Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal Homotopy

We characterize homotopical equivalences between causal DAG models, exploiting the close connections between partially ordered set representations of DAGs (posets) and finite Alexandroff topologies. Alexandroff spaces yield a directional topological space: the topology is defined by a unique minimal basis defined by an open set for each variable x, specified as the intersection of all open sets containing x. Alexandroff spaces induce a (reflexive, transitive) preorder. Alexandroff spaces satisfying the Kolmogorov T0 separation criterion, where open sets distinguish variables, converts the preordering into a partial ordering. Our approach broadly is to construct a topological representation of posets from data, and then use the poset representation to build a conventional DAG causal model. We illustrate our framework by showing how it unifies disparate algorithms and case studies proposed previously. Topology plays two key roles in causal discovery. First, topological separability constraints on datasets have been used in several previous approaches to infer causal structure from observations and interventions. Second, a diverse range ofgraphical models used to represent causal structures can be represented in a unified way in terms of a topological representation of the induced poset structure. We show that the homotopy theory of Alexandroff spaces can be exploited to significantly efficiently reduce the number of possible DAG structures, reducing the search space by several orders of magnitude.
Learning Curves for Sequential Training of Neural Networks: Self-Knowledge Transfer and Forgetting

Sequential training from task to task is becoming one of the major objects in deep learning applications such as continual learning and transfer learning. Nevertheless, it remains unclear under what conditions the trained model’s performance improves or deteriorates. To deepen our understanding of sequential training, this study provides a theoretical analysis of generalization performance in a solvable case of continual learning. We consider neural networks in the neural tangent kernel (NTK) regime that continually learn target functions from task to task, and investigate the generalization by using an established statistical mechanical analysis of kernel ridge-less regression. We ﬁrst show characteristic transitions from positive to negative transfer. More similar targets above a speciﬁc critical value can achieve positive knowledge transfer for the subsequent task while catastrophic forgetting occurs even with very similar targets. Next, we investigate a variant of continual learning where the model learns the same target function in multiple tasks. Even for the same target, the trained model shows some transfer and forgetting depending on the sample size of each task. We can guarantee that the generalization error monotonically decreases from task to task for equal sample sizes while unbalanced sample sizes deteriorate the generalization. We respectively refer to these improvement and deterioration as self -knowledge transfer and forgetting, and empirically conﬁrm them in realistic training of deep neural networks as well.
Survey on English Entity Linking on Wikidata

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.
Push-Sum Distributed Dual Averaging for Convex Optimization in Multiagent Systems With Communication Delays

The distributed convex optimization problem over the multiagent system is considered in this article, and it is assumed that each agent possesses its own cost function and communicates with its neighbors over a sequence of time-varying directed graphs. However, due to some reasons, there exist communication delays while agents receive information from other agents, and we are going to seek the optimal value of the sum of agents’ loss functions in this case. We desire to handle this problem with the push-sum distributed dual averaging (PS-DDA) algorithm. We study the effects of communication delays on the convergence results of the PS-DDA algorithm and propose an explicit bound on the convergence rate. It is proved that this algorithm converges, and the convergence result of the PS-DDA algorithm will be worse as the maximum delay value on one edge becomes larger. Our analysis indicates that the PS-DDA algorithm can converge at a rate of <inline-formula> <tex-math notation="LaTeX">${\mathcal {O}}(T^{-0.5})$ </tex-math></inline-formula> with proper step size, where <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is iteration span. We finally apply the theoretical results to numerical simulations to show the PS-DDA algorithm’s performance.
Mobility Signatures: A Tool for Characterizing Cities Using Intercity Mobility Flows

Understanding the patterns of human mobility between cities has various applications from transport engineering to spatial modeling of the spreading of contagious diseases. We adopt a city-centric, data-driven perspective to quantify such patterns and introduce the mobility signature as a tool for understanding how a city (or a region) is embedded in the wider mobility network. We demonstrate the potential of the mobility signature approach through two applications that build on mobile-phone-based data from Finland. First, we use mobility signatures to show that the well-known radiation model is more accurate for mobility flows associated with larger Finnish cities, while the traditional gravity model appears a better fit for less populated areas. Second, we illustrate how the SARS-CoV-2 pandemic disrupted the mobility patterns in Finland in the spring of 2020. These two cases demonstrate the ability of the mobility signatures to quickly capture features of mobility flows that are harder to extract using more traditional methods.
Processes and countermeasures in overtaking road accidents

Of police road accident files describing overtaking accidents, 973 were sampled from the headquarters of Nottinghamshire Constabulary, UK for 1989–1993. Of particularly detailed cases, 410 were examined in depth and salient facts extracted including the exact manoeuvre involved, the errors made by drivers, the reasons for error, and the principal behavioural countermeasures that would have prevented the accident. Particular hazards were: (1) oncoming vehicles that only came into view during the overtake, from turnings, bends and hill crests, and (2) collisions involving the same direction cohort. The most effective single countermeasure was to avoid overtaking a vehicle travelling at or near the speed limit. The study illustrates the 'structured judgement method' of accident causation research in which human interpreters are used to assign causes and processes to individual cases, but orthodox research techniques are used to standardize procedures, and to assess and ensure reliability.
Make A Long Image Short: Adaptive Token Length for Vision Transformers

The vision transformer splits each image into a sequence of tokens with fixed length and processes the tokens in the same way as words in natural language processing. More tokens normally lead to better performance but considerably increased computational cost. Motivated by the proverb"A picture is worth a thousand words"we aim to accelerate the ViT model by making a long image short. To this end, we propose a novel approach to assign token length adaptively during inference. Specifically, we first train a ViT model, called Resizable-ViT (ReViT), that can process any given input with diverse token lengths. Then, we retrieve the"token-length label"from ReViT and use it to train a lightweight Token-Length Assigner (TLA). The token-length labels are the smallest number of tokens to split an image that the ReViT can make the correct prediction, and TLA is learned to allocate the optimal token length based on these labels. The TLA enables the ReViT to process the image with the minimum sufficient number of tokens during inference. Thus, the inference speed is boosted by reducing the token numbers in the ViT model. Our approach is general and compatible with modern vision transformer architectures and can significantly reduce computational expanse. We verified the effectiveness of our methods on multiple representative ViT models (DeiT, LV-ViT, and TimesFormer) across two tasks (image classification and action recognition).
Models for characterizing Darrieus turbines and synthesizing wind speed scenarios

With the evolving global energy dynamics, new and innovative ways to integrate renewable energy technologies into residential and commercial complexes are being adopted. Techno-economic models such as NREL-SAM and HOMER are handy for evaluating such integration of renewables. However, the unavailability of libraries for vertical axis wind turbines (VAWT) and wind speed data limitations have been identified in the two platforms (NREL-SAM and HOMER). Accordingly, this project looks into developing models for evaluating power curve tables for Darrieus turbines and synthesizing wind speed scenarios. Analytical and numerical models for Darrieus turbines have been presented, and the ability to evaluate power curve tables from both models has been demonstrated. Double-Multiple Streamtube model was implemented for the analytical modelling, and 2-D CFD simulations on OpenFOAM were developed for the numerical modelling. The analytical model was found to fail for small-sized turbines, likely due to poor lift and drag coefficient values at high local angles of attack, but matched sufficiently with experimental results for large-sized turbines. Numerical simulations for Darrieus turbines were of two types - torque evaluation at constant wind speed and angular velocity, and starting characteristics for constant wind speed. Auto-regressive moving average models were implemented to characterize historical wind speed measurements, thus synthesizing wind speed scenarios. For this, the wind speeds or wind speed residuals (following detrending) were first normalized using the cumulative distribution functions, following which an ARMA model was fit.
FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

Abstract Browser fingerprinting is a stateless tracking technique that aims to combine information exposed by multiple different web APIs to create a unique identifier for tracking users across the web. Over the last decade, trackers have abused several existing and newly proposed web APIs to further enhance the browser fingerprint. Existing approaches are limited to detecting a specific fingerprinting technique(s) at a particular point in time. Thus, they are unable to systematically detect novel fingerprinting techniques that abuse different web APIs. In this paper, we propose FP-Radar, a machine learning approach that leverages longitudinal measurements of web API usage on top-100K websites over the last decade for early detection of new and evolving browser fingerprinting techniques. The results show that FP-Radar is able to early detect the abuse of newly introduced properties of already known (e.g., WebGL, Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for browser fingerprinting. To the best of our knowledge, FP-Radar is the first to detect the abuse of the Visibility API for ephemeral fingerprinting in the wild.
Mind Your Clever Neighbours: Unsupervised Person Re-identification via Adaptive Clustering Relationship Modeling

Unsupervised person re-identification (Re-ID) attracts increasing attention due to its potential to resolve the scalability problem of supervised Re-ID models. Most existing unsupervised methods adopt an iterative clustering mechanism, where the network was trained based on pseudo labels generated by unsupervised clustering. However, clustering errors are inevitable. To generate high-quality pseudo-labels and mitigate the impact of clustering errors, we propose a novel clustering relationship modeling framework for unsupervised person Re-ID. Specifically, before clustering, the relation between unlabeled images is explored based on a graph correlation learning (GCL) module and the refined features are then used for clustering to generate high-quality pseudo-labels.Thus, GCL adaptively mines the relationship between samples in a mini-batch to reduce the impact of abnormal clustering when training. To train the network more effectively, we further propose a selective contrastive learning (SCL) method with a selective memory bank update policy. Extensive experiments demonstrate that our method shows much better results than most state-of-the-art unsupervised methods on Market1501, DukeMTMC-reID and MSMT17 datasets. We will release the code for model reproduction.
Active Inference in Robotics and Artificial Agents: Survey and Challenges

Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning. Recently, it has been shown to be a promising approach to the problems of state-estimation and control under uncertainty, as well as a foundation for the construction of goal-driven behaviours in robotics and artificial agents in general. Here, we review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning; describing current achievements with a particular focus on robotics. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness. Furthermore, we connect this approach with other frameworks and discuss its expected benefits and challenges: a unified framework with functional biological plausibility using variational Bayesian inference.
Scalable Hypergraph Embedding System

Many problems such as node classification and link prediction in network data can be solved using graph embeddings, and a number of algorithms are known for constructing such embeddings. However, it is difficult to use graphs to capture non-binary relations such as communities of nodes. These kinds of complex relations are expressed more naturally as hypergraphs. While hypergraphs are a generalization of graphs, state-of-the-art graph embedding techniques are not adequate for solving prediction and classification tasks on large hypergraphs accurately in reasonable time. In this paper, we introduce FaHyEm, a novel hierarchical framework for scalable unsupervised hypergraph embedding. FaHyEm is capable of generating high quality embeddings for real-world hypergraphs with millions of nodes and hyperedges in only a couple of minutes while existing hypergraph systems either fail for such large hypergraphs or may take days to produce the embeddings.
TransZero: Attribute-guided Transformer for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which are strong prior for localization of object attribute for representing discriminative region features enabling significant visual-semantic interaction. Although few attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected. In this paper, we propose an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero. Specifically, TransZero takes a feature augmentation encoder to alleviate the cross-dataset bias between ImageNet and ZSL benchmarks and improve the transferability of visual features by reducing the entangled relative geometry relationships among region features. To learn locality-augmented visual features, TransZero employs a visual-semantic decoder to localize the most relevant image regions to each attributes from a given image under the guidance of attribute semantic information. Then, the locality-augmented visual features and semantic vectors are used for conducting effective visual-semantic interaction in a visual-semantic embedding network. Extensive experiments show that TransZero achieves a new state-of-the-art on three ZSL benchmarks. The codes are available at: https://github.com/shiming-chen/TransZero.
The Box Size Confidence Bias Harms Your Object Detector

Countless applications depend on accurate predictions with reliable confidence estimates from modern object detectors. However, it is well known that neural networks, including object detectors, produce miscalibrated confidence estimates. Recent work even suggests that detectors’ confidence predictions are biased with respect to object size and position. In object detection, the issues of conditional biases, confidence calibration, and task performance are usually explored in isolation, but, as we aim to show, they are closely related. We formally prove that the conditional confidence bias harms the performance of object detectors and empirically validate these findings. Specifically, to quantify the performance impact of the confidence bias on object detectors, we modify the histogram binning calibration to avoid performance impairment and instead improve it through calibration conditioned on the bounding box size. We further find that the confidence bias is also present in detections generated on the training data of the detector, which can be leveraged to perform the de-biasing. Moreover, we show that Test Time Augmentation (TTA) confounds this bias, which results in even more significant performance impairments on the detectors. Finally, we use our proposed algorithm to analyze a diverse set of object detection architectures and show that the conditional confidence bias harms their performance by up to 0.6 mAP and 0.8 mAP50. Code available at https://github.com/Blueblue4/Object-Detection-Confidence-Bias.
Second Rényi entropy and annulus partition function for one-dimensional quantum critical systems with boundaries

We consider the entanglement entropy in critical one-dimensional quantum systems with open boundary conditions. We show that the second Rényi entropy of an interval away from the boundary can be computed exactly, provided the same conformal boundary condition is applied on both sides. The result involves the annulus partition function. We compare our exact result with numerical computations for the critical quantum Ising chain with open boundary conditions. We find excellent agreement, and we analyse in detail the finite-size corrections, which are known to be much larger than for a periodic system.
A Wide and Deep Exploration of Radio Galaxies with Subaru HSC (WERGS). VI. Distant Filamentary Structures Pointed Out by High-z Radio Galaxies at z ∼ 4

We present the environmental properties around high-z radio galaxies (HzRGs) at z ∼ 4, which have been poorly investigated because of their rarity. We use the largest samples of HzRGs and g-dropout galaxy overdense regions at z ∼ 4, which were constructed from the Hyper Suprime-Cam Subaru Strategic Program, to characterize the HzRG environments statistically. We measure the g-dropout galaxy overdensities around 21 HzRGs whose rest-frame 1.4 GHz radio luminosities (L 1.4 GHz) are 1026–27 W Hz−1. We find that the overdensities around the faint HzRGs with L 1.4 GHz ∼ 1026.0–26.5 W Hz−1 tend to be higher than those of the g-dropout galaxies. On the other hand, no significant difference of density environments is found between the luminous HzRGs with L 1.4 GHz ∼ 1026.5–27.0 W Hz−1 and the g-dropout galaxies. The HzRGs are found to occupy more massive halos than g-dropout galaxies through a cross-correlation between the HzRGs and g-dropout galaxies. This trend is more pronounced in the faint HzRGs. These results are consistent with a scenario where HzRGs get older and more massive as the radio luminosity decreases. The HzRGs are expected to trace the progenitors of Local Cluster halos from their calculated halo mass. In addition, we find that surrounding galaxies tend to distribute along the radio jet major axis of the HzRGs at angular distances of ≲500 physical kpc. Our findings imply the onset of the filamentary structures around the HzRGs at z ∼ 4.
Blackbox Untargeted Adversarial Testing of Automatic Speech Recognition Systems

—Automatic speech recognition (ASR) systems are prevalent, particularly in applications for voice navigation and voice control of domestic appliances. The computational core of ASRs are deep neural networks (DNNs) that have been shown to be susceptible to adversarial perturbations; easily misused by attackers to generate malicious outputs. To help test the correctness of ASRS, we propose techniques that automatically generate blackbox (agnostic to the DNN), untargeted adversarial attacks that are portable across ASRs. Much of the existing work on adversarial ASR testing focuses on targeted attacks, i.e generating audio samples given an output text. Targeted techniques are not portable, customised to the structure of DNNs (whitebox) within a speciﬁc ASR. In contrast, our method attacks the signal processing stage of the ASR pipeline that is shared across most ASRs. Additionally, we ensure the generated adversarial audio samples have no human audible difference by manipulating the acoustic signal using a psychoacoustic model that maintains the signal below the thresholds of human perception. We evaluate portability and effectiveness of our techniques using three popular ASRs and three input audio datasets using the metrics - Word Error Rate (WER) of output text, Similarity to original audio and attack Success Rate on different ASRs. We found our testing techniques were portable across ASRs, with the adversarial audio samples producing high Success Rates , WERs and Similarities to the original audio.