Miracle-dz/UniFAR-FTUs huggingface dataset:
Deconvolutional Networks on Graph Data

In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct the input graph signal? "We propose Graph Deconvolutional Network (GDN) and motivate the design of GDN via a combination of inverse filters in spectral domain and de-noising layers in wavelet domain, as the inverse operation results in a high frequency amplifier and may amplify the noise. We demonstrate the effectiveness of the proposed method on several tasks including graph feature imputation and graph structure generation.
The extended Smale's 9th problem -- On computational barriers and paradoxes in estimation, regularisation, computer-assisted proofs and learning

Linear and semidefinite programming (LP, SDP), regularisation through basis pursuit (BP) and Lasso have seen great success in mathematics, statistics, data science, computer-assisted proofs and learning. The success of LP is traditionally attributed to the fact that it is"in P"for rational inputs. On the other hand, in his list of problems for the 21st century S. Smale calls for"[Computational] models which process approximate inputs and which permit round-off computations". Indeed, since e.g. the exponential function does not have an exact representation or floating-point arithmetic approximates every rational number that is not in base-2, inexact input is a daily encounter. The model allowing inaccurate input of arbitrary precision, which we call the extended model, leads to extended versions of fundamental problems such as:"Are LP and other aforementioned problems in P? "The same question can be asked for an extended version of Smale's 9th problem on the list of mathematical problems for the 21st century:"Is there a polynomial time algorithm over the real numbers which decides the feasibility of the linear system of inequalities, and if so, outputs a feasible candidate? "One can thus pose this problem in the extended model. Similarly, the optimisation problems BP, SDP and Lasso, where the task is to output a solution to a specified precision, can likewise be posed in the extended model, also considering randomised algorithms. We will collectively refer to these problems as the extended Smale's 9th problem, which we settle in both the negative and the positive yielding two surprises: (1) In mathematics, sparse regularisation, statistics, and learning, one successfully computes with non-computable functions. (2) In order to mathematically characterise this phenomenon, one needs an intricate complexity theory for, seemingly paradoxically, non-computable functions.
Quantum phases of two-dimensional 
Z2
 gauge theory coupled to single-component fermion matter

We investigate the rich quantum phase diagram of Wegner's theory of discrete Ising gauge fields interacting with $U(1)$ symmetric single-component fermion matter hopping on a two-dimensional square lattice. In particular limits the model reduces to (i) pure $\mathbb{Z}_2$ even and odd gauge theories, (ii) free fermions in a static background of deconfined $\mathbb{Z}_2$ gauge fields, (iii) the kinetic Rokhsar-Kivelson quantum dimer model at a generic dimer filling. We develop a local transformation that maps the lattice gauge theory onto a model of $\mathbb{Z}_2$ gauge-invariant spin $1/2$ degrees of freedom. Using the mapping, we perform numerical density matrix renormalization group calculations that corroborate our understanding of the limits identified above. Moreover, in the absence of the magnetic plaquette term, we reveal signatures of topologically ordered Dirac semimetal and staggered Mott insulator phases at half-filling. At strong coupling, the lattice gauge theory displays fracton phenomenology with isolated fermions being completely frozen and dimers exhibiting restricted mobility. In that limit, we predict that in the ground state dimers form compact clusters, whose hopping is suppressed exponentially in their size. We determine the band structure of the smallest clusters numerically using exact diagonalization. The rich phenomenology discussed in this paper can be probed in analog and digital quantum simulators of discrete gauge theories and in Kitaev spin-orbital liquids.
Cycle-Balanced Representation Learning For Counterfactual Inference

With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e.g., health care and computational advertising) without Randomized Controlled Trials(RCTs). However, observational data suffer from inherent missing counterfactual outcomes, and distribution discrepancy between treatment and control groups due to behaviour preference. Motivated by recent advances of representation learning in the field of domain adaptation, we propose a novel framework based on Cycle-Balanced REpresentation learning for counterfactual inference (CBRE), to solve above problems. Specifically, we realize a robust balanced representation for different groups using adversarial training, and meanwhile construct an information loop, such that preserve original data properties cyclically, which reduces information loss when transforming data into latent representation space.Experimental results on three real-world datasets demonstrate that CBRE matches/outperforms the state-of-the-art methods, and it has a great potential to be applied to counterfactual inference.
InfoGCL: Information-Aware Graph Contrastive Learning

Various graph contrastive learning models have been proposed to improve the performance of learning tasks on graph datasets in recent years. While effective and prevalent, these models are usually carefully customized. In particular, although all recent researches create two contrastive views, they differ greatly in view augmentations, architectures, and objectives. It remains an open question how to build your graph contrastive learning model from scratch for particular graph learning tasks and datasets. In this work, we aim to fill this gap by studying how graph information is transformed and transferred during the contrastive learning process and proposing an information-aware graph contrastive learning framework called InfoGCL. The key point of this framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized. We show for the first time that all recent graph contrastive learning methods can be unified by our framework. We empirically validate our theoretical analysis on both node and graph classification benchmark datasets, and demonstrate that our algorithm significantly outperforms the state-of-the-arts.
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. Recent advances in pretrained contextualized language representations have promoted the development of several domainspecific pretrained models and facilitated several downstream applications. However, there are no existing pretrained language models for mental healthcare. This paper trains and release two pretrained masked language models, i.e., MentalBERT and MentalRoBERTa, to benefit machine learning for the mental healthcare research community. Besides, we evaluate our trained domain-specific models and several variants of pretrained language models on several mental disorder detection benchmarks and demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks.
Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data.We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of [49] and the Brueghel dataset. Our code and data are available at http://imagine.enpc. fr/~shenx/SegSwap/.
Dense Hierarchical Retrieval for Open-Domain Question Answering

Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum inner product search in the retrieval process. However, current dense retrievers require splitting documents into short passages that usually contain local, partial, and sometimes biased context, and highly depend on the splitting process. As a consequence, it may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result. In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage. Specifically, a document-level retriever first identifies relevant documents, among which relevant passages are then retrieved by a passage-level retriever. The ranking of the retrieved passages will be further calibrated by examining the document-level relevance. In addition, hierarchical title structure and two negative sampling strategies (i.e., In-Doc and In-Sec negatives) are investigated. We apply DHR to large-scale open-domain QA datasets. DHR significantly outperforms the original dense passage retriever and helps an end-to-end QA system outperform the strong baselines on multiple open-domain QA benchmarks.
From theories on styles to their transfer in text: Bridging the gap with a hierarchical survey

Abstract Humans are naturally endowed with the ability to write in a particular style. They can, for instance, rephrase a formal letter in an informal way, convey a literal message with the use of figures of speech or edit a novel by mimicking the style of some well-known authors. Automating this form of creativity constitutes the goal of style transfer. As a natural language generation task, style transfer aims at rewriting existing texts, and specifically, it creates paraphrases that exhibit some desired stylistic attributes. From a practical perspective, it envisions beneficial applications, like chatbots that modulate their communicative style to appear empathetic, or systems that automatically simplify technical articles for a non-expert audience. Several style-aware paraphrasing methods have attempted to tackle style transfer. A handful of surveys give a methodological overview of the field, but they do not support researchers to focus on specific styles. With this paper, we aim at providing a comprehensive discussion of the styles that have received attention in the transfer task. We organize them in a hierarchy, highlighting the challenges for the definition of each of them and pointing out gaps in the current research landscape. The hierarchy comprises two main groups. One encompasses styles that people modulate arbitrarily, along the lines of registers and genres. The other group corresponds to unintentionally expressed styles, due to an author’s personal characteristics. Hence, our review shows how these groups relate to one another and where specific styles, including some that have not yet been explored, belong in the hierarchy. Moreover, we summarize the methods employed for different stylistic families, hinting researchers towards those that would be the most fitting for future research.
Neyman–Pearson lemma for Bayes factors

Abstract We point out that the Neyman–Pearson lemma applies to Bayes factors if we consider expected type-1 and type-2 error rates. That is, the Bayes factor is the test statistic that maximizes the expected power for a fixed expected type-1 error rate. For Bayes factors involving a simple null hypothesis, the expected type-1 error rate is just the completely frequentist type-1 error rate. Lastly we remark on connections between the Karlin–Rubin theorem and uniformly most powerful tests, and Bayes factors. This provides frequentist motivations for computing the Bayes factor and could help reconcile Bayesians and frequentists.
Interim Reporting and Managerial Short-termism Omnia

This research aims to investigate the impact of frequency of financial reporting on managerial short termism. Further, this research investigates the moderate effect of investment sensitivity on the relationship between frequent reporting and managerial short termism. Based on a sample of 76 publicly listed firms in Egyptian Stock Exchange during a five year period (2013-2017), this research employs panel model analysis to empirically test the hypothesized relationships. The results show that there is no difference in the level of investment in terms of capital expenditures, net fixed assets or advertising between quarterly reporting committed firms and quarterly reporting noncommitted firms. The results also show that there is no significant impact of frequent financial reporting on managerial short termism in the existence of investment sensitivity. This null evidence is consistent with the theoretical insight that, for frequent disclosure to affect managerial short termism, it needs to change the mix of hard information versus soft information.
Answering Open-Domain Questions of Varying Reasoning Steps from Text

We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model to perform all the necessary subtasks—retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents—in an iterative fashion. We avoid crucial assumptions of previous work that do not transfer well to real-world settings, including exploiting knowledge of the fixed number of retrieval steps required to answer each question or using structured metadata like knowledge bases or web links that have limited availability. Instead, we design a system that can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. To emulate this setting, we construct a new benchmark, called BeerQA, by combining existing one- and two-step datasets with a new collection of 530 questions that require three Wikipedia pages to answer, unifying Wikipedia corpora versions in the process. We show that our model demonstrates competitive performance on both existing benchmarks and this new benchmark. We make the new benchmark available at https://beerqa.github. io/.
Replication of period-doubling route to chaos in coupled systems with delay

In this study, replication of a period-doubling cascade in coupled systems with delay is rigorously proved under certain assumptions, which guarantee the existence of bounded solutions and replication of sensitivity. A novel definition for replication of sensitivity is utilized, in which the proximity of solutions is considered in an interval instead of a single point. Examples with simulations supporting the theoretical results concerning sensitivity and period-doubling cascade are provided.
Efficacy and safety of givosiran for acute hepatic porphyria: 24‐month interim analysis of the randomized phase 3 ENVISION study

Upregulation of hepatic delta‐aminolevulinic acid synthase 1 with accumulation of potentially toxic heme precursors delta‐aminolevulinic acid and porphobilinogen is fundamental to the pathogenesis of acute hepatic porphyria. Aims: evaluate long‐term efficacy and safety of givosiran in acute hepatic porphyria.
Prevalence of Overt and Occult Hepatitis B Virus Infection among HIV-Positive People Referring to Consultation Center for Behavioral Diseases, Kurdistan Province, Iran.

Background Based on evidence, HIV and HBV have common transmission routes; co-infection of HBV/HIV can dramatically increase disease progression. The present study aimed to determine the prevalence of overt HBV infection and OBI in HIV-positive people. Methods In this descriptive study, whole blood samples were collected from 184 HIV-positive subjects referring to the Consultation Center for Behavioral Diseases, Sanandaj, Iran, during 2014 to 2016. ELISA was used for the determination of HBV serologic markers (HBsAg and anti-HBc). To evaluate OBI, DNA was extracted only from HBsAg-negative and anti-HBc-positive samples and tested for HBV DNA by real-time PCR. Test results and patients' data were analyzed by SPSS software. Results The mean age of the study population was 39.2 ± 9.4 (SD) years, of whom 140 (76%) were male. Overall, 43 (23.3%) samples were positive for HBsAg (overt HBV infection), and 50 (27.2%) for anti-HBc. Among 31 HBsAg-negative and anti-HBc-positive samples (suspected OBI), one (3.2%) sample was positive for HBV DNA (verified seropositive OBI). HBV infection was higher among males (n = 37; 86.05%), jobless people (n = 23; 53.49%), and those with an injection HIV transmission route (n = 32; 74.43%). Conclusion We observed a high prevalence of overt HBV and one OBI among the study population. A serologic marker such as anti-HBc indicates resolved or past HBV infection. Molecular screening for HBV is valuable for the management of HIV-infected people.
A survey of OCR evaluation tools and metrics

The millions of pages of historical documents that are digitized in libraries are increasingly used in contexts that have more specific requirements for OCR quality than keyword search. How to comprehensively, efficiently and reliably assess the quality of OCR results against the background of mass digitization, when ground truth can only ever be produced for very small numbers? Due to gaps in specifications, results from OCR evaluation tools can return different results, and due to differences in implementation, even commonly used error rates are often not directly comparable. OCR evaluation metrics and sampling methods are also not sufficient where they do not take into account the accuracy of layout analysis, since for advanced use cases like Natural Language Processing or the Digital Humanities, accurate layout analysis and detection of the reading order are crucial. We provide an overview of OCR evaluation metrics and tools, describe two advanced use cases for OCR results, and perform an OCR evaluation experiment with multiple evaluation tools and different metrics for two distinct datasets. We analyze the differences and commonalities in light of the presented use cases and suggest areas for future work.
Utilizing Representation Learning for Robust Text Classification Under Datasetshift

Within One-vs-Rest (OVR) classification, a classifier differentiates a single class of interest (COI) from the rest, i.e. any other class. By extending the scope of the rest class to corruptions (dataset shift), aspects of outlier detection gain relevancy. In this work, we show that adversarially trained autoencoders (ATA) representative of autoencoder-based outlier detection methods, yield tremendous robustness improvements over traditional neural network methods such as multi-layer perceptrons (MLP) and common ensemble methods, while maintaining a competitive classification performance. In contrast, our results also reveal that deep learning methods solely optimized for classification, tend to fail completely when exposed to dataset shift.
The Human Resource Management challenge of predicting employee turnover using machine learning and system dynamics

The turnover rate is an indicator of the company situation (health), a high turnover may indicate dissatisfaction with the work conditions, conflicts with the management or lack of career opportunities, etc. The unexpected loss of high skilled employees will lead to less productivity and less quality which can threaten the company’s competitive advantage in the market. The turnover rate is difficult to predict and challenging to manage, therefore, having a warning system or tool that predicts possible resignations at an early stage may help HR managers improve their retention strategies and maintain a stable rate. The main target for this research proposal is to build a model based on HR managers observations and knowledge alongside theoretical assumptions using historical data to assist decision-making. The model will be built, constructed, and developed using machine learning and system dynamics to act as a predictive tool. Therefore, HR managers will be able to predict the turnover rate and most importantly to identify the reasons behind it, thus, they will have more flexibility in adjusting and improving retention strategies. To build this predictive model, mixed methods qualitative and quantitative will be used taking into consideration the ethical approval process of data gathering by the university of Worcester.
Improvement on Incremental Spectral Clustering

Spectral clustering (SC) is one of the most popular algorithms for finding communities in a static graph. The algorithm derives the communities through clustering the leading eigenvectors of the graph Laplacian. Many practical problems involve networks that evolve over time (for instance, the Facebook friendship network) and communities need to be updated dynamically. Since spectral clustering is an offline algorithm, any change in the graph requires a new computation of the eigenvectors of the graph Laplacian matrix, which is highly inefficient for real-time clustering of most large dynamic networks. Incremental spectral clustering (ISC) extends the method to efficiently cluster dynamic graphs [1], by incrementally updating the eigenvectors and eigenvalues, but can sometimes lead to sub-optimal performance due to approximation errors. In this paper, a modified incremental spectral algorithm (MISC) is introduced by considering higher-order approximations of the eigenvector update rule. We compared the performance of ISC and MISC through experiments on random graphs generated from dynamic stochastic block model, from Barabasi-Albert model with planted communities and real-word network data.