Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Lasso for model selection and prediction

It is known that the Thresholded Lasso (TL), SCAD or MCP correct intrinsic estimation bias of the Lasso. In this paper we propose an alternative method of improving the Lasso for predictive models with general convex loss functions which encompass normal linear models, logistic regression, quantile regression, or support vector machines. For a given penalty we order the absolute values of the Lasso nonzero coefficients and then select the final model from a small nested family by the Generalized Information Criterion. We derive exponential upper bounds on the selection error of the method. These results confirm that, at least for normal linear models, our algorithm seems to be the benchmark for the theory of model selection as it is constructive, computationally efficient and leads to consistent model selection under weak assumptions. Constructivity of the algorithm means that, in contrast to the TL, SCAD or MCP, consistent selection does not rely on the unknown parameters as the cone invertibility factor. Instead, our algorithm only needs the sample size, the number of predictors and an upper bound on the noise parameter. We show in numerical experiments on synthetic and real‐world datasets that an implementation of our algorithm is more accurate than implementations of studied concave regularizations. Our procedure is included in the R package DMRnet and available in the CRAN repository.
Modeling Symmetric Positive Definite Matrices with An Application to Functional Brain Connectivity

In neuroscience, functional brain connectivity describes the connectivity between brain regions that share functional properties. Neuroscientists often characterize it by a time series of covariance matrices between functional measurements of distributed neuron areas. An effective statistical model for functional connectivity and its changes over time is critical for better understanding the mechanisms of brain and various neurological diseases. To this end, we propose a matrix-log mean model with an additive heterogeneous noise for modeling random symmetric positive definite matrices that lie in a Riemannian manifold. The heterogeneity of error terms is introduced specifically to capture the curved nature of the manifold. We then propose to use the local scan statistics to detect change patterns in the functional connectivity. Theoretically, we show that our procedure can recover all change points consistently. Simulation studies and an application to the Human Connectome Project lend further support to the proposed methodology.
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis

Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and risk, the acquisition of certain image modalities could be limited. To address this issue, many cross-modality medical image synthesis methods have been proposed. However, the current methods cannot well model the hard-to-synthesis regions (e.g., tumor or lesion regions). To address this issue, we propose a simple but effective strategy, that is, we propose a dual-discriminator (dual-D) adversarial learning system, in which, a global-D is used to make an overall evaluation for the synthetic image, and a local-D is proposed to densely evaluate the local regions of the synthetic image. More importantly, we build an adversarial attention mechanism which targets at better modeling hard-to-synthesize regions (e.g., tumor or lesion regions) based on the local-D. Experimental results show the robustness and accuracy of our method in synthesizing fine-grained target images from the corresponding source images. In particular, we evaluate our method on two datasets, i.e., to address the tasks of generating T2 MRI from T1 MRI for the brain tumor images and generating MRI from CT. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks. And the proposed difficult-region-aware attention mechanism is also proved to be able to help generate more realistic images, especially for the hard-to-synthesize regions.
Distributed Optimal Dynamic State Estimation for Cyber Intrusion Detection in Networked DC Microgrids

In this paper, we present a novel distributed state estimation approach in networked DC microgrids to detect the false data injection in the microgrid control network. Each microgrid monitored by a distributed state estimator will detect if there is manipulated data received from their neighboring microgrids for control purposes. A dynamic model supporting the dynamic state estimation will be constructed for networked microgrids. The optimal distributed state estimation, which is robust to load disturbances but sensitive to false data injected from neighboring microgrids will be presented. To demonstrate the effectiveness of the proposed approach, we simulate a 12kV three-bus networked DC microgrids in MATLAB/Simulink. Residual information corresponding to the false data injected from neighbors validates the efficacy of the proposed approach in detecting compromised agents of neighboring microgrids.
Recommending Related Tables

Tables are an extremely powerful visual and interactive tool for structuring and manipulating data, making spreadsheet programs one of the most popular computer applications. In this paper we introduce and address the task of recommending related tables: given an input table, identifying and returning a ranked list of relevant tables. One of the many possible application scenarios for this task is to provide users of a spreadsheet program proactively with recommendations for related structured content on the Web. At its core, the related table recommendation task boils down to computing the similarity between a pair of tables. We develop a theoretically sound framework for performing table matching. Our approach hinges on the idea of representing table elements in multiple semantic spaces, and then combining element-level similarities using a discriminative learning model. Using a purpose-built test collection from Wikipedia tables, we demonstrate that the proposed approach delivers state-of-the-art performance.
An Investigation on the Effect of the Near-Fault Earthquakes on the Seismic Behavior of RC Moment Resisting Frames (MRFs) Designed Based on Iranian Seismic Code (Standard No. 2800)

Past severe earthquakes, such as Bam earthquake of 2003 and Tabas earthquake of 1978, have demonstrated that many cities in Iran are prone to be struck by near-fault earthquakes. Such earthquakes are impulsive in nature, and therefore, they are more destructive than the ordinary ground shaking. In the fourth edition of Iranian seismic code (Standard No. 2800), some changes, including a modification factor for the elastic acceleration response spectrum (EARS) have been recently recommended to reflect the effects of such probable near-fault earthquakes for the designing procedure. In this study, a numbers of 2D RC moment resisting frames (MRFs), from four to twelve story buildings, are designed linearly based on Iranian National Building Code (INBC) and Standard No. 2800 as well. Subsequently, their nonlinear models are reproduced for conducting nonlinear dynamic time history (NDTH) analysis. For this purpose, twenty impulsive ground motions are selected and scaled to be compatible with the design basis earthquake (DBE) spectrum of the abovementioned code. It is concluded that the seismic performance of the analyzed structures are not satisfactory at all; no buildings are successful to satisfy the life safety (LS) performance level posed by guidelines such as ASCE41-06 or ASCE41-13. Moreover, it is worth mentioning that even collapse prevention (CP) limit states are not also met in some cases. Therefore, the recently added modifications in the Standard No. 2800 may be inadequate to incorporate the near-fault earthquakes' effects.
Theory of spin injection in two-dimensional metals with proximity-induced spin-orbit coupling

Spin injection is a powerful experimental probe into a wealth of nonequilibrium spin-dependent phenomena displayed by materials with spin-orbit coupling (SOC). Here, we develop a theory of coupled spin-charge diffusive transport in two-dimensional spin-valve devices. The theory describes a realistic proximity-induced SOC with both spatially uniform and random components of the SOC due to adatoms and imperfections, and applies to the two dimensional electron gases found in two-dimensional materials and van der Walls heterostructures. The various charge-to-spin conversion mechanisms known to be present in diffusive metals, including the spin Hall effect and several mechanisms contributing current-induced spin polarization are accounted for. Our analysis shows that the dominant conversion mechanisms can be discerned by analyzing the nonlocal resistance of the spin-valve for different polarizations of the injected spins and as a function of the applied in-plane magnetic field.
Generalized Control Functions via Variational Decoupling

Causal estimation relies on separating the variation in the outcome due to the confounders from that due to the treatment. To achieve this separation, practitioners can use external sources of randomness that only influence the treatment called instrumental variables (IVs). Traditional IV-methods rely on structural assumptions that limit the effect that the confounders can have on both outcome and treatment. To relax these assumptions we develop a new estimator called the generalized control-function method (GCFN). GCFN's first stage called variational decoupling (VDE) recovers the residual variation in the treatment given the IV. In the second stage, GCFN regresses the outcome on the treatment and residual variation to compute the causal effect. We evaluate GCFN on simulated data and on recovering the causal effect of slave export on community trust. We show how VDE can help unify IV-estimators and non-IV-estimators.
Solving p-adic polynomial systems via iterative eigenvector algorithms

ABSTRACT We describe an implementation of a polynomial system solver to compute the approximate solutions of a 0-dimensional polynomial system with finite precision p-adic arithmetic. We also describe an improvement to an algorithm of Caruso, Roe, and Vaccon for calculating the eigenvalues and eigenvectors of a p-adic matrix.
Role of Toll-Like Receptors in the interplay between pathogen and damage associated molecular patterns

Various theoretical studies have been carried out to infer relevant protein-protein interactions among pathogens and their hosts. Such studies are generally based on preferential attachment of bacteria / virus to their human receptor homologs. We have analyzed 17 pathogenic species mainly belonging to bacterial and viral pathogenic classes, with the aim to identify the interacting human proteins which are targeted by both bacteria and virus specifically. Our study reveals that the TLRs play a crucial role between the pathogen-associated molecular patterns (PAMPs) and the damage associated molecular patterns (DAMPS). PAMPs include bacterial lipopolysaccharides (LPs), endotoxins, bacterial flagellin, lipoteichoic acid, peptidoglycan in bacterial organisms and nucleic acid variants associated with viral organisms, whereas DAMPs are associated with host biomolecules that perpetuate non-infectious inflammatory responses. We found out the presence of SOD1 and SOD2 (superoxide dismutase) is crucial, as it acts as an anti-oxidant and helps in eliminating oxidative stress by preventing damage from reactive oxygen species. Hence, such strategies can be used as new therapies for anti-inflammatory diseases with significant clinical outcomes.
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings

Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary. While there are high-quality generic seed dictionaries and pre-trained cross-lingual embeddings available for many language pairs, there is little research on how they perform on specialised tasks. In this paper, we investigate the best practices for constructing the seed dictionary for a specific domain. We evaluate the embeddings on the sequence labelling task of Curriculum Vitae parsing and show that the size of a bilingual dictionary, the frequency of the dictionary words in the domain corpora and the source of data (task-specific vs generic) influence performance. We also show that the less training data is available in the low-resource language, the more the construction of the bilingual dictionary matters, and demonstrate that some of the choices are crucial in the zero-shot transfer learning case.
Perfect Conversion of a TM Surface Wave Into a TM Leaky Wave by an Isotropic Periodic Metasurface Printed on a Grounded Dielectric Slab

This article presents an exact solution for a perfect conversion of a TM-polarized surface wave (SW) into a TM-polarized leaky wave (LW) using a reciprocal and lossless penetrable metasurface (MTS) characterized by a scalar sheet impedance, located on a grounded slab. On contrary to the known realizations of LW antennas, the optimal surface reactance modulation which is found here ensures the absence of evanescent higher-order modes of the field Floquet-wave expansion near the radiating surface. Thus, all the energy carried by the SW is used for launching the single inhomogeneous plane wave into space without accumulation of reactive energy in the higher-order modes. It is shown that the resulting penetrable MTS exhibits variation from an inductive to a capacitive reactance passing through a resonance. The present formulation complements a previous article of the authors, in which a perfect conversion from TM-polarized SW to TE-polarized LW was found for impenetrable boundary conditions. Here, the solution takes into account the grounded slab dispersion, and it is convenient for practical implementation.
Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures

With the development of Natural Language Processing, Automatic question-answering system such as Waston, Siri, Alexa, has become one of the most important NLP applications. Nowadays, enterprises try to build automatic custom service chatbots to save human resources and provide a 24-hour customer service. Evaluation of chatbots currently relied greatly on human annotation which cost a plenty of time. Thus, has initiated a new Short Text Conversation subtask called Dialogue Quality (DQ) and Nugget Detection (ND) which aim to automatically evaluate dialogues generated by chatbots. In this paper, we solve the DQ and ND subtasks by deep neural network. We proposed two models for both DQ and ND subtasks which is constructed by hierarchical structure: embedding layer, utterance layer, context layer and memory layer, to hierarchical learn dialogue representation from word level, sentence level, context level to long range context level. Furthermore, we apply gating and attention mechanism at utterance layer and context layer to improve the performance. We also tried BERT to replace embedding layer and utterance layer as sentence representation. The result shows that BERT produced a better utterance representation than multi-stack CNN for both DQ and ND subtasks and outperform other models proposed by other researches. The evaluation measures are proposed by , that is, NMD, RSNOD for DQ and JSD, RNSS for ND, which is not traditional evaluation measures such as accuracy, precision, recall and f1-score. Thus, we have done a series of experiments by using traditional evaluation measures and analyze the performance and error.
Blind source separation using Fast-ICA with a novel nonlinear function

Blind source separation(BSS) is a hotspot in signal processing, and independent component analysis (ICA) is a very effective tool for solving the BSS problem. In order to improve the performance of the separation, a new nonlinear function sin was introduced. It can replace the commonly used classical functions (tanh, gauss and pow3) and does not need to select different nonlinear functions according to the Gauss property of signals. The two Matlab simulation results show that the improved Fast-ICA algorithm with the proposed nonlinearity can not only improve the separation accuracy but also speed up the convergence of blind source separation.
Decentralized Dynamic State Estimation in Microgrids

This paper proposes a decentralized dynamic state estimation scheme for microgrids. The approach employs the voltage and current measurements in the dq0 reference frame through phasor synchronization to be able to exclude orthogonal functions from their relationship formulas. Based on that premise, we utilize a Kalman filter to dynamically estimate states of microgrids. The decoupling of measurement values to state and input vectors reduces the computational complexity. The Kalman filter considers the process noise covariances, which are modified with respect to the covariance of measured input values. Theoretical analysis and simulation results are provided for validation.
High-redshift test of gravity using enhanced growth of small structures probed by the neutral hydrogen distribution

Future 21-cm intensity mapping surveys such as SKA can provide precise information on the spatial distribution of the neutral hydrogen (HI) in the postreionization epoch. This information will allow us to test the standard Λ cold dark matter paradigm and with that the nature of gravity. In this work, we employ the SHYBONE simulations, which model galaxy formation in fðRÞ modified gravity using the IllustrisTNG model, to study the effects of modified gravity on HI abundance and power spectra. We find that the enhanced growth low-mass dark matter halos experience in fðRÞ gravity at high redshifts alters the HI power spectrum and can be observable through 21-cm intensity mapping. Our results suggest that the HI power spectrum is suppressed by ∼13% on scales k ≲ 2h Mpc−1 at z ¼ 2 for F6, a fðRÞ model which passes most observational constraints. We show that this suppression can be detectable by SKA1-MID with 1000 hours of exposure time, making HI clustering a novel test of gravity at high redshift.
From weight structures to (orthogonal) $t$-structures and back

A $t$-structure $t=(C_{t\le 0},C_{t\ge 0})$ on a triangulated category $C$ is right adjacent to a weight structure $w=(C_{w\le 0}, C_{w\ge 0})$ if $C_{t\ge 0}=C_{w\ge 0}$; then $t$ can be uniquely recovered from $w$ and vice versa. We prove that if $C$ satisfies the Brown representability property then $t$ that is adjacent to $w$ exists if and only if $w$ is smashing (i.e., coproducts respect weight decompositions); then the heart $Ht$ is the category of those functors $Hw^{op}\to Ab$ that respect products. The dual to this statement is related to results of B. Keller and P. Nicolas. We also prove that an adjacent $t$ exists whenever $w$ is a bounded weight structure on a saturated $R$-linear category $C$ (for a noetherian ring $R$); for $C=D^{perf}(X)$, where the scheme $X$ is regular and proper over $R$, this gives 1-to-1 correspondences between bounded weights structures on $C$ and the classes of those bounded $t$-structures on it such that $Ht$ has either enough projectives or injectives. We generalize this existence statement to construct (under certain assumptions) a $t$-structure $t$ on a triangulated category $C'$ such that $C$ and $C'$ are subcategories of a common triangulated category $D$ and $t$ is right orthogonal to $w$. In particular, if $X$ is proper over $R$ but not necessarily regular then one can take $C=D^{perf}(X)$, $C'=D^b_{coh}(X)$ or $C'=D^-_{coh}(X)$, and $D=D_{qc}(X)$. We also study hearts of orthogonal $t$-structures and their restrictions, and prove some statements on "reconstructing" weight structures from orthogonal $t$-structures. The main tool of this paper are virtual $t$-truncations of (cohomological) functors; these are defined in terms of weight structures and "behave as if they come from $t$-truncations" whether $t$ exists or not.
A Dynamical Systems Model for Weight Change Behavioral Interventions

We propose a dynamical systems model that captures the daily fluctuations of human weight change, incorporating both physiological and psychological factors. The model consists of an energy balance integrated with a mechanistic behavioral model inspired by the Theory of Planned Behavior (TPB); the former describes the net effect of energy intake minus energy consumption and the latter describes how important variables in a behavioral intervention can influence healthy eating habits and increased physical activity over time. A three-compartment energy balance model is presented for the general human weight change case. The model can be used to inform behavioral scientists in the design of optimized interventions for weight loss and body composition change. The novelty of our approach lies in the integration of the behavioral and mechanistic model descriptions, the representation of the behavioral intervention as a dynamical system, and its application to problems in weight loss and body composition change.
Linking Art through Human Poses

We address the discovery of composition transfer in artworks based on their visual content. Automated analysis of large art collections, which are growing as a result of art digitization among museums and galleries, is an important tool for art history and assists cultural heritage preservation. Modern image retrieval systems offer good performance on visually similar artworks, but fail in the cases of more abstract composition transfer. The proposed approach links artworks through a pose similarity of human figures depicted in images. Human figures are the subject of a large fraction of visual art from middle ages to modernity and their distinctive poses were often a source of inspiration among artists. The method consists of two steps – fast pose matching and robust spatial verification. We experimentally show that explicit human pose matching is superior to standard content-based image retrieval methods on a manually annotated art composition transfer dataset.
Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models

In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustworthiness can be useful for fraud analysts who process machine learning-generated alerts of fraudulent transactions. In this work, we present a case-based reasoning (CBR) approach that provides evidence on the trustworthiness of a prediction in the form of a visualization of similar previous instances. Different from previous works, we consider similarity of local post-hoc explanations of predictions and show empirically that our visualization can be useful for processing alerts. Furthermore, our approach is perceived useful and easy to use by fraud analysts at a major Dutch bank.