Miracle-dz/UniFAR-FTUs huggingface dataset:
Likely cavitation and radial motion of stochastic elastic spheres 2: Impulse driven

Cavitation in solids can be caused by tensile dead-load traction or impulse traction. The two different types of boundary conditions lead to different static and dynamic solutions. In addition, if the material is stochastic, i.e., the model parameters are represented by probability distributions, the expected behaviour is more complicated to describe. Here, following the first instalment of this work, we examine the static and dynamic cavitation of a stochastic material under a uniform tensile impulse traction in different spherical geometries. We find that the critical load at which a cavity forms at the centre of the sphere is the same as for the homogeneous sphere composed entirely of the material found at its centre, while the post-cavitation radial motion is non-oscillatory. However, there are some important differences in the nonlinear elastic responses. Specifically, subcritical bifurcation, with snap cavitation, is obtained in a static sphere of stochastic neo-Hookean material and also in a radially inhomogeneous sphere, whereas for composite spheres, a supercritical bifurcation, with stable cavitation, is possible as well. Given the non-deterministic material parameters, the results are characterised by probability distributions.
Multi-stage Attack: Concepts, Detection and Defence

— We live in a fast-paced world, surrounded by all kinds of technologies. However, unfortunately these technologies present weaknesses that exposes us to malicious attacks. The attackers are developing their skills and continuously find new ways of penetrating into systems where they have unauthorized access. These attacks can cause data breaches and losses and can even cause physical damage to the systems. By getting access to sensitive data, the attacker may steal the bank account details of an individual, their identity and even invade their privacy, which may cause a serious psychological impact. These attacks can cost companies huge financial losses, damage their reputation, and it might even place them in a situation where they have to face legal consequences for not being able to protect their customer’s data. This report will discuss multi-stage attacks, which is a very dangerous type of attack, because it’s not easily detectable. This attack, as it can be referred from the name, happens in multiple steps, where each step executed by its own does not seem malicious. However, all these phases executed together will cause great harm. This report will also present several proposed detection methods, as well as defence recommendations against this type of attack.
for the observability of the

— Given any measurable subset ω of a closed Riemannian manifold and given any T > 0 , we deﬁne (cid:96) T ( ω ) ∈ [0 , 1] as the smallest average time over [0 ,T ] spent by all geodesic rays in ω . Our ﬁrst main result, which is of geometric nature, states that, under regularity assumptions, 1 / 2 is the maximal possible discrepancy of (cid:96) T when taking the closure. Our second main result is of probabilistic nature: considering a regular checkerboard on the ﬂat two-dimensional torus made of n 2 square white cells, constructing random subsets ω nε by darkening cells randomly with a probability ε , we prove that the random law (cid:96) T ( ω nε ) converges in probability to ε as n → + ∞ . We discuss the consequences in terms of observability of the wave equation.
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models

We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation. While it is generally known that this approach improves the overall predictive performance, especially when considering distributional shift between training and evaluation data, we here emphasize the perspective that online adaptation turns parameters into temporally changing states and provides a form of context-length extension with memory in weights, more in line with the concept of memory in neuroscience. We pay particular attention to the speed of adaptation (in terms of sample efficiency),sensitivity to the overall distributional drift, and the computational overhead for performing gradient computations and parameter updates. Our empirical study provides insights on when online adaptation is particularly interesting. We highlight that with online adaptation the conceptual distinction between in-context learning and fine tuning blurs: both are methods to condition the model on previously observed tokens.
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation

Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies. Among them, leveraging self-supervised Vision Transformers for unsupervised semantic segmentation (USS) has been making steady progress with expressive deep features. Yet, for semantically segmenting images with complex objects, a predominant challenge remains: the lack of explicit object-level semantic encoding in patch-level features. This technical limitation often leads to inadequate segmentation of complex objects with diverse structures. To address this gap, we present a novel approach, EAGLE, which emphasizes object-centric representation learning for unsupervised semantic segmentation. Specifically, we introduce EiCue, a spectral technique providing semantic and structural cues through an eigenbasis derived from the semantic similarity matrix of deep image features and color affinity from an image. Further, by incorporating our object-centric contrastive loss with EiCue, we guide our model to learn object-level representations with intra- and inter-image object-feature consistency, thereby enhancing semantic accuracy. Extensive experiments on COCO-Stuff, Cityscapes, and Potsdam-3 datasets demonstrate the state-of-the-art USS results of EAGLE with accurate and consistent semantic segmentation across complex scenes.
Soliton Frequency Combs in Elastomer Membrane-Cavity Optomechanics

Solitons, arising from nonlinear wave-matter interactions, stand out for their intrinsic stability during wave propagation and exceptional spectral characteristics. Their applications span diverse physical systems, including telecommunications, atomic clocks, and precise measurements. In recent years, significant strides have been made in developing cavity-optomechanics based approaches to generate optical frequency combs (FCs). In this study, we present an innovative approach, never explored before, that leverages elastomer membrane (EM)-cavity optomechanics to achieve the generation of soliton FCs, a highly sought-after phenomenon in the realm of nonlinear wave-matter interactions. Our method represents a significant breakthrough due to its streamlined simplicity, relying on a single continuous-wave (CW) laser pump and an externally applied acoustic wave exciting an EM-cavity, which gives rise to phonons, quantized vibrational energy states intrinsic to the elastomer's crystalline lattice structure. The mechanical resonator and electromagnetic cavity resonance are parametrically coupled within the microwave frequency range, collectively orchestrate the process of soliton FCs formation with remarkable efficiency. Numerical simulations and experimental observations demonstrate the emergence of multiple stable localized opto-mechanical wave packets, characterized by a narrow pulses time-domain response. Crucially, by setting the acoustic wave frequency to match the natural frequency of the EM resonator, the solitons' teeth are precisely spaced, and the EM's motion is significantly amplified, giving rise to a Kerr medium. The successful realization of optomechanical stable solitons represents a monumental advancement with transformative potential across various fields, including quantum computing and spectroscopy.
When do Convolutional Neural Networks Stop Learning?

Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in computer vision tasks such as image classification, detection, segmentation, and medical image analysis. In general, an arbitrary number of epochs is used to train such neural networks. In a single epoch, the entire training data -- divided by batch size -- are fed to the network. In practice, validation error with training loss is used to estimate the neural network's generalization, which indicates the optimal learning capacity of the network. Current practice is to stop training when the training loss decreases and the gap between training and validation error increases (i.e., the generalization gap) to avoid overfitting. However, this is a trial-and-error-based approach which raises a critical question: Is it possible to estimate when neural networks stop learning based on training data? This research work introduces a hypothesis that analyzes the data variation across all the layers of a CNN variant to anticipate its near-optimal learning capacity. In the training phase, we use our hypothesis to anticipate the near-optimal learning capacity of a CNN variant without using any validation data. Our hypothesis can be deployed as a plug-and-play to any existing CNN variant without introducing additional trainable parameters to the network. We test our hypothesis on six different CNN variants and three different general image datasets (CIFAR10, CIFAR100, and SVHN). The result based on these CNN variants and datasets shows that our hypothesis saves 58.49\% of computational time (on average) in training. We further conduct our hypothesis on ten medical image datasets and compared with the MedMNIST-V2 benchmark. Based on our experimental result, we save $\approx$ 44.1\% of computational time without losing accuracy against the MedMNIST-V2 benchmark.
Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances

The Quadratic Assignment Problem (QAP) is one of the major domains in the field of evolutionary computation, and more widely in combinatorial optimization. This paper studies the phase transition of the QAP, which can be described as a dramatic change in the problem's computational complexity and satisfiability, within a narrow range of the problem parameters. To approach this phenomenon, we introduce a new QAP-SAT design of the initial problem based on submodularity to capture its difficulty with new features. This decomposition is studied experimentally using branch-and-bound and tabu search solvers. A phase transition parameter is then proposed. The critical parameter of phase transition satisfaction and that of the solving effort are shown to be highly correlated for tabu search, thus allowing the prediction of difficult instances.
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training

Highlighting particularly relevant regions of an image can improve the performance of vision-language models (VLMs) on various vision-language (VL) tasks by guiding the model to attend more closely to these regions of interest. For example, VLMs can be given a"visual prompt", where visual markers such as bounding boxes delineate key image regions. However, current VLMs that can incorporate visual guidance are either proprietary and expensive or require costly training on curated data that includes visual prompts. We introduce Contrastive Region Guidance (CRG), a training-free guidance method that enables open-source VLMs to respond to visual prompts. CRG contrasts model outputs produced with and without visual prompts, factoring out biases revealed by the model when answering without the information required to produce a correct answer (i.e., the model's prior). CRG achieves substantial improvements in a wide variety of VL tasks: When region annotations are provided, CRG increases absolute accuracy by up to 11.1% on ViP-Bench, a collection of six diverse region-based tasks such as recognition, math, and object relationship reasoning. We also show CRG's applicability to spatial reasoning, with 10% improvement on What'sUp, as well as to compositional generalization -- improving accuracy by 11.5% and 7.5% on two challenging splits from SugarCrepe -- and to image-text alignment for generated images, where we improve by up to 8.4 AUROC and 6.8 F1 points on SeeTRUE. When reference regions are absent, CRG allows us to re-rank proposed regions in referring expression comprehension and phrase grounding benchmarks like RefCOCO/+/g and Flickr30K Entities, with an average gain of 3.2% in accuracy. Our analysis explores alternative masking strategies for CRG, quantifies CRG's probability shift, and evaluates the role of region guidance strength, empirically validating CRG's design choices.
Pre-trained Model-based Actionable Warning Identification: A Feasibility Study

Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainly learn an AWI classifier from labeled warnings, are notably common. However, these approaches still face the problem of restricted performance due to the direct reliance on a limited number of labeled warnings to develop a classifier. Very recently, Pre-Trained Models (PTMs), which have been trained through billions of text/code tokens and demonstrated substantial success applications on various code-related tasks, could potentially circumvent the above problem. Nevertheless, the performance of PTMs on AWI has not been systematically investigated, leaving a gap in understanding their pros and cons. In this paper, we are the first to explore the feasibility of applying various PTMs for AWI. By conducting the extensive evaluation on 10K+ SpotBugs warnings from 10 large-scale and open-source projects, we observe that all studied PTMs are consistently 9.85%~21.12% better than the state-of-the-art ML-based AWI approaches. Besides, we investigate the impact of three primary aspects (i.e., data preprocessing, model training, and model prediction) in the typical PTM-based AWI workflow. Further, we identify the reasons for current PTMs' underperformance on AWI. Based on our findings, we provide several practical guidelines to enhance PTM-based AWI in future work.
Space Complexity of Euclidean Clustering

The $(k, z)$-Clustering problem in Euclidean space $\mathbb{R}^d$ has been extensively studied. Given the scale of data involved, compression methods for the Euclidean $(k, z)$-Clustering problem, such as data compression and dimension reduction, have received significant attention in the literature. However, the space complexity of the clustering problem, specifically, the number of bits required to compress the cost function within a multiplicative error $\varepsilon$, remains unclear in existing literature. This paper initiates the study of space complexity for Euclidean $(k, z)$-Clustering and offers both upper and lower bounds. Our space bounds are nearly tight when $k$ is constant, indicating that storing a coreset, a well-known data compression approach, serves as the optimal compression scheme. Furthermore, our lower bound result for $(k, z)$-Clustering establishes a tight space bound of $\Theta( n d )$ for terminal embedding, where $n$ represents the dataset size. Our technical approach leverages new geometric insights for principal angles and discrepancy methods, which may hold independent interest.
One-Step Multi-View Clustering Based on Transition Probability

The large-scale multi-view clustering algorithms, based on the anchor graph, have shown promising performance and efficiency and have been extensively explored in recent years. Despite their successes, current methods lack interpretability in the clustering process and do not sufficiently consider the complementary information across different views. To address these shortcomings, we introduce the One-Step Multi-View Clustering Based on Transition Probability (OSMVC-TP). This method adopts a probabilistic approach, which leverages the anchor graph, representing the transition probabilities from samples to anchor points. Our method directly learns the transition probabilities from anchor points to categories, and calculates the transition probabilities from samples to categories, thus obtaining soft label matrices for samples and anchor points, enhancing the interpretability of clustering. Furthermore, to maintain consistency in labels across different views, we apply a Schatten p-norm constraint on the tensor composed of the soft labels. This approach effectively harnesses the complementary information among the views. Extensive experiments have confirmed the effectiveness and robustness of OSMVC-TP.
Connections between Bressan's Mixing Conjecture, the Branched Optimal Transport and Combinatorial Optimization

We investigate the 1D version of the notable Bressan's mixing conjecture, and introduce various formulation in the classical optimal transport setting, the branched optimal transport setting and a combinatorial optimization. In the discrete case of the combinatorial problem, we prove the number of admissible solutions is on the Catalan number. Our investigation sheds light on the intricate relationship between mixing problem in the fluid dynamics and many other popular fields, leaving many interesting open questions in both theoretical and practical applications across disciplines.
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge

Knowledge graph question answering (KGQA) is a well-established field that seeks to provide factual answers to natural language (NL) questions by leveraging knowledge graphs (KGs). However, existing KGQA datasets suffer from two significant limitations: (1) no existing KGQA dataset requires commonsense reasoning to arrive at an answer and (2) existing KGQA datasets focus on popular entities for which large language models (LLMs) can directly answer without hallucinating and without leveraging the KG. In this work, we seek a novel KGQA dataset that supports commonsense reasoning and focuses on long-tail entities (e.g., non-mainstream and recent entities) where LLMs frequently hallucinate, and thus create the need for novel methodologies that leverage the KG for factual and attributable commonsense inference. We create a novel Commonsense Reasoning (CR) and Long-Tail (LT) KGQA dataset with two subtasks -- question answering and claim verification -- that address both limitations (1) and (2). We construct CR-LT-KGQA by building extensions to existing reasoning datasets StrategyQA and CREAK over Wikidata. While existing KGQA methods are not applicable due to their lack of commonsense inference support, baseline evaluation of LLMs on CR-LT KGQA demonstrate a high rate of hallucination. Thus, CR-LT KGQA poses significant challenges for hallucination-prone LLMs, hence paving the way for future commonsense KGQA research to provide accurate and factual answers for long-tail entities in the era of LLMs.
Observational Constraints on the Dark Energy with a Quadratic Equation of State

In this study, we introduce a novel late-time effective dark energy model characterized by a quadratic equation of state (EoS) and rigorously examine its observational constraints. Initially, we delve into the background dynamics of this model, tracing the evolution of fluctuations in linear order. Our approach involves substituting the conventional cosmological constant with a dynamically effective dark energy fluid. Leveraging a diverse array of observational datasets encompassing the Planck 2018 Cosmic Microwave Background (CMB), Type Ia Supernovae (SNe), Baryon Acoustic Oscillations (BAO), and a prior on the Hubble constant $H_0$ (R21), we constrain the model parameters. We establish the model's consistency by comparing the Hubble parameter as a function of redshift against observational Hubble data (OHD), benchmarking its performance against the Standard $\Lambda$CDM model. Additionally, our investigation delves into studies of the model's dynamical behavior by computing cosmological parameters such as the deceleration parameter, relative Hubble parameter, and the evolution of the Hubble rate. Furthermore, employing Bayesian analysis, we determine the Bayesian Evidence for our proposed model compared to the reference $\Lambda$CDM model. While our analysis unveils the favorable behavior of the model in various observational tests, the well-known cosmological tensions persist when the full dataset combination is explored.
Mixed-Strategy Nash Equilibrium for Crowd Navigation

We address the problem of finding mixed-strategy Nash equilibrium for crowd navigation. Mixed-strategy Nash equilibrium provides a rigorous model for the robot to anticipate uncertain yet cooperative human behavior in crowds, but the computation cost is often too high for scalable and real-time decision-making. Here we prove that a simple iterative Bayesian updating scheme converges to the Nash equilibrium of a mixed-strategy social navigation game. Furthermore, we propose a data-driven framework to construct the game by initializing agent strategies as Gaussian processes learned from human datasets. Based on the proposed mixed-strategy Nash equilibrium model, we develop a sampling-based crowd navigation framework that can be integrated into existing navigation methods and runs in real-time on a laptop CPU. We evaluate our framework in both simulated environments and real-world human datasets in unstructured environments. Our framework consistently outperforms both non-learning and learning-based methods on both safety and navigation efficiency and reaches human-level crowd navigation performance on top of a meta-planner.
Attention Guidance Mechanism for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) is challenging in image-to-text tasks due to the complex layouts of mathematical expressions and suffers from problems including over-parsing and under-parsing. To solve these, previous HMER methods improve the attention mechanism by utilizing historical alignment information. However, this approach has limitations in addressing under-parsing since it cannot correct the erroneous attention on image areas that should be parsed at subsequent decoding steps. This faulty attention causes the attention module to incorporate future context into the current decoding step, thereby confusing the alignment process. To address this issue, we propose an attention guidance mechanism to explicitly suppress attention weights in irrelevant areas and enhance the appropriate ones, thereby inhibiting access to information outside the intended context. Depending on the type of attention guidance, we devise two complementary approaches to refine attention weights: self-guidance that coordinates attention of multiple heads and neighbor-guidance that integrates attention from adjacent time steps. Experiments show that our method outperforms existing state-of-the-art methods, achieving expression recognition rates of 60.75% / 61.81% / 63.30% on the CROHME 2014/ 2016/ 2019 datasets.