Miracle-dz/UniFAR-FTUs huggingface dataset:
Micromechanics of vortices in granular media: connection to shear bands and implications for continuum modelling of failure in geomaterials

Recent analysis of data from triaxial tests on sand and discrete element simulations indicate the final pattern of failure is encoded in grain motions during the nascent stages of loading. We study vortices that are evident from grain displacements at the start of loading and bear a direct mathematical connection to boundary conditions, uniform continuum strain and shear bands. Motions of three grains in mutual contact, that is, 3‐cycles, manifest vortices. In the initial stages of loading, 3‐cycles initiate a rotation around a region Ω* where the shear band ultimately develops. This bias sets a course in 3‐cycle evolution, determining where they will more likely collapse. A multiscale spatial analysis of 3‐cycle temporal evolution provides quantitative evidence that the most stable, persistent 3‐cycles degrade preferentially in Ω*, until essentially depleted when the shear band is fully formed. The transition towards a clustered distribution of persistent 3‐cycles occurs early in the loading history—and coincides with the persistent localisation of vortices in Ω*. In 3D samples, no evidence of spatial clustering in persistent 3‐cycle deaths is found in samples undergoing diffuse failure, while early clustering manifests in a sample that ultimately failed by strain localisation. This study not only delivered insights into the possible structural origins of vortices in dense granular systems but also a tool for the early detection of the mode of failure—localised versus diffuse—a sample will ultimately undergo. Copyright © 2014 John Wiley & Sons, Ltd.
Markovian classification of SAR images using $\mathcal{G}^{0}_{I}$ model

When processing synthetic aperture radar (SAR) images, there is a strong need for statistical models of scattering to take into account multiplicative noise. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is manmade. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, etc.), but none of them is flexible enough to model all kinds of surfaces. Frery et al. [IEEE Transactions on Geoscience and Remote Sensing 35 (1997) 648–659] proposed a new class of distributions, G distribution, arising from the multiplicative model. Classical distributions such as K are particular cases of this new class. A special case of this class called G0 is shown able to model extremely heterogeneous clutter, such as that of urban areas. The quality of the classification obtained by mixing this model and a Markovian segmentation is high.
Heat transport through a quantum Brownian harmonic chain beyond the weak-coupling regime: An exact treatment

We consider a linear chain of quantum harmonic oscillators, in which the number of the individual oscillators is given by an arbitrary number N, and each oscillator is coupled at an arbitrary strength kappa to its nearest neighbors ("intra-coupling"), as well as the two end oscillators of the chain are coupled at an arbitrary strength c_nu to two separate baths at arbitrarily different temperatures, respectively. We derive an exact closed expression for the steady-state heat current flowing from a hot bath through the chain to a cold bath, in the Drude-Ullersma damping model going beyond the Markovian damping. This allows us to explore the behavior of heat current relative to the intra-coupling strength as a control parameter, especially in pursuit of the heat power amplification. Then it turns out that in the weak-coupling regime (kappa, c_nu << 1), the heat current is small, as expected, and almost independent of chain length N, hence violating Fourier's law of heat conduction; this is consistent to the earlier results obtained within the rotating wave approximation for the intra-coupling as well as in the Born-Markov approximation for the chain-bath coupling. Beyond the weak-coupling regime, on the other hand, we typically observe that with increase of the intra-coupling strength the heat current is gradually amplified, and reaches its maximum value at some specific coupling strength kappa_R "resonant" to a given chain-bath coupling strength. Also, the behavior of heat current versus chain length appears typically in such a way that the magnitude of current reaches its maximum with N=1 and then gradually decreases with increase of the chain length, being in fact almost N-independent in the range of N large enough. As a result, Fourier's law proves violated also in this regime.
An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio

\beginabstract Many effective attempts have been made for fake audio detection. However, they can only provide detection results but no countermeasures to curb this harm. For many related practical applications, what model or algorithm generated the fake audio also is needed. Therefore, We propose a new problem for detecting vocoder fingerprints of fake audio. Experiments are conducted on the datasets synthesized by eight state-of-the-art vocoders. We have preliminarily explored the features and model architectures. The t-SNE visualization shows that different vocoders generate distinct vocoder fingerprints. \endabstract
Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference

Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion. The source code and datasets of this paper are available at https://github.com/ngl567/EngineKG.
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations

Introduction: For supervised deep learning (DL) tasks, researchers need a large annotated dataset. In medical data science, one of the major limitations to develop DL models is the lack of annotated examples in large quantity. This is most often due to the time and expertise required to annotate. We introduce Lirot.ai, a novel platform for facilitating and crowd-sourcing image segmentations. Methods: Lirot.ai is composed of three components; an iPadOS client application named Lirot.ai-app, a back-end server named Lirot.ai-server and a python API name Lirot.ai-API. Lirot.ai-app was developed in Swift 5.6 and Lirot.ai-server is a firebase backend. Lirot.ai-API allows the management of the database. Lirot.ai-app can be installed on as many iPadOS devices as needed so that annotators may be able to perform their segmentation simultaneously and remotely. We incorporate Apple Pencil compatibility, making the segmentation faster, more accurate, and more intuitive for the expert than any other computer-based alternative. Results: We demonstrate the usage of Lirot.ai for the creation of a retinal fundus dataset with reference vasculature segmentations. Discussion and future work: We will use active learning strategies to continue enlarging our retinal fundus dataset by including a more efficient process to select the images to be annotated and distribute them to annotators.
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition

Sketch-and-project is a framework which unifies many known iterative methods for solving linear systems and their variants, as well as further extensions to non-linear optimization problems. It includes popular methods such as randomized Kaczmarz, coordinate descent, variants of the Newton method in convex optimization, and others. In this paper, we develop a theoretical framework for obtaining sharp guarantees on the convergence rate of sketch-and-project methods. Our approach is the first to: (1) show that the convergence rate improves at least linearly with the sketch size, and even faster when the data matrix exhibits certain spectral decays; and (2) allow for sparse sketching matrices, which are more efficient than dense sketches and more robust than sub-sampling methods. In particular, our results explain an observed phenomenon that a radical sparsification of the sketching matrix does not affect the per iteration convergence rate of sketch-and-project. To obtain our results, we develop new non-asymptotic spectral bounds for the expected sketched projection matrix, which are of independent interest; and we establish a connection between the convergence rates of iterative sketch-and-project solvers and the approximation error of randomized singular value decomposition, which is a widely used one-shot sketching algorithm for low-rank approximation. Our experiments support the theory and demonstrate that even extremely sparse sketches exhibit the convergence properties predicted by our framework.
Game-Theoretic Algorithms for Conditional Moment Matching

A variety of problems in econometrics and machine learning, including instrumental variable regression and Bellman residual minimization, can be formulated as satisfying a set of conditional moment restrictions (CMR). We derive a general, game-theoretic strategy for satisfying CMR that scales to nonlinear problems, is amenable to gradient-based optimization, and is able to account for finite sample uncertainty. We recover the approaches of Dikkala et al. and Dai et al. as special cases of our general framework before detailing various extensions and how to efficiently solve the game defined by CMR.
STS: Surround-view Temporal Stereo for Multi-view 3D Detection

Learning accurate depth is essential to multi-view 3D object detection. Recent approaches mainly learn depth from monocular images, which confront inherent difficulties due to the ill-posed nature of monocular depth learning. Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view Temporal Stereo (STS) technique that leverages the geometry correspondence between frames across time to facilitate accurate depth learning. Specifically, we regard the field of views from all cameras around the ego vehicle as a unified view, namely surroundview, and conduct temporal stereo matching on it. The resulting geometrical correspondence between different frames from STS is utilized and combined with the monocular depth to yield final depth prediction. Comprehensive experiments on nuScenes show that STS greatly boosts 3D detection ability, notably for medium and long distance objects. On BEVDepth with ResNet-50 backbone, STS improves mAP and NDS by 2.6% and 1.4%, respectively. Consistent improvements are observed when using a larger backbone and a larger image resolution, demonstrating its effectiveness
HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations

The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI) recommendations and computing resource-aware mobile app recommendations. In many such recommendation scenarios, contexts are drifting over time. For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time. However, most existing graph-based collaborative filtering methods are designed under the assumption of static features. Therefore, they would require frequent retraining and/or yield graphical models burgeoning in sizes, impeding their suitability for context-drifting recommendations. In this work, we propose a specifically tailor-made Hybrid Static and Adaptive Graph Embedding (HySAGE) network for context-drifting recommendations. Our key idea is to disentangle the relatively static user-item interaction and rapidly drifting contextual features. Specifically, our proposed HySAGE network learns a relatively static graph embedding from user-item interaction and an adaptive embedding from drifting contextual features. These embeddings are incorporated into an interest network to generate the user interest in some certain context. We adopt an interactive attention module to learn the interactions among static graph embeddings, adaptive contextual embeddings, and user interest, helping to achieve a better final representation. Extensive experiments on real-world datasets demonstrate that HySAGE significantly improves the performance of the existing state-of-the-art recommendation algorithms.
A Survey on Transactional Stream Processing

Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, numerous endeavors have contributed to the evolution of TSP solutions, uncovering similarities and distinctions among them. Despite these advances, a universally accepted standard approach for integrating transactional functionality with stream processing remains to be established. Existing TSP solutions predominantly concentrate on specific application characteristics and involve complex design trade-offs. This survey intends to introduce TSP and present our perspective on its future progression. Our primary goals are twofold: to provide insights into the diverse TSP requirements and methodologies, and to inspire the design and development of groundbreaking TSP systems.
Topical: Learning Repository Embeddings from Source Code using Attention

—Machine learning on source code (MLOnCode) promises to transform how software is delivered. By mining the context and relationship between software artefacts, MLOnCode augments the software developer’s capabilities with code auto-generation, code recommendation, code auto-tagging and other data-driven enhancements. For many of these tasks a script level representation of code is sufﬁcient, however, in many cases a repository level representation that takes into account various dependencies and repository structure is imperative, for example, auto-tagging repositories with topics or auto-documentation of repository code etc . Existing methods for computing repository level representations suffer from (a) reliance on natural language documentation of code (for example, README ﬁles) (b) naive aggregation of method/script-level representation, for example, by concatenation or averaging. This paper introduces Topical a deep neural network to generate repository level embeddings of publicly available GitHub code repositories directly from source code. Topical incorporates an attention mechanism that projects the source code, the full dependency graph and the script level textual information into a dense repository-level representation. To compute the repository-level representations, Topical is trained to predict the topics associated with a repository, on a dataset of publicly available GitHub repositories that were crawled along with their ground truth topic tags. Our experiments show that the embeddings computed by Topical are able to outperform multiple baselines, including baselines that naively combine the method-level representations through averaging or concatenation at the task of repository auto-tagging. Furthermore, we show that Topical’s attention mechanism out-performs naive aggregation methods when computing repository-level representations from script-level representation generated by existing methods. Topical is a lightweight framework for computing repository-level representation of code repositories that scales efﬁciently with the number of topics and dataset
Antecedent Predictions Are Dominant for Tree-Based Code Generation

Code generation focuses on the automatic conversion of natural language (NL) utterances into code snippets. The sequence-to-tree (Seq2Tree) methods, e.g., TRANX, are proposed for code generation, with the guarantee of the compilability of the generated code, which generate the subsequent Abstract Syntax Tree (AST) node relying on antecedent predictions of AST nodes. Existing Seq2Tree meth-ods tend to treat both antecedent predictions and subsequent predictions equally. However, under the AST constraints, it is difﬁcult for Seq2Tree models to produce the correct subsequent prediction based on incorrect antecedent predictions. Thus, antecedent predictions ought to receive more attention than subsequent predictions. To this end, in this paper, we propose an effective method, named APTRANX (Antecedent Prioritized TRANX), on the basis of TRANX. APTRANX contains an Antecedent Prioritized (AP) Loss, which helps the model attach importance to antecedent predictions by exploiting the position information of the generated AST nodes. With better antecedent predictions and accompanying subsequent predictions, APTRANX signiﬁcantly improves the performance. We conduct extensive experiments on several benchmark datasets, and the experimental results demonstrate the superiority and generality of our proposed method compared with the state-of-the-art methods.
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey

In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism underlying the data and for decision-making on effective responses or interventions. A great deal of research has been conducted to address this challenging problem from different angles. For estimating causal effect in observational data, assumptions such as Markov condition, faithfulness and causal sufficiency are always made. Under the assumptions, full knowledge such as, a set of covariates or an underlying causal graph, is typically required. A practical challenge is that in many applications, no such full knowledge or only some partial knowledge is available. In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge. In this survey, we review these data-driven methods on causal effect estimation for a single treatment with a single outcome of interest and focus on the challenges faced by data-driven causal effect estimation. We concisely summarise the basic concepts and theories that are essential for data-driven causal effect estimation using graphical causal modelling but are scattered around the literature. We identify and discuss the challenges faced by data-driven causal effect estimation and characterise the existing methods by their assumptions and the approaches to tackling the challenges. We analyse the strengths and limitations of the different types of methods and present an empirical evaluation to support the discussions. We hope this review will motivate more researhers to design better data-driven methods based on graphical causal modelling for the challenging problem of causal effect estimation.
Effects of head modeling errors on the spatial frequency representation of MEG

Objectives. We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM). Approach. A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.33 S m−1 was defined. The vertices were then randomly perturbed radially up to 2%, 4%, 6%, 8% and 10% of the radius. For each head perturbation case, the forward signal was calculated for dipolar sources located at 2 cm, 4 cm, 6 cm and 8 cm from the origin (center of the sphere), and for a 324 sensor array located at 10 cm to 15 cm from the origin. Equivalent current dipole (ECD) source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was then analyzed in the spatial frequency domain, and the signal and ECD errors were quantified relative to the unperturbed case. Main results. In the noiseless and high signal-to-noise ratio (SNR) case of approximately ≥6 dB, inaccuracies in our spherical BEM head conductor models lead to increased signal and ECD inaccuracies when sensor arrays are placed closer to the head. This is true especially in the case of deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays allows for an improved ECD fit and outweighs the effects of head geometry inaccuracies. Significance. OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential.
On the Linear AFL: The Non-Basic Case

The linear Arithmetic Fundamental Lemma (AFL) conjecture compares intersection numbers on Lubin--Tate deformation spaces with derivatives of orbital integrals. It has been introduced for elliptic orbits in arXiv:1803.07553 and arXiv:2010.07365. In these cases, the relevant intersection problem is formulated for the basic isogeny class. In the present article, we extend the theory to all orbits and all isogeny classes. Our main result is a reduction of the non-basic cases of the AFL to the basic ones, which is achieved by exploiting the connected-\'etale sequence. Our theory will be relevant in the global setting, where also locally non-elliptic orbits may contribute in a non-trivial way.