Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet-Packet Powered Deepfake Image Detection

As neural networks become more able to generate realistic artiﬁcial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, at the same time, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable toolbox arises to identify artiﬁcial images and other content. Previous work primarily relies on pixel-space convolutional neural networks or the Fourier transform. To the best of our knowledge, wavelet-based generative adversarial neural network (GAN) analysis and detection methods have been absent thus far. This paper aims to ﬁll this gap and describes a wavelet-based approach to GAN-generated image analysis and detection. We evaluate our method on FFHQ [19], CelebA [22], and LSUN [40] source identiﬁcation problems and ﬁnd improved or competitive performance.
Intra- and inter-fraction relative range verification in heavy-ion therapy using filtered interaction vertex imaging

Heavy-ion therapy, particularly using scanned (active) beam delivery, provides a precise and highly conformal dose distribution, with maximum dose deposition for each pencil beam at its endpoint (Bragg peak), and low entrance and exit dose. To take full advantage of this precision, robust range verification methods are required; these methods ensure that the Bragg peak is positioned correctly in the patient and the dose is delivered as prescribed. Relative range verification allows intra-fraction monitoring of Bragg peak spacing to ensure full coverage with each fraction, as well as inter-fraction monitoring to ensure all fractions are delivered consistently. To validate the proposed filtered interaction vertex imaging (IVI) method for relative range verification, a 16O beam was used to deliver 12 Bragg peak positions in a 40 mm poly-(methyl methacrylate) phantom. Secondary particles produced in the phantom were monitored using position-sensitive silicon detectors. Events recorded on these detectors, along with a measurement of the treatment beam axis, were used to reconstruct the sites of origin of these secondary particles in the phantom. The distal edge of the depth distribution of these reconstructed points was determined with logistic fits, and the translation in depth required to minimize the χ 2 statistic between these fits was used to compute the range shift between any two Bragg peak positions. In all cases, the range shift was determined with sub-millimeter precision, to a standard deviation of the mean of 220(10) μm. This result validates filtered IVI as a reliable relative range verification method, which should be capable of monitoring each energy step in each fraction of a scanned heavy-ion treatment plan.
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness

Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts. Unfortunately, efforts towards simultaneously meeting these requirements have mostly been unsuccessful. This raises an important question: Is the inability to create Compact, Accurate, and Robust Deep neural networks (CARDs) fundamental? To answer this question, we perform a large-scale analysis of popular model compression techniques which uncovers several intriguing patterns. Notably, in contrast to traditional pruning approaches (e.g., fine tuning and gradual magnitude pruning), we find that"lottery ticket-style"approaches can surprisingly be used to produce CARDs, including binary-weight CARDs. Specifically, we are able to create extremely compact CARDs that, compared to their larger counterparts, have similar test accuracy and matching (or better) robustness -- simply by pruning and (optionally) quantizing. Leveraging the compactness of CARDs, we develop a simple domain-adaptive test-time ensembling approach (CARD-Decks) that uses a gating module to dynamically select appropriate CARDs from the CARD-Deck based on their spectral-similarity with test samples. The proposed approach builds a"winning hand'' of CARDs that establishes a new state-of-the-art (on RobustBench) on CIFAR-10-C accuracies (i.e., 96.8% standard and 92.75% robust) and CIFAR-100-C accuracies (80.6% standard and 71.3% robust) with better memory usage than non-compressed baselines (pretrained CARDs and CARD-Decks available at https://github.com/RobustBench/robustbench). Finally, we provide theoretical support for our empirical findings.
Quantum phase transition dynamics in the two-dimensional transverse-field Ising model

The quantum Kibble-Zurek mechanism (QKZM) predicts universal dynamical behavior near the quantum phase transitions (QPTs). It is now well understood for the one-dimensional quantum matter. Higher-dimensional systems, however, remain a challenge, complicated by the fundamentally different character of the associated QPTs and their underlying conformal field theories. In this work, we take the first steps toward theoretical exploration of the QKZM in two dimensions for interacting quantum matter. We study the dynamical crossing of the QPT in the paradigmatic Ising model by a joint effort of modern state-of-the-art numerical methods, including artificial neural networks and tensor networks. As a central result, we quantify universal QKZM behavior close to the QPT. We also note that, upon traversing further into the ferromagnetic regime, deviations from the QKZM prediction appear. We explain the observed behavior by proposing an extended QKZM taking into account spectral information as well as phase ordering. Our work provides a testing platform for higher-dimensional quantum simulators.
XCiT: Cross-Covariance Image Transformers

Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yields global interactions between all tokens ,i.e. words or image patches, and enables flexible modelling of image data beyond the local interactions of convolutions. This flexibility, however, comes with a quadratic complexity in time and memory, hindering application to long sequences and high-resolution images. We propose a"transposed"version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries. The resulting cross-covariance attention (XCA) has linear complexity in the number of tokens, and allows efficient processing of high-resolution images. Our cross-covariance image transformer (XCiT) is built upon XCA. It combines the accuracy of conventional transformers with the scalability of convolutional architectures. We validate the effectiveness and generality of XCiT by reporting excellent results on multiple vision benchmarks, including image classification and self-supervised feature learning on ImageNet-1k, object detection and instance segmentation on COCO, and semantic segmentation on ADE20k.
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA

We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to extend the identifiability theory of deep generative models for a very broad class of structured models. While previous works have shown identifiability for specific classes of time-series models, our theorems extend this to more general temporal structures as well as to models with more complex structures such as spatial dependencies. In particular, we establish the major result that identifiability for this framework holds even in the presence of noise of unknown distribution. Finally, as an example of our framework's flexibility, we introduce the first nonlinear ICA model for time-series that combines the following very useful properties: it accounts for both nonstationarity and autocorrelation in a fully unsupervised setting; performs dimensionality reduction; models hidden states; and enables principled estimation and inference by variational maximum-likelihood.
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task

In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling. To encourage the model to learn better encoding ability, we propose a column selection auxiliary task to empower the encoder with the relevance matching capability by using explicit learning targets. Furthermore, we propose two value filling methods to build the bridge from the existing zero-shot semantic parsers to real-world applications, considering most of the existing parsers ignore the values filling in the synthesized SQL. With experiments on Spider, our proposed framework improves over the baselines on the execution accuracy and exact set match accuracy when database contents are unavailable, and detailed analysis sheds light on future work.
Exploring the Properties and Evolution of Neural Network Eigenspaces during Training

We investigate properties and the evolution of the emergent inference process inside neural networks using layer saturation [1] and logistic regression probes [2]. We demonstrate that the difficulty of a problem, defined by the number of classes and complexity of the visual domain, as well as the number of parameters in neural network layers affect the predictive performance in an antagonistic manner. We further show that this relationship can be measured using saturation. This opens the possibility of detecting over- and under-parameterization of neural networks. We further show that the observed effects are independent of previously reported pathological patterns like the "tail pattern" described in [1]. Finally, we study the emergence of saturation patterns during training, showing that saturation patterns emerge early during training. This allows for early analysis and potentially increased cycle-time during experiments.
Automatic Curricula via Expert Demonstrations

We propose Automatic Curricula via Expert Demonstrations (ACED), a reinforcement learning (RL) approach that combines the ideas of imitation learning and curriculum learning in order to solve challenging robotic manipulation tasks with sparse reward functions. Curriculum learning solves complicated RL tasks by introducing a sequence of auxiliary tasks with increasing difficulty, yet how to automatically design effective and generalizable curricula remains a challenging research problem. ACED extracts curricula from a small amount of expert demonstration trajectories by dividing demonstrations into sections and initializing training episodes to states sampled from different sections of demonstrations. Through moving the reset states from the end to the beginning of demonstrations as the learning agent improves its performance, ACED not only learns challenging manipulation tasks with unseen initializations and goals, but also discovers novel solutions that are distinct from the demonstrations. In addition, ACED can be naturally combined with other imitation learning methods to utilize expert demonstrations in a more efficient manner, and we show that a combination of ACED with behavior cloning allows pick-and-place tasks to be learned with as few as 1 demonstration and block stacking tasks to be learned with 20 demonstrations.
A Fait Accompli? An Empirical Study into the Absence of Consent to Third-Party Tracking in Android Apps

Third-party tracking allows companies to collect users' behavioural data and track their activity across digital devices. This can put deep insights into users' private lives into the hands of strangers, and often happens without users' awareness or explicit consent. EU and UK data protection law, however, requires consent, both 1) to access and store information on users' devices and 2) to legitimate the processing of personal data as part of third-party tracking, as we analyse in this paper. This paper further investigates whether and to what extent consent is implemented in mobile apps. First, we analyse a representative sample of apps from the Google Play Store. We find that most apps engage in third-party tracking, but few obtained consent before doing so, indicating potentially widespread violations of EU and UK privacy law. Second, we examine the most common third-party tracking libraries in detail. While most acknowledge that they rely on app developers to obtain consent on their behalf, they typically fail to put in place robust measures to ensure this: disclosure of consent requirements is limited; default consent implementations are lacking; and compliance guidance is difficult to find, hard to read, and poorly maintained.
Implications of Eccentric Observations on Binary Black Hole Formation Channels

Orbital eccentricity is one of the most robust discriminators for distinguishing between dynamical and isolated formation scenarios of binary black hole mergers using gravitational-wave observatories such as LIGO and Virgo. Using state-of-the-art cluster models, we show how selection effects impact the detectable distribution of eccentric mergers from clusters. We show that the observation (or lack thereof) of eccentric binary black hole mergers can significantly constrain the fraction of detectable systems that originate from dynamical environments, such as dense star clusters. After roughly 150 observations, observing no eccentric binary signals would indicate that clusters cannot make up the majority of the merging binary black hole population in the local universe (95% credibility). However, if dense star clusters dominate the rate of eccentric mergers and a single system is confirmed to be measurably eccentric in the first and second gravitational-wave transient catalogs, clusters must account for at least 14% of detectable binary black hole mergers. The constraints on the fraction of detectable systems from dense star clusters become significantly tighter as the number of eccentric observations grows and will be constrained to within 0.5 dex once 10 eccentric binary black holes are observed.
Quantum Monte Carlo studies with microscopic two- and three-body interactions of a trimer scaling function

Lucas Madeira, ∗ Tobias Frederico, Stefano Gandolfi, Lauro Tomio, and Marcelo T. Yamashita Instituto de F́ısica de São Carlos, Universidade de São Paulo, CP 369, 13560-970, São Carlos, São Paulo, Brazil Instituto Tecnológico de Aeronáutica, DCTA, 12228-900 São José dos Campos, Brazil Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Instituto de F́ısica Teórica, Universidade Estadual Paulista, 01405-900 São Paulo, Brazil (Dated: June 18, 2021)
Scenario Generation of Wind Farm Power for Real-Time System Operation

This work proposes a method of wind farm scenario generation to support real-time optimization tools and presents key findings therein. This work draws upon work from the literature and presents an efficient and scalable method for producing an adequate number of scenarios for a large fleet of wind farms while capturing both spatial and temporal dependencies. The method makes probabilistic forecasts using conditional heteroscedastic regression for each wind farm and time horizon. Past training data is transformed (using the probabilistic forecasting models) into standard normal samples. A Gaussian copula is estimated from the normalized samples and used in real-time to enforce proper spatial and temporal dependencies. The method is evaluated using historical data from MISO and performance within the MISO real-time look-ahead framework is discussed.
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction

Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and responses to treatment. Better estimations of prognosis would support treatment planning and patient support. Advances in machine learning have informed development of clinical predictive models, but their integration into clinical practice is almost non-existent. One reasons for this is the lack of interpretability of models. In this paper, we use a novel brain tumour dataset to compare two interpretable rule list models against popular machine learning approaches for brain tumour survival prediction. All models are quantitatively evaluated using standard performance metrics. The rule lists are also qualitatively assessed for their interpretability and clinical utility. The interpretability of the “black box” machine learning models is evaluated using two post-hoc explanation techniques, LIME and SHAP. Our results show that the rule lists were only slightly outperformed by the black box models. We demonstrate that rule list algorithms produced simple decision lists that align Email addresses: Colleen.Charlton@camh.ca (Colleen E. Charlton), michael.poon@ed.ac. uk (Michael Tin Chung Poon), paul.brennan@ed.ac. uk (Paul M. Brennan), jdf@ed.ac. uk (Jacques D. Fleuriot) Current address for C.E. Charlton: Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada ar X iv :2 10 6. 09 42 4v 1 [ cs . L G ] 1 7 Ju n 20 21 with clinical expertise. By comparison, post-hoc interpretability methods applied to “black box” models may produce unreliable explanations of local model predictions. Model interpretability is essential for understanding differences in predictive performance and for integration into clinical practice.
Scrambled Translation Problem: A Problem of Denoising UNMT

In this paper and we identify an interesting kind of error in the output of Unsupervised Neural Machine Translation (UNMT) systems like Undreamt1. We refer to this error type as Scrambled Translation problem. We observe that UNMT models which use word shuffle noise (as in case of Undreamt) can generate correct words and but fail to stitch them together to form phrases. As a result and words of the translated sentence look scrambled and resulting in decreased BLEU. We hypothesise that the reason behind scrambled translation problem is ’shuffling noise’ which is introduced in every input sentence as a denoising strategy. To test our hypothesis and we experiment by retraining UNMT models with a simple retraining strategy. We stop the training of the Denoising UNMT model after a pre-decided number of iterations and resume the training for the remaining iterations- which number is also pre-decided- using original sentence as input without adding any noise. Our proposed solution achieves significant performance improvement UNMT models that train conventionally. We demonstrate these performance gains on four language pairs and viz. and English-French and English-German and English-Spanish and Hindi-Punjabi. Our qualitative and quantitative analysis shows that the retraining strategy helps achieve better alignment as observed by attention heatmap and better phrasal translation and leading to statistically significant improvement in BLEU scores.
Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects

A critical decision point when training predictors using multiple studies is whether these studies should be combined or treated separately. We compare two multi-study learning approaches in the presence of potential heterogeneity in predictor-outcome relationships across datasets. We consider 1) merging all of the datasets and training a single learner, and 2) multi-study ensembling, which involves training a separate learner on each dataset and combining the predictions resulting from each learner. In a linear regression setting, we show analytically and confirm via simulation that merging yields lower prediction error than ensembling when the predictor-outcome relationships are relatively homogeneous across studies. However, as cross-study heterogeneity increases, there exists a transition point beyond which ensembling outperforms merging. We provide analytic expressions for the transition point in various scenarios, study asymptotic properties, and illustrate how transition point theory can be used for deciding when studies should be combined with an application from metabolomics.
BELT: Block-wise Missing Embedding Learning Transformer

Matrix completion has attracted attention in many ﬁelds, including statistics, applied mathematics, and electrical engineering. Most of the works focus on the independent sampling models under which the observed entries are sampled independently. Motivated by applications in the integration of multiple Electronic Health Record (EHR) datasets, we propose the method B lock-wise missing E mbedding L earning T ransformer (BELT) to treat row-wise/column-wise missingness. Speciﬁcally, BELT can recover block-wise missing matrices eﬃciently when every pair of matrices has an overlap. Our idea is to exploit the orthogonal Procrustes problem to align the eigenspace of the two sub-matrices using their overlap, then complete the missing blocks by the inner product of the two low-rank components. Besides, we prove the statistical rate for the eigenspace of the underlying matrix, which is comparable to the rate under the independently missing assumption. Simulation studies show that the method performs well under a variety of conﬁgurations. In the real data analysis, the method is applied to two tasks: (i) the integrating of several point-wise mutual information matrices built by English EHR and Chinese medical text data, and (ii) the machine translation between English and Chinese medical concepts. Our method shows an advantage over existing methods.
Automatic Main Character Recognition for Photographic Studies

Main characters in images are the most important humans that catch the viewer’s attention upon first look, and they are emphasized by properties such as size, position, color saturation, and sharpness of focus. Identifying the main character in images plays an important role in traditional photographic studies and media analysis, but the task is performed manually and is, thus, slow and laborious. Furthermore, selection of main characters can be sometimes subjective. In this paper, we analyze the feasibility of solving the main character recognition needed for photographic studies automatically and propose a method for identifying the main characters. The proposed method uses machine learning based human pose estimation along with traditional computer vision approaches for this task. We approach the task as a binary classification problem where each detected human is classified either as a main character or not. To evaluate both the subjectivity of the task and the performance of our method, we collected a dataset of 300 varying images from multiple sources and asked five people, a photographic researcher and four other persons, to annotate the main characters. Our analysis showed a relatively high agreement between different annotators. The proposed method achieved a promising F1 score of 0.83 on the full image set and 0.96 on a subset evaluated as most clear and important cases by the photographic researcher.
Asymptotic observables and the swampland

We show that constraints on scalar ﬁeld potentials and towers of light massive states in asymptotic limits of scalar ﬁeld space (as posited by the de Sitter Conjecture and the Swampland Distance Conjecture, respectively) are correlated with the prospects for deﬁning asymptotic observables in expanding FRW cosmologies. The observations of a “census taker” in an eternally inﬂating cosmology are further related to the question of whether certain domain walls satisfy a version of the Weak Gravity Conjecture. This suggests that answers to fundamental questions about asymptotic observables in cosmology could help shed light on the Swampland program, and vice versa.