Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Noise and Efficiency Issues in Graph-Based Machine Learning Models From the Perspective of Adversarial Attack

Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are invariably affected by the plethora of redundant and erroneous edges present within the constructed graphs. In this paper, we propose treating these noisy edges as adversarial attack and use a spectral adversarial robustness evaluation method to diminish the impact of noisy edges on the performance of graph algorithms. Our method identifies those points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms. Our experiments with spectral clustering, one of the most representative and widely utilized graph algorithms, reveal that our methodology not only substantially elevates the precision of the algorithm but also greatly accelerates its computational efficiency by leveraging only a select number of robust data points.
High-Frequency Ultrasound Imaging of the Intestine in Normal Subjects and Patients with Intestinal Parasites

High-frequency ultrasound imaging was used to evaluate the intestinal walls of the duodenum and colon in patients with intestinal parasitic infections. Ultrasound images were obtained from 100 consecutive patients with symptomatic intestinal parasitic diseases and 40 healthy controls. High-frequency annular array transducer of 7.5 MHz was used to obtain B-mode ultrasound gray-scale and color images of the duodenum and colon with and without water contrast. The diagnosis of parasitic infections was based on clinical presentation, serial stool microscopy, and finding of parasites in duodenal aspirates. We demonstrated normal duodenum and colon echoanatomy in control subjects. In patients with giardiasis, the lesions of the duodenum and colon were associated with increased dimensions and wall thickness compared to healthy controls (p < 0.05). The ultrasound features of giardial lesions were characterized by increased wall echogenicity, flattening or loss of duodenal folds, and/or colonic haustration, hyperechoic floating foci demonstrating chaotic motility, increased perilesional tissue echogenicity, and altered colonic peristalsis. In amebic lesions there were hyperechoic floating foci with bulk motility. There is loss of wall thickness at amebic ulcer sites or wall thickening at amebic granuloma. Helminths were visualized as large hyperechoic linear or curvilinear foci with serpentine or jolting motility. In conclusion, high-frequency B-mode ultrasound imaging with water contrast demonstrated details of duodenal and colonic echoanatomy in normal subjects and patients with giardiasis.
Where's the "up"?! A Comprehensive (bottom-up) Study on the Security of Arm Cortex-M Systems

Arm Cortex-M processors are the most widely used 32-bit microcontrollers among embedded and Internetof-Things devices. Despite the widespread usage, there has been little effort in summarizing their hardware security features, characterizing the limitations and vulnerabilities of their hardware and software stack, and systematizing the research on securing these systems. The goals and contributions of this paper are multi-fold. First, we analyze the hardware security limitations and issues of Cortex-M systems. Second, we conducted a deep study of the software stack designed for Cortex-M and revealed its limitations, which is accompanied by an empirical analysis of 1,797 real-world firmware from seven hardware vendors. Third, we categorize the reported bugs in Cortex-M software systems. Finally, we systematize the efforts that aim at securing Cortex-M systems and evaluate them in terms of the protections they offer, run-time performance, required hardware features, etc. Based on the insights, we develop a set of recommendations for the research community and MCU software developers.
Optimizing the method of images for regularized Stokeslets using theory and experiments of spheres moving near boundaries

The general system of images for regularized Stokeslets (GSIRS) developed by Cortez and Varela (2015) is used extensively to model Stokes flow phenomena such as microorganisms swimming near a boundary. Our collaborative team uses dynamically similar scaled macroscopic experiments to test theories for forces and torques on spheres moving near a boundary and use these data and the method of regularized Stokeslets (MRS) created by Cortez et al. (2015) to calibrate the GSIRS. We find excellent agreement between theory and experiments, which provides the first experimental validation of such series solutions for spheres moving near an infinite plane boundary. We test two surface discretization methods commonly used in the literature: the six-patch method and the spherical centroidal Voronoi tessellation (SCVT) method. Our data show that the SCVT method provides the most accurate results when the motional symmetry is broken by the presence of a boundary. We use theory and the MRS to find optimal values for the regularization parameter in free space and show that the optimal regularization parameter values can be fit with simple formulae when using the SCVT method, so that other researchers have an easy reference. We also present a regularization function with higher order accuracy when compared with the regularization function previously introduced by Cortez et al. (2005). The simulated force and torque values compare very well with experiments and theory for a wide range of boundary distances. But, we find the simulations lose accuracy when the gap between the edge of the sphere and the wall is smaller than the average distance between grid points using the SCVT method. Our computational parameters and MATLAB and PYTHON implementations of the Lee and Leal (1980) theory provide researchers with important resources to optimize the numerical simulations of spheres moving near boundaries.
Validation of symmetry-induced high moment velocity and temperature scaling laws in a turbulent channel flow.

The symmetry-based turbulence theory has been used to derive new scaling laws for the streamwise velocity and temperature moments of arbitrary order. For this, it has been applied to an incompressible turbulent channel flow driven by a pressure gradient with a passive scalar equation coupled in. To derive the scaling laws, symmetries of the classical Navier-Stokes and the thermal energy equations have been used together with statistical symmetries, i.e., the statistical scaling and translation symmetries of the multipoint moment equations. Specifically, the multipoint moments are built on the instantaneous velocity and temperature fields other than in the classical approach, where moments are based on the fluctuations of these fields. With this instantaneous approach, a linear system of multipoint correlation equations has been obtained, which greatly simplifies the symmetry analysis. The scaling laws have been derived in the limit of zero viscosity and heat conduction, i.e., Re_{τ}→∞ and Pr>1, and they apply in the center of the channel, i.e., they represent a generalization of the deficit law, thus extending the work of Oberlack et al. [Phys. Rev. Lett. 128, 024502 (2022)0031-900710.1103/PhysRevLett.128. 024502]. The scaling laws are all power laws, with the exponent of the high moments all depending exclusively on those of the first and second moments. To validate the new scaling laws, the data from a large number of direct numerical simulations (DNS) for different Reynolds and Prandtl numbers have been used. The results show a very high accuracy of the scaling laws to represent the DNS data. The statistical scaling symmetry of the multipoint moment equations, which characterizes intermittency, has been the key to the new results since it generates a constant in the exponent of the final scaling law. Most important, since this constant is independent of the order of the moments, it clearly indicates anomalous scaling.
Chemical contrast in STM imaging of transition metal aluminides

The present manuscript reviews recent scanning tunnelling microscopy (STM) studies of transition metal (TM) aluminide surfaces. It provides a general perspective on the contrast between Al atoms and TM atoms in STM imaging. A general trend is the much stronger bias dependence of TM atoms, or TM-rich regions of the surface. This dependence can be attenuated by the local chemical arrangements and environments. Al atoms can show a stronger bias dependence when their chemical environment, such as their immediate subsurface, is populated with TM. All this is well explained in light of combined results of STM and both theoretical and experimental electronic and crystallographic structure determinations. Since STM probes the Fermi surface, the electronic structure in the vicinity of the Fermi level (EF) is essential for understanding contrast and bias dependence. Hence, partial density of states provides information about the TM d band position and width, s–p–d hybridization or interactions, or charge transfer between constituent elements. In addition, recent developments in STM image simulations are very interesting for elucidating chemical contrast at Al–TM alloy surfaces, and allow direct atomic identification, when the surface does not show too much disorder. Overall, we show that chemically-specific imaging is often possible
Elementary first-order model checking for sparse graphs

It is known that for subgraph-closed graph classes the first-order model checking problem is fixed-parameter tractable if and only if the class is nowhere dense [Grohe, Kreutzer, Siebertz, STOC 2014]. However, the dependency on the formula size is non-elementary, and in fact, this is unavoidable even for the class of all trees [Frick and Grohe, LICS 2002]. On the other hand, it is known that the dependency is elementary for classes of bounded degree [Frick and Grohe, LICS 2002] as well as for classes of bounded pathwidth [Lampis, ICALP 2023]. In this paper we generalise these results and almost completely characterise subgraph-closed graph classes for which the model checking problem is fixed-parameter tractable with an elementary dependency on the formula size. Those are the graph classes for which there exists a number $d$ such that for every $r$, some tree of depth $d$ and size bounded by an elementary function of $r$ is avoided as an $({\leq} r)$-subdivision in all graphs in the class. In particular, this implies that if the class in question excludes a fixed tree as a topological minor, then first-order model checking for graphs in the class is fixed-parameter tractable with an elementary dependency on the formula size.
Reducing Coverage Depth in DNA Storage: A Combinatorial Perspective on Random Access Efficiency

We investigate the fundamental limits of the recently proposed random access coverage depth problem for DNA data storage. Under this paradigm, it is assumed that the user information consists of $k$ information strands, which are encoded into $n$ strands via some generator matrix $G$. In the sequencing process, the strands are read uniformly at random, since each strand is available in a large number of copies. In this context, the random access coverage depth problem refers to the expected number of reads (i.e., sequenced strands) until it is possible to decode a specific information strand, which is requested by the user. The goal is to minimize the maximum expectation over all possible requested information strands, and this value is denoted by $T_{\max}(G)$. This paper introduces new techniques to investigate the random access coverage depth problem, which capture its combinatorial nature. We establish two general formulas to find $T_{max}(G)$ for arbitrary matrices. We introduce the concept of recovery balanced codes and combine all these results and notions to compute $T_{\max}(G)$ for MDS, simplex, and Hamming codes. We also study the performance of modified systematic MDS matrices and our results show that the best results for $T_{\max}(G)$ are achieved with a specific mix of encoded strands and replication of the information strands.
ARCNet: An Asymmetric Residual Wavelet Column Correction Network for Infrared Image Destriping

Infrared image destriping seeks to restore high-quality content from degraded images. Recent works mainly address this task by leveraging prior knowledge to separate stripe noise from the degraded image. However, constructing a robust decoupling model for that purpose remains challenging, especially when significant similarities exist between the stripe noise and vertical background structure. Addressing that, we introduce Asymmetric Residual wavelet Column correction Network (ARCNet) for image destriping, aiming to consistently preserve spatially precise high-resolution representations. Our neural model leverages a novel downsampler, residual haar discrete wavelet transform (RHDWT), stripe directional prior knowledge and data-driven learning to induce a model with enriched feature representation of stripe noise and background. In our technique, the inverse wavelet transform is replaced by transposed convolution for feature upsampling, which can suppress noise crosstalk and encourage the network to focus on robust image reconstruction. After each sampling, a proposed column non-uniformity correction module (CNCM) is leveraged by our method to enhance column uniformity, spatial correlation, and global self-dependence between each layer component. CNCM can establish structural characteristics of stripe noise and utilize contextual information at long-range dependencies to distinguish stripes with varying intensities and distributions. Extensive experiments on synthetic data, real data, and infrared small target detection tasks show that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively by a considerable margin. Our code will be made publicly available at \url{https://github.com/xdFai}.
Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study

Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to address critical challenges of blockchain technology, including scalability, security, privacy, and interoperability. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains. Then, we discuss emerging solutions that demonstrate the effectiveness of GAI in addressing various challenges of blockchain, such as detecting unknown blockchain attacks and smart contract vulnerabilities, designing key secret sharing schemes, and enhancing privacy. Moreover, we present a case study to demonstrate that GAI, specifically the generative diffusion model, can be employed to optimize blockchain network performance metrics. Experimental results clearly show that, compared to a baseline traditional AI approach, the proposed generative diffusion model approach can converge faster, achieve higher rewards, and significantly improve the throughput and latency of the blockchain network. Additionally, we highlight future research directions for GAI in blockchain applications, including personalized GAI-enabled blockchains, GAI-blockchain synergy, and privacy and security considerations within blockchain ecosystems.
Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset

Electronic health record (EHR) is more and more popular, and it comes with applying machine learning solutions to resolve various problems in the domain. This growing research area also raises the need for EHRs accessibility. Medical Information Mart for Intensive Care (MIMIC) dataset is a popular, public, and free EHR dataset in a raw format that has been used in numerous studies. However, despite of its popularity, it is lacking benchmarking work, especially with recent state of the art works in the field of deep learning with time-series tabular data. The aim of this work is to fill this lack by providing a benchmark for latest version of MIMIC dataset, MIMIC-IV. We also give a detailed literature survey about studies that has been already done for MIIMIC-III.
Boundary terms, branes and AdS/BCFT in first-order gravity

We provide an account of the issue of Gibbons-Hawking-York-like boundary terms for a gravity theory defined on a Riemman-Cartan spacetime. We further discuss different criteria for introducing boundary terms in some familiar first-order gravity theories with both on-shell vanishing and non-vanishing torsion, along with considerations regarding the thermodynamics of black holes and profiles of the End-of-the-World branes. Our analysis confirms the expected geodesic profile of the End-of-the-World brane in the BF formulation of Jackiw-Teitelboim gravity. Finally, we present the first realisation of the AdS/BCFT duality for spacetime with torsion.
Revising clustering and small-worldness in brain networks

As more connectome data become available, the question of how to best analyse the structure of biological neural networks becomes increasingly pertinent. In brain networks, knowing that two areas are connected is often not sufficient, as the directionality and weight of the connection affect the dynamics in crucial ways. Still, the methods commonly used to estimate network properties, such as clustering and small-worldness, usually disregard features encoded in the directionality and strength of network connections. To address this issue, we propose using fully-weighted and directed clustering measures that provide higher sensitivity to non-random structural features. Using artificial networks, we demonstrate the problems with methods routinely used in the field and how fully-weighted and directed methods can alleviate them. Specifically, we highlight their robustness to noise and their ability to address thresholding issues, particularly in inferred networks. We further apply our method to the connectomes of different species and uncover regularities and correlations between neuronal structures and functions that cannot be detected with traditional clustering metrics. Finally, we extend the notion of small-worldness in brain networks to account for weights and directionality and show that some connectomes can no longer be considered ``small-world''. Overall, our study makes a case for a combined use of fully-weighted and directed measures to deal with the variability of brain networks and suggests the presence of complex patterns in neural connectivity that can only be revealed using such methods.
The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games

Solving feedback Stackelberg games with nonlinear dynamics and coupled constraints, a common scenario in practice, presents significant challenges. This work introduces an efficient method for computing local feedback Stackelberg policies in multi-player general-sum dynamic games, with continuous state and action spaces. Different from existing (approximate) dynamic programming solutions that are primarily designed for unconstrained problems, our approach involves reformulating a feedback Stackelberg dynamic game into a sequence of nested optimization problems, enabling the derivation of Karush-Kuhn-Tucker (KKT) conditions and the establishment of a second-order sufficient condition for local feedback Stackelberg policies. We propose a Newton-style primal-dual interior point method for solving constrained linear quadratic (LQ) feedback Stackelberg games, offering provable convergence guarantees. Our method is further extended to compute local feedback Stackelberg policies for more general nonlinear games by iteratively approximating them using LQ games, ensuring that their KKT conditions are locally aligned with those of the original nonlinear games. We prove the exponential convergence of our algorithm in constrained nonlinear games. In a feedback Stackelberg game with nonlinear dynamics and (nonconvex) coupled costs and constraints, our experimental results reveal the algorithm's ability to handle infeasible initial conditions and achieve exponential convergence towards an approximate local feedback Stackelberg equilibrium.
The rigidity of eigenvalues on K\"ahler manifolds with positive Ricci lower bound

In this work, optimal rigidity results for eigenvalues on K\"ahler manifolds with positive Ricci lower bound are established. More precisely, for those K\"ahler manifolds whose first eigenvalue agrees with the Ricci lower bound, we show that the complex projective space is the only one with the largest multiplicity of the first eigenvalue. Moreover, there is a specific gap between the largest and the second largest multiplicity. In the K\"ahler--Einstein case, almost rigidity results for eigenvalues are also obtained.
GenPluSSS: A Genetic Algorithm Based Plugin for Measured Subsurface Scattering Representation

This paper presents a plugin that adds a representation of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on a combination of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering method (GenSSS). The proposed plugin has been implemented using Mitsuba renderer, which is an open source rendering software. The proposed plugin has been validated on measured subsurface scattering data. It's shown that the proposed plugin visualizes homogeneous and heterogeneous subsurface scattering effects, accurately, compactly and computationally efficiently.
numaPTE: Managing Page-Tables and TLBs on NUMA Systems

Memory management operations that modify page-tables, typically performed during memory allocation/deallocation, are infamous for their poor performance in highly threaded applications, largely due to process-wide TLB shootdowns that the OS must issue due to the lack of hardware support for TLB coherence. We study these operations in NUMA settings, where we observe up to 40x overhead for basic operations such as munmap or mprotect. The overhead further increases if page-table replication is used, where complete coherent copies of the page-tables are maintained across all NUMA nodes. While eager system-wide replication is extremely effective at localizing page-table reads during address translation, we find that it creates additional penalties upon any page-table changes due to the need to maintain all replicas coherent. In this paper, we propose a novel page-table management mechanism, called numaPTE, to enable transparent, on-demand, and partial page-table replication across NUMA nodes in order to perform address translation locally, while avoiding the overheads and scalability issues of system-wide full page-table replication. We then show that numaPTE's precise knowledge of page-table sharers can be leveraged to significantly reduce the number of TLB shootdowns issued upon any memory-management operation. As a result, numaPTE not only avoids replication-related slowdowns, but also provides significant speedup over the baseline on memory allocation/deallocation and access control operations. We implement numaPTEin Linux on x86_64, evaluate it on 4- and 8-socket systems, and show that numaPTE achieves the full benefits of eager page-table replication on a wide range of applications, while also achieving a 12% and 36% runtime improvement on Webserver and Memcached respectively due to a significant reduction in TLB shootdowns.