Miracle-dz/UniFAR-FTUs huggingface dataset:
CLOUD: Contrastive Learning of Unsupervised Dynamics

Developing agents that can perform complex control tasks from high dimensional observations such as pixels is challenging due to difficulties in learning dynamics efficiently. In this work, we propose to learn forward and inverse dynamics in a fully unsupervised manner via contrastive estimation. Specifically, we train a forward dynamics model and an inverse dynamics model in the feature space of states and actions with data collected from random exploration. Unlike most existing deterministic models, our energy-based model takes into account the stochastic nature of agent-environment interactions. We demonstrate the efficacy of our approach across a variety of tasks including goal-directed planning and imitation from observations. Project videos and code are at this https URL.
An ancestral role of pericentrin in centriole formation through SAS-6 recruitment

The centrosome is composed of two centrioles surrounded by a microtubule-nucleating pericentriolar matrix (PCM). Centrioles regulate matrix assembly. Here we ask whether the matrix also regulates centriole assembly. To define the interaction between the matrix and individual centriole components, we take advantage of a heterologous expression system using fission yeast. Importantly, its centrosome, the spindle pole body (SPB), has matrix but no centrioles. Surprisingly, we observed that the SPB can recruit several animal centriole components. Pcp1/pericentrin, a conserved matrix component that is often upregulated in cancer, recruits a critical centriole constituent, SAS-6. We further show that this novel interaction is conserved and important for centriole biogenesis and elongation in animals. We speculate that the Pcp1/pericentrin-SAS-6 interaction surface was conserved for one billion years of evolution after centriole loss in yeasts, due to its conserved binding to calmodulin. This study reveals an ancestral relationship between pericentrin and the centriole, where both regulate each other assembly, ensuring mutual localisation. Short summary The pericentriolar matrix (PCM) is not only important for microtubule-nucleation but also can regulate centriole biogenesis. Ito et al. reveal an ancestral interaction between the centriole protein SAS-6 and the PCM component pericentrin, which regulates centriole biogenesis and elongation.
DualNet: Locate Then Detect Effective Payload with Deep Attention Network

Network intrusion detection (NID) is an essential defense strategy that is used to discover the trace of suspicious user behaviour in large-scale cyberspace, and machine learning (ML), due to its capability of automation and intelligence, has been gradually adopted as a mainstream hunting method in recent years. However, traditional ML based network intrusion detection systems (NIDSs) are not effective to recognize unknown threats and their high detection rate often comes with the cost of high false alarms, which leads to the problem of alarm fatigue. To address the above problems, in this paper, we propose a novel neural network based detection system, DualNet, which is constructed with a general feature extraction stage and a crucial feature learning stage. DualNet can rapidly reuse the spatial-temporal features in accordance with their importance to facilitate the entire learning process and simultaneously mitigate several optimization problems occurred in deep learning (DL). We evaluate the DualNet on two benchmark cyber attack datasets, NSL-KDD and UNSW-NB15. Our experiment shows that DualNet outperforms classical ML based NIDSs and is more effective than existing DL methods for NID in terms of accuracy, detection rate and false alarm rate.
ROGER: Reconstructing orbits of galaxies in extreme regions using machine learning techniques

We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We use a sample of 34 massive, M200 > 1015h−1M⊙, galaxy clusters in the MultiDark Planck 2 (MDLP2) simulation at redshift zero. We select all galaxies with stellar mass M⋆ ≥ 108.5h−1M⊙, as computed by the semi-analytic model of galaxy formation SAG, that are located in, and in the vicinity of, these clusters and classify them according to their orbits. We train ROGER to retrieve the original classification of the galaxies from their projected phase-space positions. For each galaxy, ROGER gives as output the probability of being a cluster galaxy, a galaxy that has recently fallen into a cluster, a backsplash galaxy, an infalling galaxy, or an interloper. We discuss the performance of the machine learning methods and potential uses of our code. Among the different methods explored, we find the K-Nearest Neighbours algorithm achieves the best performance.
Domain Adaptation in LiDAR Semantic Segmentation

LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems during their decision making processes. Deep neural networks are achieving state-of-the-art results on large public benchmarks on this task. Unfortunately, finding models that generalize well or adapt to additional domains, where data distribution is different, remains a major challenge. This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. Our approach combines novel ideas on top of the current state-of-the-art approaches and yields new state-of-the-art results. We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space. Besides, we propose a learning-based approach that aligns the distribution of the semantic classes of the target domain to the source domain. The presented ablation study shows how each part contributes to the final performance. Our strategy is shown to outperform previous approaches for domain adaptation with comparisons run on three different domains.
Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text

Current approaches to open-domain question answering often make crucial assumptions that prevent them from generalizing to real-world settings, including the access to parameterized retrieval systems well-tuned for the task, access to structured metadata like knowledge bases and web links, or a priori knowledge of the complexity of questions to be answered (e.g., single-hop or multi-hop). To address these limitations, we propose a unified system to answer open-domain questions of arbitrary complexity directly from text that works with off-the-shelf retrieval systems on arbitrary text collections. We employ a single multi-task model to perform all the necessary subtasks---retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents---in an iterative fashion. To emulate a more realistic setting, we also constructed a new unified benchmark by collecting about 200 multi-hop questions that require three Wikipedia pages to answer, and combining them with existing datasets. We show that our model not only outperforms state-of-the-art systems on several existing benchmarks that exclusively feature single-hop or multi-hop open-domain questions, but also achieves strong performance on the new benchmark.
Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset

Modern social intelligence includes the ability to watch videos and answer questions about social and theory-of-mind-related content, e.g., for a scene in Harry Potter, “Is the father really upset about the boys flying the car? ” Social visual question answering (social VQA) is emerging as a valuable methodology for studying social reasoning in both humans (e.g., children with autism) and AI agents. However, this problem space spans enormous variations in both videos and questions. We discuss methods for creating and characterizing social VQA datasets, including 1) crowdsourcing versus in-house authoring, including sample comparisons of two new datasets that we created (TinySocial-Crowd and TinySocial-InHouse) and the previously existing Social-IQ dataset; 2) a new rubric for characterizing the difficulty and content of a given video; and 3) a new rubric for characterizing question types. We close by describing how having well-characterized social VQA datasets will enhance the explainability of AI agents and can also inform assessments and educational interventions for people.
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing

The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height ≤ t. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2.2x speed-up in decoding time and a ~5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GraPPa.
Rethinking the Competition Between Detection and ReID in Multiobject Tracking

Due to balanced accuracy and speed, one-shot models which jointly learn detection and identification embeddings, have drawn great attention in multi-object tracking (MOT). However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm. This leads to inferior performance compared with existing two-stage methods. In this paper, we first dissect the reasoning process for these two tasks, which reveals that the competition between them inevitably would destroy task-dependent representations learning. To tackle this problem, we propose a novel reciprocal network (REN) with a self-relation and cross-relation design so that to impel each branch to better learn task-dependent representations. The proposed model aims to alleviate the deleterious tasks competition, meanwhile improve the cooperation between detection and ReID. Furthermore, we introduce a scale-aware attention network (SAAN) that prevents semantic level misalignment to improve the association capability of ID embeddings. By integrating the two delicately designed networks into a one-shot online MOT system, we construct a strong MOT tracker, namely CSTrack. Our tracker achieves the state-of-the-art performance on MOT16, MOT17 and MOT20 datasets, without other bells and whistles. Moreover, CSTrack is efficient and runs at 16.4 FPS on a single modern GPU, and its lightweight version even runs at 34.6 FPS. The complete code has been released at https://github.com/JudasDie/SOTS
Digital Transformation: Environmental Friend or Foe? Panel Discussion at the Australasian Conference on Information Systems 2019

The advent of digital technologies such as social media, mobile, analytics, cloud computing and internet-of-things has provided unique opportunities for organizations to engage in innovations that are affordable, easy-to-use, easy-to-learn and easy-to-implement. Transformations through such technologies often have positive impacts on business processes, products and services. As such, organizations have managed to increase productivity and efficiency, reduce cycle time and make substantial gains through digital transformation. Such transformations have also been positively associated with reducing harmful environmental impacts by providing organizations alternative ways of undertaking their business activities. However, in recent times, especially with an abundance of technologies being available at near-zero costs, questions regarding the potential negative impacts of digital transformation on the environment have arisen. The morass of the ubiquitous technologies around us necessitates the continuing creation of large data centers, that are increasing their capacity yielding a negative impact on the environment. Considering this dialectical contradiction, a panel was conducted at the Australasian Conference on Information Systems (ACIS) in Perth, Australia, in 2019. Its aim was to invigorate the dialogue regarding the impact of digital transformation on environmental sustainability and suggested some directions for future research in this area.
Calculate Center-of-Inertia Frequency and System RoCoF Using PMU Data

The power system frequency is important for the system overall stability. However, there does not exist a single measurement point of the system frequency due to the distributed nature of the system inertia and the small inconsistency of different generator rotors' electrical speeds in one synchronized system. This paper proposed a new approach to calculate the system center-of-inertia (CoI) frequency and the rate-of-change-of-frequency (RoCoF) more accurately using PMU data at multiple locations. The CoI frequency and the RoCoF value were further used to assist fast estimation of the imbalance MW amount of a frequency event. Test results using actual measurements in the U.S. Eastern Interconnection system validated the effectiveness of the proposed method.
A modified Bayesian Convolutional Neural Network for Breast Histopathology Image Classification and Uncertainty Quantification

Convolutional neural network (CNN) based classification models have been successfully used on histopathological images for the detection of diseases. Despite its success, CNN may yield erroneous or overfitted results when the data is not sufficiently large or is biased. To overcome these limitations of CNN and to provide uncertainty quantification Bayesian CNN is recently proposed. However, we show that Bayesian-CNN still suffers from inaccuracies, especially in negative predictions. In the present work, we extend the Bayesian-CNN to improve accuracy and the rate of convergence. The proposed model is called modified Bayesian-CNN. The novelty of the proposed model lies in an adaptive activation function that contains a learnable parameter for each of the neurons. This adaptive activation function dynamically changes the loss function thereby providing faster convergence and better accuracy. The uncertainties associated with the predictions are obtained since the model learns a probability distribution on the network parameters. It reduces overfitting through an ensemble averaging over networks, which in turn improves accuracy on the unknown data. The proposed model demonstrates significant improvement by nearly eliminating overfitting and remarkably reducing (about 38%) the number of false-negative predictions. We found that the proposed model predicts higher uncertainty for images having features of both the classes. The uncertainty in the predictions of individual images can be used to decide when further human-expert intervention is needed. These findings have the potential to advance the state-of-the-art machine learning-based automatic classification for histopathological images.
Pre-trained Model for Chinese Word Segmentation with Meta Learning

Recent researches show that pre-trained models such as BERT (Devlin et al., 2019) are beneficial for Chinese Word Segmentation tasks. However, existing approaches usually finetune pre-trained models directly on a separate downstream Chinese Word Segmentation corpus. These recent methods don't fully utilize the prior knowledge of existing segmentation corpora, and don't regard the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks. In this work, we propose a Pre-Trained Model for Chinese Word Segmentation, which can be abbreviated as PTM-CWS. PTM-CWS model employs a unified architecture for different segmentation criteria, and is pre-trained on a joint multi-criteria corpus with meta learning algorithm. Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge, reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora.
Sharper convergence bounds of Monte Carlo Rademacher Averages through Self-Bounding functions

We derive sharper probabilistic concentration bounds for the Monte Carlo Empirical Rademacher Averages (MCERA), which are proved through recent results on the concentration of self-bounding functions. Our novel bounds allow obtaining sharper bounds to (Local) Rademacher Averages. We also derive novel variance-aware bounds for the special case where only one vector of Rademacher random variables is used to compute the MCERA. Then, we leverage the framework of self-bounding functions to derive novel probabilistic bounds to the supremum deviations, that may be of independent interest.
Theoretical opportunities for rural innovation and entrepreneurship research

Even though rural entrepreneurship and innovation has been studied for decades, the advent of social media, mobile, analytics, cloud computing and internet of things - also referred as digital technologies - (Nambisan 2013, Yoo et al. 2012) has provided new opportunities and challenges for this vast discipline. As a result, we see new business models, new processes, products and services offered using new digital technologies. Such changes challenge the orthodox view of IT entrepreneurship and innovation, opening new avenues for researches and challenges the existing theoretical understanding. This book chapter is an attempt to understand the existing literature on rural innovation and entrepreneurship in information systems discipline and identify opportunities for rural entrepreneurship and innovation in the digital era.
AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting

We address the problem of image-based crowd counting. In particular, we propose a new problem called unlabeled scene-adaptive crowd counting. Given a new target scene, we would like to have a crowd counting model specifically adapted to this particular scene based on the target data that capture some information about the new scene. In this paper, we propose to use one or more unlabeled images from the target scene to perform the adaptation. In comparison with the existing problem setups (e.g. fully supervised), our proposed problem setup is closer to the real-world applications of crowd counting systems. We introduce a novel AdaCrowd framework to solve this problem. Our framework consists of a crowd counting network and a guiding network. The guiding network predicts some parameters in the crowd counting network based on the unlabeled images from a particular scene. This allows our model to adapt to different target scenes. The experimental results on several challenging benchmark datasets demonstrate the effectiveness of our proposed approach compared with other alternative methods. Code is available at https://github.com/maheshkkumar/adacrowd
Towards falsifiable interpretability research

Methods for understanding the decisions of and mechanisms underlying deep neural networks (DNNs) typically rely on building intuition by emphasizing sensory or semantic features of individual examples. For instance, methods aim to visualize the components of an input which are "important" to a network's decision, or to measure the semantic properties of single neurons. Here, we argue that interpretability research suffers from an over-reliance on intuition-based approaches that risk-and in some cases have caused-illusory progress and misleading conclusions. We identify a set of limitations that we argue impede meaningful progress in interpretability research, and examine two popular classes of interpretability methods-saliency and single-neuron-based approaches-that serve as case studies for how overreliance on intuition and lack of falsifiability can undermine interpretability research. To address these concerns, we propose a strategy to address these impediments in the form of a framework for strongly falsifiable interpretability research. We encourage researchers to use their intuitions as a starting point to develop and test clear, falsifiable hypotheses, and hope that our framework yields robust, evidence-based interpretability methods that generate meaningful advances in our understanding of DNNs.
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits

In the contextual linear bandit setting, algorithms built on the optimism principle fail to exploit the structure of the problem and have been shown to be asymptotically suboptimal. In this paper, we follow recent approaches of deriving asymptotically optimal algorithms from problem-dependent regret lower bounds and we introduce a novel algorithm improving over the state-of-the-art along multiple dimensions. We build on a reformulation of the lower bound, where context distribution and exploration policy are decoupled, and we obtain an algorithm robust to unbalanced context distributions. Then, using an incremental primal-dual approach to solve the Lagrangian relaxation of the lower bound, we obtain a scalable and computationally efficient algorithm. Finally, we remove forced exploration and build on confidence intervals of the optimization problem to encourage a minimum level of exploration that is better adapted to the problem structure. We demonstrate the asymptotic optimality of our algorithm, while providing both problem-dependent and worst-case finite-time regret guarantees. Our bounds scale with the logarithm of the number of arms, thus avoiding the linear dependence common in all related prior works. Notably, we establish minimax optimality for any learning horizon in the special case of non-contextual linear bandits. Finally, we verify that our algorithm obtains better empirical performance than state-of-the-art baselines.
Fusion of Dual Spatial Information for Hyperspectral Image Classification

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance. The task of spectral-spatial hyperspectral image (HSI) classification has remained challenging because of high intraclass spectrum variability and low interclass spectral variability. This fact has made the extraction of spatial information highly active. In this work, a novel HSI classification framework using the fusion of dual spatial information is proposed, in which the dual spatial information is built by both exploiting pre-processing feature extraction and post-processing spatial optimization. In the feature extraction stage, an adaptive texture smoothing method is proposed to construct the structural profile (SP), which makes it possible to precisely extract discriminative features from HSIs. The SP extraction method is used here for the first time in the remote sensing community. Then, the extracted SP is fed into a spectral classifier. In the spatial optimization stage, a pixel-level classifier is used to obtain the class probability followed by an extended random walker-based spatial optimization technique. Finally, a decision fusion rule is utilized to fuse the class probabilities obtained by the two different stages. Experiments performed on three data sets from different scenes illustrate that the proposed method can outperform other state-of-the-art classification techniques. In addition, the proposed feature extraction method, i.e., SP, can effectively improve the discrimination between different land covers.