Miracle-dz/UniFAR-FTUs huggingface dataset:
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks

This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Specifically, we construct a distinct generative model and design an objective function that encourages the generative model to produce causal, compact, and faithful explanations. This is achieved by isolating the causal factors in the latent space of graphs by maximizing the information flow measurements. We theoretically analyze the cause-effect relationships in the proposed causal graph, identify node attributes as confounders between graphs and GNN predictions, and circumvent such confounder effect by leveraging the backdoor adjustment formula. Our framework is compatible with any GNNs, and it does not require access to the process by which the target GNN produces its predictions. In addition, it does not rely on the linear-independence assumption of the explained features, nor require prior knowledge on the graph learning tasks. We show a proof-of-concept of OrphicX on canonical classification problems on graph data. In particular, we analyze the explanatory subgraphs obtained from explanations for molecular graphs (i.e., Mutag) and quantitatively evaluate the explanation performance with frequently occurring subgraph patterns. Empirically, we show that OrphicX can effectively identify the causal semantics for generating causal explanations, significantly outperforming its alternatives11This project is supported by the Internal Research Fund at The Hong Kong Polytechnic University P0035763. HW is partially supported by NSF Grant IIS-2127918 and an Amazon Faculty Research Award.
Human Response to an AI-Based Decision Support System: A User Study on the Effects of Accuracy and Bias

Artificial Intelligence (AI) is increasingly used to build Decision Support Systems (DSS) across many domains. This paper describes a series of experiments designed to observe human response to different characteristics of a DSS such as accuracy and bias, particularly the extent to which participants rely on the DSS, and the performance they achieve. In our experiments, participants play a simple online game inspired by so-called “wildcat” (i.e., exploratory) drilling for oil. The landscape has two layers: a visible layer describing the costs (ter-rain), and a hidden layer describing the reward (oil yield). The final score of a participant is computed as rewards minus costs. Participants in the control group play the game without receiving any assistance, while in treatment groups they are assisted by a DSS suggesting places to drill. For certain treatments, the DSS does not consider costs, but only rewards, which introduces a bias that is observable by users. Between subjects, we vary the accuracy and bias of the DSS, and observe the participants’ total score, time to completion, the extent to which they follow or ignore suggestions. We also measure the acceptability of the DSS in an exit survey. Our results show that participants tend to score better with the DSS, that the score increase is due to users following the DSS advice, and related to the difficulty of the game and the accuracy of the DSS. We observe that this setting elicits mostly rational behavior from participants, who place a moderate amount of trust in the DSS and show neither algorithmic aversion (under-reliance) nor automation bias (over-reliance). However, their stated willingness to accept the DSS in the exit survey seems less sensitive to the accuracy of the DSS than their behavior, suggesting that users are only partially aware of the (lack of) accuracy of the DSS. The game constitutes a research platform intentionally designed to study decision support in the absence of pre-existing expertise, which makes it an interesting model for studying algorithmic reliance.
Analytic theory for the tangential YORP produced by the asteroid regolith

The tangential YORP effect is a radiation pressure torque produced by asymmetric thermal emission by structures on the asteroid surface. As such structures, previous works considered boulders of different shapes lying on the surface of the asteroid. We study the tangential YORP produced by the rough interface of the asteroid's regolith. We create an approximate analytic theory of heat conduction on a slightly non-flat sinusoidal surface. We analyze the published data on the small-scale shape of the asteroid (162173) Ryugu and estimate its tangential YORP due to the surface roughness. We derive an analytic formula that expresses the TYORP of a sinusoidal surface in terms of its geometric and thermal properties. TYORP is maximal at the thermal parameter of the order of unity and for the shape irregularities of the order of the thermal wavelength. Application of this equation to Ryugu predicts TYORP, which is 5-70 times greater than its normal YORP effect. The contribution of the small-scale regolith roughness to the YORP effect of the asteroid can be comparable to the normal YORP and the tangential YORP produced by boulders. The same theory can describe roughness on the asteroid boulders, thus adding a new term to the previously considered TYORP created by boulders.
Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT

Since 2016, deep learning (DL) has advanced tomographic imaging with re-markable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suﬀer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of diﬀerent methods, and discuss relevant issues and fu-ture directions.
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series

Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study. Designing flexible and scalable generative models is a very challenging task due to the high dimensionality of the data, as well as the need to represent temporal dependencies and marginal network structure. Here we introduce DAMNETS, a scalable deep generative model for network time series. DAMNETS outperforms competing methods on all of our measures of sample quality, over both real and synthetic data sets.
LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents

Identifying keyphrases (KPs) from text documents is a fundamental task in natural language processing and information retrieval. Vast majority of the benchmark datasets for this task are from the scientific domain containing only the document title and abstract information. This limits keyphrase extraction (KPE) and keyphrase generation (KPG) algorithms to identify keyphrases from human-written summaries that are often very short (approx 8 sentences). This presents three challenges for real-world applications: human-written summaries are unavailable for most documents, the documents are almost always long, and a high percentage of KPs are directly found beyond the limited context of title and abstract. Therefore, we release two extensive corpora mapping KPs of ~1.3M and ~100K scientific articles with their fully extracted text and additional metadata including publication venue, year, author, field of study, and citations for facilitating research on this real-world problem.
LogicInference: A New Dataset for Teaching Logical Inference to seq2seq Models

Machine learning models such as Transformers or LSTMs struggle with tasks that are compositional in nature such as those involving reasoning/inference. Although many datasets exist to evaluate compositional generalization, when it comes to evaluating inference abilities, options are more limited. This paper presents LogicInference, a new dataset to evaluate the ability of models to perform logical inference. The dataset focuses on inference using propositional logic and a small subset of first-order logic, represented both in semi-formal logical notation, as well as in natural language. We also report initial results using a collection of machine learning models to establish an initial baseline in this dataset.
A Framework of Meta Functional Learning for Regularising Knowledge Transfer

—Machine learning classiﬁers’ capability is largely dependent on the scale of available training data and limited by the model overﬁtting in data-scarce learning tasks. To address this problem, this work proposes a novel framework of Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Based on this framework, we formulate three variants of MFL: MFL with Prototypes (MFL-P) which learns a functional by auxiliary prototypes, Composite MFL (ComMFL) that transfers knowledge from both functional space and representational space, and MFL with Iterative Updates (MFL-IU) which improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Moreover, we generalise these variants for knowledge transfer regularisation from binary classiﬁers to multi-class classiﬁers. Extensive experiments on two few-shot learning scenarios, Few-Shot Learning (FSL) and Cross-Domain Few-Shot Learning (CD-FSL), show that meta functional learning for knowledge transfer regularisation can improve FSL classiﬁers.
Spectral Line Depth Variability in Radial Velocity Spectra

Stellar active regions, including spots and faculae, can create radial velocity (RV) signals that interfere with the detection and mass measurements of low-mass exoplanets. In doing so, these active regions affect each spectral line differently, but the origin of these differences is not fully understood. Here we explore how spectral line variability correlated with S-index (Ca H and K emission) is related to the atomic properties of each spectral line. Next, we develop a simple analytic stellar atmosphere model that can account for the largest sources of line variability with S-index. Then, we apply this model to HARPS spectra of α Cen B to explain Fe i line depth changes in terms of a disk-averaged temperature difference between active and quiet regions on the visible hemisphere of the star. This work helps establish a physical basis for understanding how stellar activity manifests differently in each spectral line and may help future work mitigating the impact of stellar activity on exoplanet RV surveys.
Absorption studies of the most diffuse gas in the Large Scale Structure

As the Universe evolves, it develops a web of ﬁlamentary structure of matter. This cosmic web is ﬁlled with gas, with the most diffuse gas lying in the intergalactic regions. At low redshift, the gas is predominantly warm-hot, and one of its best tracers is X-ray absorption in sightlines to background quasars. In this Chapter, we present the theoretical background for the formation of the warm-hot intergalactic medium (WHIM) and present the physical properties of the WHIM from cosmological hydro-dynamical simulations. We discuss the feasibility of detecting the WHIM with X-ray absorption lines, with high-resolution and high signal-to-noise spectra. We present detailed discussion of observing techniques, including the WHIM ionization balance, observable lines, the curve of growth, and the diagnostics using the X-ray lines. We present the current efforts of detecting the WHIM with gratings on-board Chandra and XMM-Newton observatories. We discuss the criti-cality of WHIM detections reported in literature, where robust detections are likely from the circumgalactic medium of intervening galaxies, or intra-group medium, rather than truly diffuse gas in the intergalactic medium. Secure detections of the most diffuse gas in the low redshift large scale structure may have to await next generation of X-ray telescopes. We end our Chapter with the discussion of future missions carrying dispersive and non-dispersive spectrometers. We present ﬁgure-Taotao of-merit parameters for line detectibility as well as for the number of WHIM systems that can be detected with future missions. These will deﬁne our ability to account for the missing low-redshift baryons and to understand the evolution of the Universe over half of its life.
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information

The impact of Vehicle-to-Everything (V2X) communications on platoon control performance is investigated. Platoon control is essentially a sequential stochastic decision problem (SSDP), which can be solved by Deep Reinforcement Learning (DRL) to deal with both the control constraints and uncertainty in the platoon leading vehicle’s behavior. In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality. Our objective is to find the specific set of information that should be shared among the vehicles for the construction of the most appropriate state space. SSDP models are conceived for platoon control under different information topologies (IFT) by taking into account ‘just sufficient’ information. Furthermore, theorems are established for comparing the performance of their optimal policies. In order to determine whether a piece of information should or should not be transmitted for improving the DRL-based control policy, we quantify its value by deriving the conditional KL divergence of the transition models. More meritorious information is given higher priority in transmission, since including it in the state space has a higher probability in offsetting the negative effect of having higher state dimensions. Finally, simulation results are provided to illustrate the theoretical analysis.
TransductGAN: a Transductive Adversarial Model for Novelty Detection

Novelty detection, a widely studied problem in machine learning, is the problem of detecting a novel class of data that has not been previously observed. A common setting for novelty detection is inductive whereby only examples of the negative class are available during training time. Transductive novelty detection on the other hand has only witnessed a recent surge in interest, it not only makes use of the negative class during training but also incorporates the (unlabeled) test set to detect novel examples. Several studies have emerged under the transductive setting umbrella that have demonstrated its advantage over its inductive counterpart. Depending on the assumptions about the data, these methods go by different names (e.g. transductive novelty detection, semi-supervised novelty detection, positive-unlabeled learning, out-of-distribution detection). With the use of generative adversarial networks (GAN), a segment of those studies have adopted a transductive setup in order to learn how to generate examples of the novel class. In this study, we propose TransductGAN, a transductive generative adversarial network that attempts to learn how to generate image examples from both the novel and negative classes by using a mixture of two Gaussians in the latent space. It achieves that by incorporating an adversarial autoencoder with a GAN network, the ability to generate examples of novel data points offers not only a visual representation of novelties, but also overcomes the hurdle faced by many inductive methods of how to tune the model hyperparameters at the decision rule level. Our model has shown superior performance over state-of-the-art inductive and transductive methods. Our study is fully reproducible with the code available publicly.
Morphological Demodulation Method Based on Improved Singular Spectrum Decomposition and Its Application in Rolling Bearing Fault Diagnosis

： Aiming at the difficulty of fault feature extraction of rolling bearing under strong background noise and interference sources, a morphological demodulation method based on improved singular spectrum decomposition (SSD) is proposed to detect bearing fault. Firstly, in order to avoid the empirical selection of embedding dimension length in singular spectrum analysis, a novel self-adaptive signal processing method named SSD is used to analyze the vibration signal. In this method, track matrix first is built and embedding dimension length is adaptively selected, and then several single components can be obtained by decomposing the non-stationary signal. However, boundary effect will appear in component reconstruction process of SSD. Aiming at this problem, a waveform matching extension method is used to improve the SSD, and several single components respectively located in high frequency to low frequency can be obtained, which are closer to the actual components curve. Besides, in order to extract the useful fault characteristic information of single components, a self-complementary top-hat transform is proposed to analyze the single components, which its optimal structure elements scale can be obtained by feature energy ratio. Finally, the fault types of bearing can be accurately identified by the spectrum analysis of morphological demodulation results. Simulation and practical signal analysis prove the validity of the proposed method.
Organizational Search, Capability Reconfiguration andCapability Reorientation: A Framework of Organizational Responses to Perceived Capability Gaps

Organizational decision-makers perceive capability gaps when they expect that organizational aspirations can no longer be achieved with an existing capability configuration. In this study I develop a framework for explaining the factors that influence the perception of and the reactions to such capability gaps. In particular, I argue that perception of capability gaps initiates organizational search processes that can trigger four different responses: capability renewal, capability replacement, capability redeployment, and capability replication. Capability replacement and renewal involve capability reconfiguration; capability redeployment and replication serve the purpose of capability reorientation. This study contributes to research on strategic change from a competence-based perspective by shifting attention from separate capabilities toward those particular mechanisms that enable firms to cope with technological change. In addition, this study integrates behavioral concepts with the dynamic capabilities perspective by putting forward the notion of aspirations as baseline to which decision-makers assess the usefulness of capability configurations and by analyzing the role of attention and slack as moderators of those search processes that follow the perception of capability gaps.
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real‐world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance.
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension

Value function based reinforcement learning (RL) algorithms, for example, $Q$-learning, learn optimal policies from datasets of actions, rewards, and state transitions. However, when the underlying state transition dynamics are stochastic and evolve on a high-dimensional space, generating independent and identically distributed (IID) data samples for creating these datasets poses a significant challenge due to the intractability of the associated normalizing integral. In these scenarios, Hamiltonian Monte Carlo (HMC) sampling offers a computationally tractable way to generate data for training RL algorithms. In this paper, we introduce a framework, called \textit{Hamiltonian $Q$-Learning}, that demonstrates, both theoretically and empirically, that $Q$ values can be learned from a dataset generated by HMC samples of actions, rewards, and state transitions. Furthermore, to exploit the underlying low-rank structure of the $Q$ function, Hamiltonian $Q$-Learning uses a matrix completion algorithm for reconstructing the updated $Q$ function from $Q$ value updates over a much smaller subset of state-action pairs. Thus, by providing an efficient way to apply $Q$-learning in stochastic, high-dimensional settings, the proposed approach broadens the scope of RL algorithms for real-world applications.
Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Autonomous crop monitoring is a difficult task due to the complex structure of plants. Occlusions from leaves can make it impossible to obtain complete views about all fruits of, e.g., pepper plants. Therefore, accurately estimating the shape and volume of fruits from partial information is crucial to enable further advanced automation tasks such as yield estimation and automated fruit picking. In this paper, we present an approach for mapping fruits on plants and estimating their shape by matching superellipsoids. Our system segments fruits in images and uses their masks to generate point clouds of the fruits. To combine sequences of acquired point clouds, we utilize a real-time 3D mapping framework and build up a fruit map based on truncated signed distance fields. We cluster fruits from this map and use optimized superellipsoids for matching to obtain accurate shape estimates. In our experiments, we show in various simulated scenarios with a robotic arm equipped with an RGB-D camera that our approach can accurately estimate fruit volumes. Additionally, we provide qualitative results of estimated fruit shapes from data recorded in a commercial glasshouse environment.
State space models vs. multi-step predictors in predictive control: Are state space models complicating safe data-driven designs?

This paper contrasts recursive state space models and direct multi-step predictors for linear predictive control. We provide a tutorial exposition for both model structures to solve the following problems: 1. stochastic optimal control; 2. system identification; 3. stochastic optimal control based on the estimated model. Throughout the paper, we provide detailed discussions of the benefits and limitations of these two model parametrizations for predictive control and highlight the relation to existing works. Additionally, we derive a novel (partially tight) constraint tightening for stochastic predictive control with parametric uncertainty in the multi-step predictor.