n$ and their conjugates $\bar{z}_1,\dots,\bar{z}_n$. In this article we define an integer valued index for vector fields $v$ with isolated singularity at $\mathbf{0}$ on real analytic varieties $V_f:=f^{-1}(0)$ defined by mixed functions $f$ with isolated critical point at $\mathbf{0}$. We call this index the mixed GSV-index and it generalizes the classical GSV-index defined by Gomez-Mont, Seade and Verjovsky, i.e., if the function $f$ is holomorphic, then the mixed GSV-index coincides with the GSV-index. Furthermore, the mixed GSV-index is a lifting to $\mathbb{Z}$ of the $\mathbb{Z}_2$-valued real GSV-index defined by Aguilar, Seade and Verjovsky. As applications we prove that the mixed GSV-index is equal to the Poincar\'e-Hopf index of $v$ on a Milnor fiber. If $f$ also satisfies the strong Milnor condition, i.e., for every $\epsilon>0$ (small enough) the map $\frac{f}{\|f\|}\colon \mathbb{S}_\epsilon \setminus L_f \to \mathbb{S}^1$ is a fiber bundle, we prove that the mixed GSV-index is equal to the curvatura integra of $f$ defined by Cisneros-Molina, Grulha and Seade based on the curvatura integra defined by Kervaire.
Detecting Concept Drift for the reliability prediction of Software Defects using Instance Interpretation

In the context of Just-In-Time Software Defect Prediction (JIT-SDP), Concept drift (CD) can occur due to changes in the software development process, the complexity of the software, or changes in user behavior that may affect the stability of the JIT-SDP model over time. Additionally, the challenge of class imbalance in JIT-SDP data poses a potential risk to the accuracy of CD detection methods if rebalancing is implemented. This issue has not been explored to the best of our knowledge. Furthermore, methods to check the stability of JIT-SDP models over time by considering labeled evaluation data have been proposed. However, it should be noted that future data labels may not always be available promptly. We aim to develop a reliable JIT-SDP model using CD point detection directly by identifying changes in the interpretation of unlabeled simplified and resampled data. To evaluate our approach, we first obtained baseline methods based on model performance monitoring to identify CD points on labeled data. We then compared the output of the proposed methods with baseline methods based on performance monitoring of threshold-dependent and threshold-independent criteria using well-known performance measures in CD detection methods, such as accuracy, MDR, MTD, MTFA, and MTR. We also utilize the Friedman statistical test to assess the effectiveness of our approach. As a result, our proposed methods show higher compatibility with baseline methods based on threshold-independent criteria when applied to rebalanced data, and with baseline methods based on threshold-dependent criteria when applied to simple data.
Exploring the Adaptive Behaviors of Particle Lenia: A Perturbation-Response Analysis for Computational Agency

A firm cognitive subject or ``individual'' is presupposed for the emergence of mind. However, with the development of recent information technology, the ``individual'' has become more dispersed in society and the cognitive subject has become increasingly unstable and adaptive, necessitating an update in our understanding of the ``individual''. Autopoiesis serves as a model of the cognitive subject, which is unstable and requires effort to maintain itself to adapt to the environment. In this study, we evaluated adaptivity for a highly extensible multi-particle system model Particle Lenia through the response perturbation. As a result, we found that Particle Lenia has a particle configuration that is both temporally unstable and has multiple stable states. This result suggests that Particle Lenia can express adaptive characteristics and is expected to be used as a computational model toward building an autopoietic cognitive agent.
Sharp quantitative rigidity results for maps from $S^2$ to $S^2$ of general degree

As the energy of any map $v$ from $S^2$ to $S^2$ is at least $4\pi \vert deg(v)\vert$ with equality if and only if $v$ is a rational map one might ask whether maps with small energy defect $\delta_v=E(v)-4\pi \vert deg(v)\vert$ are necessarily close to a rational map. While such a rigidity statement turns out to be false for maps of general degree, we will prove that any map $v$ with small energy defect is essentially given by a collection of rational maps that describe the behaviour of $v$ at very different scales and that the corresponding distance is controlled by a quantitative rigidity estimate of the form $dist^2\leq C \delta_v(1+\vert\log\delta_v\vert)$ which is indeed sharp.