OpenRaiser/Intern-Atlas huggingface dataset:
Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach
Venue: arXiv cs.CV 2023
Year: 2023

In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.
$\mathcal{P}$-matchings Parameterized by Treewidth
Venue: arXiv cs.DM 2023
Year: 2023

A \emph{matching} is a subset of edges in a graph $G$ that do not share an endpoint. A matching $M$ is a \emph{$\mathcal{P}$-matching} if the subgraph of $G$ induced by the endpoints of the edges of $M$ satisfies property $\mathcal{P}$. For example, if the property $\mathcal{P}$ is that of being a matching, being acyclic, or being disconnected, then we obtain an \emph{induced matching}, an \emph{acyclic matching}, and a \emph{disconnected matching}, respectively. In this paper, we analyze the problems of the computation of these matchings from the viewpoint of Parameterized Complexity with respect to the parameter \emph{treewidth}.
Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraints
Venue: arXiv cs.AI 2023
Year: 2023

Many constraint satisfaction and optimisation problems can be solved effectively by encoding them as instances of the Boolean Satisfiability problem (SAT). However, even the simplest types of constraints have many encodings in the literature with widely varying performance, and the problem of selecting suitable encodings for a given problem instance is not trivial. We explore the problem of selecting encodings for pseudo-Boolean and linear constraints using a supervised machine learning approach. We show that it is possible to select encodings effectively using a standard set of features for constraint problems; however we obtain better performance with a new set of features specifically designed for the pseudo-Boolean and linear constraints. In fact, we achieve good results when selecting encodings for unseen problem classes. Our results compare favourably to AutoFolio when using the same feature set. We discuss the relative importance of instance features to the task of selecting the best encodings, and compare several variations of the machine learning method.
SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration
Venue: arXiv cs.CV 2023
Year: 2023

Point cloud registration is to estimate a transformation to align point clouds collected in different perspectives. In learning-based point cloud registration, a robust descriptor is vital for high-accuracy registration. However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization to encode initial features. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Under high-intensity noise, SphereNet increases the feature matching recall by more than 25 percentage points on 3DMatch-noise. In addition, it sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5\% and 75.6\% registration recall and also has the best generalization ability on unseen datasets.
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
Venue: arXiv cs.LG 2023
Year: 2023

We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given $n$ samples from a multivariate Gaussian distribution with $p$ variables, the goal is to estimate the $p \times p$ inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator based on an $\ell_0$-penalized version of the pseudolikelihood function, while most earlier approaches are based on the $\ell_1$-relaxation. Our estimator can be formulated as a convex mixed integer program (MIP) which can be difficult to compute at scale using off-the-shelf commercial solvers. To solve the MIP, we propose a custom nonlinear branch-and-bound (BnB) framework that solves node relaxations with tailored first-order methods. As a by-product of our BnB framework, we propose large-scale solvers for obtaining good primal solutions that are of independent interest. We derive novel statistical guarantees (estimation and variable selection) for our estimator and discuss how our approach improves upon existing estimators. Our numerical experiments on real/synthetic datasets suggest that our method can solve, to near-optimality, problem instances with $p = 10^4$ -- corresponding to a symmetric matrix of size $p \times p$ with $p^2/2$ binary variables. We demonstrate the usefulness of GraphL0BnB versus various state-of-the-art approaches on a range of datasets.
CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space
Venue: arXiv cs.SE 2023
Year: 2023

Deep neural networks (DNNs) have demonstrated their outperformance in various software systems, but also exhibit misbehavior and even result in irreversible disasters. Therefore, it is crucial to identify the misbehavior of DNN-based software and improve DNNs' quality. Test input prioritization is one of the most appealing ways to guarantee DNNs' quality, which prioritizes test inputs so that more bug-revealing inputs can be identified earlier with limited time and manual labeling efforts. However, the existing prioritization methods are still limited from three aspects: certifiability, effectiveness, and generalizability. To overcome the challenges, we propose CertPri, a test input prioritization technique designed based on a movement cost perspective of test inputs in DNNs' feature space. CertPri differs from previous works in three key aspects: (1) certifiable: it provides a formal robustness guarantee for the movement cost; (2) effective: it leverages formally guaranteed movement costs to identify malicious bug-revealing inputs; and (3) generic: it can be applied to various tasks, data, models, and scenarios. Extensive evaluations across 2 tasks (i.e., classification and regression), 6 data forms, 4 model structures, and 2 scenarios (i.e., white-box and black-box) demonstrate CertPri's superior performance. For instance, it significantly improves 53.97% prioritization effectiveness on average compared with baselines. Its robustness and generalizability are 1.41~2.00 times and 1.33~3.39 times that of baselines on average, respectively.
Design and Execution Challenges for Cybersecurity Serious Games: An Overview
Venue: arXiv cs.HC 2023
Year: 2023

Serious games are increasingly being used in cybersecurity education to engage and educate users. Several studies with cybersecurity serious games have shown that they are successful in educating users and the users also find them both fun and engaging. Meanwhile, several studies have also reported issues in identifying real life effects of the game and even the long-term effects that they have. Based on our experience with enterprise cybersecurity games and games from recent literature, we discuss a few key challenges that must be considered while designing and evaluating serious games for cybersecurity awareness.
RIS-Aided Index Modulation with Greedy Detection over Rician Fading Channels
Venue: arXiv cs.IT 2023
Year: 2023

Index modulation schemes for reconfigurable intelligent surfaces (RIS)-assisted systems are envisioned as promising technologies for fifth-generation-advanced and sixth-generation (6G) wireless communication systems to enhance various system capabilities such as coverage area and network capacity. In this paper, we consider a receive diversity RIS-assisted wireless communication system employing IM schemes, namely, space-shift keying (SSK) for binary modulation and spatial modulation (SM) for M-ary modulation for data transmission. The RIS lies in close proximity to the transmitter, and the transmitted data is subjected to a fading environment with a prominent line-of-sight component modeled by a Rician distribution. A receiver structure based on a greedy detection rule is employed to select the receive diversity branch with the highest received signal energy for demodulation. The performance of the considered system is evaluated by obtaining a series-form expression for the probability of erroneous index detection (PED) of the considered target antenna using a characteristic function approach. In addition, closed-form and asymptotic expressions at high and low signal-to-noise ratios (SNRs) for the bit error rate (BER) for the SSK-based system, and the SM-based system employing M-ary phase-shift keying and M-ary quadrature amplitude modulation schemes, are derived. The dependencies of the system performance on the various parameters are corroborated via numerical results. The asymptotic expressions and results of PED and BER at high and low SNR values lead to the observation of a performance saturation and the presence of an SNR value as a point of inflection, which is attributed to the greedy detector's structure.
A comparative analysis of SRGAN models
Venue: arXiv cs.CV 2023
Year: 2023

In this study, we evaluate the performance of multiple state-of-the-art SRGAN (Super Resolution Generative Adversarial Network) models, ESRGAN, Real-ESRGAN and EDSR, on a benchmark dataset of real-world images which undergo degradation using a pipeline. Our results show that some models seem to significantly increase the resolution of the input images while preserving their visual quality, this is assessed using Tesseract OCR engine. We observe that EDSR-BASE model from huggingface outperforms the remaining candidate models in terms of both quantitative metrics and subjective visual quality assessments with least compute overhead. Specifically, EDSR generates images with higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values and are seen to return high quality OCR results with Tesseract OCR engine. These findings suggest that EDSR is a robust and effective approach for single-image super-resolution and may be particularly well-suited for applications where high-quality visual fidelity is critical and optimized compute.
Justifications for Generalizations of Approval Voting
Venue: arXiv cs.GT 2023
Year: 2023

Approval voting is a common method of preference aggregation where voters vote by ``approving'' of a subset of candidates and the winner(s) are those who are approved of by the largest number of voters. In approval voting, the degree to which a vote impacts a candidate's score depends only on if that voter approved of the candidate or not, i.e., it is independent of which, or how many, other candidates they approved of. Recently, there has been interest in satisfaction approval voting and quadratic voting both of which include a trade-off between approving of more candidates and how much support each selected candidate gets. Approval voting, satisfaction approval voting, and quadratic voting, can all be viewed as voting where a vote is viewed as analogous to a vector with a different unit norm ($\mathcal{L}^{\infty}$, $\mathcal{L}^{1}$, and $\mathcal{L}^2$ respectively). This suggests a generalization where one can view a vote as analogous to a normalized unit vector under an arbitrary $\mathcal{L}^p$-norm. In this paper, we look at various general methods for justifying voting methods and investigate the degree to which these serve as justifications for these generalizations of approval voting.
FACTS: Facial Animation Creation using the Transfer of Styles
Venue: arXiv cs.GR 2023
Year: 2023

The ability to accurately capture and express emotions is a critical aspect of creating believable characters in video games and other forms of entertainment. Traditionally, this animation has been achieved with artistic effort or performance capture, both requiring costs in time and labor. More recently, audio-driven models have seen success, however, these often lack expressiveness in areas not correlated to the audio signal. In this paper, we present a novel approach to facial animation by taking existing animations and allowing for the modification of style characteristics. Specifically, we explore the use of a StarGAN to enable the conversion of 3D facial animations into different emotions and person-specific styles. We are able to maintain the lip-sync of the animations with this method thanks to the use of a novel viseme-preserving loss.
Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots
Venue: arXiv cs.CR 2023
Year: 2023

Recent advances in natural language processing and machine learning have led to the development of chatbot models, such as ChatGPT, that can engage in conversational dialogue with human users. However, the ability of these models to generate toxic or harmful responses during a non-toxic multi-turn conversation remains an open research question. Existing research focuses on single-turn sentence testing, while we find that 82\% of the individual non-toxic sentences that elicit toxic behaviors in a conversation are considered safe by existing tools. In this paper, we design a new attack, \toxicbot, by fine-tuning a chatbot to engage in conversation with a target open-domain chatbot. The chatbot is fine-tuned with a collection of crafted conversation sequences. Particularly, each conversation begins with a sentence from a crafted prompt sentences dataset. Our extensive evaluation shows that open-domain chatbot models can be triggered to generate toxic responses in a multi-turn conversation. In the best scenario, \toxicbot achieves a 67\% activation rate. The conversation sequences in the fine-tuning stage help trigger the toxicity in a conversation, which allows the attack to bypass two defense methods. Our findings suggest that further research is needed to address chatbot toxicity in a dynamic interactive environment. The proposed \toxicbot can be used by both industry and researchers to develop methods for detecting and mitigating toxic responses in conversational dialogue and improve the robustness of chatbots for end users.
Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
Venue: arXiv cs.CV 2023
Year: 2023

We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis. However, existing methodologies grapple with the challenge of data scarcity during the training phase, leading to over-fitting. To mitigate this issue, we introduce a novel Unsupervised Dense Few-shot Medical Image Segmentation Model Training Pipeline (DenseMP) that capitalizes on unsupervised dense pre-training. DenseMP is composed of two distinct stages: (1) segmentation-aware dense contrastive pre-training, and (2) few-shot-aware superpixel guided dense pre-training. These stages collaboratively yield a pre-trained initial model specifically designed for few-shot medical image segmentation, which can subsequently be fine-tuned on the target dataset. Our proposed pipeline significantly enhances the performance of the widely recognized few-shot segmentation model, PA-Net, achieving state-of-the-art results on the Abd-CT and Abd-MRI datasets. Code will be released after acceptance.
Towards a Rosetta Stone for (meta)data: Learning from natural language to improve semantic and cognitive interoperability
Venue: arXiv cs.DB 2023
Year: 2023

In order to effectively manage the overwhelming influx of data, it is crucial to ensure that data is findable, accessible, interoperable, and reusable (FAIR). While ontologies and knowledge graphs have been employed to enhance FAIRness, challenges remain regarding semantic and cognitive interoperability. We explore how English facilitates reliable communication of terms and statements, and transfer our findings to a framework of ontologies and knowledge graphs, while treating terms and statements as minimal information units. We categorize statement types based on their predicates, recognizing the limitations of modeling non-binary predicates with multiple triples, which negatively impacts interoperability. Terms are associated with different frames of reference, and different operations require different schemata. Term mappings and schema crosswalks are therefore vital for semantic interoperability. We propose a machine-actionable Rosetta Stone Framework for (meta)data, which uses reference terms and schemata as an interlingua to minimize mappings and crosswalks. Modeling statements rather than a human-independent reality ensures cognitive familiarity and thus better interoperability of data structures. We extend this Rosetta modeling paradigm to reference schemata, resulting in simple schemata with a consistent structure across statement types, empowering domain experts to create their own schemata using the Rosetta Editor, without requiring knowledge of semantics. The Editor also allows specifying textual and graphical display templates for each schema, delivering human-readable data representations alongside machine-actionable data structures. The Rosetta Query Builder derives queries based on completed input forms and the information from corresponding reference schemata. This work sets the conceptual ground for the Rosetta Stone Framework that we plan to develop in the future.
Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimization
Venue: arXiv cs.LG 2023
Year: 2023

In the context of digital twins, structural health monitoring (SHM) constitutes the backbone of condition-based maintenance, facilitating the interconnection between virtual and physical assets. Guided wave propagation (GWP) is commonly employed for the inspection of structures in SHM. However, GWP is sensitive to variations in the material properties of the structure, leading to false alarms. In this direction, uncertainty quantification (UQ) is regularly applied to improve the reliability of predictions. Computational mechanics is a useful tool for the simulation of GWP, and is often applied for UQ. Even so, the application of UQ methods requires numerous simulations, while large-scale, transient numerical GWP solutions increase the computational cost. Reduced order models (ROMs) are commonly employed to provide numerical results in a limited amount of time. In this paper, we propose a machine learning (ML)-based ROM, mentioned as BO-ML-ROM, to decrease the computational time related to the simulation of the GWP. The ROM is integrated with a Bayesian optimization (BO) framework, to adaptively sample the parameters for the ROM training. The finite element method is used for the simulation of the high-fidelity models. The formulated ROM is used for forward UQ of the GWP in an aluminum plate with varying material properties. To determine the influence of each parameter perturbation, a global, variance-based sensitivity analysis is implemented based on Sobol' indices. It is shown that Bayesian optimization outperforms one-shot sampling methods, both in terms of accuracy and speed-up. The predicted results reveal the efficiency of BO-ML-ROM for GWP and demonstrate its value for UQ.
Visualization of Mobility Digital Twin: Framework Design, Case Study, and Future Challenges
Venue: arXiv cs.NI 2023
Year: 2023

A Mobility Digital Twin is an emerging implementation of digital twin technology in the transportation domain, which creates digital replicas for various physical mobility entities, such as vehicles, drivers, and pedestrians. Although a few work have investigated the applications of mobility digital twin recently, the extent to which it can facilitate safer autonomous vehicles remains insufficiently explored. In this paper, we first propose visualization of mobility digital twin, which aims to augment the existing perception systems in connected and autonomous vehicles through twinning high-fidelity and manipulable geometry representations for causal traffic participants, such as surrounding pedestrians and vehicles, in the digital space. An end-to-end system framework, including image data crowdsourcing, preprocessing, offloading, and edge-assisted 3D geometry reconstruction, is designed to enable real-world development of the proposed visualization of mobility digital twin. We implement the proposed system framework and conduct a case study to assess the twinning fidelity and physical-to-digital synchronicity within different image sampling scenarios and wireless network conditions. Based on the case study, future challenges of the proposed visualization of mobility digital twin are discussed toward the end of the paper.
JAZZVAR: A Dataset of Variations found within Solo Piano Performances of Jazz Standards for Music Overpainting
Venue: arXiv cs.SD 2023
Year: 2023

Jazz pianists often uniquely interpret jazz standards. Passages from these interpretations can be viewed as sections of variation. We manually extracted such variations from solo jazz piano performances. The JAZZVAR dataset is a collection of 502 pairs of Variation and Original MIDI segments. Each Variation in the dataset is accompanied by a corresponding Original segment containing the melody and chords from the original jazz standard. Our approach differs from many existing jazz datasets in the music information retrieval (MIR) community, which often focus on improvisation sections within jazz performances. In this paper, we outline the curation process for obtaining and sorting the repertoire, the pipeline for creating the Original and Variation pairs, and our analysis of the dataset. We also introduce a new generative music task, Music Overpainting, and present a baseline Transformer model trained on the JAZZVAR dataset for this task. Other potential applications of our dataset include expressive performance analysis and performer identification.