Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved approximation algorithm for maximizing a DR-submodular function over a convex set

Maximizing a DR-submodular function subject to a general convex set is an NP-hard problem arising from many applications in combinatorial optimization and machine learning. While it is highly desirable to design efficient approximation algorithms under this general setting where neither the objective function is monotonic nor the feasible set is down-closed, our main contribution is to present a 0.25-approximation Frank-Wolfe type of algorithm with a sub-exponential time-complexity under the value oracle model.
SGDR: Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation

Disentangled representation is a powerful technique to tackle domain shift problem in medical image analysis in unsupervised domain adaptation setting. However, previous methods only focus on exacting domain-invariant feature and ignore whether exacted feature is meaningful for downstream tasks. We propose a novel framework, called semantic-guided disentangled representation (SGDR), an effective method to exact semantically meaningful feature for segmentation task to improve performance of cross modality medical image segmentation in unsupervised domain adaptation setting. To exact the meaningful domain-invariant features of different modality, we introduce a content discriminator to force the content representation to be embedded to the same space and a feature discriminator to exact the meaningful representation. We also use pixel-level annotations to guide the encoder to learn features that are meaningful for segmentation task. We validated our method on two public datasets and experiment results show that our approach outperforms the state of the art methods on two evaluation metrics by a signiﬁcant margin.
Estimating the Ratio of Means in a Zero-inflated Poisson Mixture Model

The problem of estimating the ratio of the means of a two-component Poisson mixture model is considered, when each component is subject to zero-inﬂation, i.e., excess zero counts. The resulting zero-inﬂated Poisson mixture (ZIPM) model can be treated as a three-component Poisson mixture model with one degenerate component. The EM algorithm is applied to obtain frequentist estimators and their standard errors, the latter determined via an explicit expression for the observed information matrix. Bayes and empirical Bayes estimators also are obtained by means of conjugate priors and their data-based variants. Lastly, the ZIPM distribution and the ZTP (zero-truncated Poisson) distribution are compared.
Knowledge Mining with Scene Text for Fine-Grained Recognition

Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scene text for fine-grained recognition, which might be irrelevant when it is not significantly related to objects/scenes. We propose an end-to-end trainable network that mines implicit contextual knowledge behind scene text image and enhance the semantics and correlation to fine-tune the image representation. Unlike the existing methods, our model integrates three modalities: visual feature extraction, text semantics extraction, and correlating background knowledge to fine-grained image classification. Specifically, we employ KnowBert to retrieve relevant knowledge for semantic representation and combine it with image features for fine-grained classification. Experiments on two benchmark datasets, Con-Text, and Drink Bottle, show that our method outperforms the state-of-the-art by 3.72% mAP and 5.39% mAp, respectively. To further validate the effectiveness of the proposed method, we create a new dataset on crowd activity recognition for the evaluation. The source code and new dataset of this work are available at this repository11https://github.com/lanfeng4659/KnowledgeMiningWithSceneText.
A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction

Feng Abstract —Interventional magnetic resonance imaging (i-MRI) for surgical guidance could help visualize the interventional process such as deep brain stimulation (DBS), improving the surgery performance and patient outcome. Different from retrospective reconstruction in conventional dynamic imaging, i-MRI for DBS has to acquire and reconstruct the interventional images sequentially online. Here we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. By using an initializer and Conv-LSTM blocks, the priors from the preoperative reference image and intra-operative frames were exploited for reconstructing the current frame. Data consistency for radial sampling was implemented by a soft-projection method. An adversarial learning strategy was adopted to improve the reconstruction accuracy. A set of interventional images based on the pre-operative and post-operative MR images were simulated for algorithm validation. Results showed with only 10 radial spokes, ConvLR provided the best performance compared with state-of-the-art methods, giving an acceleration up to 40 folds. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking

Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not. Building an interpretable neural text classifier for RRP promotes the understanding of why a research paper is predicted as replicable or non-replicable and therefore makes its real-world application more reliable and trustworthy. However, the prior works on model interpretation mainly focused on improving the model interpretability at the word/phrase level, which are insufficient especially for long research papers in RRP. Furthermore, the existing methods cannot utilize a large size of unlabeled dataset to further improve the model interpretability. To address these limitations, we aim to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to further leverage the large corpus of unlabeled datasets to boost the interpretability in addition to improving prediction performance as existing works have done. In this work, we propose the Variational Contextual Consistency Sentence Masking (VCCSM) method to automatically extract key sentences based on the context in the classifier, using both labeled and unlabeled datasets. Results of our experiments on RRP along with European Convention of Human Rights (ECHR) datasets demonstrate that VCCSM is able to improve the model interpretability for the long document classification tasks using the area over the perturbation curve and post-hoc accuracy as evaluation metrics.
Current Source Localization Using Deep Prior with Depth Weighting

This paper proposes a novel neuronal current source localization method based on Deep Prior that represents a more complicated prior distribution of current source in the brain using convolutional neural networks. Deep Prior has been suggested as an unsupervised learning approach that does not require a large amount of training data, and randomly-initialized neural networks are used as implicit priors for solving inverse problems. In our previous work, a Deep-Prior-based current source localization method has been proposed but the performance was not almost the same as those of conventional approaches, such as standardized low-resolution brain electromagnetic tomography (sLORETA). In order to improve the Deep-Prior-based approach, in this paper, a depth weight of the current source is introduced for Deep Prior, where depth weighting amounts to assigning more penalty to the superficial currents. Its effectiveness is confirmed by experiments of current source estimation on simulated MEG data.
On Time Stepping Schemes Considering Switching Behaviors for Power System Electromagnetic Transient Simulation

Several difficulties will appear when typical electromagnetic transient simulation, using the implicit trapezoidal method and fixed step sizes, is applied to power systems with switching behaviors. These difficulties are addressed by different aspects of time stepping schemes in the literature. This paper first details the different aspects and reviews corresponding methods. Some misunderstanding in the literature is clarified. Issues that may be encountered by the existing methods are concurrently revealed. Based on the detailed review, the paper then puts forward a novel time stepping scheme which fully addresses the difficulties. The effectiveness of the proposed scheme is demonstrated via numerical case studies.
Behavioural alterations induced by intermittent ethanol intake and noise exposure in adolescent rats

Alcohol intake and exposure to noise are common activities of human adolescents performed in entertainment contexts worldwide that can induce behavioural disturbances. Therefore, the aim of the present work was to investigate in an experimental model of adolescent animals whether noise exposure and intermittent ethanol intake, when present individually or sequentially, might be able to modify different behaviours. Adolescent Wistar rats of both sexes were subjected to voluntary intermittent ethanol intake for 1 week followed by exposure to noise for 2 h and tested in a battery of behavioural tasks. Data show that males exposed to noise experienced a deficit in associative memory (AM), increase in anxiety‐like behaviours (ALB) and altered reaction to novelty (RN) when compared with sham animals, whereas females also showed an increase in risk assessment behaviours (RABs) and a decrease in exploratory activity (EA). In contrast, ethanol intake induced an increase in RAB and RN in males and females, whereas females also showed a deficit in AM and EA as well as an increase in ALB. When ethanol was ingested before noise exposure, most parameters were counteracted both in male and females, but differed among sexes. In consequence, it could be hypothesized that an environmental acute stressor like noise might trigger a behavioural counteracting induced by a previous repeated exposure to a chemical agent such as ethanol, leading to a compensation of a non‐adaptive behaviour and reaching a better adjustment to the environment.
Evaluation of occupational health and safety risk factors in Turkish marine fish hatcheries

In this study, the approaches of employees in marine fish hatcheries to occupational health and safety were determined. Cronbach’s α coefficient was calculated as 0.858 in the reliability of the scale and scale factors based on internal consistency. In addition,factor analysis according to the varimax method was found to be approximately 64%. It was determined that 68% of the employees were not aware of the relevant laws. In addition,85% of the employees are aware of the dangers and risks in their workplaces. In the event of a negative situation in the workplace,the rate of those who know their legal rights is 73% (p ≤ 0.05). In addition,91% of the employees stated that they would use their right to object in a risky situation and quit the job. Sixty per cent of the employees stated that their jobs can satisfy them sociologically and economically throughout their life.
Introduction to the special issue: advances in understanding the cognitive neuroscience of aging with multivariate methods

As the aging population across the world continues to grow, there is an ever-increasing need to understand how age and factors associated with aging impact cognition. Cognitive aging research has expanded rapidly over the past several decades focusing on the inherent relationship between the brain and subsequent behavior across the lifespan. For example, early work in structural neuroimaging focused on identification of age-related volumetric declines in the hippocampus and frontal cortices (Raz, 2000; Raz et al., 2005; for a recent review, see, Lockhart & DeCarli, 2014), linking these declines to age-related declines in cognitive performance (Fjell & Walhovd, 2020; Raz & Rodrigue, 2006). Early functional neuroimaging studies largely focused on cross-sectional differences between young and older adults in univariate measures of regional brain structure and function (e.g., amplitude and location of the fMRI BOLD signal) and their relationship to cognition. This foundational work has led to theories and conclusions regarding agerelated deficits in the engagement of core cognitive processes as well as age-related shifts in neural engagement and cognitive operations. One of the prominent findings in early functional neuroimaging work in aging is that of functional reorganization. Specifically, under-recruitment, or an age-related reduction in BOLD activation in a given brain region, has led to the idea of neural deficits in engaging necessary cognitive resources for completing a behavioral task (e.g., attention or memory; Grady, 2008). At the same time, over-recruitment, or an age-related increase in BOLD activation with a given brain region, has been met with the idea of compensatory processing in aging (Cabeza et al., 2002; Cabeza & Dennis, 2013). For example, a consistent poster-to-anterior-shift in aging activation across perceptual, attentional, and memory tasks has led to the conclusion that older adults engage in less overall processing within sensory cortices (e.g., occipital cortex), while over recruiting regions (e.g., frontal cortex) involved in higher-order cognition compared to younger adults (Davis et al., 2008). Other work has focused on age-related decreases in the overall BOLD signal within highly specialized brain regions when viewing or processing discrete categories of stimuli (e.g., fusiform gyrus activity when viewing face stimuli; Park et al., 2004). The
Complete Genome Sequence of Burkholderia cenocepacia Phage Paku

Burkholderia cenocepacia is able to cause infections in cystic fibrosis patients. B. cenocepacia phage Paku has a 42,727-bp genome sharing a phiKMV-like genome arrangement. T7-like tail components were identified in parallel with a tyrosine integrase, suggesting that Paku might exhibit a temperate lifestyle, an atypical feature for an Autographiviridae phage. ABSTRACT Burkholderia cenocepacia is able to cause infections in cystic fibrosis patients. B. cenocepacia phage Paku has a 42,727-bp genome sharing a phiKMV-like genome arrangement. T7-like tail components were identified in parallel with a tyrosine integrase, suggesting that Paku might exhibit a temperate lifestyle, an atypical feature for an Autographiviridae phage.
Likelihood Scores for Sparse Signal and Change-Point Detection

We consider here the identification of change-points on large-scale data streams. The objective is to find the most efficient way of combining information across data stream so that detection is possible under the smallest detectable change magnitude. The challenge comes from the sparsity of change-points when only a small fraction of data streams undergo change at any point in time. The most successful approach to the sparsity issue so far has been the application of hard thresholding such that only local scores from data streams exhibiting significant changes are considered and added. However the identification of an optimal threshold is a difficult one. In particular it is unlikely that the same threshold is optimal for different levels of sparsity. We propose here a sparse likelihood score for identifying a sparse signal. The score is a likelihood ratio for testing between the null hypothesis of no change against an alternative hypothesis in which the change-points or signals are barely detectable. By the Neyman-Pearson Lemma this score has maximum detection power at the given alternative. The outcome is that we have a scoring of data streams that is successful in detecting at the boundary of the detectable region of signals and change-points. The likelihood score can be seen as a soft thresholding approach to sparse signal and change-point detection in which local scores that indicate small changes are down-weighted much more than local scores indicating large changes. We are able to show sharp optimality of the sparsity likelihood score in the sense of achieving successful detection at the minimum detectable order of change magnitude as well as the best constant with respect this order of change.
Efficient quantum algorithm for solving structured problems via multistep quantum computation

In classical computation, a problem can be solved in multiple steps where calculated results of each step can be copied and used repeatedly. While in quantum computation, it is difficult to realize a similar multi-step computation process because the no-cloning theorem forbids making copies of an unknown quantum state perfectly. We find a method based on quantum resonant transition to protect and reuse an unknown quantum state that encodes calculated results of an intermediate step without making copies of the state, and present a quantum algorithm that solves a problem via a multi-step quantum computation process. This algorithm can achieve an exponential speedup over classical algorithms in solving a type of structured search problems.
Generalizing treatment effects with incomplete covariates: Identifying assumptions and multiple imputation algorithms

We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (RCT) to a target population described by a set of covariates from observational data. Available methods such as inverse propensity sampling weighting are not designed to handle missing values, which are however common in both data sources. In addition to coupling the assumptions for causal effect identifiability and for the missing values mechanism and to defining appropriate estimation strategies, one difficulty is to consider the specific structure of the data with two sources and treatment and outcome only available in the RCT. We propose three multiple imputation strategies to handle missing values when generalizing treatment effects, each handling the multisource structure of the problem differently (separate imputation, joint imputation with fixed effect, joint imputation ignoring source information). As an alternative to multiple imputation, we also propose a direct estimation approach that treats incomplete covariates as semidiscrete variables. The multiple imputation strategies and the latter alternative rely on different sets of assumptions concerning the impact of missing values on identifiability. We discuss these assumptions and assess the methods through an extensive simulation study. This work is motivated by the analysis of a large registry of over 20,000 major trauma patients and an RCT studying the effect of tranexamic acid administration on mortality in major trauma patients admitted to intensive care units. The analysis illustrates how the missing values handling can impact the conclusion about the effect generalized from the RCT to the target population.
Are Instrumental Variables Really That Instrumental? Endogeneity Resolution in Regression Models for Comparative Studies

We provide a justification for why, and when, endogeneity will not cause bias in the interpretation of the coefficients in a regression model. This technique can be a viable alternative to, or even used alongside, the instrumental variable method. We show that when performing any comparative study, it is possible to measure the true change in the coefficients under a broad set of conditions. Our results hold, as long as the product of the covariance structure between the explanatory variables and the covariance between the error term and the explanatory variables are equal, within the same system at different time periods or across multiple systems at the same point in time.
Rough Volatility: Fact or Artefact?

We investigate the statistical evidence for the use of ‘rough’ fractional processes with Hurst exponent $$H< 0.5$$ H < 0.5 for modeling the volatility of financial assets, using a model-free approach. We introduce a non-parametric method for estimating the roughness of a function based on discrete sample, using the concept of normalized p-th variation along a sequence of partitions. Detailed numerical experiments based on sample paths of fractional Brownian motion and other fractional processes reveal good finite sample performance of our estimator for measuring the roughness of sample paths of stochastic processes. We then apply this method to estimate the roughness of realized volatility signals based on high-frequency observations. Detailed numerical experiments based on stochastic volatility models show that, even when the instantaneous volatility has diffusive dynamics with the same roughness as Brownian motion, the realized volatility exhibits rough behaviour corresponding to a Hurst exponent significantly smaller than 0.5. Comparison of roughness estimates for realized and instantaneous volatility in fractional volatility models with different values of Hurst exponent shows that, irrespective of the roughness of the spot volatility process, realized volatility always exhibits ‘rough’ behaviour with an apparent Hurst index $$\widehat{H}<0.5$$ H ^ < 0.5 . These results suggest that the origin of the roughness observed in realized volatility time series lies in the estimation error rather than the volatility process itself.
A Memristive Based Design of a Core Digital Circuit for Elliptic Curve Cryptography

The new emerging non-volatile memory (NVM) devices known as memristors could be the promising candidate for future digital architecture, owing to their nanoscale size and its ability to integrate with the exciting CMOS technology. In this paper, a combination of memristor devices and CMOS transistors are working together to form a hybrid CMOS-memristor circuit for XAX- Module, a core element for the finite field multiplier. The proposed design was implemented using Pt /TaOx/Ta memristor device and simulated in Cadence Virtuoso. The simulation results demonstrate the design functionality. The proposed module appears to be efficient in terms of layout area, delay and power consumption since the design utilizes the hybrid CMOS/memristor gates.
HELoC: Hierarchical Contrastive Learning of Source Code Representation

Abstract syntax trees (ASTs) play a crucial role in source code representation. However, due to the large number of nodes in an AST and the typically deep AST hierarchy, it is challenging to learn the hierarchical structure of an AST effectively. In this paper, we propose HELoC, a hierarchical contrastive learning model for source code representation. To effectively learn the AST hierarchy, we use contrastive learning to allow the network to predict the AST node level and learn the hierarchical relationships between nodes in a self-supervised manner, which makes the representation vectors of nodes with greater differences in AST levels farther apart in the embedding space. By using such vectors, the structural similarities between code snippets can be measured more precisely. In the learning process, a novel GNN (called Residual Self-attention Graph Neural Network, RSGNN) is designed, which enables HELoC to focus on embedding the local structure of an AST while capturing its overall structure. HELoC is self-supervised and can be applied to many source code related downstream tasks such as code classification, code clone detection, and code clustering after pre-training. Our extensive experiments demonstrate that HELoC outperforms the state-of-the-art source code representation models.