OpenRaiser/Intern-Atlas huggingface dataset:
A Study of Left Before Treatment Complete Emergency Department Patients: An Optimized Explanatory Machine Learning Framework
Venue: arXiv cs.AI 2022
Year: 2022

The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adverse effects. This paper proposes a framework for studying the factors that affect LBTC outcomes in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization--one of the main challenges of machine learning model development. Three metaheuristic optimization algorithms are employed for optimizing the parameters of extreme gradient boosting (XGB), which are simulated annealing (SA), adaptive simulated annealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED. The designed algorithms are trained and tested using four data groups resulting from the feature selection phase. The model with the best predictive performance is interpreted using SHaply Additive exPlanations (SHAP) method. The findings show that ATSA-XGB outperformed other mode configurations with an accuracy, area under the curve (AUC), sensitivity, specificity, and F1-score of 86.61%, 87.50%, 85.71%, 87.51%, and 86.60%, respectively. The degree and the direction of effects of each feature were determined and explained using the SHAP method.
Some Results on Approximability of Minimum Sum Vertex Cover
Venue: arXiv cs.CC 2022
Year: 2022

We study the Minimum Sum Vertex Cover problem, which asks for an ordering of vertices in a graph that minimizes the total cover time of edges. In particular, n vertices of the graph are visited according to an ordering, and for each edge this induces the first time it is covered. The goal of the problem is to find the ordering which minimizes the sum of the cover times over all edges in the graph. In this work we give the first explicit hardness of approximation result for Minimum Sum Vertex Cover. In particular, assuming the Unique Games Conjecture, we show that the Minimum Sum Vertex Cover problem cannot be approximated within 1.0748. The best approximation ratio for Minimum Sum Vertex Cover as of now is 16/9, due to a recent work of Bansal, Batra, Farhadi, and Tetali. We also study Minimum Sum Vertex Cover problem on regular graphs. In particular, we show that in this case the problem is hard to approximate within 1.0157. We also revisit an approximation algorithm for regular graphs outlined in the work of Feige, Lovász, and Tetali, to show that Minimum Sum Vertex Cover can be approximated within 1.225 on regular graphs.
Efficient Induction of Language Models Via Probabilistic Concept Formation
Venue: arXiv cs.CL 2022
Year: 2022

This paper presents a novel approach to the acquisition of language models from corpora. The framework builds on Cobweb, an early system for constructing taxonomic hierarchies of probabilistic concepts that used a tabular, attribute-value encoding of training cases and concepts, making it unsuitable for sequential input like language. In response, we explore three new extensions to Cobweb -- the Word, Leaf, and Path variants. These systems encode each training case as an anchor word and surrounding context words, and they store probabilistic descriptions of concepts as distributions over anchor and context information. As in the original Cobweb, a performance element sorts a new instance downward through the hierarchy and uses the final node to predict missing features. Learning is interleaved with performance, updating concept probabilities and hierarchy structure as classification occurs. Thus, the new approaches process training cases in an incremental, online manner that it very different from most methods for statistical language learning. We examine how well the three variants place synonyms together and keep homonyms apart, their ability to recall synonyms as a function of training set size, and their training efficiency. Finally, we discuss related work on incremental learning and directions for further research.
Asymmetric Cross-Scale Alignment for Text-Based Person Search
Venue: arXiv cs.CV 2022
Year: 2022

Text-based person search (TBPS) is of significant importance in intelligent surveillance, which aims to retrieve pedestrian images with high semantic relevance to a given text description. This retrieval task is characterized with both modal heterogeneity and fine-grained matching. To implement this task, one needs to extract multi-scale features from both image and text domains, and then perform the cross-modal alignment. However, most existing approaches only consider the alignment confined at their individual scales, e.g., an image-sentence or a region-phrase scale. Such a strategy adopts the presumable alignment in feature extraction, while overlooking the cross-scale alignment, e.g., image-phrase. In this paper, we present a transformer-based model to extract multi-scale representations, and perform Asymmetric Cross-Scale Alignment (ACSA) to precisely align the two modalities. Specifically, ACSA consists of a global-level alignment module and an asymmetric cross-attention module, where the former aligns an image and texts on a global scale, and the latter applies the cross-attention mechanism to dynamically align the cross-modal entities in region/image-phrase scales. Extensive experiments on two benchmark datasets CUHK-PEDES and RSTPReid demonstrate the effectiveness of our approach. Codes are available at \href{url}{https://github.com/mul-hjh/ACSA}.
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models
Venue: arXiv cs.AI 2022
Year: 2022

Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this prognostic study of 6.8 million patients, our denoising sequence-to-sequence prediction model of multiple outcomes outperformed state-of-the-art models scuh pretrained BERT on a broad range of patient outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep bidirectional and autoregressive representation improves patient outcome prediction.
Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints
Venue: arXiv cs.CV 2022
Year: 2022

Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models. To this end, we propose a self-training framework based on the domain-agnostic geometrical constraints. Specifically, we train a neural network to predict the 2D keypoints of a satellite and then use PnP to estimate the pose. The poses of target samples are regarded as latent variables to formulate the task as a minimization problem. Furthermore, we leverage fine-grained segmentation to tackle the information loss issue caused by abstracting the satellite as sparse keypoints. Finally, we iteratively solve the minimization problem in two steps: pseudo-label generation and network training. Experimental results show that our method adapts well to the target domain. Moreover, our method won the 1st place on the sunlamp task of the second international Satellite Pose Estimation Competition.
DAS: Neural Architecture Search via Distinguishing Activation Score
Venue: arXiv cs.LG 2022
Year: 2022

Neural Architecture Search (NAS) is an automatic technique that can search for well-performed architectures for a specific task. Although NAS surpasses human-designed architecture in many fields, the high computational cost of architecture evaluation it requires hinders its development. A feasible solution is to directly evaluate some metrics in the initial stage of the architecture without any training. NAS without training (WOT) score is such a metric, which estimates the final trained accuracy of the architecture through the ability to distinguish different inputs in the activation layer. However, WOT score is not an atomic metric, meaning that it does not represent a fundamental indicator of the architecture. The contributions of this paper are in three folds. First, we decouple WOT into two atomic metrics which represent the distinguishing ability of the network and the number of activation units, and explore better combination rules named (Distinguishing Activation Score) DAS. We prove the correctness of decoupling theoretically and confirmed the effectiveness of the rules experimentally. Second, in order to improve the prediction accuracy of DAS to meet practical search requirements, we propose a fast training strategy. When DAS is used in combination with the fast training strategy, it yields more improvements. Third, we propose a dataset called Darts-training-bench (DTB), which fills the gap that no training states of architecture in existing datasets. Our proposed method has 1.04$\times$ - 1.56$\times$ improvements on NAS-Bench-101, Network Design Spaces, and the proposed DTB.
Dubbing in Practice: A Large Scale Study of Human Localization With Insights for Automatic Dubbing
Venue: arXiv cs.CL 2022
Year: 2022

We investigate how humans perform the task of dubbing video content from one language into another, leveraging a novel corpus of 319.57 hours of video from 54 professionally produced titles. This is the first such large-scale study we are aware of. The results challenge a number of assumptions commonly made in both qualitative literature on human dubbing and machine-learning literature on automatic dubbing, arguing for the importance of vocal naturalness and translation quality over commonly emphasized isometric (character length) and lip-sync constraints, and for a more qualified view of the importance of isochronic (timing) constraints. We also find substantial influence of the source-side audio on human dubs through channels other than the words of the translation, pointing to the need for research on ways to preserve speech characteristics, as well as semantic transfer such as emphasis/emotion, in automatic dubbing systems.
A Closer Look at Some Recent Proof Compression-Related Claims
Venue: arXiv cs.CC 2022
Year: 2022

Gordeev and Haeusler [GH19] claim that each tautology $ρ$ of minimal propositional logic can be proved with a natural deduction of size polynomial in $|ρ|$. This builds on work from Hudelmaier [Hud93] that found a similar result for intuitionistic propositional logic, but for which only the height of the proof was polynomially bounded, not the overall size. They arrive at this result by transforming a proof in Hudelmaier's sequent calculus into an equivalent tree-like proof in Prawitz's system of natural deduction, and then compressing the tree-like proof into an equivalent DAG-like proof in such a way that a polynomial bound on the height and foundation implies a polynomial bound on the overall size. Our paper, however, observes that this construction was performed only on minimal implicational logic, which we show to be weaker than the minimal propositional logic for which they claim the result (see Section 4.2). Simply extending the logic systems used to cover minimal propositional logic would not be sufficient to recover the results of the paper, as it would entirely disrupt proofs of a number of the theorems that are critical to proving the main result. Relying heavily on their aforementioned work, Gordeev and Haeusler [GH20] claim to establish NP=PSPACE. The argument centrally depends on the polynomial bound on proof size in minimal propositional logic. Since we show that that bound has not been correctly established by them, their purported proof does not correctly establish NP=PSPACE.
Matrix Based Adaptive Short Block Cipher
Venue: arXiv cs.CR 2022
Year: 2022

Every day, millions of credit cards are swiped and transactions are carried out across the world. Due to numerous forms of unethical digital activities, users are vulnerable to credit card fraud, phishing, identity theft, etc. This paper outlines a novel block encryption algorithm involving multiple private keys and a resilient trapdoor function that ensures data security while maintaining an optimal run time and space complexity. The proposed scheme consists of an irrepressible trapdoor based on a depressed cubic function and a unique key generation algorithm that uses Fibonacci sequences and invertible square matrices for improved security. The paper involves data obtained from comprehensive cryptanalysis exploiting the strengths and weaknesses of the system and comments on its potential large-scale industry applications.
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Venue: arXiv cs.LG 2022
Year: 2022

Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.
Analysis of Integrating Blockchain Technologies into Multi-Agent Systems
Venue: arXiv cs.MA 2022
Year: 2022

Multi-Agent Systems, a division of Intelligent Systems diversely applied in multiple disciplines. Desired for their efficiency in solving complex problems at a low cost. However, identified vulnerabilities include system security, integrity, and identity management. Blockchain Technologies was chosen for analysis in providing a suitable solution due to features of transparency, encryption, and trust.
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations
Venue: arXiv cs.LG 2022
Year: 2022

Networks have become indispensable and ubiquitous structures in many fields to model the interactions among different entities, such as friendship in social networks or protein interactions in biological graphs. A major challenge is to understand the structure and dynamics of these systems. Although networks evolve through time, most existing graph representation learning methods target only static networks. Whereas approaches have been developed for the modeling of dynamic networks, there is a lack of efficient continuous time dynamic graph representation learning methods that can provide accurate network characterization and visualization in low dimensions while explicitly accounting for prominent network characteristics such as homophily and transitivity. In this paper, we propose the Piecewise-Velocity Model (PiVeM) for the representation of continuous-time dynamic networks. It learns dynamic embeddings in which the temporal evolution of nodes is approximated by piecewise linear interpolations based on a latent distance model with piecewise constant node-specific velocities. The model allows for analytically tractable expressions of the associated Poisson process likelihood with scalable inference invariant to the number of events. We further impose a scalable Kronecker structured Gaussian Process prior to the dynamics accounting for community structure, temporal smoothness, and disentangled (uncorrelated) latent embedding dimensions optimally learned to characterize the network dynamics. We show that PiVeM can successfully represent network structure and dynamics in ultra-low two-dimensional spaces. It outperforms relevant state-of-art methods in downstream tasks such as link prediction. In summary, PiVeM enables easily interpretable dynamic network visualizations and characterizations that can further improve our understanding of the intrinsic dynamics of time-evolving networks.
Event-Driven Testing For Edge Applications
Venue: arXiv cs.DC 2022
Year: 2022

With the rise of the Internet of Things (IoT) and Edge computing, a considerable amount of system services are moving from reliable Cloud data centers to less reliable infrastructures closer to the end-users. However, the constrained resources, unreliable communication, and varying operating conditions of IoT pose significant complexities for software vendors in testing their applications. Although several emulators exist for testing IoT systems, numerous issues can be pointed out, such as lacking support for logical dependencies and advanced fault injection capabilities, requiring manual validation of the system's behavior, or focusing on a specific platform and language. To address these limitations, we propose Frisbee: a framework for the automated testing of IoT applications. Frisbee accelerates the testing process by simplifying the spin-up of distributed virtual testbeds over Kubernetes and the description of `what-if' scenarios that integrate complex sequences of workloads and faultloads.
Designing Autonomous Markets for Stablecoin Monetary Policy
Venue: arXiv cs.CR 2022
Year: 2022

We develop a new type of automated market maker (AMM) that helps to maintain stability and long-term viability in a stablecoin. This primary market AMM (P-AMM) is an autonomous mechanism for pricing minting and redemption of stablecoins in all possible states and is designed to achieve several desirable properties. We first cover several case studies of current ad hoc stablecoin issuance and redemption mechanisms, several of which have contributed to recent stablecoin de-peggings, and formulate desirable properties of a P-AMM that support stability and usability. We then design a P-AMM redemption curve and show that it satisfies these properties, including bounded loss for both the protocol and stablecoin holders. We further show that this redemption curve is path independent and has properties of path deficiency in extended settings involving trading fees and a separate minting curve. This means that system health weakly improves relative to the path independent setting along any trading curve and that there is no incentive to strategically subdivide redemptions. Finally, we show how to implement the P-AMM efficiently on-chain.
Computational Complexity of Flattening Fixed-Angle Orthogonal Chains
Venue: arXiv cs.CG 2022
Year: 2022

Planar/flat configurations of fixed-angle chains and trees are well studied in the context of polymer science, molecular biology, and puzzles. In this paper, we focus on a simple type of fixed-angle linkage: every edge has unit length (equilateral), and each joint has a fixed angle of $90^\circ$ (orthogonal) or $180^\circ$ (straight). When the linkage forms a path (open chain), it always has a planar configuration, namely the zig-zag which alternating the $90^\circ$ angles between left and right turns. But when the linkage forms a cycle (closed chain), or is forced to lie in a box of fixed size, we prove that the flattening problem -- deciding whether there is a planar noncrossing configuration -- is strongly NP-complete. Back to open chains, we turn to the Hydrophobic-Hydrophilic (HP) model of protein folding, where each vertex is labeled H or P, and the goal is to find a folding that maximizes the number of H-H adjacencies. In the well-studied HP model, the joint angles are not fixed. We introduce and analyze the fixed-angle HP model, which is motivated by real-world proteins. We prove strong NP-completeness of finding a planar noncrossing configuration of a fixed-angle orthogonal equilateral open chain with the most H--H adjacencies, even if the chain has only two H vertices. (Effectively, this lets us force the chain to be closed.)
Deep Causal Learning for Robotic Intelligence
Venue: arXiv cs.RO 2022
Year: 2022

This invited review discusses causal learning in the context of robotic intelligence. The paper introduced the psychological findings on causal learning in human cognition, then it introduced the traditional statistical solutions on causal discovery and causal inference. The paper reviewed recent deep causal learning algorithms with a focus on their architectures and the benefits of using deep nets and discussed the gap between deep causal learning and the needs of robotic intelligence.
Efficiently Hardening SGX Enclaves against Memory Access Pattern Attacks via Dynamic Program Partitioning
Venue: arXiv cs.CR 2022
Year: 2022

Intel SGX is known to be vulnerable to a class of practical attacks exploiting memory access pattern side-channels, notably page-fault attacks and cache timing attacks. A promising hardening scheme is to wrap applications in hardware transactions, enabled by Intel TSX, that return control to the software upon unexpected cache misses and interruptions so that the existing side-channel attacks exploiting these micro-architectural events can be detected and mitigated. However, existing hardening schemes scale only to small-data computation, with a typical working set smaller than one or few times (e.g., $8$ times) of a CPU data cache. This work tackles the data scalability and performance efficiency of security hardening schemes of Intel SGX enclaves against memory-access pattern side channels. The key insight is that the size of TSX transactions in the target computation is critical, both performance- and security-wise. Unlike the existing designs, this work dynamically partitions target computations to enlarge transactions while avoiding aborts, leading to lower performance overhead and improved side-channel security. We materialize the dynamic partitioning scheme and build a C++ library to monitor and model cache utilization at runtime. We further build a data analytical system using the library and implement various external oblivious algorithms. Performance evaluation shows that our work can effectively increase transaction size and reduce the execution time by up to two orders of magnitude compared with the state-of-the-art solutions.
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
Venue: arXiv cs.MM 2022
Year: 2022

Blind watermarking provides powerful evidence for copyright protection, image authentication, and tampering identification. However, it remains a challenge to design a watermarking model with high imperceptibility and robustness against strong noise attacks. To resolve this issue, we present a framework Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks. For the invertible part, we develop a diffusion and extraction module (DEM) and a fusion and split module (FSM) to embed and extract watermarks symmetrically in an invertible way. For the non-invertible part, we introduce a non-invertible attention-based module (NIAM) and the noise-specific selection module (NSM) to solve the asymmetric extraction under a strong noise attack. Extensive experiments demonstrate that our framework outperforms the current state-of-the-art methods of imperceptibility and robustness significantly. Our framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks. The code will be available in https://github.com/rmpku/CIN.