Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical and Near-infrared Excesses are Correlated in T Tauri Stars

Accretion is one of the defining characteristics of classical T Tauri stars, fueled by the presence of a circumstellar disk comprised of dust and gas. Accretion produces a UV and optical excess, while re-radiated emission at the inner edge of the dust component of the disk produces a near-infrared (NIR) excess. The interplay between stars and their disks helps regulate protoplanetary disk evolution and dispersal, which is key to a full understanding of planet formation. To investigate the relations between NIR excess and optical excess in both single and binary stars, we used an archival sample of spectroscopically characterized members of the Taurus star-forming region (τ ∼ 1–2 Myr) with measured luminosities, spectral types, and optical veiling. We combined the archival sample with the Two Micron All Sky Survey and Wide-field Infrared Survey Explorer NIR photometry and high-resolution imaging surveys. We found that NIR and optical excesses are correlated in multiple NIR photometric bands, suggesting that they are closely related, likely because more massive disks have higher inner dust disk walls and are also associated with higher accretion rates. We also found that multiplicity has no impact on accretion or inner disk properties in a sample with a wide range of separations, but the sample was too small to specifically investigate close binaries, where the effects of multiplicity on disk properties should be most significant.
A Bayesian Hierarchical Time Series Model for Reconstructing Hydroclimate from Multiple Proxies

We propose a Bayesian hierarchical model which produces probabilistic reconstructions of hydroclimatic variability in Queensland Australia. The model provides a standardised approach to hydroclimate reconstruction using multiple palaeoclimate proxy records derived from natural archives such as speleothems, ice cores and tree rings. The method combines time-series modelling with inverse prediction to quantify the relationships between a given hydroclimate index and relevant proxies over an instrumental period and subsequently reconstruct the hydroclimate back through time. We present case studies for Brisbane and Fitzroy catchments focusing on two hydroclimate indices, the Rainfall Index (RFI) and the Standardised Precipitation-Evapotranspiration Index (SPEI). The probabilistic nature of the reconstructions allows us to estimate the probability that a hydroclimate index in any reconstruction year was lower (higher) than the minimum (maximum) value observed over the instrumental period. In Brisbane, the RFI is unlikely (probabilities<20%) to have exhibited extremes beyond the minimum/maximum values observed between 1889 and 2017. However, in Fitzroy there are several years during the reconstruction period where the RFI is likely (>50% probability) to have exhibited behaviour beyond the minimum/maximum of what has been observed. For SPEI, the probability of observing such extremes since the end of the instrumental period in 1889 doesn't exceed 50% in any reconstruction year in Brisbane or Fitzroy.
Evaluation of Neuromorphic Spike Encoding of Sound Using Information Theory

The problem of spike encoding of sound consists in transforming a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural networks, where it is the first and most crucial stage of processing. Many algorithms have been proposed to perform spike encoding of sound. However, a systematic approach to quantitatively evaluate their performance is currently lacking. We propose the use of an information-theoretic framework to solve this problem. Specifically, we evaluate the coding efficiency of four spike encoding algorithms on two coding tasks that consist of coding the fundamental characteristics of sound: frequency and amplitude. The algorithms investigated are: Independent Spike Coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire coding. Using the tools of information theory, we estimate the information that the spikes carry on relevant aspects of an input stimulus. We find disparities in the coding efficiencies of the algorithms, where Leaky Integrate-and-Fire coding performs best. The information-theoretic analysis of their performance on these coding tasks provides insight on the encoding of richer and more complex sound stimuli.
A Mental-Model Centric Landscape of Human-AI Symbiosis

There has been significant recent interest in developing AI agents capable of effectively interacting and teaming with humans. While each of these works try to tackle a problem quite central to the problem of human-AI interaction, they tend to rely on myopic formulations that obscure the possible inter-relatedness and complementarity of many of these works. The human-aware AI framework was a recent effort to provide a unified account for human-AI interaction by casting them in terms of their relationship to various mental models. Unfortunately, the current accounts of human-aware AI are insufficient to explain the landscape of the work doing in the space of human-AI interaction due to their focus on limited settings. In this paper, we aim to correct this shortcoming by introducing a significantly general version of human-aware AI interaction scheme, called generalized human-aware interaction (GHAI), that talks about (mental) models of six types. Through this paper, we will see how this new framework allows us to capture the various works done in the space of human-AI interaction and identify the fundamental behavioral patterns supported by these works. We will also use this framework to identify potential gaps in the current literature and suggest future research directions to address these shortcomings.
Sharp threshold for rigidity of random graphs

We consider the Erdős–Rényi evolution of random graphs, where a new uniformly distributed edge is added to the graph in every step. For every fixed d⩾1$d\geqslant 1$ , we show that with high probability, the graph becomes rigid in Rd$\mathbb {R}^d$ at the very moment its minimum degree becomes d$d$ , and it becomes globally rigid in Rd$\mathbb {R}^d$ at the very moment its minimum degree becomes d+1$d+1$ .
Graph-based Extractive Explainer for Recommendations

Explanations in a recommender system assist users make informed decisions among a set of recommended items. Extensive research attention has been devoted to generate natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable, and personalized at the same time. In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of proposed solution.
Does Ridesourcing Respond to Unplanned Rail Disruptions? A Natural Experiment Analysis of Mobility Resilience and Disparity

Urban rail transit networks provide critical access to opportunities and livelihood in many urban systems. Ensuring that these services are resilient (that is, exhibiting efficient response to and recovery from disruptions) is a key economic and social priority. Increasingly, the ability of urban rail systems to cope with disruptions is a function of a complex patchwork of mobility options, wherein alternative modes can complement and fill service gaps. This study analyzes the role of ridesourcing in providing adaptive mobility capacity that could be leveraged to fill no-notice gaps in rail transit services, addressing the question of distributional impacts of resilience. Using a natural experiment, we systematically identify 28 major transit disruptions over the period of one year in Chicago and match them, both temporally and spatially, with ridesourcing trip data. Using multilevel mixed modeling, we quantify variation in the adaptive use of on-demand mobility across the racially and economically diverse city of Chicago. Our findings show that the gap-filling potential of adaptive ridesourcing during rail transit disruptions is significantly influenced by station-, community-, and district-level factors. Specifically, greater shifts to ridesourcing occur during weekdays, nonholidays, and more severe disruptions, in community areas that have higher percentages of White residents and transit commuters, and in the more affluent North district of the city. These findings suggest that while ridesourcing appears to provide adaptive capacity during rail disruptions, its benefits do not appear to be equitable for lower-income communities of color that already experience limited mobility options. Research implications for mobility operator collaborations to support mobility as a service are discussed. This study builds a more comprehensive understanding of transit service resilience, variation in vulnerability, and the complementarity of ridesourcing to existing transport networks during disruptions.
Bound states at disclinations: an additive rule of real and reciprocal space topology

Focusing on the two-dimensional (2D) Su-Schrieffer-Heeger (SSH) model, we propose an additive rule between the real-space topological invariant s of disclinations (related to the Burgers vector B) and the reciprocal-space topological invariant p of bulk wave functions (the vectored Zak phase). The disclination-induced bound states in the 2D SSH model appear only if (s + p/2π) is nonzero modulo the lattice constant. These disclination-bound states are robust against perturbations respecting C 4 point group symmetry and other perturbations within an amplitude determined by p. Besides the disclination-bound states, the proposed additive rule also suggests that a half-bound state extends over only half of a sample and a hybrid-bound state, which always have a nonvanishing component of s + p/2π.
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows

This work demonstrates the application of a particular branch of causal inference and deep learning models: \emph{causal-Graphical Normalizing Flows (c-GNFs)}. In a recent contribution, scholars showed that normalizing flows carry certain properties, making them particularly suitable for causal and counterfactual analysis. However, c-GNFs have only been tested in a simulated data setting and no contribution to date have evaluated the application of c-GNFs on large-scale real-world data. Focusing on the \emph{AI for social good}, our study provides a counterfactual analysis of the impact of the International Monetary Fund (IMF) program on child poverty using c-GNFs. The analysis relies on a large-scale real-world observational data: 1,941,734 children under the age of 18, cared for by 567,344 families residing in the 67 countries from the Global-South. While the primary objective of the IMF is to support governments in achieving economic stability, our results find that an IMF program reduces child poverty as a positive side-effect by about 1.2$\pm$0.24 degree (`0' equals no poverty and `7' is maximum poverty). Thus, our article shows how c-GNFs further the use of deep learning and causal inference in AI for social good. It shows how learning algorithms can be used for addressing the untapped potential for a significant social impact through counterfactual inference at population level (ACE), sub-population level (CACE), and individual level (ICE). In contrast to most works that model ACE or CACE but not ICE, c-GNFs enable personalization using \emph{`The First Law of Causal Inference'}.
The stable uniqueness theorem for equivariant Kasparov theory

This paper examines and strengthens the Cuntz–Thomsen picture of equivariant Kasparov theory for arbitrary second-countable locally compact groups, in which elements are given by certain pairs of cocycle representations between C-dynamical systems. The main result is a stable uniqueness theorem that generalizes a fundamental characterization of ordinary KK-theory by Lin and Dadarlat–Eilers. Along the way, we prove an equivariant Cuntz–Thomsen picture analog of the fact that the equivalence relation of homotopy agrees with the (a priori stronger) equivalence relation of stable operator homotopy. The results proved in this paper will be employed as the technical centerpiece in forthcoming work of the authors to classify certain amenable group actions on Kirchberg algebras by equivariant Kasparov theory.
Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network

The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many applications, tactile sensors have yet to be widely deployed in industrial robotics settings; part of the challenge lies in identifying slip and other events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high durability and reliability, and are built from inexpensive, off-the-shelf components. We train a temporal convolution neural network to detect slip, achieving high detection accuracies while displaying robustness to the speed and direction of the slip motion. Further, we test our detector on two manipulation tasks involving a variety of common objects and demonstrate successful generalization to real-world scenarios not seen during training. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.
Toward more generalized Malicious URL Detection Models

This paper reveals a data bias issue that can profoundly hinder the performance of machine learning models in malicious URL detection. We describe how such bias can be diagnosed using interpretable machine learning techniques and further argue that such biases naturally exist in the real world security data for training a classification model. To counteract these challenges, we propose a debiased training strategy that can be applied to most deep-learning based models to alleviate the negative effects of the biased features. The solution is based on the technique of adversarial training to train deep neural networks learning invariant embedding from biased data. Through extensive experimentation, we substantiate that our innovative strategy fosters superior generalization capabilities across both CNN-based and RNN-based detection models. The findings presented in this work not only expose a latent issue in the field but also provide an actionable remedy, marking a significant step forward in the pursuit of more reliable and robust malicious URL detection.
Towards Automatic Transcription of Polyphonic Electric Guitar Music: A New Dataset and a Multi-Loss Transformer Model

In this paper, we propose a new dataset named EGDB, that contains transcriptions of the electric guitar performance of 240 tablatures rendered with different tones. Moreover, we benchmark the performance of two well-known transcription models proposed originally for the piano on this dataset, along with a multi-loss Transformer model that we newly propose. Our evaluation on this dataset and a separate set of real-world recordings demonstrate the influence of timbre on the accuracy of guitar sheet transcription, the potential of using multiple losses for Transformers, as well as the room for further improvement for this task.
An Unsupervised Attentive-Adversarial Learning Framework for Single Image Deraining

—Single image deraining has been an important topic in low-level computer vision tasks. The atmospheric veiling effect (which is generated by rain accumulation, similar to fog) usually appears with the rain. Most deep learning-based single image deraining methods mainly focus on rain streak removal by disregarding this effect, which leads to low-quality deraining performance. In addition, these methods are trained only on synthetic data, hence they do not take into account real-world rainy images. To address the above issues, we propose a novel unsupervised attentive-adversarial learning framework (UALF) for single image deraining that trains on both synthetic and real rainy images while simultaneously capturing both rain streaks and rain accumulation features. UALF consists of a Rain-fog2Clean (R2C) transformation block and a Clean2Rain-fog (C2R) transformation block. In R2C, to better characterize the rain-fog fusion feature and to achieve high-quality deraining performance, we employ an attention rain-fog feature extraction network (ARFE) to exploit the self-similarity of global and local rain-fog information by learning the spatial feature correlations. Moreover, to improve the transformation ability of C2R, we design a rain-fog feature decoupling and reorganization network (RFDR) by embedding a rainy image degradation model and a mixed discriminator to preserve richer texture details. Extensive experiments on benchmark rain-fog and rain datasets show that UALF outperforms state-of-the-art deraining methods. We also conduct defogging performance evaluation experiments to further demonstrate the effectiveness of UALF.
Single-Query Verifiable Proof-of-Sequential-Work

We propose a proof-of-sequential-work ( PoSW ) that can be veriﬁed with only a single query to the random oracle for each random challenge. Proofs-of-sequential-work are protocols that facilitate a veriﬁer to eﬃciently verify if a prover has executed a speciﬁed number of computations sequentially. Denoting this number of sequential computations with N , the prover with poly ( N ) parallelism must take Ω ( N )-sequential time while the veriﬁer veriﬁes the computation in O (log N )-sequential time using upto O (log N ) parallelism. We propose a PoSW that allows any veriﬁer, even the one with no parallelism, to verify using just a single sequential computation on a single challenge. All the existing PoSW s [10,5,2,6] mandate a prover to compute a sequence of responses from a random oracle against N -rounds of queries. Then the prover commits this sequence using a commitment scheme (e.g., Merkle root (like) commitment) predeﬁned in the PoSW s. Now the veriﬁer asks the prover to provide a set of proofs against t randomly chosen checkpoints, called challenges, in the computed sequence. The veriﬁer ﬁnds out the commitment from each of these proofs spending O (log N ) rounds of queries to the oracle. It can be reduced to a single round of queries only if the veriﬁer owns O (log N ) parallelism [6]. The veriﬁer in our PoSW demands no parallelism but uses a single query to the random oracle in order to verify each of the t challenges. The key observation is that the commitment schemes themselves in the prior works
Numeric Encoding Options with Automunge

Mainstream practice in machine learning with tabular data may take for granted that any feature engineering beyond scaling for numeric sets is superfluous in context of deep neural networks. This paper will offer arguments for potential benefits of extended encodings of numeric streams in deep learning by way of a survey of options for numeric transformations as available in the Automunge open source python library platform for tabular data pipelines, where transformations may be applied to distinct columns in"family tree"sets with generations and branches of derivations. Automunge transformation options include normalization, binning, noise injection, derivatives, and more. The aggregation of these methods into family tree sets of transformations are demonstrated for use to present numeric features to machine learning in multiple configurations of varying information content, as may be applied to encode numeric sets of unknown interpretation. Experiments demonstrate the realization of a novel generalized solution to data augmentation by noise injection for tabular learning, as may materially benefit model performance in applications with underserved training data.
TransDreamer: Reinforcement Learning with Transformer World Models

The Dreamer agent provides various benefits of Model-Based Reinforcement Learning (MBRL) such as sample efficiency, reusable knowledge, and safe planning. However, its world model and policy networks inherit the limitations of recurrent neural networks and thus an important question is how an MBRL framework can benefit from the recent advances of transformers and what the challenges are in doing so. In this paper, we propose a transformer-based MBRL agent, called TransDreamer. We first introduce the Transformer State-Space Model, a world model that leverages a transformer for dynamics predictions. We then share this world model with a transformer-based policy network and obtain stability in training a transformer-based RL agent. In experiments, we apply the proposed model to 2D visual RL and 3D first-person visual RL tasks both requiring long-range memory access for memory-based reasoning. We show that the proposed model outperforms Dreamer in these complex tasks.
Active Diffusion of Self-Propelled Particles in Flexible Polymer Networks

Biopolymer networks having a meshwork topology, e.g., extracellular matrix and mucus gels, are ubiquitous. It is an open question to understand how self-propelled agents such as Janus colloidal particles diffuse through such a biopolymer network. Here, we computationally explore this issue in-depth by explicitly modeling three-dimensional biopolymer networks and performing Langevin dynamics simulations of active diffusion of the self-propelled tracers therein. We show that the active tracer performs distinct diffusion dynamics depending on the mesh-to-particle size and P\'eclet number (Pe). When the particle is smaller than the mesh size, it moves as if in free space with a decreased mobility depending on the polymer occupation density and Pe. However, when the particle size is increased to be comparable to the mesh size, the active particles explore the polymer network using the trapped-and-hopping mechanism. We study the trapped time distribution, flight length distribution, the mean-squared displacement, and the long-time diffusivity at varying Pe. If the particle is larger than the mesh, it captures the collective viscoelastic dynamics from the polymer network at short times and the simple diffusion of the total system at large times. Finally, we discuss the scaling behavior of the long-time diffusivity with Pe, where we find a range of Pe that yields a nontrivial power law. The latter turns out to arise from a large fluctuation of trapped, activated tracers in conjugation with responsive polymer networks.
Uniting Control and Data Parallelism: Towards Scalable Memory-Driven Dynamic Graph Processing

Control parallelism and data parallelism is mostly reasoned and optimized as separate functions. Because of this, workloads that are irregular, fine-grain and dynamic such as dynamic graph processing become very hard to scale. An experimental research approach to computer architecture that synthesizes prior techniques of parallel computing along with new innovations is proposed in this paper. We establish the background and motivation of the research undertaking and provide a detailed description of the proposed omputing system that is highly parallel non-von Neumann, memory-centric and memory-driven. We also present a message-driven (or even-driven) programming model called"diffusive computation"and provide insights into its properties using SSSP and Triangle Counting problems as examples.