Miracle-dz/UniFAR-FTUs huggingface dataset:
Provenance-aware Discovery of Functional Dependencies on Integrated Views

The automatic discovery of functional dependencies (FDs) has been widely studied as one of the hardest problems in data profiling. Existing approaches have focused on making the FD computation efficient while inspecting single relations at a time. In this paper, for the first time we address the problem of inferring FDs for multiple relations as they occur in integrated views by solely using the functional dependencies of the base relations of the view itself. To this purpose, we leverage logical inference and selective mining and show that we can discover most of the exact FDs from the base relations and avoid the full computation of the FDs for the integrated view itself, while at the same time preserving the lineage of FDs of base relations. We propose algorithms to speedup the inferred FD discovery process and mine FDs on-the-fly only from necessary data partitions. We present InFine (INferred FunctIoNal dEpendency), an end-to-end solution to discover inferred FDs on integrated views by leveraging provenance information of base relations. Our experiments on a range of real-world and synthetic datasets demonstrate the benefits of our method over existing FD discovery methods that need to rerun the discovery process on the view from scratch and cannot exploit lineage information on the FDs. We show that InFine outperforms traditional methods necessitating the full integrated view computation by one to two order of magnitude in terms of runtime. It is also the most memory efficient method while preserving FD provenance information using mainly inference from base table with negligible execution time.
Deciding twin-width at most 4 is NP-complete

We show that determining if an $n$-vertex graph has twin-width at most 4 is NP-complete, and requires time $2^{\Omega(n/\log n)}$ unless the Exponential-Time Hypothesis fails. Along the way, we give an elementary proof that $n$-vertex graphs subdivided at least $2 \log n$ times have twin-width at most 4. We also show how to encode trigraphs $H$ (2-edge colored graphs involved in the definition of twin-width) into graphs $G$, in the sense that every $d$-sequence (sequence of vertex contractions witnessing that the twin-width is at most $d$) of $G$ inevitably creates $H$ as an induced subtrigraph, whereas there exists a partial $d$-sequence that actually goes from $G$ to $H$. We believe that these facts and their proofs can be of independent interest.
Competency Lifecycle Roadmap: Toward Performance Readiness

Abstract : Workforce effectiveness relies on two critical characteristics: competence and readiness. This technical note describes the Competency Lifecycle Roadmap (CLR), a preliminary roadmap for understanding and building workforce readiness developed by the Computer Security Incident Response Team (CSIRT) Development and Training team at the CERT Program, part of Carnegie Mellon University's Software Engineering Institute. This note provides an early look at the roadmap, highlights some of its uses to date, and discusses potential next steps in its development and transition.
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling

Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model and a recurrent neural network variant. ES-RNN achieves a 9.4% improvement in absolute error in the Makridakis-4 Forecasting Competition. This improvement and similar outperformance from other hybrid models have primarily been demonstrated only on univariate datasets. Difficulties with applying hybrid forecast methods to multivariate data include (i) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, (ii) challenges associated with auto-correlation inherent in the data, as well as (iii) complex dependency (cross-correlation) between the covariates that may be hard to capture. This paper presents Multivariate Exponential Smoothing Long Short Term Memory (MES-LSTM), a generalized multivariate extension to ES-RNN, that overcomes these challenges. MES-LSTM utilizes a vectorized implementation. We test MES-LSTM on several aggregated coronavirus disease of 2019 (COVID-19) morbidity datasets and find our hybrid approach shows consistent, significant improvement over pure statistical and deep learning methods at forecast accuracy and prediction interval construction.
Dynamics of social interaction: Modeling the genesis of scientific collaboration

Collaboration plays a key role in knowledge production. Here, we show that patterns of interaction during conferences can be used to predict who will subsequently form a new collaboration, even when interaction is prescribed rather than freely chosen. We introduce a novel longitudinal dataset tracking patterns of interaction among hundreds of scientists during multi-day conferences encompassing different scientific fields over the span of 5 years. We find that participants who formed new collaborations interacted 63% more on average than those who chose not to form new teams, and that those assigned to a higher interaction scenario had more than an eightfold increase in their odds of collaborating. We propose a simple mathematical framework for the process of team formation that incorporates this observation as well as the effect of memory beyond interaction time. The model accurately reproduces the collaborations formed across all conferences and outperforms seven other candidate models. This work not only suggests that encounters between individuals at conferences play an important role in shaping the future of science, but that these encounters can be designed to better catalyze collaborations.
General Theory of Josephson Diodes

Motivated by recent progress in the superconductivity nonreciprocal phenomena, we study the general theory of Josephson diodes. The central ingredient for Josephson diodes is the asymmetric proximity process inside the tunneling barrier. From the symmetry breaking point of view, there are two types of Josephson diodes, inversion breaking and time-reversal breaking. For the inversion breaking case, applying voltage bias could effectively tune the proximity process like the voltage-dependent Rashba coupling or electric polarization giving rise to $I_c(V)\neq I_c(-V)$ and $I_{r+}\neq I_{r-}$. For the time-reversal breaking case, the current flow could adjust the internal time-reversal breaking field like magnetism or time-reversal breaking electron-electron pairing, which leads to $I_{c+}\neq I_{c-}$. All these results provide a complete understanding and the general principles of realizing Josephson diodes, especially the recently found NbSe$_2$/Nb$_3$Br$_8$/NbSe$_2$ Josephson diodes.
Locally optimal detection of unknown signals in non-Gaussian Markov noise

The authors address the application of locally optimum (LO) signal detection techniques to environments in which there is no prior knowledge of the noise density and spectral properties. Specific algorithms are introduced, and the performance of these algorithms is examined. Unlike previous algorithms, these techniques place few assumptions on the properties of the noise, and they perform well under a wide variety of circumstances. The algorithms presented were tested using noise with Cauchy, Laplace, and Gaussian mixture density functions. In all cases the LO procedures showed significant improvement over the direct use of the magnitude of the DFT (discrete Fourier transform), at least for small signal levels. In general, the results were most dramatic when testing with noise densities with very heavy tails, such as the Cauchy and Gaussian mixture cases. <<ETX>>
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as node classification and graph property prediction. Nonetheless, existing systems are inefficient to train large graphs with billions of nodes and edges with GPUs. The main bottlenecks are the process of preparing data for GPUs - subgraph sampling and feature retrieving. This paper proposes BGL, a distributed GNN training system designed to address the bottlenecks with a few key ideas. First, we propose a dynamic cache engine to minimize feature retrieving traffic. By a co-design of caching policy and the order of sampling, we find a sweet spot of low overhead and high cache hit ratio. Second, we improve the graph partition algorithm to reduce cross-partition communication during subgraph sampling. Finally, careful resource isolation reduces contention between different data preprocessing stages. Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20.68x on average.
Mental Health and Neuropsychiatric Aftermath 35 Years After the Chernobyl Catastrophe: Current State and Future Perspectives

Abstract Objective: The Chernobyl nuclear power plant (ChNPP) disaster that happened in Ukraine on the 26th of April 1986 still represents the most severe nuclear accident in human history. Its consequences, especially those involving mental health are increasingly emerging as long-term detrimental effects. Therefore, the aim of the present paper was to review the results of some of authors’ studies and their personal reflections on this topic. Method: The authors selected and commented on the findings mainly derived from their contributions on the prevalence of long-term psychopathological symptoms and neuropsychiatric disorders in diferent groups of exposed and non exposed individuals, including the workers at the NPP the so-called liquidators (CUWs), the most exposed group, evacuees and people living in more or less contaminated areas. Results: The main findings derived from a series of studies carried out by the authors throughout the following decades after the disaster indicate the high prevalence of cerebrovascular diseases, organic mental and depressive disorders, cognitive impairment and even dementia that increase with the irradiation dose mainly amongst the liquidators. The organic disorders are probably related to a peculiar effect of radiation on left, dominant brain hemisphere. Interestingly, recent studies revealed abnormalities of the serotonin transporter and other genes disorders possibly at the basis of depression of exposed individuals. Conclusions: The high prevalence of neuropsychiatric disorders amongst irradiated subjects following the ChNPP disaster highlights the impact of radiation exposure on the lifelong onset of neuropsychiatric disorders, for too long neglected by international agencies. Such findings require to be deepened in the future possibly within the frame of the so-called “ecological psychiatry”.
Intelli-Paint: Towards Developing Human-like Painting Agents

The generation of well-designed artwork is often quite time-consuming and assumes a high degree of proficiency on part of the human painter. In order to facilitate the human painting process, substantial research efforts have been made on teaching machines how to"paint like a human", and then using the trained agent as a painting assistant tool for human users. However, current research in this direction is often reliant on a progressive grid-based division strategy wherein the agent divides the overall image into successively finer grids, and then proceeds to paint each of them in parallel. This inevitably leads to artificial painting sequences which are not easily intelligible to human users. To address this, we propose a novel painting approach which learns to generate output canvases while exhibiting a more human-like painting style. The proposed painting pipeline Intelli-Paint consists of 1) a progressive layering strategy which allows the agent to first paint a natural background scene representation before adding in each of the foreground objects in a progressive fashion. 2) We also introduce a novel sequential brushstroke guidance strategy which helps the painting agent to shift its attention between different image regions in a semantic-aware manner. 3) Finally, we propose a brushstroke regularization strategy which allows for ~60-80% reduction in the total number of required brushstrokes without any perceivable differences in the quality of the generated canvases. Through both quantitative and qualitative results, we show that the resulting agents not only show enhanced efficiency in output canvas generation but also exhibit a more natural-looking painting style which would better assist human users express their ideas through digital artwork.
Spectrum of digitized QCD: Glueballs in a 
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 gauge theory

Quantum simulations of QCD require digitization of the infinite-dimensional gluon field. Schemes for doing this with the minimum amount of qubits are desirable. We present a practical digitization for $SU(3)$ gauge theories via its discrete subgroup $S(1080)$. Using a modified action that allows classical simulations down to $a\approx 0.08$ fm, the low-lying glueball spectrum is computed with percent-level precision at multiple lattice spacings and shown to extrapolate to the continuum limit $SU(3)$ results. This suggests that this digitization scheme is sufficient for precision quantum simulations of QCD.
A CNN based method for Sub-pixel Urban Land Cover Classification using Landsat-5 TM and Resourcesat-1 LISS-IV Imagery

Time series data of urban land cover is of great utility in analyzing urban growth patterns, changes in distribution of impervious surface and vegetation and resulting impacts on urban micro climate. While Landsat data is ideal for such analysis due to the long time series of free imagery, traditional per-pixel hard classification fails to yield full potential of the Landsat data. This paper proposes a sub-pixel classification method that leverages the temporal overlap of Landsat-5 TM and Resourcesat-1 LISS-IV sensors. We train a convolutional neural network to predict fractional land cover maps from 30m Landsat-5 TM data. The reference land cover fractions are estimated from a hard-classified 5.8m LISS-IV image for Bengaluru from 2011. Further, we demonstrate the generalizability and superior performance of the proposed model using data for Mumbai from 2009 and comparing it to the results obtained using a Random Forest classifier. For both Bengaluru (2011) and Mumbai (2009) data, Mean Absolute Percentage Error of our CNN model is in the range of 7.2 to 11.3 for both built-up and vegetation fraction prediction at the 30m cell level. Unlike most recent studies where validation is conducted using data for a limited spatial extent, our model has been trained and validated using data for the complete spatial extent of two mega cities for two different time periods. Hence it can reliably generate 30m built-up and vegetation fraction maps from Landsat-5 TM time series data to analyze long term urban growth patterns.
Improvement of Indoor Radio Coverage at 60 GHz in NLOS Configuration

In the current development of new technologies, the world of communications is experiencing significant growth thanks to the integration of wireless communications in millimeter band. In this context, the purpose of this paper is to assess the contribution that the use of a metallic reflector panel can introduce to indoor radio coverage at 60 GHz in an NLOS (corridor) configuration. The results obtained by measurement show that the use of such a panel can lead to a significant path loss reduction up to 12 dB, which improves the reception power.
Gendered Language in Resumes and its Implications for Algorithmic Bias in Hiring

Despite growing concerns around gender bias in NLP models used in algorithmic hiring, there is little empirical work studying the extent and nature of gendered language in resumes.Using a corpus of 709k resumes from IT firms, we train a series of models to classify the gender of the applicant, thereby measuring the extent of gendered information encoded in resumes.We also investigate whether it is possible to obfuscate gender from resumes by removing gender identifiers, hobbies, gender sub-space in embedding models, etc.We find that there is a significant amount of gendered information in resumes even after obfuscation.A simple Tf-Idf model can learn to classify gender with AUROC=0.75, and more sophisticated transformer-based models achieve AUROC=0.8. We further find that gender predictive values have low correlation with gender direction of embeddings – meaning that, what is predictive of gender is much more than what is “gendered” in the masculine/feminine sense.We discuss the algorithmic bias and fairness implications of these findings in the hiring context.
Toward Imagined Speech based Smart Communication System: Potential Applications on Metaverse Conditions

Metaverse provides an alternative platform for human interaction in the virtual world. Since virtual platform holds few restrictions in changing the surrounding environments or the appearance of the avatars, it can serve as a platform that reflects human thoughts or even dreams at least in the metaverse world. When it is merged together with the current brain-computer interface (BCI) technology, which enables system control via brain signals, a new paradigm of human interaction through mind may be established in the metaverse conditions. Recent BCI systems are aiming to provide user-friendly and intuitive means of communication using brain signals. Imagined speech has become an alternative neuro-paradigm for communicative BCI since it relies directly on a person’s speech production process, rather than using speech-unrelated neural activity as the means of communication. In this paper, we propose a brain-to-speech (BTS) system for real-world smart communication using brain signals. Also, we show a demonstration of imagined speech based smart home control through communication with a virtual assistant, which can be one of the future applications of brain-metaverse system. We performed pseudo-online analysis using imagined speech electroencephalography data of nine subjects to investigate the potential use of virtual BTS system in the real-world. Average accuracy of 46.54 ± 9.37 % and 75.56 ± 5.83 % was acquired in the thirteen-class and binary pseudo-online analysis, respectively. Our results support the potential of imagined speech based smart communication to be applied in the metaverse world.
The Dual PC Algorithm for Structure Learning

independence the algorithm, carry the tests within the PC algorithm by leveraging the inverse relationship between covariance and precision matrices. By exploiting block matrix inversions we can simultaneously perform tests on partial correlations of complementary (or dual) conditioning sets. The multiple CI tests of the dual algorithm proceed by marginal and full-order CI relationships and progressively to central-order ones. dual outperforms the classic PC algorithm both in of run time and in recovering the underlying network structure, in the presence of from
Taxonomic and geographic bias in 50 years of research on the behaviour and ecology of galagids

Identifying knowledge gaps and taxonomic and geographic bias in the literature is invaluable for guiding research towards a more representative understanding of animal groups. Galagids are nocturnal African primates and, for many species, detailed information on their behaviour and ecology is unavailable. To identify gaps and bias in the literature we reviewed published peer-reviewed research articles on galagid behaviour and ecology over a 50-year period from January 1971 to December 2020. Using the Web of Science and Google Scholar databases, we identified 758 articles, assessed 339 full texts for eligibility and included 211 in the review. Species of Otolemur have been extensively researched in comparison to other genera (78.2% of studies; Euoticus: 13.3% of studies; Galago: 66.4% of studies; Galagoides: 20.9% of studies; Paragalago: 22.3% of studies; Sciurocheirus: 15.2% of studies). The most common category of research was physiology (55.0% of studies), followed by behavioural ecology (47.4% of studies), and fewer studies were on genetics and taxonomy (16.1% of studies) and habitat and distribution (14.2% of studies). Text mining revealed that the word ‘behaviour’ was the most common word used in abstracts and keywords, and few words were related to ecology. Negative binomial regression revealed that mean body mass and geographic range size were significant positive predictors of the total number of scientific outputs on each species. Research on wild populations was carried out in only 24 (60%) of the 40 countries galagids are thought to inhabit. Studies were undertaken in locations with lower mean annual temperatures and higher human population densities over warmer and less populated areas. We encourage a more equal sampling effort both taxonomically and geographically that in particular addresses the paucity of research on smaller species and those with restricted ranges. Research on in situ populations, especially in warmer and remote areas, is urgently needed, particularly in West, Central and some Southern African countries.
High-dimensional logistic entropy clustering

Minimization of the (regularized) entropy of classification probabilities is a versatile class of discriminative clustering methods. The classification probabilities are usually defined through the use of some classical losses from supervised classification and the point is to avoid modelisation of the full data distribution by just optimizing the law of the labels conditioned on the observations. We give the first theoretical study of such methods, by specializing to logistic classification probabilities. We prove that if the observations are generated from a two-component isotropic Gaussian mixture, then minimizing the entropy risk over a Euclidean ball indeed allows to identify the separation vector of the mixture. Furthermore, if this separation vector is sparse, then penalizing the empirical risk by a `1-regularization term allows to infer the separation in a high-dimensional space and to recover its support, at standard rates of sparsity problems. Our approach is based on the local convexity of the logistic entropy risk, that occurs if the separation vector is large enough, with a condition on its norm that is independent from the space dimension. This local convexity property also guarantees fast rates in a classical, low-dimensional setting. CREST, ENSAI, Univ. Rennes, LumenAI CREST, ENSAI, Univ. Rennes LMO, Univ. Paris-Saclay, SNCF