Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronizability in weighted simplicial complexes

We present a formula for determining synchronizability in large, randomized and weighted simplicial complexes. This formula leverages eigenratios and costs to assess complete synchronizability under diverse network topologies and intensity distributions. We systematically vary coupling strengths (pairwise and three-body), degree and intensity distributions to identify the synchronizability of these simplicial complexes. For all scenarios, eigenratios and costs reliably gauge synchronizability, eliminating the need for explicit connectivity matrices and eigenvalue calculations. This efficient approach offers a general formula for manipulating synchronizability in diffusively coupled identical systems with higher-order interactions simply by manipulating degrees, weights, and coupling strengths. We validate our findings with simplicial complexes of R\"ossler oscillators and confirm that the results are independent of the number of oscillators, connectivity components and distributions of degrees and intensities. Finally, we validate the theory by considering a real-world applicability using a weighted complex system.
zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training

The recent advancements in deep learning have brought about significant changes in various aspects of people's lives. Meanwhile, these rapid developments have raised concerns about the legitimacy of the training process of deep neural networks. To protect the intellectual properties of AI developers, directly examining the training process by accessing the model parameters and training data is often prohibited for verifiers. In response to this challenge, we present zero-knowledge deep learning (zkDL), an efficient zero-knowledge proof for deep learning training. To address the long-standing challenge of verifiable computations of non-linearities in deep learning training, we introduce zkReLU, a specialized proof for the ReLU activation and its backpropagation. zkReLU turns the disadvantage of non-arithmetic relations into an advantage, leading to the creation of FAC4DNN, our specialized arithmetic circuit design for modelling neural networks. This design aggregates the proofs over different layers and training steps, without being constrained by their sequential order in the training process. With our new CUDA implementation that achieves full compatibility with the tensor structures and the aggregated proof design, zkDL enables the generation of complete and sound proofs in less than a second per batch update for an 8-layer neural network with 10M parameters and a batch size of 64, while provably ensuring the privacy of data and model parameters. To our best knowledge, we are not aware of any existing work on zero-knowledge proof of deep learning training that is scalable to million-size networks.
Distractor generation for multiple-choice questions with predictive prompting and large language models

Large Language Models (LLMs) such as ChatGPT have demonstrated remarkable performance across various tasks and have garnered significant attention from both researchers and practitioners. However, in an educational context, we still observe a performance gap in generating distractors -- i.e., plausible yet incorrect answers -- with LLMs for multiple-choice questions (MCQs). In this study, we propose a strategy for guiding LLMs such as ChatGPT, in generating relevant distractors by prompting them with question items automatically retrieved from a question bank as well-chosen in-context examples. We evaluate our LLM-based solutions using a quantitative assessment on an existing test set, as well as through quality annotations by human experts, i.e., teachers. We found that on average 53% of the generated distractors presented to the teachers were rated as high-quality, i.e., suitable for immediate use as is, outperforming the state-of-the-art model. We also show the gains of our approach 1 in generating high-quality distractors by comparing it with a zero-shot ChatGPT and a few-shot ChatGPT prompted with static examples.
GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration

The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning. This study proposes a more dynamic airspace configuration (DAC) approach that could increase throughput and accommodate fluctuating traffic, ideal for emergencies. The proposed approach constructs the airspace as a constraints-embedded graph, compresses its dimensions, and applies a spectral clustering-enabled adaptive algorithm to generate collaborative airport groups and evenly distribute workloads among them. Under various traffic conditions, our experiments demonstrate a 50% reduction in workload imbalances. This research could ultimately form the basis for a recommendation system for optimized airspace configuration. Code available at https://github.com/KeFenge2022/GraphDAC.git.
Distributed Dynamic Programming and an O.D.E. Framework of Distributed TD-Learning for Networked Multi-Agent Markov Decision Processes

— The primary objective of this paper is to investigate distributed dynamic programming (DP) and distributed temporal difference (TD) learning algorithms for networked multi-agent Markov decision problems (MAMDPs). In our study, we adopt a distributed multi-agent framework where individual agents have access only to their own rewards, lacking insights into the rewards of other agents. Additionally, each agent has the ability to share its parameters with neighboring agents through a communication network, represented by a graph. Our contributions can be summarized in two key points: 1) We introduce a novel distributed DP, inspired by the averaging consensus method in the continuous-time domain. The convergence of this DP is assessed through control theory perspectives. 2) Building upon the aforementioned DP, we devise a new distributed TD-learning algorithm and prove its convergence. A standout feature of our proposed distributed DP is its incorporation of two independent dynamic systems, each with a distinct role. This characteristic sets the stage for a novel distributed TD-learning strategy, the convergence of which can be directly established using the Borkar-Meyn theorem.
Existence, Stability and Slow Dynamics of Spikes in a 1D Minimal Keller--Segel Model with Logistic Growth

We analyze the existence, linear stability, and slow dynamics of localized 1D spike patterns for a Keller--Segel model of chemotaxis that includes the effect of logistic growth of the cellular population. Our analysis of localized patterns for this two-component reaction-diffusion (RD) model is based, not on the usual limit of a large chemotactic drift coefficient, but instead on the singular limit of an asymptotically small diffusivity $d_2=\epsilon^2\ll 1$ of the chemoattractant concentration field. In the limit $d_2\ll 1$, steady-state and quasi-equilibrium 1D multi-spike patterns are constructed asymptotically. To determine the linear stability of steady-state $N$-spike patterns we analyze the spectral properties associated with both the ''large'' ${\mathcal O}(1)$ and the ''small'' $o(1)$ eigenvalues associated with the linearization of the Keller--Segel model. By analyzing a nonlocal eigenvalue problem characterizing the large eigenvalues, it is shown that $N$-spike equilibria can be destabilized by a zero-eigenvalue crossing leading to a competition instability if the cellular diffusion rate $d_1$ exceeds a threshold, or from a Hopf bifurcation if a relaxation time constant $\tau$ is too large. In addition, a matrix eigenvalue problem that governs the stability properties of an $N$-spike steady-state with respect to the small eigenvalues is derived. From an analysis of this matrix problem, an explicit range of $d_1$ where the $N$-spike steady-state is stable to the small eigenvalues is identified. Finally, for quasi-equilibrium spike patterns that are stable on an ${\mathcal O}(1)$ time-scale, we derive a differential algebraic system (DAE) governing the slow dynamics of a collection of localized spikes. Unexpectedly, our analysis is rather closely related to the analysis of spike patterns for the Gierer--Meinhardt RD system.
Rating-based Reinforcement Learning

This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, based on human relative preferences over sample pairs, the proposed rating-based reinforcement learning approach is based on human evaluation of individual trajectories without relative comparisons between sample pairs. The rating-based reinforcement learning approach builds on a new prediction model for human ratings and a novel multi-class loss function. We conduct several experimental studies based on synthetic ratings and real human ratings to evaluate the effectiveness and benefits of the new rating-based reinforcement learning approach.
A spatial spectral domain integral equation solver for electromagnetic scattering in dielectric layered media

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Multiplicity dependence of intra-jet properties in pp collisions at $\sqrt{s}$ = 13 TeV with ALICE

Recent results in high-multiplicity pp collisions show features similar to those that are associated with the formation of a quark$-$gluon plasma in heavy-ion collisions. Investigating the modification of the intra-jet properties as a function of event multiplicity in pp collisions can provide deeper insight into the nature of these effects. We will present the recent measurements of multiplicity dependence of charged-particle jet properties (average charged particle multiplicity and fragmentation functions) for leading charged-particle jets. Jets are reconstructed using anti-$k_{\rm T}$ jet finding algorithm with radius parameter $R$ = 0.4 in the jet $p_{\rm T}$ range from 5$-$110 GeV/$c$ at midrapidity in pp collisions at $\sqrt{s}$ = 13 TeV with ALICE.
Numerical analysis of a baryon and its dilatation modes in holographic QCD

We investigate a baryon and its dilatation modes in holographic QCD based on the Sakai-Sugimoto model, which is expressed as a 1+4 dimensional U($N_f$) gauge theory in the flavor space. For spatially rotational symmetric systems, we apply a generalized version of the Witten Ansatz, and reduce 1+4 dimensional holographic QCD into a 1+2 dimensional Abelian Higgs theory in a curved space. In the reduced theory, the holographic baryon is described as a two-dimensional topological object of an Abrikosov vortex. We numerically calculate the baryon solution of holographic QCD using a fine and large lattice with spacing of 0.04 fm and size of 10 fm. Using the relation between the baryon size and the zero-point location of the Higgs field in the description with the Witten Ansatz, we investigate a various-size baryon through this vortex description. As time-dependent size-oscillation modes (dilatation modes) of a baryon, we numerically obtain the lowest excitation energy of 577 MeV and deduce the dilatational excitation of a nucleon to be the Roper resonance N$^*$(1440).
Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging

Many nuclear safety applications need fast, portable, and accurate imagers to better locate radiation sources. The Rotating Scatter Mask (RSM) system is an emerging device with the potential to meet these needs. The main challenge is the under-determined nature of the data acquisition process: the dimension of the measured signal is far less than the dimension of the image to be reconstructed. To address this challenge, this work aims to fuse model-based sparsity-promoting regularization and a data-driven deep neural network denoising image prior to perform image reconstruction. An efficient algorithm is developed and produces superior reconstructions relative to current approaches.
Olio: A Semantic Search Interface for Data Repositories

Search and information retrieval systems are becoming more expressive in interpreting user queries beyond the traditional weighted bag-of-words model of document retrieval. For example, searching for a flight status or a game score returns a dynamically generated response along with supporting, pre-authored documents contextually relevant to the query. In this paper, we extend this hybrid search paradigm to data repositories that contain curated data sources and visualization content. We introduce a semantic search interface, Olio, that provides a hybrid set of results comprising both auto-generated visualization responses and pre-authored charts to blend analytical question-answering with content discovery search goals. We specifically explore three search scenarios - question-and-answering, exploratory search, and design search over data repositories. The interface also provides faceted search support for users to refine and filter the conventional best-first search results based on parameters such as author name, time, and chart type. A preliminary user evaluation of the system demonstrates that Olio’s interface and the hybrid search paradigm collectively afford greater expressivity in how users discover insights and visualization content in data repositories.
Eco-Friendly Sensing for Human Activity Recognition

With the increasing number of IoT devices, there is a growing demand for energy-free sensors. Human activity recognition holds immense value in numerous daily healthcare applications. However, the majority of current sensing modalities consume energy, thus limiting their sustainable adoption. In this paper, we present a novel activity recognition system that not only operates without requiring energy for sensing but also harvests energy. Our proposed system utilizes photovoltaic cells, attached to the wrist and shoes, as eco-friendly sensing devices for activity recognition. By capturing photovoltaic readings and employing a deep transformer model with powerful learning capabilities, the system effectively recognizes user activities. To ensure robust performance across various subjects, time periods, and lighting conditions, the system incorporates feature extraction and different processing modules. The evaluation of the proposed system on realistic indoor and outdoor environments demonstrated its ability to recognize activities with an accuracy of 91.7%.
Multiobjective optimization approach to shape and topology optimization of plane trusses with various aspect ratios

A multiobjective optimization method is proposed for obtaining the optimal plane trusses simultaneously for various aspect ratios of the initial ground structure as a set of Pareto optimal solutions generated through a single optimization process. The shape and topology are optimized simultaneously to minimize the compliance under constraint on the total structural volume. The strain energy of each member is divided into components of two coordinate directions on the plane. The force density method is used for alleviating difficulties due to existence of coalescent or melting nodes. It is shown in the numerical example that sufficiently accurate optimal solutions are obtained by comparison with those obtained by the linear weighted sum approach that requires solving a single-objective optimization problem many times.
Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions

The hard margin loss function has been at the core of the support vector machine (SVM) research from the very beginning due to its generalization capability.On the other hand, the cardinality constraint has been widely used for feature selection, leading to sparse solutions. This paper studies the sparse SVM with the hard-margin loss (SSVM-HM) that integrates the virtues of both worlds. However, SSVM-HM is one of the most challenging models to solve. In this paper, we cast the problem as a composite optimization with the cardinality constraint. We characterize its local minimizers in terms of {\rm P}-stationarity that well captures the combinatorial structure of the problem. We then propose an inexact proximal augmented Lagrangian method (iPAL). The different parts of the inexactness measurements from the {\rm P}-stationarity are controlled at different scales in a way that the generated sequence converges both globally and at a linear rate. This matches the best convergence theory for composite optimization. To make iPAL practically efficient, we propose a gradient-Newton method in a subspace for the iPAL subproblem. This is accomplished by detecting active samples and features with the help of the proximal operator of the hard margin loss and the projection of cardinality constraint. Extensive numerical results on both simulated and real datasets demonstrate that the proposed method is fast, produces sparse solution of high accuracy, and can lead to effective reduction on active samples and features when compared with several leading solvers.
Deep Learning and Computer Vision for Glaucoma Detection: A Review

Glaucoma is the leading cause of irreversible blindness worldwide and poses significant diagnostic challenges due to its reliance on subjective evaluation. However, recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. In this paper, we survey recent studies on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and visual field images, with a particular emphasis on deep learning-based methods. We provide an updated taxonomy that organizes methods into architectural paradigms and includes links to available source code to enhance the reproducibility of the methods. Through rigorous benchmarking on widely-used public datasets, we reveal performance gaps in generalizability, uncertainty estimation, and multimodal integration. Additionally, our survey curates key datasets while highlighting limitations such as scale, labeling inconsistencies, and bias. We outline open research challenges and detail promising directions for future studies. This survey is expected to be useful for both AI researchers seeking to translate advances into practice and ophthalmologists aiming to improve clinical workflows and diagnosis using the latest AI outcomes.
Classical and non-classical central limit theorems for random sums of independent random variables of a double sequence

Since the appearance of H. Robbins article (1948), the central limit theorems for random sums have been studied for about 70 years. The central limit theorems for random sums of independent random variables play a very important role in various disciplines such as statistics, financial mathematics, insurance, etc. The purpose of this paper is to randomize some well-known classical and non-classical central limit theorems for sums of independent (not necessarily identically distributed) random variables of a double sequence, with the conditions for determining their validity. The results obtained in this paper are extensions and generalizations of known ones.
The flow of ideas in word embeddings

The flow of ideas has been extensively studied by physicists, psychologists, and machine learning engineers. This paper adopts specific tools from microrheology to investigate the similarity-based flow of ideas. We introduce a random walker in word embeddings and study its behavior. Such similarity-mediated random walks through the embedding space show signatures of anomalous diffusion commonly observed in complex structured systems such as biological cells and complex fluids. The paper concludes by proposing the application of popular tools employed in the study of random walks and diffusion of particles under Brownian motion to assess quantitatively the incorporation of diverse ideas in a document. Overall, this paper presents a self-referenced method combining microrheology and machine learning concepts to explore the meandering tendencies of language models and their potential association with creativity.
Stable Adam Optimization for 16-bit Neural Networks Training

In this research, we address critical concerns related to the numerical instability observed in 16-bit computations of machine learning models. Such instability, particularly when employing popular optimization algorithms like Adam, often leads to unstable training of deep neural networks. This not only disrupts the learning process but also poses significant challenges in deploying dependable models in real-world applications. Our investigation identifies the epsilon hyperparameter as the primary source of this instability. A nuanced exploration reveals that subtle adjustments to epsilon within 16-bit computations can enhance the numerical stability of Adam, enabling more stable training of 16-bit neural networks. We propose a novel, dependable approach that leverages updates from the Adam optimizer to bolster the stability of the learning process. Our contributions provide deeper insights into optimization challenges in low-precision computations and offer solutions to ensure the stability of deep neural network training, paving the way for their dependable use in various applications.
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models

The rise in popularity of text-to-image generative artificial intelligence (AI) has attracted widespread public interest. We demonstrate that this technology can be attacked to generate content that subtly manipulates its users. We propose a Backdoor Attack on text-to-image Generative Models (BAGM), which upon triggering, infuses the generated images with manipulative details that are naturally blended in the content. Our attack is the first to target three popular text-to-image generative models across three stages of the generative process by modifying the behaviour of the embedded tokenizer, the language model or the image generative model. Based on the penetration level, BAGM takes the form of a suite of attacks that are referred to as surface, shallow and deep attacks in this article. Given the existing gap within this domain, we also contribute a comprehensive set of quantitative metrics designed specifically for assessing the effectiveness of backdoor attacks on text-to-image models. The efficacy of BAGM is established by attacking state-of-the-art generative models, using a marketing scenario as the target domain. To that end, we contribute a dataset of branded product images. Our embedded backdoors increase the bias towards the target outputs by more than five times the usual, without compromising the model robustness or the generated content utility. By exposing generative AI's vulnerabilities, we encourage researchers to tackle these challenges and practitioners to exercise caution when using pre-trained models. Relevant code, input prompts and supplementary material can be found at https://github.com/JJ-Vice/BAGM, and the dataset is available at: https://ieee-dataport.org/documents/marketable-foods-mf-dataset. Keywords: Generative Artificial Intelligence, Generative Models, Text-to-Image generation, Backdoor Attacks, Trojan, Stable Diffusion.