Miracle-dz/UniFAR-FTUs huggingface dataset:
Thech. Report: Genuinization of Speech waveform PMF for speaker detection spoofing and countermeasures

In the context of spoofing attacks in speaker recognition systems, we observed that the waveform probability mass function (PMF) of genuine speech differs significantly from the PMF of speech resulting from the attacks. This is true for synthesized or converted speech as well as replayed speech. We also noticed that this observation seems to have a significant impact on spoofing detection performance. In this article, we propose an algorithm, denoted genuinization, capable of reducing the waveform distribution gap between authentic speech and spoofing speech. Our genuinization algorithm is evaluated on ASVspoof 2019 challenge datasets, using the baseline system provided by the challenge organization. We first assess the influence of genuinization on spoofing performance. Using genuinization for the spoofing attacks degrades spoofing detection performance by up to a factor of 10. Next, we integrate the genuinization algorithm in the spoofing countermeasures and we observe a huge spoofing detection improvement in different cases. The results of our experiments show clearly that waveform distribution plays an important role and must be taken into account by anti-spoofing systems.
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance

Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP). While numerous studies aim to improve the overall LP performance of GNNs, none have explored its varying performance across different nodes and its underlying reasons. To this end, we aim to demystify which nodes will perform better from the perspective of their local topology. Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors. We empirically demonstrate that TC has a higher correlation with LP performance than other node-level topological metrics like degree and subgraph density, offering a better way to identify low-performing nodes than using cold-start. With TC, we discover a novel topological distribution shift issue in which newly joined neighbors of a node tend to become less interactive with that node's existing neighbors, compromising the generalizability of node embeddings for LP at testing time. To make the computation of TC scalable, We further propose Approximated Topological Concentration (ATC) and theoretically/empirically justify its efficacy in approximating TC and reducing the computation complexity. Given the positive correlation between node TC and its LP performance, we explore the potential of boosting LP performance via enhancing TC by re-weighting edges in the message-passing and discuss its effectiveness with limitations. Our code is publicly available at https://github.com/YuWVandy/Topo_LP_GNN.
Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab

We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1) it is compatible with Google Colab which allows automatic execution on cloud environment; (2) it supports two dimensional time-dependent PDEs; (3) it provides simple interface for definition of the residual loss, boundary condition and initial loss, together with their weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it allows for customizing the number of layers and neurons per layer, as well as for arbitrary activation function; (6) the learning rate and number of epochs are available as parameters; (7) it automatically differentiates PINN with respect to spatial and temporal variables; (8) it provides routines for plotting the convergence (with running average), initial conditions learnt, 2D and 3D snapshots from the simulation and movies (9) it includes a library of problems: (a) non-stationary heat transfer; (b) wave equation modeling a tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor growth simulations.
An Exploration of Task-Decoupling on Two-Stage Neural Post Filter for Real-Time Personalized Acoustic Echo Cancellation

Deep learning based techniques have been popularly adopted in acoustic echo cancellation (AEC). Utilization of speaker representation has extended the frontier of AEC, thus attracting many researchers’ interest in personalized acoustic echo cancellation (PAEC). Meanwhile, task-decoupling strategies are widely adopted in speech enhancement. To further explore the task-decoupling approach, we propose to use a two-stage task-decoupling post-filter (TDPF) in PAEC. Furthermore, a multi-scale local-global speaker representation is applied to improve speaker extraction in PAEC. Experimental results indicate that the task-decoupling model can yield better performance than a single joint network. The optimal approach is to decouple the echo cancellation from noise and interference speech suppression. Based on the task-decoupling sequence, optimal training strategies for the two-stage model are explored afterwards.
FeatSense – A Feature-Based Registration Algorithm with GPU-Accelerated TSDF-Mapping Backend for NVIDIA Jetson Boards

This paper presents FeatSense, a feature-based GPU-accelerated SLAM system for high-resolution LiDARs, combined with a map generation algorithm for real-time gener-ation of large Truncated Signed Distance Fields (TSDFs) on em-bedded hardware. FeatSense uses LiDAR point cloud features for odometry estimation and point cloud registration. The registered point clouds are integrated into a global Truncated Signed Distance Field (TSDF) representation. FeatSense is intended to run on embedded systems with integrated GPU-accelerator like NVIDIA Jetson boards. In this paper, we present a real-time capable TSDF-SLAM system specially tailored for closely coupled CPU/GPU systems. The implementation is evaluated in various structured and unstructured environments and benchmarked against existing reference datasets. The main contribution of this paper is the ability to register up to 128 scan lines of an Ouster OS1-128 LiDAR at 10Hz on a NVIDIA AGX Xavier while achieving a TSDF map generation speedup by a factor of 100 compared to previous work on the same power budget.
Domain Knowledge Graph Construction Via A Simple Checker

With the availability of large language models, there is a growing interest for semiconductor chip design companies to leverage the technologies. For those companies, deployment of a new methodology must include two important considerations: confidentiality and scalability. In this context, this work tackles the problem of knowledge graph construction from hardware-design domain texts. We propose an oracle-checker scheme to leverage the power of GPT3.5 and demonstrate that the essence of the problem is in distillation of domain expert's background knowledge. Using RISC-V unprivileged ISA specification as an example, we explain key ideas and discuss practicality of our proposed oracle-checker approach.
Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection

Constant evolution and the emergence of new cyberattacks require the development of advanced techniques for defense. This paper aims to measure the impact of a supervised filter (classifier) in network anomaly detection. We perform our experiments by employing a hybrid anomaly detection approach in network flow data. For this purpose, we extended a state-of-the-art autoencoder-based anomaly detection method by prepending a binary classifier acting as a prefilter for the anomaly detector. The method was evaluated on the publicly available real-world dataset UGR'16. Our empirical results indicate that the hybrid approach does offer a higher detection rate of known attacks than a standalone anomaly detector while still retaining the ability to detect zero-day attacks. Employing a supervised binary prefilter has increased the AUC metric by over 11%, detecting 30% more attacks while keeping the number of false positives approximately the same.
QR-Tag: Angular Measurement and Tracking with a QR-Design Marker

Directional information measurement has many applications in domains such as robotics, virtual and augmented reality, and industrial computer vision. Conventional methods either require pre-calibration or necessitate controlled environments. The state-of-the-art MoireTag approach exploits the Moire effect and QR-design to continuously track the angular shift precisely. However, it is still not a fully QR code design. To overcome the above challenges, we propose a novel snapshot method for discrete angular measurement and tracking with scannable QR-design patterns that are generated by binary structures printed on both sides of a glass plate. The QR codes, resulting from the parallax effect due to the geometry alignment between two layers, can be readily measured as angular information using a phone camera. The simulation results show that the proposed non-contact object tracking framework is computationally efficient with high accuracy.
Computation-Limited Signals: A Channel Capacity Regime Constrained by Computational Complexity

In this letter, we introduce the computational-limited (comp-limited) signals, a communication capacity regime in which the signal time computational complexity overhead is the key constraint -- rather than power or bandwidth -- to the overall communication capacity. We present the Spectro-Computational (SC) analysis, a novel mathematical framework that enhances classic concepts of information theory -- such as throughput, spectral efficiency and capacity -- to account for the signal processing computational complexity overhead. We consider a specific Shannon regime under which capacity is expected to get arbitrarily large as channel resources grow. Under that regime, we identify the conditions under which the time complexity overhead causes capacity to decrease rather than increasing, thereby creating the case for the comp-limited regime. We also provide examples of the SC analysis and show the OFDM waveform is comp-limited unless the lower-bound computational complexity of the $N$-point DFT problem verifies as $\Omega(N)$, which remains an open challenge.
Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.
On growth error bound and Kurdyka-{\L}ojasiewicz condition

In this paper, by using the proximal point algorithm, we provide a more accessible and elementary proof of the fact that Kurdyka-{\L}ojasiewicz conditions imply the growth error bounds for convex functions which has been established before via a subgradient flow. We further extend the result for nonconvex functions. Finally we show that every definable function in an o-minimal structure must satisfy a growth error bound.
GSLB: The Graph Structure Learning Benchmark

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://github.com/GSL-Benchmark/GSLB.
Selective Demonstrations for Cross-domain Text-to-SQL

Large language models (LLMs) with in-context learning have demonstrated impressive generalization capabilities in the cross-domain text-to-SQL task, without the use of in-domain annotations. However, incorporating in-domain demonstration examples has been found to greatly enhance LLMs' performance. In this paper, we delve into the key factors within in-domain examples that contribute to the improvement and explore whether we can harness these benefits without relying on in-domain annotations. Based on our findings, we propose a demonstration selection framework ODIS which utilizes both out-of-domain examples and synthetically generated in-domain examples to construct demonstrations. By retrieving demonstrations from hybrid sources, ODIS leverages the advantages of both, showcasing its effectiveness compared to baseline methods that rely on a single data source. Furthermore, ODIS outperforms state-of-the-art approaches on two cross-domain text-to-SQL datasets, with improvements of 1.1 and 11.8 points in execution accuracy, respectively.
Scaling limit of an equilibrium surface under the Random Average Process

We consider the equilibrium surface of the Random Average Process started from an inclined plane, as seen from the height of the origin, obtained in [Ferrari&Fontes, 1998], where its fluctuations were shown to be of order of the square root of the distance to the origin in one dimension, and the square root of the log of that distance in two dimensions (and constant in higher dimensions). Remarkably, even if not pointed out explicitly in [Ferrari&Fontes, 1998], the correlation structure of those fluctuations is given in terms of the Green's function of a certain random walk, and thus corresponds to those of Discrete Gaussian Free Fields. In the present paper we obtain the scaling limit of those fluctuations in one and two dimensions, in terms of Gaussian processes, in the sense of finite dimensional distributions. In one dimension, the limit is given by Brownian Motion; in two dimensions, we get a process with a discontinuous covariance function.
Automatic Aspect Extraction from Scientific Texts

Being able to extract from scientific papers their main points, key insights, and other important information, referred to here as aspects, might facilitate the process of conducting a scientific literature review. Therefore, the aim of our research is to create a tool for automatic aspect extraction from Russian-language scientific texts of any domain. In this paper, we present a cross-domain dataset of scientific texts in Russian, annotated with such aspects as Task, Contribution, Method, and Conclusion, as well as a baseline algorithm for aspect extraction, based on the multilingual BERT model fine-tuned on our data. We show that there are some differences in aspect representation in different domains, but even though our model was trained on a limited number of scientific domains, it is still able to generalize to new domains, as was proved by cross-domain experiments. The code and the dataset are available at \url{https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts}.
What modes of escape are suitable for evacuees from tsunami disasters? - various levels of infrastructure and population

The Great East Japan Earthquake of March 11th, 2011 was distinct from the 1995 Kobe earthquake, especially in the causes of deaths. The former was characterized mainly by deaths due to drowning from the tsunami, while the latter was characterized by deaths due to structures that collapsed in the earthquake. The tsunamis also required evacuees to escape distances of over 15km, and the Evacuation Action Plan (EAP) had recommended walking mode as the only official mode of escape. This research focuses on the Tohoku Area from a macroscopic perspective. Data were used from a survey by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) with 10,601 evacuees at shelters. The Evacuation Assessment Support System (EASY) was formulated using the following parameters: distance to shelters, population density and evacuation speed. Residents living in Fukushima Prefecture did not have the advantage of escaping by walking. Iwate Prefecture had the advantage of car mode, but the EAP does not encourage this.
How Nested Bars Enhance, Modulate, and Are Destroyed by Gas Inflows

Gas flows in the presence of two independently rotating nested bars remain not fully understood but are likely to play an important role in fueling the central black hole. We use high-resolution hydrodynamical simulations with detailed models of subgrid physics to study this problem. Our results show that the inner bar in double-barred galaxies can help drive gas flow from the nuclear ring to the center. In contrast, gas inflow usually stalls at the nuclear ring in single-barred galaxies. The inner bar causes a quasiperiodic inflow with a frequency determined by the difference between the two bar pattern speeds. We find that the star formation rate is higher in the model with two bars than in that with one bar. The inner bar in our model gradually weakens and dissolves due to gas inflow over a few billion years. Star formation produces metal-rich/α-poor stars, which slows the weakening of the inner bar but does not halt its eventual decay. We also present a qualitative comparison of the gas morphology and kinematics in our simulations with those of observed double-barred galaxies.
Elastic turbulence in planar channel flows -- Turbulence with no drag and enhanced mixing

The efficient mixing of different species is a withstanding problem in micro-scale systems, e.g., drug delivery and disease diagnosis devices. There, classic inertial turbulence is hindered by the small geometries and flow rates, and only viscous diffusion remains active. A chaotic fluid motion can be recovered by conferring upon the viscous fluid elastic properties (e.g., through the addition of small amounts of polymers). Elastic turbulence (ET) often occurs in curvilinear channels, while less certain is its existence in straight channels. Here, we show with fully resolved 3D numerical simulations that ET in straight channels can be sustained and that, although it does not increase drag, it provides an additional mixing mechanism on top of the viscous diffusion, which can be measured in experiments. Spanning the Reynolds number across three orders of magnitude, we show that once inertia becomes sub-dominant, all the flows are governed by elasticity and characterised by similar dynamical properties. We therefore propose a unified view of ET and the commonly dubbed elasto-inertial turbulence. Exploiting this novel state for mixing enhancement in microfluidic devices can make them simpler and more efficient.
Tectonique et géodynamique actuelle de l'arc alpin - Approche sismotectonique et modélisation numérique

Le regime tectonique actuel de la chaine des Alpes centrales/occidentales est analyse grâce a une compilation de 389 mecanismes au foyer. Cette synthese met en evidence l'importance des phenomenes extensifs, caracterisant l'ensemble de la 'haute' chaine. Les directions d'extension sont orientees perpendiculairement a l'orogene, tandis qu'en domaine externe, les directions de compression sont orientees en eventail perpendiculaire a l'arc alpin, dans un contexte globalement decrochant (derivant localement vers l'extension ou la compression). Un modele de re-equilibrage gravitaire est avance et teste par modelisation numerique en elements finis, d'une part en 2.5D, d'autre part en 2D (coupe ECORS). Ces etudes de modelisation montrent l'importance des phenomenes d'etalement gravitaire dans la chaine, aboutissant a un regime de deformation/contraintes proche de celui observe par les outils sismotectoniques et permet de proposer un regime tectonique actuel post-collisionnel au niveau des Alpes centrales/occidentales. Des etudes de quantifications de la deformation montrent des taux de deformation faibles, de l'ordre de 1 a 3 mm/a pour les calculs sur base GPS et de 0.1 a 0.4 mm/a pour les calculs sur une base sismologique (la difference entre les deux quantifications posant la question des deformations asismiques dans la chaine). Les taux de rotation montrent des rotations faibles, de 2 a 2.5 degres/Ma, probablement associe a la rotation antihoraire de la plaque Apulienne qui entrainerait la rotation antihoraire du coeur de l'arc, tandis que des rotations horaires observees en domaine externe resulteraient de la rotation de blocs situes le long des grands decrochements dextres de la bordure occidentale de l'orogene.
Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features

Nowadays, multimedia forensics faces unprecedented challenges due to the rapid advancement of multimedia generation technology thereby making Image Manipulation Localization (IML) crucial in the pursuit of truth. The key to IML lies in revealing the artifacts or inconsistencies between the tampered and authentic areas, which are evident under pixel-level features. Consequently, existing studies treat IML as a low-level vision task, focusing on allocating tampered masks by crafting pixel-level features such as image RGB noises, edge signals, or high-frequency features. However, in practice, tampering commonly occurs at the object level, and different classes of objects have varying likelihoods of becoming targets of tampering. Therefore, object semantics are also vital in identifying the tampered areas in addition to pixel-level features. This necessitates IML models to carry out a semantic understanding of the entire image. In this paper, we reformulate the IML task as a high-level vision task that greatly benefits from low-level features. Based on such an interpretation, we propose a method to enhance the Masked Autoencoder (MAE) by incorporating high-resolution inputs and a perceptual loss supervision module, which is termed Perceptual MAE (PMAE). While MAE has demonstrated an impressive understanding of object semantics, PMAE can also compensate for low-level semantics with our proposed enhancements. Evidenced by extensive experiments, this paradigm effectively unites the low-level and high-level features of the IML task and outperforms state-of-the-art tampering localization methods on all five publicly available datasets.