OpenRaiser/Intern-Atlas huggingface dataset:
Scaling Limits of Quantum Repeater Networks
Venue: arXiv cs.NI 2023
Year: 2023

Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. However, due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed. The goal of this work is to maximize the overall length, or scalability of QRNs such that long-distance quantum communications is achieved while application-specific quality-of-service (QoS) requirements are satisfied. In particular, a novel joint optimization framework that aims at maximizing QRN scalability, while satisfying QoS constraints on the end-to-end fidelity and rate is proposed. The proposed approach optimizes the number of QRN repeater nodes, their separation distance, and the number of distillation rounds to be performed at both link and end-to-end levels. Extensive simulations are conducted to analyze the tradeoffs between QRN scalability, rate, and fidelity under gate and measurement errors. The obtained results characterize the QRN scaling limits for a given QoS requirement. The proposed approach offers a promising solution and design guidelines for future QRN deployments.
SatAIOps: Revamping the Full Life-Cycle Satellite Network Operations
Venue: arXiv cs.SE 2023
Year: 2023

Recently advanced non-geostationary (NGSO) satellite networks represented by large constellations and advanced payloads provide great promises for enabling high-quality Internet connectivity to any place on Earth. However, the traditional approach to satellite operations cannot address the new challenges in the NGSO satellite networks imposed by the significant increase in complexity, security, resilience, and environmental concerns. Therefore, a reliable, sustainable, and efficient approach is required for the entire life-cycle of satellite network operations. This paper provides a timely response to the new challenges and proposes a novel approach called "SatAIOps" as an overall solution. Through our discussion on the current challenges of the advanced satellite networks, SatAIOps and its functional modules in the entire life-cycle of satellites are proposed, with some example technologies given. SatAIOps provides a new perspective for addressing operational challenges with trustworthy and responsible AI technologies. It enables a new framework for evolving and collaborative efforts from research and industry communities.
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
Venue: arXiv cs.LG 2023
Year: 2023

We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled gradient descent methods and is theoretically grounded in maximally informative summary statistics. Amortized variational inference is restricted by the expressive power of the chosen variational distribution and the availability of training data in the form of joint data and parameter samples, which often lead to approximation errors such as the amortization gap. To address this issue, we propose an iterative framework that refines the current amortized posterior approximation at each step. Our approach involves alternating between two steps: (1) constructing a training dataset consisting of pairs of summarized data residuals and parameters, where the summarized data residual is generated using a gradient-based summary statistic, and (2) training a conditional generative model -- a normalizing flow in our examples -- on this dataset to obtain a probabilistic update of the unknown parameter. This procedure leads to iterative refinement of the amortized posterior approximations without the need for extra training data. We validate our method in a controlled setting by applying it to a stylized problem, and observe improved posterior approximations with each iteration. Additionally, we showcase the capability of our method in tackling realistically sized problems by applying it to transcranial ultrasound, a high-dimensional, nonlinear inverse problem governed by wave physics, and observe enhanced posterior quality through better image reconstruction with the posterior mean.
Automating privacy decisions -- where to draw the line?
Venue: arXiv cs.CY 2023
Year: 2023

Users are often overwhelmed by privacy decisions to manage their personal data, which can happen on the web, in mobile, and in IoT environments. These decisions can take various forms -- such as decisions for setting privacy permissions or privacy preferences, decisions responding to consent requests, or to intervene and ``reject'' processing of one's personal data --, and each can have different legal impacts. In all cases and for all types of decisions, scholars and industry have been proposing tools to better automate the process of privacy decisions at different levels, in order to enhance usability. We provide in this paper an overview of the main challenges raised by the automation of privacy decisions, together with a classification scheme of the existing and envisioned work and proposals addressing automation of privacy decisions.
On the Stability of Approximate Message Passing with Independent Measurement Ensembles
Venue: arXiv cs.IT 2023
Year: 2023

Approximate message passing (AMP) is a scalable, iterative approach to signal recovery. For structured random measurement ensembles, including independent and identically distributed (i.i.d.) Gaussian and rotationally-invariant matrices, the performance of AMP can be characterized by a scalar recursion called state evolution (SE). The pseudo-Lipschitz (polynomial) smoothness is conventionally assumed. In this work, we extend the SE for AMP to a new class of measurement matrices with independent (not necessarily identically distributed) entries. We also extend it to a general class of functions, called controlled functions which are not constrained by the polynomial smoothness; unlike the pseudo-Lipschitz function that has polynomial smoothness, the controlled function grows exponentially. The lack of structure in the assumed measurement ensembles is addressed by leveraging Lindeberg-Feller. The lack of smoothness of the assumed controlled function is addressed by a proposed conditioning technique leveraging the empirical statistics of the AMP instances. The resultants grant the use of the SE to a broader class of measurement ensembles and a new class of functions.
BRF: eBPF Runtime Fuzzer
Venue: arXiv cs.CR 2023
Year: 2023

The eBPF technology in the Linux kernel has been widely adopted for different applications, such as networking, tracing, and security, thanks to the programmability it provides. By allowing user-supplied eBPF programs to be executed directly in the kernel, it greatly increases the flexibility and efficiency of deploying customized logic. However, eBPF also introduces a new and wide attack surface: malicious eBPF programs may try to exploit the vulnerabilities in the eBPF subsystem in the kernel. Fuzzing is a promising technique to find such vulnerabilities. Unfortunately, our experiments with the state-of-the-art kernel fuzzer, Syzkaller, shows that it cannot effectively fuzz the eBPF runtime, those components that are in charge of executing an eBPF program, for two reasons. First, the eBPF verifier (which is tasked with verifying the safety of eBPF programs) rejects many fuzzing inputs because (1) they do not comply with its required semantics or (2) they miss some dependencies, i.e., other syscalls that need to be issued before the program is loaded. Second, Syzkaller fails to attach and trigger the execution of eBPF programs most of the times. This paper introduces the BPF Runtime Fuzzer (BRF), a fuzzer that can satisfy the semantics and dependencies required by the verifier and the eBPF subsystem. Our experiments show, in 48-hour fuzzing sessions, BRF can successfully execute 8x more eBPF programs compared to Syzkaller. Moreover, eBPF programs generated by BRF are much more expressive than Syzkaller's. As a result, BRF achieves 101% higher code coverage. Finally, BRF has so far managed to find 4 vulnerabilities (some of them have been assigned CVE numbers) in the eBPF runtime, proving its effectiveness.
Comparing Variation in Tokenizer Outputs Using a Series of Problematic and Challenging Biomedical Sentences
Venue: arXiv cs.CL 2023
Year: 2023

Background & Objective: Biomedical text data are increasingly available for research. Tokenization is an initial step in many biomedical text mining pipelines. Tokenization is the process of parsing an input biomedical sentence (represented as a digital character sequence) into a discrete set of word/token symbols, which convey focused semantic/syntactic meaning. The objective of this study is to explore variation in tokenizer outputs when applied across a series of challenging biomedical sentences. Method: Diaz [2015] introduce 24 challenging example biomedical sentences for comparing tokenizer performance. In this study, we descriptively explore variation in outputs of eight tokenizers applied to each example biomedical sentence. The tokenizers compared in this study are the NLTK white space tokenizer, the NLTK Penn Tree Bank tokenizer, Spacy and SciSpacy tokenizers, Stanza/Stanza-Craft tokenizers, the UDPipe tokenizer, and R-tokenizers. Results: For many examples, tokenizers performed similarly effectively; however, for certain examples, there were meaningful variation in returned outputs. The white space tokenizer often performed differently than other tokenizers. We observed performance similarities for tokenizers implementing rule-based systems (e.g. pattern matching and regular expressions) and tokenizers implementing neural architectures for token classification. Oftentimes, the challenging tokens resulting in the greatest variation in outputs, are those words which convey substantive and focused biomedical/clinical meaning (e.g. x-ray, IL-10, TCR/CD3, CD4+ CD8+, and (Ca2+)-regulated). Conclusion: When state-of-the-art, open-source tokenizers from Python and R were applied to a series of challenging biomedical example sentences, we observed subtle variation in the returned outputs.
Black-Box Statistical Prediction of Lossy Compression Ratios for Scientific Data
Venue: arXiv cs.DC 2023
Year: 2023

Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely on trial and error to assess lossy compression performance. As a strong data-driven effort toward quantifying lossy compressibility of scientific datasets, we provide a statistical framework to predict compression ratios of lossy compressors. Our method is a two-step framework where (i) compressor-agnostic predictors are computed and (ii) statistical prediction models relying on these predictors are trained on observed compression ratios. Proposed predictors exploit spatial correlations and notions of entropy and lossyness via the quantized entropy. We study 8+ compressors on 6 scientific datasets and achieve a median percentage prediction error less than 12%, which is substantially smaller than that of other methods while achieving at least a 8.8x speedup for searching for a specific compression ratio and 7.8x speedup for determining the best compressor out of a collection.
Learning Better Contrastive View from Radiologist's Gaze
Venue: arXiv cs.CV 2023
Year: 2023

Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims to minimizing distances between positive pairs. These methods usually apply random data augmentation to input images, expecting the augmented views of the same images to be similar and positively paired. However, random augmentation may overlook image semantic information and degrade the quality of augmented views in contrastive learning. This issue becomes more challenging in medical images since the abnormalities related to diseases can be tiny, and are easy to be corrupted (e.g., being cropped out) in the current scheme of random augmentation. In this work, we first demonstrate that, for widely-used X-ray images, the conventional augmentation prevalent in contrastive pre-training can affect the performance of the downstream diagnosis or classification tasks. Then, we propose a novel augmentation method, i.e., FocusContrast, to learn from radiologists' gaze in diagnosis and generate contrastive views for medical images with guidance from radiologists' visual attention. Specifically, we track the gaze movement of radiologists and model their visual attention when reading to diagnose X-ray images. The learned model can predict visual attention of the radiologists given a new input image, and further guide the attention-aware augmentation that hardly neglects the disease-related abnormalities. As a plug-and-play and framework-agnostic module, FocusContrast consistently improves state-of-the-art contrastive learning methods of SimCLR, MoCo, and BYOL by 4.0~7.0% in classification accuracy on a knee X-ray dataset.
M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
Venue: arXiv cs.CV 2023
Year: 2023

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M$^2$DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at \url{https://github.com/PurdueDigitalTwin/M2DAR}.
Federated Learning over Harmonized Data Silos
Venue: arXiv cs.LG 2023
Year: 2023

Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data. Most of the existing work operates on unstructured data, such as images or text, or on structured data assumed to be consistent across the different sites. However, sites often have different schemata, data formats, data values, and access patterns. The field of data integration has developed many methods to address these challenges, including techniques for data exchange and query rewriting using declarative schema mappings, and for entity linkage. Therefore, we propose an architectural vision for an end-to-end Federated Learning and Integration system, incorporating the critical steps of data harmonization and data imputation, to spur further research on the intersection of data management information systems and machine learning.
LoViT: Long Video Transformer for Surgical Phase Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Current approaches are limited since they train spatial feature extractors using frame-level supervision that could lead to incorrect predictions due to similar frames appearing at different phases, and poorly fuse local and global features due to computational constraints which can affect the analysis of long videos commonly encountered in surgical interventions. In this paper, we present a two-stage method, called Long Video Transformer (LoViT) for fusing short- and long-term temporal information that combines a temporally-rich spatial feature extractor and a multi-scale temporal aggregator consisting of two cascaded L-Trans modules based on self-attention, followed by a G-Informer module based on ProbSparse self-attention for processing global temporal information. The multi-scale temporal head then combines local and global features and classifies surgical phases using phase transition-aware supervision. Our approach outperforms state-of-the-art methods on the Cholec80 and AutoLaparo datasets consistently. Compared to Trans-SVNet, LoViT achieves a 2.4 pp (percentage point) improvement in video-level accuracy on Cholec80 and a 3.1 pp improvement on AutoLaparo. Moreover, it achieves a 5.3 pp improvement in phase-level Jaccard on AutoLaparo and a 1.55 pp improvement on Cholec80. Our results demonstrate the effectiveness of our approach in achieving state-of-the-art performance of surgical phase recognition on two datasets of different surgical procedures and temporal sequencing characteristics whilst introducing mechanisms that cope with long videos.
Physics-enhanced Gaussian Process Variational Autoencoder
Venue: arXiv cs.LG 2023
Year: 2023

Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data. Using video clips as input data, the encoder may be used to describe the movement of an object in the video without ground truth data (unsupervised learning). Even though the object's dynamics is typically based on first principles, this prior knowledge is mostly ignored in the existing literature. Thus, we propose a physics-enhanced variational autoencoder that places a physical-enhanced Gaussian process prior on the latent dynamics to improve the efficiency of the variational autoencoder and to allow physically correct predictions. The physical prior knowledge expressed as linear dynamical system is here reflected by the Green's function and included in the kernel function of the Gaussian process. The benefits of the proposed approach are highlighted in a simulation with an oscillating particle.
Physics-informed Convolutional Recurrent Surrogate Model for Reservoir Simulation with Well Controls
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a novel surrogate model for modeling subsurface fluid flow with well controls using a physics-informed convolutional recurrent neural network (PICRNN). The model uses a convolutional long-short term memory (ConvLSTM) to capture the spatiotemporal dependencies of the state evolution dynamics in the porous flow. The ConvLSTM is linked to the state space equations, enabling the incorporation of a discrete-time sequence of well control. The model requires initial state condition and a sequence of well controls as inputs, and predicts the state variables of the system, such as pressure, as output. By minimizing the residuals of reservoir flow state-space equations, the network is trained without the need for labeled data. The model is designed to serve as a surrogate model for predicting future reservoir states based on the initial reservoir state and input engineering controls. Boundary conditions are enforced into the state-space equations so no additional loss term is needed. Three numerical cases are studied, demonstrating the model's effectiveness in predicting reservoir dynamics based on future well/system controls. The proposed model provides a new approach for efficient and accurate prediction of subsurface fluid flow, with potential applications in optimal control design for reservoir engineering.
Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm
Venue: arXiv cs.SI 2023
Year: 2023

What are the relations between the edge weights and the topology in real-world graphs? Given only the topology of a graph, how can we assign realistic weights to its edges based on the relations? Several trials have been done for edge-weight prediction where some unknown edge weights are predicted with most edge weights known. There are also existing works on generating both topology and edge weights of weighted graphs. Differently, we are interested in generating edge weights that are realistic in a macroscopic scope, merely from the topology, which is unexplored and challenging. To this end, we explore and exploit the patterns involving edge weights and topology in real-world graphs. Specifically, we divide each graph into layers where each layer consists of the edges with weights at least a threshold. We observe consistent and surprising patterns appearing in multiple layers: the similarity between being adjacent and having high weights, and the nearly-linear growth of the fraction of edges having high weights with the number of common neighbors. We also observe a power-law pattern that connects the layers. Based on the observations, we propose PEAR, an algorithm assigning realistic edge weights to a given topology. The algorithm relies on only two parameters, preserves all the observed patterns, and produces more realistic weights than the baseline methods with more parameters.
Optimizing pre-scheduled, intermittently-observed MDPs
Venue: arXiv cs.RO 2023
Year: 2023

A challenging category of robotics problems arises when sensing incurs substantial costs. This paper examines settings in which a robot wishes to limit its observations of state, for instance, motivated by specific considerations of energy management, stealth, or implicit coordination. We formulate the problem of planning under uncertainty when the robot's observations are intermittent but their timing is known via a pre-declared schedule. After having established the appropriate notion of an optimal policy for such settings, we tackle the problem of joint optimization of the cumulative execution cost and the number of state observations, both in expectation under discounts. To approach this multi-objective optimization problem, we introduce an algorithm that can identify the Pareto front for a class of schedules that are advantageous in the discounted setting. The algorithm proceeds in an accumulative fashion, prepending additions to a working set of schedules and then computing incremental changes to the value functions. Because full exhaustive construction becomes computationally prohibitive for moderate-sized problems, we propose a filtering approach to prune the working set. Empirical results demonstrate that this filtering is effective at reducing computation while incurring only negligible reduction in quality. In summarizing our findings, we provide a characterization of the run-time vs quality trade-off involved.