OpenRaiser/Intern-Atlas huggingface dataset:
An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems: Extended Version
Venue: arXiv cs.AI 2023
Year: 2023

Decision mining enables the discovery of decision rules from event logs or streams, and constitutes an important part of in-depth analysis and optimisation of business processes. So far, decision mining has been merely applied in an ex-post way resulting in a snapshot of decision rules for the given chunk of log data. Online decision mining, by contrast, enables continuous monitoring of decision rule evolution and decision drift. Hence this paper presents an end-to-end approach for the discovery as well as monitoring of decision points and the corresponding decision rules during runtime, bridging the gap between online control flow discovery and decision mining. The approach provides automatic decision support for process-aware information systems with efficient decision drift discovery and monitoring. For monitoring, not only the performance, in terms of accuracy, of decision rules is taken into account, but also the occurrence of data elements and changes in branching frequency. The paper provides two algorithms, which are evaluated on four synthetic and one real-life data set, showing feasibility and applicability of the approach. Overall, the approach fosters the understanding of decisions in business processes and hence contributes to an improved human-process interaction.
Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction
Venue: arXiv cs.CV 2023
Year: 2023

Adaptive radiotherapy is a growing field of study in cancer treatment due to it's objective in sparing healthy tissue. The standard of care in several institutions includes longitudinal cone-beam computed tomography (CBCT) acquisitions to monitor changes, but have yet to be used to improve tumor control while managing side-effects. The aim of this study is to demonstrate the clinical value of pre-treatment CBCT acquired daily during radiation therapy treatment for head and neck cancers for the downstream task of predicting severe toxicity occurrence: reactive feeding tube (NG), hospitalization and radionecrosis. For this, we propose a deformable 3D classification pipeline that includes a component analyzing the Jacobian matrix of the deformation between planning CT and longitudinal CBCT, as well as clinical data. The model is based on a multi-branch 3D residual convolutional neural network, while the CT to CBCT registration is based on a pair of VoxelMorph architectures. Accuracies of 85.8% and 75.3% was found for radionecrosis and hospitalization, respectively, with similar performance as early as after the first week of treatment. For NG tube risk, performance improves with increasing the timing of the CBCT fraction, reaching 83.1% after the $5_{th}$ week of treatment.
Cross-Tool and Cross-Behavior Perceptual Knowledge Transfer for Grounded Object Recognition
Venue: arXiv cs.RO 2023
Year: 2023

Humans learn about objects via interaction and using multiple perceptions, such as vision, sound, and touch. While vision can provide information about an object's appearance, non-visual sensors, such as audio and haptics, can provide information about its intrinsic properties, such as weight, temperature, hardness, and the object's sound. Using tools to interact with objects can reveal additional object properties that are otherwise hidden (e.g., knives and spoons can be used to examine the properties of food, including its texture and consistency). Robots can use tools to interact with objects and gather information about their implicit properties via non-visual sensors. However, a robot's model for recognizing objects using a tool-mediated behavior does not generalize to a new tool or behavior due to differing observed data distributions. To address this challenge, we propose a framework to enable robots to transfer implicit knowledge about granular objects across different tools and behaviors. The proposed approach learns a shared latent space from multiple robots' contexts produced by respective sensory data while interacting with objects using tools. We collected a dataset using a UR5 robot that performed 5,400 interactions using 6 tools and 6 behaviors on 15 granular objects and tested our method on cross-tool and cross-behavioral transfer tasks. Our results show the less experienced target robot can benefit from the experience gained from the source robot and perform recognition on a set of novel objects. We have released the code, datasets, and additional results: https://github.com/gtatiya/Tool-Knowledge-Transfer.
Abstract Visual Reasoning Enabled by Language
Venue: arXiv cs.AI 2023
Year: 2023

While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexible intelligence. The Abstraction and Reasoning Corpus (ARC), a visual intelligence benchmark introduced by François Chollet, aims to assess how close AI systems are to human-like cognitive abilities. Most current approaches rely on carefully handcrafted domain-specific program searches to brute-force solutions for the tasks present in ARC. In this work, we propose a general learning-based framework for solving ARC. It is centered on transforming tasks from the vision to the language domain. This composition of language and vision allows for pre-trained models to be leveraged at each stage, enabling a shift from handcrafted priors towards the learned priors of the models. While not yet beating state-of-the-art models on ARC, we demonstrate the potential of our approach, for instance, by solving some ARC tasks that have not been solved previously.
Validation of a Hospital Digital Twin with Machine Learning
Venue: arXiv cs.AI 2023
Year: 2023

Recently there has been a surge of interest in developing Digital Twins of process flows in healthcare to better understand bottlenecks and areas of improvement. A key challenge is in the validation process. We describe a work in progress for a digital twin using an agent based simulation model for determining bed turnaround time for patients in hospitals. We employ a strategy using machine learning for validating the model and implementing sensitivity analysis.
Adaptive Weighted Multiview Kernel Matrix Factorization with its application in Alzheimer's Disease Analysis -- A clustering Perspective
Venue: arXiv cs.LG 2023
Year: 2023

Recent technology and equipment advancements provide with us opportunities to better analyze Alzheimer's disease (AD), where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper we propose a novel model to leverage data from all different modalities/views, which can learn the weights of each view adaptively. Different from previous vanilla Non-negative Matrix Factorization which assumes data is linearly separable, we propose a simple yet efficient method based on kernel matrix factorization, which is not only able to deal with non-linear data structure but also can achieve better prediction accuracy. Experimental results on ADNI dataset demonstrate the effectiveness of our proposed method, which indicate promising prospects of kernel application in AD analysis.
End-to-end Face-swapping via Adaptive Latent Representation Learning
Venue: arXiv cs.CV 2023
Year: 2023

Taking full advantage of the excellent performance of StyleGAN, style transfer-based face swapping methods have been extensively investigated recently. However, these studies require separate face segmentation and blending modules for successful face swapping, and the fixed selection of the manipulated latent code in these works is reckless, thus degrading face swapping quality, generalizability, and practicability. This paper proposes a novel and end-to-end integrated framework for high resolution and attribute preservation face swapping via Adaptive Latent Representation Learning. Specifically, we first design a multi-task dual-space face encoder by sharing the underlying feature extraction network to simultaneously complete the facial region perception and face encoding. This encoder enables us to control the face pose and attribute individually, thus enhancing the face swapping quality. Next, we propose an adaptive latent codes swapping module to adaptively learn the mapping between the facial attributes and the latent codes and select effective latent codes for improved retention of facial attributes. Finally, the initial face swapping image generated by StyleGAN2 is blended with the facial region mask generated by our encoder to address the background blur problem. Our framework integrating facial perceiving and blending into the end-to-end training and testing process can achieve high realistic face-swapping on wild faces without segmentation masks. Experimental results demonstrate the superior performance of our approach over state-of-the-art methods.
Stabilized training of joint energy-based models and their practical applications
Venue: arXiv cs.LG 2023
Year: 2023

The recently proposed Joint Energy-based Model (JEM) interprets discriminatively trained classifier $p(y|x)$ as an energy model, which is also trained as a generative model describing the distribution of the input observations $p(x)$. The JEM training relies on "positive examples" (i.e. examples from the training data set) as well as on "negative examples", which are samples from the modeled distribution $p(x)$ generated by means of Stochastic Gradient Langevin Dynamics (SGLD). Unfortunately, SGLD often fails to deliver negative samples of sufficient quality during the standard JEM training, which causes a very unbalanced contribution from the positive and negative examples when calculating gradients for JEM updates. As a consequence, the standard JEM training is quite unstable requiring careful tuning of hyper-parameters and frequent restarts when the training starts diverging. This makes it difficult to apply JEM to different neural network architectures, modalities, and tasks. In this work, we propose a training procedure that stabilizes SGLD-based JEM training (ST-JEM) by balancing the contribution from the positive and negative examples. We also propose to add an additional "regularization" term to the training objective -- MI between the input observations $x$ and output labels $y$ -- which encourages the JEM classifier to make more certain decisions about output labels. We demonstrate the effectiveness of our approach on the CIFAR10 and CIFAR100 tasks. We also consider the task of classifying phonemes in a speech signal, for which we were not able to train JEM without the proposed stabilization. We show that a convincing speech can be generated from the trained model. Alternatively, corrupted speech can be de-noised by bringing it closer to the modeled speech distribution using a few SGLD iterations. We also propose and discuss additional applications of the trained model.
Preference-Aware Delivery Planning for Last-Mile Logistics
Venue: arXiv cs.AI 2023
Year: 2023

Optimizing delivery routes for last-mile logistics service is challenging and has attracted the attention of many researchers. These problems are usually modeled and solved as variants of vehicle routing problems (VRPs) with challenging real-world constraints (e.g., time windows, precedence). However, despite many decades of solid research on solving these VRP instances, we still see significant gaps between optimized routes and the routes that are actually preferred by the practitioners. Most of these gaps are due to the difference between what's being optimized, and what the practitioners actually care about, which is hard to be defined exactly in many instances. In this paper, we propose a novel hierarchical route optimizer with learnable parameters that combines the strength of both the optimization and machine learning approaches. Our hierarchical router first solves a zone-level Traveling Salesman Problem with learnable weights on various zone-level features; with the zone visit sequence fixed, we then solve the stop-level vehicle routing problem as a Shortest Hamiltonian Path problem. The Bayesian optimization approach is then introduced to allow us to adjust the weights to be assigned to different zone features used in solving the zone-level Traveling Salesman Problem. By using a real-world delivery dataset provided by the Amazon Last Mile Routing Research Challenge, we demonstrate the importance of having both the optimization and the machine learning components. We also demonstrate how we can use route-related features to identify instances that we might have difficulty with. This paves ways to further research on how we can tackle these difficult instances.
Improved Bounds for Covering Paths and Trees in the Plane
Venue: arXiv cs.CG 2023
Year: 2023

A covering path for a planar point set is a path drawn in the plane with straight-line edges such that every point lies at a vertex or on an edge of the path. A covering tree is defined analogously. Let $π(n)$ be the minimum number such that every set of $n$ points in the plane can be covered by a noncrossing path with at most $π(n)$ edges. Let $τ(n)$ be the analogous number for noncrossing covering trees. Dumitrescu, Gerbner, Keszegh, and Tóth (Discrete & Computational Geometry, 2014) established the following inequalities: \[\frac{5n}{9} - O(1) < π(n) < \left(1-\frac{1}{601080391}\right)n, \quad\text{and} \quad\frac{9n}{17} - O(1) < τ(n)\leqslant \left\lfloor\frac{5n}{6}\right\rfloor.\] We report the following improved upper bounds: \[π(n)\leqslant \left(1-\frac{1}{22}\right)n, \quad\text{and}\quad τ(n)\leqslant \left\lceil\frac{4n}{5}\right\rceil.\] In the same context we study rainbow polygons. For a set of colored points in the plane, a perfect rainbow polygon is a simple polygon that contains exactly one point of each color in its interior or on its boundary. Let $ρ(k)$ be the minimum number such that every $k$-colored point set in the plane admits a perfect rainbow polygon of size $ρ(k)$. Flores-Peñaloza, Kano, Martínez-Sandoval, Orden, Tejel, Tóth, Urrutia, and Vogtenhuber (Discrete Mathematics, 2021) proved that $20k/19 - O(1) <ρ(k) < 10k/7 + O(1).$ We report the improved upper bound $ρ(k)< 7k/5 + O(1)$. To obtain the improved bounds we present simple $O(n\log n)$-time algorithms that achieve paths, trees, and polygons with our desired number of edges.
Sample Efficient Multimodal Semantic Augmentation for Incremental Summarization
Venue: arXiv cs.CL 2023
Year: 2023

In this work, we develop a prompting approach for incremental summarization of task videos. We develop a sample-efficient few-shot approach for extracting semantic concepts as an intermediate step. We leverage an existing model for extracting the concepts from the images and extend it to videos and introduce a clustering and querying approach for sample efficiency, motivated by the recent advances in perceiver-based architectures. Our work provides further evidence that an approach with richer input context with relevant entities and actions from the videos and using these as prompts could enhance the summaries generated by the model. We show the results on a relevant dataset and discuss possible directions for the work.
Semantically Consistent Multi-view Representation Learning
Venue: arXiv cs.LG 2023
Year: 2023

In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly concentrate on the learning process in the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel Semantically Consistent Multi-view Representation Learning (SCMRL), which makes efforts to excavate underlying multi-view semantic consensus information and utilize the information to guide the unified feature representation learning. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module, which are elegantly integrated by the contrastive learning strategy to simultaneously align semantic labels of both view-specific feature representations and the learned unified feature representation. In this way, the consensus information in the semantic space can be effectively exploited to constrain the learning process of unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority.
Policy Mirror Descent Inherently Explores Action Space
Venue: arXiv cs.LG 2023
Year: 2023

Explicit exploration in the action space was assumed to be indispensable for online policy gradient methods to avoid a drastic degradation in sample complexity, for solving general reinforcement learning problems over finite state and action spaces. In this paper, we establish for the first time an $\tilde{\mathcal{O}}(1/ε^2)$ sample complexity for online policy gradient methods without incorporating any exploration strategies. The essential development consists of two new on-policy evaluation operators and a novel analysis of the stochastic policy mirror descent method (SPMD). SPMD with the first evaluation operator, called value-based estimation, tailors to the Kullback-Leibler divergence. Provided the Markov chains on the state space of generated policies are uniformly mixing with non-diminishing minimal visitation measure, an $\tilde{\mathcal{O}}(1/ε^2)$ sample complexity is obtained with a linear dependence on the size of the action space. SPMD with the second evaluation operator, namely truncated on-policy Monte Carlo (TOMC), attains an $\tilde{\mathcal{O}}(\mathcal{H}_{\mathcal{D}}/ε^2)$ sample complexity, where $\mathcal{H}_{\mathcal{D}}$ mildly depends on the effective horizon and the size of the action space with properly chosen Bregman divergence (e.g., Tsallis divergence). SPMD with TOMC also exhibits stronger convergence properties in that it controls the optimality gap with high probability rather than in expectation. In contrast to explicit exploration, these new policy gradient methods can prevent repeatedly committing to potentially high-risk actions when searching for optimal policies.
Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network
Venue: arXiv cs.CV 2023
Year: 2023

Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. Although recent geostationary satellite resolution has improved, the long-term analysis of climate applications is limited to using multiple satellites from the past to the present due to the different resolutions. To solve this problem, we proposed warp and refine network (WR-Net). WR-Net is divided into an optical flow warp component and a warp image refinement component. We used the TV-L1 algorithm instead of deep learning-based approaches to extract the optical flow warp component. The deep-learning-based model is trained on the human-centric view of the RGB channel and does not work on geostationary satellites, which is gray-scale one-channel imagery. The refinement network refines the warped image through a multi-temporal fusion layer. We evaluated WR-Net by interpolation of temporal resolution at 4 min intervals to 2 min intervals in large-scale GK2A geostationary meteorological satellite imagery. Furthermore, we applied WR-Net to the future frame prediction task and showed that the explicit use of optical flow can help future frame prediction.
Next-Generation URLLC with Massive Devices: A Unified Semi-Blind Detection Framework for Sourced and Unsourced Random Access
Venue: arXiv cs.IT 2023
Year: 2023

This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with massive devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference information (RI) is embedded in the access signal to resolve the inherent ambiguities incurred by the unknown CSI. At the receiver, we further develop a successive interference cancellation-based semi-blind detection scheme, where a bilinear generalized approximate message passing algorithm is utilized for joint channel and signal estimation (JCSE), while the embedded RI is exploited for ambiguity elimination. Particularly, a rank selection approach and a RI-aided initialization strategy are incorporated to reduce the algorithmic computational complexity and to enhance the JCSE reliability, respectively. Besides, four enabling techniques are integrated to satisfy the stringent latency and reliability requirements of massive URLLC. Numerical results demonstrate that the proposed semi-blind detection framework offers a better scalability-latency-reliability tradeoff than the state-of-the-art detection schemes dedicated to sourced or unsourced RA.
NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker
Venue: arXiv cs.CL 2023
Year: 2023

Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.
Self-Supervised Learning for Group Equivariant Neural Networks
Venue: arXiv cs.CV 2023
Year: 2023

This paper proposes a method to construct pretext tasks for self-supervised learning on group equivariant neural networks. Group equivariant neural networks are the models whose structure is restricted to commute with the transformations on the input. Therefore, it is important to construct pretext tasks for self-supervised learning that do not contradict this equivariance. To ensure that training is consistent with the equivariance, we propose two concepts for self-supervised tasks: equivariant pretext labels and invariant contrastive loss. Equivariant pretext labels use a set of labels on which we can define the transformations that correspond to the input change. Invariant contrastive loss uses a modified contrastive loss that absorbs the effect of transformations on each input. Experiments on standard image recognition benchmarks demonstrate that the equivariant neural networks exploit the proposed equivariant self-supervised tasks.