Miracle-dz/UniFAR-FTUs huggingface dataset:
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines

Recent breakthroughs in geometric deep learning (DL) and the availability of large computer-aided design (CAD) datasets have advanced the research on learning CAD modeling processes and relating them to real objects. In this context, 3D reverse engineering of CAD models from 3D scans is considered to be one of the most sought-after goals for the CAD industry. However, recent efforts continue to make multiple simplifying assumptions and applications in real-world settings remain limited. The SHARP Challenge 2023 aims at pushing the research a step closer to the real-world scenario of CAD reverse engineering through dedicated datasets and tracks. In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance track solutions. All proposed datasets1 along with useful routines and the evaluation metrics2 are publicly available.
Energy distribution and substructure formation in astrophysical MHD simulations

During substructure formation in magnetized astrophysical plasma, dissipation of magnetic energy facilitated by magnetic reconnection affects the system dynamics by heating and accelerating the ejected plasmoids. Numerical simulations are a crucial tool for investigating such systems. In astrophysical simulations, the energy dissipation, reconnection rate and substructure formation critically depend on the onset of reconnection of numerical or physical origin. In this paper, we hope to assess the reliability of the state-of-the-art numerical codes, PLUTO and KORAL by quantifying and discussing the impact of dimensionality, resolution, and code accuracy on magnetic energy dissipation, reconnection rate, and substructure formation. We quantitatively compare results obtained with relativistic and non-relativistic, resistive and non-resistive, as well as two- and three-dimensional setups performing the Orszag-Tang test problem. We find sufficient resolution in each model, for which numerical error is negligible and the resolution does not significantly affect the magnetic energy dissipation and reconnection rate. The non-relativistic simulations show that at sufficient resolution, magnetic and kinetic energies convert to internal energy and heat the plasma. In the relativistic system, energy components undergo mutual conversion during the simulation time, which leads to a substantial increase in magnetic energy at 20% and 90% of the total simulation time of 10 light-crossing times—the magnetic field is amplified by a factor of five due to relativistic shocks. We also show that the reconnection rate in all our simulations is higher than 0.1, indicating plasmoid-mediated regime. It is shown that in KORAL simulations more substructures are captured than in PLUTO simulations.
Sorting Signed Permutations by Reversals in Nearly-Linear Time

Given a signed permutation on $n$ elements, we need to sort it with the fewest reversals. This is a fundamental algorithmic problem motivated by applications in comparative genomics, as it allows to accurately model rearrangements in small genomes. The first polynomial-time algorithm was given in the foundational work of Hannenhalli and Pevzner [J. ACM'99]. Their approach was later streamlined and simplified by Kaplan, Shamir, and Tarjan [SIAM J. Comput. '99] and their framework has eventually led to an algorithm that works in $\mathcal{O}(n^{3/2}\sqrt{\log n})$ time given by Tannier, Bergeron, and Sagot [Discr. Appl. Math.'07]. However, the challenge of finding a nearly-linear time algorithm remained unresolved. In this paper, we show how to leverage the results on dynamic graph connectivity to obtain a surprisingly simple $\mathcal{O}(n \log^2 n / \log \log n)$ time algorithm for this problem.
Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction

Diffusion model has emerged as a potential tool to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Nevertheless, these prevailing diffusion models predominantly focus on the sinogram or image domains, which can lead to instability during model training, potentially culminating in convergence towards local minimal solutions. The wavelet transform serves to disentangle image contents and features into distinct frequency-component bands at varying scales, adeptly capturing diverse directional structures. Employing the wavelet transform as a guiding sparsity prior significantly enhances the robustness of diffusion models. In this study, we present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction. Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with an optimization procedure. Furthermore, we perform the low-frequency and high-frequency generative models on wavelet's decomposed components rather than the original sinogram, ensuring the stability of model training. Our method is rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform. The experimental results demonstrated that the proposed method outperformed existing state-of-the-art methods both quantitatively and qualitatively.
Minimum Width for Deep, Narrow MLP: A Diffeomorphism and the Whitney Embedding Theorem Approach

Recently, there has been significant attention on determining the minimum width for the universal approximation property of deep, narrow MLPs. Among these challenges, approximating a continuous function under the uniform norm is important and challenging, with the gap between its lower and upper bound being hard to narrow. In this regard, we propose a novel upper bound for the minimum width, given by $\operatorname{max}(2d_x+1, d_y) + \alpha(\sigma)$, to achieve uniform approximation in deep narrow MLPs, where $0\leq \alpha(\sigma)\leq 2$ represents the constant depending on the activation function. We demonstrate this bound through two key proofs. First, we establish that deep, narrow MLPs with little additional width can approximate diffeomorphisms. Secondly, we utilize the Whitney embedding theorem to show that any continuous function can be approximated by embeddings, further decomposed into linear transformations and diffeomorphisms.
Breaking the Interference and Fading Gridlock in Backscatter Communications: State-of-the-Art, Design Challenges, and Future Directions

As the Internet of Things (IoT) advances by leaps and bounds, a multitude of devices are becoming interconnected, marking the onset of an era where all things are connected. While this growth opens up opportunities for novel products and applications, it also leads to increased energy demand and battery reliance for IoT devices, creating a significant bottleneck that hinders sustainable progress. At this juncture, backscatter communication (BackCom), as a low-power and passive communication method, emerges as one of the promising solutions to this energy impasse by reducing the manufacturing costs and energy consumption of IoT devices. However, BackCom systems face challenges such as complex interference environments, including direct link interference (DLI) and mutual interference (MI) between tags, which can severely disrupt the efficiency of BackCom networks. Moreover, double-path fading is another major issue that leads to the degraded system performance. To fully unleash the potential of BackComs, the purpose of this paper is to furnish a comprehensive review of existing solutions with a focus on combatting these specific interference challenges and overcoming dual-path fading, offering an insightful analysis and comparison of various strategies for effectively mitigating these issues. Specifically, we begin by introducing the preliminaries for the BackCom, including its history, operating mechanisms, main architectures, etc, providing a foundational understanding of the field. Then, we delve into fundamental issues related to BackCom systems, such as solutions for the DLI, the MI, and the double-path fading. This paper thoroughly provides state-of-the-art advances for each case, particularly highlighting how the latest innovations in theoretical approaches and system design can strategically address these challenges.
Estimating the Fraction of First-Year Hemodialysis Deaths Attributable to Potentially Modi ﬁ able Risk Factors: Results from the DOPPS

Purpose: Mortality among ﬁ rst-year hemodialysis (HD) patients remains unacceptably high. To address this problem, we estimate the proportions of early HD deaths that are potentially preventable by modifying known risk factors. Methods: We included 15,891 HD patients (within 60 days of starting HD) from 21 countries in the Dialysis Outcomes and Practice Patterns Study (1996 – 2015), a prospective cohort study. Using Cox regression adjusted for potential confounders, we estimated the fraction of ﬁ rst-year deaths attributable to one or more of twelve modi ﬁ able risk factors (the population attributable fraction, AF) identi ﬁ ed from the published literature by comparing predicted survival based on risk factors observed vs counterfactually set to reference levels. Results: The highest AFs were for catheter use (22%), albumin <3.5 g/dL (19%), and creatinine <6 mg/dL (12%). AFs were 5%-9% for no pre-HD nephrology care, no residual urine volume, systolic blood pressure <130 or ≥ 160 mm Hg, phosphorus <3.5 or ≥ 5.5 mg/dL, hemoglobin <10 or ≥ 12 g/dL, and white blood cell count >10,000/ μ L. AFs for ferritin, calcium, and PTH were <3%. Overall, 65% (95% CI: 59%-71%) of deaths were attributable to these 12 risk factors. Additionally, the AF for C-reactive protein >10 mg/L was 21% in facilities where it was routinely measured. Conclusion: A substantial proportion of ﬁ rst-year HD deaths could be prevented by successfully modifying a few risk factors. Highest priorities should be decreasing catheter use and limiting malnutrition/in ﬂ ammation whenever possible. ﬁ risk results the DOPPS [abstract]. Am Soc
Adaptive Attack Detection in Text Classification: Leveraging Space Exploration Features for Text Sentiment Classification

Adversarial example detection plays a vital role in adaptive cyber defense, especially in the face of rapidly evolving attacks. In adaptive cyber defense, the nature and characteristics of attacks continuously change, making it crucial to have robust mechanisms in place to detect and counter these threats effectively. By incorporating adversarial example detection techniques, adaptive cyber defense systems can enhance their ability to identify and mitigate attacks that attempt to exploit vulnerabilities in machine learning models or other systems. Adversarial examples are inputs that are crafted by applying intentional perturbations to natural inputs that result in incorrect classification. In this paper, we propose a novel approach that leverages the power of BERT (Bidirectional Encoder Representations from Transformers) and introduces the concept of Space Exploration Features. We utilize the feature vectors obtained from the BERT model's output to capture a new representation of feature space to improve the density estimation method.
Independent set in k-Claw-Free Graphs: Conditional χ-boundedness and the Power of LP/SDP Relaxations

This paper studies $k$-claw-free graphs, exploring the connection between an extremal combinatorics question and the power of a convex program in approximating the maximum-weight independent set in this graph class. For the extremal question, we consider the notion, that we call \textit{conditional $\chi$-boundedness} of a graph: Given a graph $G$ that is assumed to contain an independent set of a certain (constant) size, we are interested in upper bounding the chromatic number in terms of the clique number of $G$. This question, besides being interesting on its own, has algorithmic implications (which have been relatively neglected in the literature) on the performance of SDP relaxations in estimating the value of maximum-weight independent set. For $k=3$, Chudnovsky and Seymour (JCTB 2010) prove that any $3$-claw-free graph $G$ with an independent set of size three must satisfy $\chi(G) \leq 2 \omega(G)$. Their result implies a factor $2$-estimation algorithm for the maximum weight independent set via an SDP relaxation (providing the first non-trivial result for maximum-weight independent set in such graphs via a convex relaxation). An obvious open question is whether a similar conditional $\chi$-boundedness phenomenon holds for any $k$-claw-free graph. Our main result answers this question negatively. We further present some evidence that our construction could be useful in studying more broadly the power of convex relaxations in the context of approximating maximum weight independent set in $k$-claw free graphs. In particular, we prove a lower bound on families of convex programs that are stronger than known convex relaxations used algorithmically in this context.
Ruliology: Linking Computation, Observers and Physical Law

Stephen Wolfram has recently outlined an unorthodox, multicomputational approach to fundamental theory, encompassing not only physics but also mathematics in a structure he calls The Ruliad, understood to be the entangled limit of all possible computations. In this framework, physical laws arise from the the sampling of the Ruliad by observers (including us). This naturally leads to several conceptual issues, such as what kind of object is the Ruliad? What is the nature of the observers carrying out the sampling, and how do they relate to the Ruliad itself? What is the precise nature of the sampling? This paper provides a philosophical examination of these questions, and other related foundational issues, including the identification of a limitation that must face any attempt to describe or model reality in such a way that the modeller-observers are included
Sarrus-inspired Deployable Polyhedral Mechanisms

Deployable polyhedral mechanisms (DPMs) have witnessed flourishing growth in recent years because of their potential applications in robotics, space exploration, structure engineering, etc. This paper firstly presents the construction, mobility and kinematics of a family of Sarrus-inspired deployable polyhedral mechanisms. By carrying out expansion operation and implanting Sarrus linkages along the straight-line motion paths, deployable tetrahedral, cubic and dodecahedral mechanisms are identified and constructed following tetrahedral, octahedral and icosahedral symmetry, respectively. Three paired transformations with synchronized radial motion between Platonic and Archimedean polyhedrons are revealed, and their significant symmetric properties are perfectly remained in each work configuration. Subsequently, with assistant of equivalent prismatic joints, the equivalent analysis strategy for mobility of multiloop polyhedral mechanisms is proposed to significantly simplify the calculation process. This paper hence presents the construction method and equivalent analysis of the Sarrus-inspired DPMs that are not only valuable in theoretical investigation, but also have great potential in practical applications such as mechanical metamaterials, deployable architectures and space exploration.
Seroprevalence of hepatitis B and C virus and HIV markers among blood donors from Shahre-Kord, Iran (2004-2006)

Objective: To determine the prevalence of HBV, HCV and HIV infection in voluntary blood donors from Shahre Kord, a central province of Iran, during 2004-2006 Design: Retrospective, descriptive and analytical study based on data from Serology Laboratory of the Blood Transfusion Center (BTC) at Shahre-Kord, Iran Setting: The BTC, Shahre-Kord, Iran Subjects: 35,124 apparently healthy voluntary blood donors Intervention: Detection of HBsAg, anti-HBc, anti-HCV and anti-HIV markers in blood samples using immunoenzymatic tests and Western blotting, as determined by the BTC Main Outcome Measures: Analysis of data obtained from the Serology Laboratory of the BTC, Shahre-Kord, Iran to determine prevalence Results: Overall prevalence estimates were: 0.1 for HBsAg, 0.07 for anti-HBc, 0.2 for anti-HCV and 0.002 for anti-HIV antibody. There was a statistically significant increase in the overall prevalence of HBsAg and HBcAb (from 0.015 to 0.02) and the prevalence of HCVAb (from 0.06 to 0.48) during the study period (p < 0.05). Three male donors were co-infected by HCV and HBV. Conclusion: This study suggests the need to investigate risk factors and risk groups for these infections in Iran. In the light of these results, an effective control and training program should be implemented.
An Improved Greedy Curvature Bound in Finite-Horizon String Optimization with an Application to a Sensor Coverage Problem

We study the optimization problem of choosing strings of finite length to maximize string submodular functions on string matroids, which is a broader class of problems than maximizing set submodular functions on set matroids. We provide a lower bound for the performance of the greedy algorithm in our problem, and then prove that our bound is superior to the greedy curvature bound of Conforti and Cornuéjols, Our bound has lower computational complexity than most previously proposed curvature bounds. Finally, we demonstrate the strength of our result on a sensor coverage problem.
A Pseudo-Boolean Polynomials Approach for Image Edge Detection

We introduce a novel approach for image edge detection based on pseudo-Boolean polynomials for image patches. We show that patches covering edge regions in the image result in pseudo-Boolean polynomials with higher degrees compared to patches that cover blob regions. The proposed approach is based on reduction of polynomial degree and equivalence properties of penalty-based pseudo-Boolean polynomials.
Volt/VAR Optimization in the Presence of Attacks: A Real-Time Co-Simulation Study

Traditionally, Volt/VAR optimization (VVO) is performed in distribution networks through legacy devices such as on-load tap changers (OLTCs), voltage regulators (VRs), and capacitor banks. With the amendment in IEEE 1547 standard, distributed energy resources (DERs) can now provide reactive power support to the grid. For this, renewable energy-based DERs, such as PV, are interfaced with the distribution networks through smart inverters (SIs). Due to the intermittent nature of such resources, VVO transforms into a dynamic problem that requires extensive communication between the VVO controller and devices performing the VVO scheme. This communication, however, can be potentially tampered with by an adversary rendering the VVO ineffective. In this regard, it is important to assess the impact of cyberattacks on the VVO scheme. This paper develops a real-time co-simulation setup to assess the effect of cyberattacks on VVO. The setup consists of a real-time power system simulator, a communication network emulator, and a master controller in a system-in-the-loop (SITL) setup. The DNP3 communication protocol is adopted for the underlying communication infrastructure. The results show that corrupted communication messages can lead to violation of voltage limits, increased number of setpoint updates of VRs, and economic loss.
Bridging Distribution Learning and Image Clustering in High-Dimensional Space

Distribution learning focuses on learning the probability density function from a set of data samples. In contrast, clustering aims to group similar objects together in an unsupervised manner. Usually, these two tasks are considered unrelated. However, the relationship between the two may be indirectly correlated, with Gaussian Mixture Models (GMM) acting as a bridge. In this paper, we focus on exploring the correlation between distribution learning and clustering, with the motivation to fill the gap between these two fields, utilizing an autoencoder (AE) to encode images into a high-dimensional latent space. Then, Monte-Carlo Marginalization (MCMarg) and Kullback-Leibler (KL) divergence loss are used to fit the Gaussian components of the GMM and learn the data distribution. Finally, image clustering is achieved through each Gaussian component of GMM. Yet, the “curse of dimensionality” poses severe challenges for most clustering algorithms. Compared with the classic Expectation-Maximization (EM) Algorithm, experimental results show that MCMarg and KL divergence can greatly alleviate the difficulty. Based on the experimental results, we believe distribution learning can exploit the potential of GMM in image clustering within high-dimensional space.
DBNet: Leveraging DBMS for Network Automation

We present DBNet, a data-driven network automation framework built on top of a DBMS. DBNet utilizes key primitives of a DBMS including tables, procedures, transactions, logging, and access control to serve the functions of a data-centric network control plane. DBNet accomplishes this functionality by storing mirrored network device states, executing automation programs on these mirror states within the DBMS, and proxying state updates out to the physical devices upon changes to mirror/local state. The framework also stores network telemetry data, performs analytics on the data, uses the analytics to motivate control plane actions, and provides provenance logging features on the actions taken. We apply DBNet to motivating cloud network infrastructure examples and show how developers can use DBNet's interface to express rich user-defined policies. Our preliminary case studies show that the overhead to run DBNet is trivial in the timescales generally relevant for network automation.
Convergence of non-linear diagonal frame filtering for regularizing inverse problems

Inverse problems are key issues in several scientific areas, including signal processing and medical imaging. Since inverse problems typically suffer from instability with respect to data perturbations, a variety of regularization techniques have been proposed. In particular, the use of filtered diagonal frame decompositions has proven to be effective and computationally efficient. However, existing convergence analysis applies only to linear filters and a few non-linear filters such as soft thresholding. In this paper, we analyze filtered diagonal frame decompositions with general non-linear filters. In particular, our results generalize SVD-based spectral filtering from linear to non-linear filters as a special case. As a first approach, we establish a connection between non-linear diagonal frame filtering and variational regularization, allowing us to use results from variational regularization to derive the convergence of non-linear spectral filtering. In the second approach, as our main theoretical results, we relax the assumptions involved in the variational case while still deriving convergence. Furthermore, we discuss connections between non-linear filtering and plug-and-play regularization and explore potential benefits of this relationship.