Miracle-dz/UniFAR-FTUs huggingface dataset:
Simple and maximally robust processes with no classical common-cause or direct-cause explanation

Guided by the intuition of coherent superposition of causal relations, recent works presented quantum processes without classical common-cause and direct-cause explanation, that is, processes which cannot be written as probabilistic mixtures of quantum common-cause and quantum direct-cause relations (CCDC). In this work, we analyze the minimum requirements for a quantum process to fail to admit a CCDC explanation and present "simple" processes, which we prove to be the most robust ones against general noise. These simple processes can be realized by preparing a maximally entangled state and applying the identity quantum channel, thus not requiring an explicit coherent mixture of common-cause and direct-cause, exploiting the possibility of a process to have both relations simultaneously. We then prove that, although all bipartite direct-cause processes are bipartite separable operators, there exist bipartite separable processes which are not direct-cause. This shows that the problem of deciding weather a process is direct-cause process is not equivalent to entanglement certification and points out the limitations of entanglement methods to detect non-classical CCDC processes. We also present a semi-definite programming hierarchy that can detect and quantify the non-classical CCDC robustnesses of every non-classical CCDC process. Among other results, our numerical methods allow us to show that the simple processes presented here are likely to be also the maximally robust against white noise. Finally, we explore the equivalence between bipartite direct-cause processes and bipartite processes without quantum memory, to present a separable process which cannot be realized as a process without quantum memory.
Self-supervised Cross-silo Federated Neural Architecture Search

Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties. In the training process of FL, no party has a global view of data distributions or model architectures of other parties. Thus the manually-designed architectures may not be optimal. In the past, Neural Architecture Search (NAS) has been applied to FL to address this critical issue. However, existing Federated NAS approaches require prohibitive communication and computation effort, as well as the availability of high-quality labels. In this work, we present Self-supervised Vertical Federated Neural Architecture Search (SS-VFNAS) for automating FL where participants hold feature-partitioned data, a common cross-silo scenario called Vertical Federated Learning (VFL). In the proposed framework, each party first conducts NAS using self-supervised approach to find a local optimal architecture with its own data. Then, parties collaboratively improve the local optimal architecture in a VFL framework with supervision. We demonstrate experimentally that our approach has superior performance, communication efficiency and privacy compared to Federated NAS and is capable of generating high-performance and highly-transferable heterogeneous architectures even with insufficient overlapping samples, providing automation for those parties without deep learning expertise.
On Statistical Bias In Active Learning: How and When To Fix It

Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize this bias and investigate the situations in which it can be harmful and sometimes even helpful. We further introduce novel corrective weights to remove bias when doing so is beneficial. Through this, our work not only provides a useful mechanism that can improve the active learning approach, but also an explanation of the empirical successes of various existing approaches which ignore this bias. In particular, we show that this bias can be actively helpful when training overparameterized models -- like neural networks -- with relatively little data.
A Hybrid 2-stage Neural Optimization for Pareto Front Extraction

Classification, recommendation, and ranking problems often involve competing goals with additional constraints (e.g., to satisfy fairness or diversity criteria). Such optimization problems are quite challenging, often involving non-convex functions along with considerations of user preferences in balancing trade-offs. Pareto solutions represent optimal frontiers for jointly optimizing multiple competing objectives. A major obstacle for frequently used linear-scalarization strategies is that the resulting optimization problem might not always converge to a global optimum. Furthermore, such methods only return one solution point per run. A Pareto solution set is a subset of all such global optima over multiple runs for different trade-off choices. Therefore, a Pareto front can only be guaranteed with multiple runs of the linear-scalarization problem, where all runs converge to their respective global optima. Consequently, extracting a Pareto front for practical problems is computationally intractable with substantial computational overheads, limited scalability, and reduced accuracy. We propose a robust, low cost, two-stage, hybrid neural Pareto optimization approach that is accurate and scales (compute space and time) with data dimensions, as well as number of functions and constraints. The first stage (neural network) efficiently extracts a weak Pareto front, using Fritz-John conditions as the discriminator, with no assumptions of convexity on the objectives or constraints. The second stage (efficient Pareto filter) extracts the strong Pareto optimal subset given the weak front from stage 1. Fritz-John conditions provide us with theoretical bounds on approximation error between the true and network extracted weak Pareto front. Numerical experiments demonstrates the accuracy and efficiency on a canonical set of benchmark problems and a fairness optimization task from prior works.
Disambiguating Symbolic Expressions in Informal Documents

We propose the task of disambiguating symbolic expressions in informal STEM documents in the form of LaTeX files - that is, determining their precise semantics and abstract syntax tree - as a neural machine translation task. We discuss the distinct challenges involved and present a dataset with roughly 33,000 entries. We evaluated several baseline models on this dataset, which failed to yield even syntactically valid LaTeX before overfitting. Consequently, we describe a methodology using a transformer language model pre-trained on sources obtained from arxiv.org, which yields promising results despite the small size of the dataset. We evaluate our model using a plurality of dedicated techniques, taking the syntax and semantics of symbolic expressions into account.
The taut polynomial and the Alexander polynomial

Landry, Minsky and Taylor defined the taut polynomial of a veering triangulation. Its specialisations generalise the Teichmüller polynomial of a fibred face of the Thurston norm ball. We prove that the taut polynomial of a veering triangulation is equal to a certain twisted Alexander polynomial of the underlying manifold. Thus, the Teichmüller polynomials are just specialisations of twisted Alexander polynomials. We also give formulae relating the taut polynomial and the untwisted Alexander polynomial. There are two formulae, depending on whether the maximal free abelian cover of a veering triangulation is edge‐orientable or not. Furthermore, we consider 3‐manifolds obtained by Dehn filling a veering triangulation. In this case, we give formulae that relate the specialisation of the taut polynomial under a Dehn filling and the Alexander polynomial of the Dehn‐filled manifold. This extends a theorem of McMullen connecting the Teichmüller polynomial and the Alexander polynomial to the non‐fibred setting, and improves it in the fibred case. We also prove a sufficient and necessary condition for the existence of an orientable fibred class in the cone over a fibred face of the Thurston norm ball.
BERTaú: Itaú BERT for digital customer service

In the last few years, three major topics received increased interest: deep learning, NLP and conversational agents. Bringing these three topics together to create an amazing digital customer experience and indeed deploy in production and solve real-world problems is something innovative and disruptive. We introduce a new Portuguese financial domain language representation model called BERTau. BERTau is an uncased BERT-base trained from scratch with data from the Itau virtual assistant chatbot solution. The novelty of this contribution lies in that BERTau pretrained language model requires less data, reaches state-of-the-art performance in three NLP tasks, and generates a smaller and lighter model that makes the deployment feasible. We developed three tasks to validate our model: information retrieval with Frequently Asked Questions (FAQ) from Itau bank, sentiment analysis from our virtual assistant data, and a NER solution. All proposed tasks are real-world solutions in production on our environment and the usage of a specialist model proved to be effective when compared to Google BERT multilingual and the Facebook’s DPRQuestionEncoder, available at Hugging Face. BERTau improves the performance in 22% of FAQ Retrieval MRR metric, 2.1% in Sentiment Analysis F1 score, 4.4% in NER F1 score. It can also represent the same sequence in up to 66% fewer tokens when compared to "shelf models".
Eﬀiciency of Query Evaluation Under Guarded TGDs: The Unbounded Arity Case

The paper analyzes the parameterized complexity of evaluating Ontology Mediated Queries (OMQs) based on Guarded TGDs (GT-GDs) and Unions of Conjunctive Queries (UCQs), in the setting where relational symbols might have unbounded arity and where the parameter is the size of the OMQ. It establishes exact criteria for ﬁxed-parameter tractability (fpt) evaluation of recursively enu-merable classes of such OMQs (under the widely held Exponential Time Hypothesis). One of the main technical tools introduced in the paper is an fpt-reduction from deciding parameterized uniform CSPs to parameterized OMQ evaluation. A fundamental feature of the reduction is preservation of measures which are known to be essential for classifying classes of parameterized uniform CSPs: submodular width (according to the well known result of Marx for unbounded-arity schemas) and treewidth (according to the well known result of Grohe for bounded-arity schemas). As such, the reduction can be employed to obtain hardness results for evaluation of classes of parameterized OMQs both in the unbounded and in the bounded arity case. Previously, in the case of bounded arity schemas, this has been tackled using a technique requiring full introspection into the construction employed by Grohe.
Thermonuclear X-Ray Bursts with Late Secondary Peaks Observed from 4U 1608–52

We report the temporal and spectral analysis of three thermonuclear X-ray bursts from 4U 1608−52, observed by the Neutron Star Interior Composition Explorer (NICER) during and just after the outburst observed from the source in 2020. In two of the X-ray bursts, we detect secondary peaks 30 and 18 s after the initial peaks. The secondary peaks show a fast rise exponential decay-like shape resembling a thermonuclear X-ray burst. Time-resolved X-ray spectral analysis reveals that the peak flux, blackbody temperature, and apparent emitting radius values of the initial peaks are in agreement with X-ray bursts previously observed from 4U 1608−52, while the same values for the secondary peaks tend toward the lower end of the distribution of bursts observed from this source. The third X-ray burst, which happened during much lower accretion rates, did not show any evidence for a deviation from an exponential decay and was significantly brighter than the previous bursts. We present the properties of the secondary peaks and discuss the events within the framework of short recurrence time bursts or bursts with secondary peaks. We find that the current observations do not fit in standard scenarios and challenge our understanding of flame spreading.
Self-Attention Meta-Learner for Continual Learning

Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. One of its main challenging, catastrophic forgetting, is caused by the neural networks non-optimal ability to learn in non-stationary distributions. In most settings of the current approaches, the agent starts from randomly initialized parameters and is optimized to master the current task regardless of the usefulness of the learned representation for future tasks. Moreover, each of the future tasks uses all the previously learned knowledge although parts of this knowledge might not be helpful for its learning. These cause interference among tasks, especially when the data of previous tasks is not accessible. In this paper, we propose a new method, named Self-Attention Meta-Learner (SAM), which learns a prior knowledge for continual learning that permits learning a sequence of tasks, while avoiding catastrophic forgetting. SAM incorporates an attention mechanism that learns to select the particular relevant representation for each future task. Each task builds a specific representation branch on top of the selected knowledge, avoiding the interference between tasks. We evaluate the proposed method on the Split CIFAR-10/100 and Split MNIST benchmarks in the task agnostic inference. We empirically show that we can achieve a better performance than several state-of-the-art methods for continual learning by building on the top of selected representation learned by SAM. We also show the role of the meta-attention mechanism in boosting informative features corresponding to the input data and identifying the correct target in the task agnostic inference. Finally, we demonstrate that popular existing continual learning methods gain a performance boost when they adopt SAM as a starting point.
Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling

This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only one control system is controlled, while the rest of the control systems are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control $2\times$ more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is $18\times$ larger than a round-robin scheduling baseline.
Refining Deep Generative Models via Discriminator Gradient Flow

Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-world data. However, generation quality is generally inconsistent for any given model and can vary dramatically between samples. We introduce Discriminator Gradient flow (DGflow), a new technique that improves generated samples via the gradient flow of entropy-regularized f-divergences between the real and the generated data distributions. The gradient flow takes the form of a non-linear Fokker-Plank equation, which can be easily simulated by sampling from the equivalent McKean-Vlasov process. By refining inferior samples, our technique avoids wasteful sample rejection used by previous methods (DRS&MH-GAN). Compared to existing works that focus on specific GAN variants, we show our refinement approach can be applied to GANs with vector-valued critics and even other deep generative models such as VAEs and Normalizing Flows. Empirical results on multiple synthetic, image, and text datasets demonstrate that DGflow leads to significant improvement in the quality of generated samples for a variety of generative models, outperforming the state-of-the-art Discriminator Optimal Transport (DOT) and Discriminator Driven Latent Sampling (DDLS) methods.
Exotic symmetries, duality, and fractons in 2+1-dimensional quantum field theory

We discuss nonstandard continuum quantum field theories in 2+1 dimensions. They exhibit exotic global symmetries, a subtle spectrum of charged excitations, and dualities similar to dualities of systems in 1+1 dimensions. These continuum models represent the low-energy limits of certain known lattice systems. One key aspect of these continuum field theories is the universal and important role played by discontinuous field configurations. In two companion papers, we will present 3+1-dimensional versions of these systems. In particular, we will discuss continuum quantum field theories of some models of fractons.
Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Convergence Analysis

This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming. Unlike classical approximate dynamic programming approaches, we first approximate the optimal policy by means of neural networks in the spirit of deep reinforcement learning, and then the value function by Monte Carlo regression. This is achieved in the dynamic programming recursion by performance or hybrid iteration, and regress now methods from numerical probabilities. We provide a theoretical justification of these algorithms. Consistency and rate of convergence for the control and value function estimates are analyzed and expressed in terms of the universal approximation error of the neural networks, and of the statistical error when estimating network function, leaving aside the optimization error. Numerical results on various applications are presented in a companion paper (arxiv.org/abs/1812.05916) and illustrate the performance of the proposed algorithms.
Associations between self-reported and objective face recognition abilities are only evident in above- and below-average recognisers

The 20-Item Prosopagnosia Items (PI-20) was recently introduced as a self-report measure of face recognition abilities and as an instrument to help the diagnosis of prosopagnosia. In general, studies using this questionnaire have shown that observers have moderate to strong insights into their face recognition abilities. However, it remains unknown whether these insights are equivalent for the whole range of face recognition abilities. The present study investigates this issue using the Mandarin version of the PI-20 and the Cambridge Face Memory Test Chinese (CFMT-Chinese). Our results showed a moderate negative association between the PI-20 and the CFMT-Chinese. However, this association was driven by people with low and high face recognition ability, but absent in people within the typical range of face recognition performance. The implications of these results for the study of individual differences and the diagnosis of prosopagnosia are discussed.
Adenocarcinoma Arising From a Gastric Duplication Cyst With Lymph Node Metastasis

Gastric duplication cysts (GDCs) are a relatively rare congenital anomalies and are mostly diagnosed in the early years of life. Herein, we report a very rare surgical case of adenocarcinoma arising from a GDC with lymph node metastasis. A 78-year-old woman was referred to our hospital because of elevated serum levels of cancer antigen (CA) 19-9. Endoscopic ultrasound, contrast fistulography, and computed tomography showed a cystic lesion communicating with the lesser curvature of the stomach. The serum levels of CA 19-9 were high, and fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging demonstrated a slightly enlarged lymph node with high FDG uptake after four months. The size of the cyst was unchanged. It was diagnosed as a GDC. The enlarged lymph node was highly likely to be malignant. Hence, we performed a distal gastrectomy involving the origin of entry and whole body of the GDC with en bloc regional lymphadenectomy. The postoperative pathology was consistent with GDC with moderately differentiated adenocarcinoma and lymph node metastasis. Adjuvant chemotherapy with tegafur-gimeracil-oteracil potassium (S-1) was administered for 12 months. At present, the patient is alive, with no recurrence of the lesion even four years after the operation. GDCs in adults are rare and may predispose to malignancy. Early diagnosis and prompt surgical intervention are important for favorable outcomes.
A Rare Presentation of Cerebrovascular Accident in a COVID-19 Patient: A Case Report

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded, positive-sense, enveloped ribonucleic acid (RNA) virus. SARS-CoV-2 and its associated disease coronavirus disease 2019 (COVID-19) has caused a global pandemic in the year 2019-2020. COVID-19 has caused widespread death, economic burden, and overcrowding of hospitals. As of September 2020, there is no reliable pharmacological treatment for patients affected by COVID-19. Herein we present a case of a 41-year-old Caucasian female who presented to the emergency department with flu-like symptoms for the past five days. The patient was admitted for COVID-19 symptoms and subsequently developed COVID-19 associated thrombotic syndrome and ischemic stroke. Below, we discuss risk factors, pathology, and rare manifestations resulting from COVID-19 infection. COVID-19 primarily affects the lungs, but a review of the current literature shows limited cases of ischemic stroke and diffuse thrombosis induced by infection of the novel COVID-19 in relatively healthy individuals with minimal risk factors.