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Diffusion models have revolutionized image synthesis, garnering significant research interest in recent years. Diffusion is an iterative algorithm in which samples are generated step-by-step, starting from pure noise. This process introduces the notion of diffusion trajectories, i.e., paths from the standard Gaussian distribution to the target image distribution. In this context, we study discriminative algorithms operating on these trajectories. Specifically, given a pre-trained diffusion model, we consider the problem of classifying images as part of the training dataset, generated by the model or originating from an external source. Our approach demonstrates the presence of patterns across steps that can be leveraged for classification. We also conduct ablation studies, which reveal that using higher-order gradient features to characterize the trajectories leads to significant performance gains and more robust algorithms.

In this work, we establish theoretical and practical connections between vertex indexing for sparse graph/network compression and matrix ordering for sparse matrix-vector multiplication and variable elimination. We present a fundamental analysis of adjacency access locality in vertex ordering from the perspective of graph composition of, or decomposition into, elementary compact graphs. We introduce an algebraic indexing approach that maintains the advantageous features of existing methods, mitigates their shortcomings, and adapts to the degree distribution. The new method demonstrates superior and versatile performance in graph compression across diverse types of graphs. It also renders proportional improvement in the efficiency of matrix-vector multiplications for subspace iterations in response to random walk queries on a large network., Comment: 7 pages, 5 figures, 1 table

We propose a generic procedure for assessing 6D object pose estimates. Our approach relies on the evaluation of discrepancies in the geometry of the observed object, in particular its respective estimated back-projection in 3D, against a putative functional shape representation comprising mixtures of Gaussian Processes, that act as a template. Each Gaussian Process is trained to yield a fragment of the object's surface in a radial fashion with respect to designated reference points. We further define a pose confidence measure as the average probability of pixel back-projections in the Gaussian mixture. The goal of our experiments is two-fold. a) We demonstrate that our functional representation is sufficiently accurate as a shape template on which the probability of back-projected object points can be evaluated, and, b) we show that the resulting confidence scores based on these probabilities are indeed a consistent quality measure of pose.

This paper presents a comprehensive analysis of the generalized spectral structure of the modularity matrix $B$, which is introduced by Newman as the kernel matrix for the quadratic-form expression of the modularity function $Q$ used for community detection. The analysis is then seamlessly extended to the resolution-parametrized modularity matrix $B(\gamma)$, where $\gamma$ denotes the resolution parameter. The modularity spectral analysis provides fresh and profound insights into the $\gamma$-dynamics within the framework of modularity maximization for community detection. It provides the first algebraic explanation of the resolution limit at any specific $\gamma$ value. Among the significant findings and implications, the analysis reveals that (1) the maxima of the quadratic function with $B(\gamma)$ as the kernel matrix always reside in the Fiedler space of the normalized graph Laplacian $L$ or the null space of $L$, or their combination, and (2) the Fiedler value of the graph Laplacian $L$ marks the critical $\gamma$ value in the transition of candidate community configuration states between graph division and aggregation. Additionally, this paper introduces and identifies the Fiedler pseudo-set (FPS) as the de facto critical region for the state transition. This work is expected to have an immediate and long-term impact on improvements in algorithms for modularity maximization and on model transformations., Comment: 11 pages, 3 figures, 1 table

The healthcare sector is increasingly vulnerable to cyberattacks due to its growing digitalization. Patient data, including medical records and financial information, are at risk, potentially leading to identity theft and patient safety concerns. The European Union and other organizations identify key areas for healthcare system improvement, yet the industry still grapples with inadequate security practices. In response, the HEIR project offers a comprehensive cybersecurity approach, promoting security features from various regulatory frameworks and introducing tools such as the Secure Healthcare Framework and Risk Assessment for Medical Applications (RAMA). These measures aim to enhance digital health security and protect sensitive patient data while facilitating secure data access and privacy-aware techniques. In a rapidly evolving threat landscape, HEIR presents a promising framework for healthcare cybersecurity., Comment: 70 pages, 48 figures

Recently it has been shown that using diffusion models for inverse problems can lead to remarkable results. However, these approaches require a closed-form expression of the degradation model and can not support complex degradations. To overcome this limitation, we propose a method (INDigo) that combines invertible neural networks (INN) and diffusion models for general inverse problems. Specifically, we train the forward process of INN to simulate an arbitrary degradation process and use the inverse as a reconstruction process. During the diffusion sampling process, we impose an additional data-consistency step that minimizes the distance between the intermediate result and the INN-optimized result at every iteration, where the INN-optimized image is composed of the coarse information given by the observed degraded image and the details generated by the diffusion process. With the help of INN, our algorithm effectively estimates the details lost in the degradation process and is no longer limited by the requirement of knowing the closed-form expression of the degradation model. Experiments demonstrate that our algorithm obtains competitive results compared with recently leading methods both quantitatively and visually. Moreover, our algorithm performs well on more complex degradation models and real-world low-quality images.

As integrated circuit technologies are moving to smaller technology nodes, Electromigration (EM) has become one of the most challenging problems facing the EDA industry. While numerical approaches have been widely deployed since they can handle complicated interconnect structures, they tend to be much slower than analytical approaches. In this paper, we present a fast semi-analytical approach, based on the matrix exponential, for the solution of Korhonen's stress equation at discrete spatial points of interconnect trees, which enables the analytical calculation of EM stress at any time and point independently. The proposed approach is combined with the extended Krylov subspace method to accurately simulate large EM models and accelerate the calculation of the final solution. Experimental evaluation on OpenROAD benchmarks demonstrates that our method achieves 0.5% average relative error over the COMSOL industrial tool while being up to three orders of magnitude faster.

The rapid growth of circuit complexity has rendered Model Order Reduction (MOR) a key enabler for the efficient simulation of large circuit models. MOR techniques based on moment-matching are well established due to their simplicity and computational performance in the reduction process. However, moment-matching methods based on the ordinary Krylov subspace are usually inadequate to accurately approximate the original circuit behavior, and at the same time do not produce reduced-order models as compact as needed. In this paper, we present a moment-matching method which utilizes the extended and the asymmetric extended Krylov subspace (EKS and AEKS), while it allows the parallel computation of the transfer function in order to deal with circuits that have many terminals. The proposed method can handle large-scale regular and singular circuits and generate accurate and efficient reduced-order models for circuit simulation. Experimental results on industrial IBM power grids demonstrate that the EKS method can achieve an error reduction up to 85.28% over a standard Krylov subspace method, while the AEKS method greatly reduces the runtime of EKS, introducing a negligible overhead in the reduction error., Comment: arXiv admin note: substantial text overlap with arXiv:2007.01948

Citizen Science aims to engage people in research activities on important issues related to their well-being. Smart Cities aim to provide them with services that improve the quality of their life. Both concepts have seen significant growth in the last years, and can be further enhanced by combining their purposes with IoT technologies that allow for dynamic and large-scale communication and interaction. However, exciting and retaining the interest of participants is a key factor for such initiatives. In this paper we suggest that engagement in Citizen Science projects applied on Smart Cities infrastructure can be enhanced through contextual and structural game elements realized through augmented audio interactive mechanisms. Our inter-disciplinary framework is described through the paradigm of a collaborative bird call recognition game, in which users collect and submit audio data, which are then classified and used for augmenting physical space with virtual soundscape maps. We discuss the Playful Learning, Internet of Audio Things, and Bird Monitoring principles that shaped the design of our paradigm, and analyze its potential technical implementation.

We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as a unified foundation for studying and connecting many important graph problems in theory and practice. We describe topological relations among graphlets (graph elements) in rigorous mathematics language and from the perspective of graph encoding. We uncover, characterize and utilize algebraic and numerical relations in graphlet counts/frequencies. We present a novel algorithm for efficiently counting small subgraphs as a practical product of our theoretical findings., Comment: 25 pages, 8 figures, 2 tables

Aims: Our Gulf War Illness (GWI) study conducts combinatorial screening of many interactive neural and humoral biomarkers in order to establish predictive, diagnostic, and therapeutic targets. We encounter obstacles at every stage of the biomarker discovery process, from sample acquisition, bio-marker extraction to multi-aspect, multi-way interaction analysis, due to the study complexity and lack of support for complex data problem solutions. We introduce a novel data platform, named ROSALIND, to overcome the challenges, foster healthy and vital collaborations and advance scientific inquiries. Main methods: ROSALIND is a researcher-centered, study-specific data platform. It provides vital support of individual creativity and effort in collaborative research. We follow the principles etched in the platform name - ROSALIND stands for resource organisms with self-governed accessibility, linkability, integrability, neutrality, and dependability. We translate, encode and implement the principles in the platform with novel use of advanced concepts and techniques to ensure and protect data integrity and research integrity. From a researcher's vantage point, ROSALIND embodies nuance utilities and advanced functionalities in one system, beyond conventional storage, archive and data management. Key findings: The deployment of ROSALIND in our GWI study in recent 12 months has accelerated the pace of data experiment and analysis, removed numerous error sources, and increased research quality and productivity. Significance: ROSALIND seems the first to address data integrity and research integrity in tandem with digital measures and means. It also promises a new type of distributed research networks with individualized data platforms connected in various self-organized collaboration configurations., Comment: Authorship disagreement

We introduce a new method for temporal pattern analysis of scientific collaboration networks. We investigate in particular virus research activities through five epidemic or pandemic outbreaks in the recent two decades and in the ongoing pandemic with COVID-19. Our method embodies two innovative components. The first is a simple model of temporal collaboration networks with time segmented in publication time and convolved in citation history, to effectively capture and accommodate collaboration activities at mixed time scales. The second component is the novel use of graphlets to encode topological structures and to detect change and persistence in collaboration activities over time. We discover in particular two unique and universal roles of bi-fork graphlet in (1) identifying bridges among triangle clusters and (2) quantifying grassroots as the backbone of every collaboration network. We present a number of intriguing patterns and findings about the virus-research activities., Comment: To appear in the Proceedings of High Performance Extreme Computing (HPEC) 2020

We introduce the computational problem of graphlet transform of a sparse large graph. Graphlets are fundamental topology elements of all graphs/networks. They can be used as coding elements to encode graph-topological information at multiple granularity levels for classifying vertices on the same graph/network as well as for making differentiation or connection across different networks. Network/graph analysis using graphlets has growing applications. We recognize the universality and increased encoding capacity in using multiple graphlets, we address the arising computational complexity issues, and we present a fast method for exact graphlet transform. The fast graphlet transform establishes a few remarkable records at once in high computational efficiency, low memory consumption, and ready translation to high-performance program and implementation. It is intended to enable and advance network/graph analysis with graphlets, and to introduce the relatively new analysis apparatus to graph theory, high-performance graph computation, and broader applications., Comment: To appear in the Proceedings of High Performance Extreme Computing (HPEC) 2020

During the past decade, Model Order Reduction (MOR) has become key enabler for the efficient simulation of large circuit models. MOR techniques based on moment-matching are well established due to their simplicity and computational performance in the reduction process. However, moment-matching methods based on the ordinary Krylov subspace are usually inadequate to accurately approximate the original circuit behavior. In this paper, we present a moment-matching method which is based on the extended Krylov subspace and exploits the superposition property in order to deal with many terminals. The proposed method can handle large-scale regular and singular circuits and generate accurate and efficient reduced-order models for circuit simulation. Experimental results on industrial IBM power grids demonstrate that our method achieves an error reduction up to 83.69% over a standard Krylov subspace method.

Motivated by the fast adoption of WebAssembly, we propose the first functional pipeline to support the superoptimization of WebAssembly bytecode. Our pipeline works over LLVM and Souper. We evaluate our superoptimization pipeline with 12 programs from the Rosetta code project. Our pipeline improves the code section size of 8 out of 12 programs. We discuss the challenges faced in superoptimization of WebAssembly with two case studies., Comment: 4 pages, 3 figures. Proceedings of MoreVMs: Workshop on Modern Language Runtimes, Ecosystems, and VMs (2020)

During the last years, a well studied biological substrate, namely Physarum polycephalum, has been proven efficient on finding appropriate and efficient solutions in hard to solve complex mathematical problems. The plasmodium of P. polycephalum is a single-cell that serves as a prosperous bio-computational example. Consequently, it has been successfully utilized in the past to solve a variety of path problems in graphs and combinatorial problems. In this work, this interesting behaviour is mimicked by a robust unconventional computational model, drawing inspiration from the notion of Cellular and Learning Automata. Namely, we employ principles of Cellular Automata (CAs) enriched with learning capabilities to develop a robust computational model, able of modelling appropriately the aforementioned biological substrate and, thus, capturing its computational capabilities. CAs are very efficient in modelling biological systems and solving scientific problems, owing to their ability of incarnating essential properties of a system where global behaviour arises as an effect of simple components, interacting locally. The resulting computational tool, after combining CAs with learning capabilities, should be appropriate for modelling the behaviour of living organisms. Thus, the inherent abilities and computational characteristics of the proposed bio-inspired model are stressed towards the experimental verification of Physarum's ability to model Logic Gates, while trying to find minimal paths in properly configured mazes with food sources. The presented simulation results for various Logic Gates are found in good agreement, both qualitatively and quantitatively, with the corresponding experimental results, proving the efficacy of this unconventional bio-inspired model and providing useful insights for its enhanced usage in various computing applications.

We introduce a nonlinear method for directly embedding large, sparse, stochastic graphs into low-dimensional spaces, without requiring vertex features to reside in, or be transformed into, a metric space. Graph data and models are prevalent in real-world applications. Direct graph embedding is fundamental to many graph analysis tasks, in addition to graph visualization. We name the novel approach SG-t-SNE, as it is inspired by and builds upon the core principle of t-SNE, a widely used method for nonlinear dimensionality reduction and data visualization. We also introduce t-SNE-$\Pi$, a high-performance software for 2D, 3D embedding of large sparse graphs on personal computers with superior efficiency. It empowers SG-t-SNE with modern computing techniques for exploiting in tandem both matrix structures and memory architectures. We present elucidating embedding results on one synthetic graph and four real-world networks.

Close miking represents a widely employed practice of placing a microphone very near to the sound source in order to capture more direct sound and minimize any pickup of ambient sound, including other, concurrently active sources. It is used by the audio engineering community for decades for audio recording, based on a number of empirical rules that were evolved during the recording practice itself. But can this empirical knowledge and close miking practice be systematically verified? In this work we aim to address this question based on an analytic methodology that employs techniques and metrics originating from the sound source separation evaluation field. In particular, we apply a quantitative analysis of the source separation capabilities of the close miking technique. The analysis is applied on a recording dataset obtained at multiple positions of a typical musical hall, multiple distances between the microphone and the sound source multiple microphone types and multiple level differences between the sound source and the ambient acoustic component. For all the above cases we compute the Source to Interference Ratio (SIR) metric. The results obtained clearly demonstrate an optimum close-miking performance that matches the current empirical knowledge of professional audio recording.

Calculation of near-neighbor interactions among high dimensional, irregularly distributed data points is a fundamental task to many graph-based or kernel-based machine learning algorithms and applications. Such calculations, involving large, sparse interaction matrices, expose the limitation of conventional data-and-computation reordering techniques for improving space and time locality on modern computer memory hierarchies. We introduce a novel method for obtaining a matrix permutation that renders a desirable sparsity profile. The method is distinguished by the guiding principle to obtain a profile that is block-sparse with dense blocks. Our profile model and measure capture the essential properties affecting space and time locality, and permit variation in sparsity profile without imposing a restriction to a fixed pattern. The second distinction lies in an efficient algorithm for obtaining a desirable profile, via exploring and exploiting multi-scale cluster structure hidden in but intrinsic to the data. The algorithm accomplishes its task with key components for lower-dimensional embedding with data-specific principal feature axes, hierarchical data clustering, multi-level matrix compression storage, and multi-level interaction computations. We provide experimental results from case studies with two important data analysis algorithms. The resulting performance is remarkably comparable to the BLAS performance for the best-case interaction governed by a regularly banded matrix with the same sparsity.