Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditions for eigenvalue configurations of two real symmetric matrices: a signature approach

For two real symmetric matrices, their eigenvalue configuration is the arrangement of their eigenvalues on the real line. In this paper, we provide quantifier-free necessary and sufficient conditions for two symmetric matrices to realize a given eigenvalue configuration. The basic idea is to generate a set of polynomials in the entries of the two matrices whose roots can be counted to uniquely determine the eigenvalue configuration. This result can be seen as ageneralization of Descartes' rule of signs to the case of two real univariate polynomials.
The effect of elevated CO 2 concentration and soil pH on the relationship between plant growth and rhizosphere denitrification potential

The effect of CO2 concentration on plant growth and the size of the rhizosphere denitrifier population was investigated for ryegrass grown at 3 different soil pH values (pH 4.3, 5.9 and 7.0). Soil microcosms were planted with ryegrass and maintained under constant growth conditions at either ambient (450ppm) or elevated (720ppm) CO2 concentration. At harvest, the rhizosphere soil was collected and subjected to a potential denitrification assay to provide an estimate of the size of the denitrifier population present. Ryegrass dry matter production varied across the pH range studied and contrary to other studies, elevated CO2 concentration did not consistently increase growth. Plant growth was reduced by ™ 35% and 23% at pH 4.3 and pH 5.9, respectively, under elevated CO2 concentration. At pH 7.0, however, plant growth was increased by ™ 45% under elevated CO2. Potential denitrification rates within the rhizosphere followed a similar pattern to plant growth in the different treatments, suggesting that plant growth and the size of denitrifier population within the rhizosphere are coupled. This study investigates the relationship between plant growth and rhizosphere denitrification potential, thereby providing an estimate of the size of the denitrifier population under increased CO2 concentration and soil pH.
Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction

Automatic Modulation Recognition (AMR) plays a crucial role in wireless communication systems. Deep learning AMR strategies have achieved tremendous success in recent years. Modulated signals exhibit long temporal dependencies, and extracting global features is crucial in identifying modulation schemes. Traditionally, human experts analyze patterns in constellation diagrams to classify modulation schemes. Classical convolutional-based networks, due to their limited receptive fields, excel at extracting local features but struggle to capture global relationships. To address this limitation, we introduce a novel hybrid deep framework named TLDNN, which incorporates the architectures of the transformer and long short-term memory (LSTM). We utilize the self-attention mechanism of the transformer to model the global correlations in signal sequences while employing LSTM to enhance the capture of temporal dependencies. To mitigate the impact like RF fingerprint features and channel characteristics on model generalization, we propose data augmentation strategies known as segment substitution (SS) to enhance the model's robustness to modulation-related features. Experimental results on widely-used datasets demonstrate that our method achieves state-of-the-art performance and exhibits significant advantages in terms of complexity. Our proposed framework serves as a foundational backbone that can be extended to different datasets. We have verified the effectiveness of our augmentation approach in enhancing the generalization of the models, particularly in few-shot scenarios. Code is available at \url{https://github.com/AMR-Master/TLDNN}.
FlashVideo: A Framework for Swift Inference in Text-to-Video Generation

In the evolving field of machine learning, video generation has witnessed significant advancements with autoregressive-based transformer models and diffusion models, known for synthesizing dynamic and realistic scenes. However, these models often face challenges with prolonged inference times, even for generating short video clips such as GIFs. This paper introduces FlashVideo, a novel framework tailored for swift Text-to-Video generation. FlashVideo represents the first successful adaptation of the RetNet architecture for video generation, bringing a unique approach to the field. Leveraging the RetNet-based architecture, FlashVideo reduces the time complexity of inference from $\mathcal{O}(L^2)$ to $\mathcal{O}(L)$ for a sequence of length $L$, significantly accelerating inference speed. Additionally, we adopt a redundant-free frame interpolation method, enhancing the efficiency of frame interpolation. Our comprehensive experiments demonstrate that FlashVideo achieves a $\times9.17$ efficiency improvement over a traditional autoregressive-based transformer model, and its inference speed is of the same order of magnitude as that of BERT-based transformer models.
A Knowledge Compilation Map for Quantum Information

Quantum computing is finding promising applications in optimization, machine learning and physics, leading to the development of various models for representing quantum information. Because these representations are often studied in different contexts (many-body physics, machine learning, formal verification, simulation), little is known about fundamental trade-offs between their succinctness and the runtime of operations to update them. We therefore analytically investigate three widely-used quantum state representations: matrix product states (MPS), decision diagrams (DDs), and restricted Boltzmann machines (RBMs). We map the relative succinctness of these data structures and provide the complexity for relevant query and manipulation operations. Further, to chart the balance between succinctness and operation efficiency, we extend the concept of rapidity with support for the non-canonical data structures studied in this work, showing in particular that MPS is at least as rapid as some DDs. By providing a knowledge compilation map for quantum state representations, this paper contributes to the understanding of the inherent time and space efficiency trade-offs in this area.
Local compactness in MT-algebras

In our previous work, we introduced McKinsey-Tarski algebras (MT-algebras for short) as an alternative pointfree approach to topology. Here we study local compactness in MT-algebras. We establish the Hofmann-Mislove theorem for sober MT-algebras, using which we develop the MT-algebra versions of such well-known dualities in pointfree topology as Hofmann-Lawson, Isbell, and Stone dualities. This yields a new perspective on these classic results.
Train-Free Segmentation in MRI with Cubical Persistent Homology

We describe a new general method for segmentation in MRI scans using Topological Data Analysis (TDA), offering several advantages over traditional machine learning approaches. It works in three steps, first identifying the whole object to segment via automatic thresholding, then detecting a distinctive subset whose topology is known in advance, and finally deducing the various components of the segmentation. Although convoking classical ideas of TDA, such an algorithm has never been proposed separately from deep learning methods. To achieve this, our approach takes into account, in addition to the homology of the image, the localization of representative cycles, a piece of information that seems never to have been exploited in this context. In particular, it offers the ability to perform segmentation without the need for large annotated data sets. TDA also provides a more interpretable and stable framework for segmentation by explicitly mapping topological features to segmentation components. By adapting the geometric object to be detected, the algorithm can be adjusted to a wide range of data segmentation challenges. We carefully study the examples of glioblastoma segmentation in brain MRI, where a sphere is to be detected, as well as myocardium in cardiac MRI, involving a cylinder, and cortical plate detection in fetal brain MRI, whose 2D slices are circles. We compare our method to state-of-the-art algorithms.
Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation

Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model during test-time, leveraging the current test data. In real-world scenarios, test data streams are not always independent and identically distributed (i.i.d.). Instead, they are frequently temporally correlated, making them non-i.i. d. Many existing methods struggle to cope with this scenario. In response, we propose a diversity-aware and category-balanced buffer that can simulate an i.i. d. data stream, even in non-i.i. d. scenarios. Combined with a diversity and entropy-weighted entropy loss, we show that a stable adaptation is possible on a wide range of corruptions and natural domain shifts, based on ImageNet. We achieve state-of-the-art results on most considered benchmarks.
Static Deadlock Detection for Rust Programs

Rust relies on its unique ownership mechanism to ensure thread and memory safety. However, numerous potential security vulnerabilities persist in practical applications. New language features in Rust pose new challenges for vulnerability detection. This paper proposes a static deadlock detection method tailored for Rust programs, aiming to identify various deadlock types, including double lock, conflict lock, and deadlock associated with conditional variables. With due consideration for Rust's ownership and lifetimes, we first complete the pointer analysis. Then, based on the obtained points-to information, we analyze dependencies among variables to identify potential deadlocks. We develop a tool and conduct experiments based on the proposed method. The experimental results demonstrate that our method outperforms existing deadlock detection methods in precision.
PLE-SLAM: A Visual-Inertial SLAM Based on Point-Line Features and Efficient IMU Initialization

Visual-inertial SLAM is crucial in various fields, such as aerial vehicles, industrial robots, and autonomous driving. The fusion of camera and inertial measurement unit (IMU) makes up for the shortcomings of a signal sensor, which significantly improves the accuracy and robustness of localization in challenging environments. This article presents PLE-SLAM, an accurate and real-time visual-inertial SLAM algorithm based on point-line features and efficient IMU initialization. First, we use parallel computing methods to extract features and compute descriptors to ensure real-time performance. Adjacent short line segments are merged into long line segments, and isolated short line segments are directly deleted. Second, a rotation-translation-decoupled initialization method is extended to use both points and lines. Gyroscope bias is optimized by tightly coupling IMU measurements and image observations. Accelerometer bias and gravity direction are solved by an analytical method for efficiency. To improve the system's intelligence in handling complex environments, a scheme of leveraging semantic information and geometric constraints to eliminate dynamic features and A solution for loop detection and closed-loop frame pose estimation using CNN and GNN are integrated into the system. All networks are accelerated to ensure real-time performance. The experiment results on public datasets illustrate that PLE-SLAM is one of the state-of-the-art visual-inertial SLAM systems.
Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation methods struggle with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed to address these challenges. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science.
On the Parameter Estimation of Sinusoidal Models for Speech and Audio Signals

In this paper, we examine the parameter estimation performance of three well-known sinusoidal models for speech and audio. The first one is the standard Sinusoidal Model (SM), which is based on the Fast Fourier Transform (FFT). The second is the Exponentially Damped Sinusoidal Model (EDSM) which has been proposed in the last decade, and utilizes a subspace method for parameter estimation, and finally the extended adaptive Quasi-Harmonic Model (eaQHM), which has been recently proposed for AM-FM decomposition, and estimates the signal parameters using Least Squares on a set of basis function that are adaptive to the local characteristics of the signal. The parameter estimation of each model is briefly described and its performance is compared to the others in terms of signal reconstruction accuracy versus window size on a variety of synthetic signals and versus the number of sinusoids on real signals. The latter include highly non stationary signals, such as singing voices and guitar solos. The advantages and disadvantages of each model are presented via synthetic signals and then the application on real signals is discussed. Conclusively, eaQHM outperforms EDS in medium-to-large window size analysis, whereas EDSM yields higher reconstruction values for smaller analysis window sizes. Thus, a future research direction appears to be the merge of adaptivity of the eaQHM and parameter estimation robustness of the EDSM in a new paradigm for high-quality analysis and resynthesis of general audio signals.
Sharp Analysis of Power Iteration for Tensor PCA

We investigate the power iteration algorithm for the tensor PCA model introduced in Richard and Montanari (2014). Previous work studying the properties of tensor power iteration is either limited to a constant number of iterations, or requires a non-trivial data-independent initialization. In this paper, we move beyond these limitations and analyze the dynamics of randomly initialized tensor power iteration up to polynomially many steps. Our contributions are threefold: First, we establish sharp bounds on the number of iterations required for power method to converge to the planted signal, for a broad range of the signal-to-noise ratios. Second, our analysis reveals that the actual algorithmic threshold for power iteration is smaller than the one conjectured in literature by a polylog(n) factor, where n is the ambient dimension. Finally, we propose a simple and effective stopping criterion for power iteration, which provably outputs a solution that is highly correlated with the true signal. Extensive numerical experiments verify our theoretical results.
GBSS: a global building semantic segmentation dataset for large-scale remote sensing building extraction

Semantic segmentation techniques for extracting building footprints from high-resolution remote sensing images have been widely used in many fields such as urban planning. However, large-scale building extraction demands higher diversity in training samples. In this paper, we construct a Global Building Semantic Segmentation (GBSS) dataset (The dataset will be released), which comprises 116.9k pairs of samples (about 742k buildings) from six continents. There are significant variations of building samples in terms of size and style, so the dataset can be a more challenging benchmark for evaluating the generalization and robustness of building semantic segmentation models. We validated through quantitative and qualitative comparisons between different datasets, and further confirmed the potential application in the field of transfer learning by conducting experiments on subsets.
Zero-Shot Position Debiasing for Large Language Models

Fine-tuning has been demonstrated to be an effective method to improve the domain performance of large language models (LLMs). However, LLMs might fit the dataset bias and shortcuts for prediction, leading to poor generation performance. Previous works have proven that LLMs are prone to exhibit position bias, i.e., leveraging information positioned at the beginning or end, or specific positional cues within the input. Existing debiasing methods for LLMs require external bias knowledge or annotated non-biased samples, which is lacking for position debiasing and impractical in reality. In this work, we propose a zero-shot position debiasing (ZOE) framework to mitigate position bias for LLMs. ZOE leverages unsupervised responses from pre-trained LLMs for debiasing without relying on any external knowledge. To improve the quality of unsupervised responses, we propose a MSA module to prune these responses. Experiments on eight datasets and five tasks show that ZOE consistently outperforms existing methods in mitigating three types of position biases. Besides, ZOE achieves this by sacrificing only a small performance on biased samples, which is general and effective. To facilitate the reproducibility of the results, we share the code of all methods and datasets on https://anonymous.4open. science/r/ZOE-F06B.
Cheetah: Natural Language Generation for 517 African Languages

Low-resource African languages pose unique challenges for natural language processing (NLP) tasks, including natural language generation (NLG). In this paper, we develop Cheetah, a massively multilingual NLG language model for African languages. Cheetah supports 517 African languages and language varieties, allowing us to address the scarcity of NLG resources and provide a solution to foster linguistic diversity. We demonstrate the effectiveness of Cheetah through comprehensive evaluations across six generation downstream tasks. In five of the six tasks, Cheetah significantly outperforms other models, showcasing its remarkable performance for generating coherent and contextually appropriate text in a wide range of African languages. We additionally conduct a detailed human evaluation to delve deeper into the linguistic capabilities of Cheetah. The introduction of Cheetah has far-reaching benefits for linguistic diversity. By leveraging pretrained models and adapting them to specific languages, our approach facilitates the development of practical NLG applications for African communities. The findings of this study contribute to advancing NLP research in low-resource settings, enabling greater accessibility and inclusion for African languages in a rapidly expanding digital landscape. We publicly release our models for research.
Life History of Millepora Hydrocorals : New Ecological and Evolutionary Perspectives from Population Genetic Approaches

Evaluating life history of species carries important implications for conservation biology. A total of 3651 colonies of the fire coral Millepora platyphylla was measured, georeferenced and collected in 5 different habitats in Moorea to evaluate the biological and ecological context of the population maintenance and renewal. This thesis has demonstrated that the population structure of this species varies greatly between lagoonal and fore reef habitats. Using newly developed microsatellite markers, we have shown that M. platyphylla relies heavily on clonal reproduction via fragmentation (80%) and that the fragments are distributed in perfect alignment with wave energy dispersal. Clonal lineages with clones shared among habitats revealed the ability of a single genotype to express different phenotypes depending on its exposure to swell wave energy. Surprisingly, M. platyphylla invests in a vulnerable morphology to wave-induced breakage in high energy reef habitats. Furthermore, parentage analysis revealed a high contribution from self-seeding (58%), limited dispersal of sexual propagules and sibling aggregations. At last, we have demonstrated intracolonial genotypic variability, mostly from somatic mutations (mosaicism), which creates novel genetic diversity within the population. The interaction of these processes generates a high level of genetic and phenotypic variation within the population and allows for local replenishment and the persistence of this fire coral species in Moorea, a marginal habitat. These life history strategies thus increase the adaptive potential and resilience of M. platyphylla in response to rapid and unpredictable environmental changes.
Approximating Single-Source Personalized PageRank with Absolute Error Guarantees

Personalized PageRank (PPR) is an extensively studied and applied node proximity measure in graphs. For a pair of nodes $s$ and $t$ on a graph $G=(V,E)$, the PPR value $\pi(s,t)$ is defined as the probability that an $\alpha$-discounted random walk from $s$ terminates at $t$, where the walk terminates with probability $\alpha$ at each step. We study the classic Single-Source PPR query, which asks for PPR approximations from a given source node $s$ to all nodes in the graph. Specifically, we aim to provide approximations with absolute error guarantees, ensuring that the resultant PPR estimates $\hat{\pi}(s,t)$ satisfy $\max_{t\in V}\big|\hat{\pi}(s,t)-\pi(s,t)\big|\le\varepsilon$ for a given error bound $\varepsilon$. We propose an algorithm that achieves this with high probability, with an expected running time of - $\widetilde{O}\big(\sqrt{m}/\varepsilon\big)$ for directed graphs, where $m=|E|$; - $\widetilde{O}\big(\sqrt{d_{\mathrm{max}}}/\varepsilon\big)$ for undirected graphs, where $d_{\mathrm{max}}$ is the maximum node degree in the graph; - $\widetilde{O}\left(n^{\gamma-1/2}/\varepsilon\right)$ for power-law graphs, where $n=|V|$ and $\gamma\in\left(\frac{1}{2},1\right)$ is the extent of the power law. These sublinear bounds improve upon existing results. We also study the case when degree-normalized absolute error guarantees are desired, requiring $\max_{t\in V}\big|\hat{\pi}(s,t)/d(t)-\pi(s,t)/d(t)\big|\le\varepsilon_d$ for a given error bound $\varepsilon_d$, where the graph is undirected and $d(t)$ is the degree of node $t$. We give an algorithm that provides this error guarantee with high probability, achieving an expected complexity of $\widetilde{O}\left(\sqrt{\sum_{t\in V}\pi(s,t)/d(t)}\big/\varepsilon_d\right)$. This improves over the previously known $O(1/\varepsilon_d)$ complexity.