Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization

Although pre-trained language models (PLMs) have achieved great success and become a milestone in NLP, abstractive conversational summarization remains a challenging but less studied task. The difficulty lies in two aspects. One is the lack of large-scale conversational summary data. Another is that applying the existing pre-trained models to this task is tricky because of the structural dependence within the conversation and its informal expression, etc. In this work, we first build a large-scale (11M) pretraining dataset called RCS, based on the multi-person discussions in the Reddit community. We then present TANet, a thread-aware Transformer-based network. Unlike the existing pre-trained models that treat a conversation as a sequence of sentences, we argue that the inherent contextual dependency among the utterances plays an essential role in understanding the entire conversation and thus propose two new techniques to incorporate the structural information into our model. The first is thread-aware attention which is computed by taking into account the contextual dependency within utterances. Second, we apply thread prediction loss to predict the relations between utterances. We evaluate our model on four datasets of real conversations, covering types of meeting transcripts, customer-service records, and forum threads. Experimental results demonstrate that TANET achieves a new state-of-the-art in terms of both automatic evaluation and human judgment.
Excimer formation of pyrene labeled lipids interpreted by means of coarse-grained molecular dynamics simulations

The excimer formation dynamics of pyrene-labeled molecules in lipid bilayers depends on molecular motion over distances of the order of 1-2 nm. From the concentration dependence of the excimer photoemission curve, it is possible to derive a value for the lipid self-diﬀusion coeﬃcient. This technique has been intensively used in the past twenty years, leading to rather large numerical values for self-diﬀusion compared with other approaches based on ﬂuorescent probes tracking. In most cases, the interpretation of the experimental data rely on models for diﬀusion limited 2d reaction rates, or comparison with 2d lattice random walks. Our approach uses realistic molecular dynamics trajectories to reinterpret these experiments. Based on a well established coarse-grained model for lipid MD simulations (Martini), we show how to relate simulation results to experimental data on excimer formation. Our procedure is quite general and is applicable to all diﬀusion-limited kinetic processes. Key to our approach is the determination of the acceleration factor of lipid coarse-grained numerical models compared to reality. We ﬁnd a signiﬁcant reduction of the diﬀusion coeﬃcient values, in particular when interleaﬂet association is taken into account. Our work does not point to deviation from a diﬀusion-limited mechanism but indicates that the excimer formation across bilayer leaﬂets could be hindered.
The Short-term Effect of COVID-19 Pandemic on China's Crude Oil Futures Market: A Study Based on Multifractal Analysis

The ongoing COVID-19 shocked financial markets globally, including China’s crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China’s first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring. [ FROM AUTHOR]
Conservation laws with discontinuous flux function on networks: a splitting algorithm

In this article, we present an extension of the splitting algorithm proposed in [22] to networks of conservation laws with piecewise linear discontinuous flux functions in the unknown. We start with the discussion of a suitable Riemann solver at the junction and then describe a strategy how to use the splitting algorithm on the network. In particular, we focus on two types of junctions, i.e., junctions where the number of outgoing roads does not exceed the number of incoming roads (dispersing type) and junctions with two incoming and one outgoing road (merging type). Finally, numerical examples demonstrate the accuracy of the splitting algorithm by comparisons to the exact solution and other approaches used in the literature.
Pareto Conditioned Networks

In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process. The set of optimal policies can grow exponentially with the number of objectives, and recovering all solutions requires an exhaustive exploration of the entire state space. We propose Pareto Conditioned Networks (PCN), a method that uses a single neural network to encompass all non-dominated policies. PCN associates every past transition with its episode's return. It trains the network such that, when conditioned on this same return, it should reenact said transition. In doing so we transform the optimization problem into a classification problem. We recover a concrete policy by conditioning the network on the desired Pareto-efficient solution. Our method is stable as it learns in a supervised fashion, thus avoiding moving target issues. Moreover, by using a single network, PCN scales efficiently with the number of objectives. Finally, it makes minimal assumptions on the shape of the Pareto front, which makes it suitable to a wider range of problems than previous state-of-the-art multi-objective reinforcement learning algorithms.
Can Caroli-de Gennes-Matricon and Majorana vortex states be distinguished in the presence of impurities?

Majorana zero modes states (MZMs) are predicted to appear as bound states in vortices of topological superconductors. MZMs are pinned at zero energy and have zero charge due to particle-hole symmetry. MZMs in vortices of topological superconductors tend to coexist with other subgap states, named Caroli-de Gennes-Matricon (CdGM) states. The distinction between MZMs and CdGM is limited since current experiments rely on zero-bias peak measurements obtained via scanning tunneling spectroscopy. In this work, we show that a local impurity potential can push CdGM states to zero energy. Furthermore, the ﬁnite charge in CdGM states can also drop to zero under the same mechanism. We establish, through exploration of the impurity parameter space, that these two phenomena generally happen in consonance. This means that energy and charge shifts in CdGM may be enough to imitate spectroscopic signatures of MZMs.
Augmentation-Free Graph Contrastive Learning

Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data. Despite its remarkable success, existing GCL methods highly rely on an augmentation scheme to learn the representations invariant across different augmentation views. In this work, we revisit such a convention in GCL through examining the effect of augmentation techniques on graph data via the lens of spectral theory. We found that graph augmentations preserve the low-frequency components and perturb the middle-and high-frequency components of the graph, which contributes to the success of GCL algorithms on homophilic graphs but hinder its application on heterophilic graphs, due to the high-frequency preference of heterophilic data. Motivated by this, we propose a novel, theoretically-principled, and augmentation-free GCL method, named AF-GCL, that (1) leverages the features aggregated by Graph Neural Network to construct the self-supervision signal instead of augmentations and therefore (2) is less sensitive to the graph homophily degree. Theoretically, We present the performance guarantee for AF-GCL as well as an analysis for understanding the efficacy of AF-GCL. Extensive experiments on 14 benchmark datasets with varying degrees of heterophily show that AF-GCL presents competitive or better performance on homophilic graphs and outperforms all existing state-of-the-art GCL methods on heterophilic graphs with significantly less computational overhead.
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets, however, are often difficult to obtain to fuel the development of advanced, high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, data augmentation plays a crucial role in boosting model performance while reducing manual efforts for data preparation, by algorithmically expanding training datasets. Beyond traditional data augmentation techniques, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representations and generate highly realistic samples. Since 2017, there has been a growth of research into GANs for image augmentation or synthesis in agriculture for improved model performance. This paper presents an overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture (https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects. Challenges and opportunities of GANs are discussed for future research.
Quantum sensing with diamond NV centers under megabar pressures

: Megabar pressures are of crucial importance for cutting-edge studies of condensed matter physics and geophysics. With the development of diamond anvil cell, laboratory studies of high pressure have entered the megabar era for decades. However, it is still challenging to implement in-situ magnetic sensing under ultrahigh pressures. Here, we demonstrate optically detected magnetic resonance of diamond nitrogen-vacancy (NV) centers, a promising quantum sensor of strain and magnetic fields, up to 1.4 Mbar. We quantify the reduction and blueshifts of NV fluorescence under high pressures. We demonstrate coherent manipulation of NV electron spins and extend its working pressure to megabar region. These results shed new light on our understanding of diamond NV centers and will benefit quantum sensing under extreme conditions.
Estimation of the uncertainty in wind power forecasting

Wind power experiences a tremendous development in Europe. Though, the intermittence of wind generation causes difficulties in the management of power systems. Moreover, in the context of the deregulation of electricity markets, wind energy is penalized by its intermittent nature. It is recognized today that the forecasting of wind power for horizons up to 2/3-day ahead eases the integration of wind generation. Wind power forecasts are traditionally provided in the form of point predictions, which correspond to the most-likely power production for a given horizon. That sole information is not sufficient for developing optimal management or trading strategies. Therefore, we investigate on possible ways for estimating the uncertainty of wind power forecasts. The characteristics of the prediction uncertainty are described by a thorough study of the performance of some of the state-of-the-art approaches, and by underlining the influence of some variables e.g. level of predicted power on distributions of prediction errors. Then, a generic method for the estimation of prediction intervals is introduced. This statistical method is non-parametric and utilizes fuzzy logic concepts for integrating expertise on the prediction uncertainty characteristics. By estimating several prediction intervals at once, one obtains predictive distributions of wind power output. The proposed method is evaluated in terms of its reliability, sharpness and resolution. In parallel, we explore the potential use of ensemble predictions for skill forecasting. Wind power ensemble forecasts are obtained either by converting meteorological ensembles (from ECMWF and NCEP) to power or by applying a poor man's temporal approach. A proposal for the definition of prediction risk indices is given, reflecting the disagreement between ensemble members over a set of successive look-ahead times. Such risk indices may comprise a more comprehensive signal on the expected level of uncertainty in an operational environment. A probabilistic relation between classes of risk indices and the level of forecast error is shown. In a final part, the trading application is considered for demonstrating the value of uncertainty estimation when predicting wind generation. It is explained how to integrate that uncertainty information in a decision-making process accounting for the sensitivity of end-users to regulation costs. The benefits of having a probabilistic view of wind power forecasting are clearly shown.
Real order total variation with applications to the loss functions in learning schemes

Loss function are an essential part in modern data-driven approach, such as bi-level training scheme and machine learnings. In this paper we propose a loss function consisting of a $r$-order (an)-isotropic total variation semi-norms $TV^r$, $r\in \mathbb{R}^+$, defined via the Riemann-Liouville (R-L) fractional derivative. We focus on studying key theoretical properties, such as the lower semi-continuity and compactness with respect to both the function and the order of derivative $r$, of such loss functions.
Improving sustainability of striped catfish (Pangasianodon hypophthalmus) farming in the Mekong Delta, Vietnam through recirculation technology

The aim of this thesis was to document improvements in sustainability indicators of striped catfish (Pangasianodon hypophthalmus, Sauvage, 1878) production through the application of recirculation and waste treatment techniques. To be able to document improvements in sustainability, in each system studied the same set of twenty sustainability indicators were measured. Indicators related to the use of fingerlings, water, diesel oil, electricity, labor, chemicals and antibiotics. Also, indicators related to nutrient utilization efficiencies and waste discharge were monitored. In addition, a sampling scheme, allowing to calculate organic matter, nitrogen, phosphorous and chemical oxygen demand mass balances covering a full production cycle and applicable in different production systems, was developed. Overall, from a sustainability point of view, striped catfish culture in ponds compared well to other important aquaculture species. Although favorable, it was concluded that water, chemicals and antibiotics use, survival, and the amounts of waste discharged could be further reduced through recirculation and treatment of solid wastes. The realized improvements through RAS technology and waste treatment technology were quantified in lab or pilot scale experiments. Large improvements were realized for water, antibiotic and chemical use, survival, waste discharge and color grade of striped catfish fillets at harvest. In addition, in RAS, utilization efficiencies of nutrients supplied through feeding were improved. Solid wastes removed from ponds or RAS could be partially re-used by making compost or producing methane for generating electricity. Another approach tested was the integration of a denitrification reactor in the recirculation system, which allowed to decompose solid waste and reduce nitrogen discharge. Denitrification in RAS did not affect fish growth, nutrient retention efficiencies and the quality of the fish fillets produced, and thus also improved sustainability of striped catfish farming. In conclusion, application of recirculation and waste treatment techniques tested in this thesis improved the sustainability for striped catfish culture. The challenge remains to scale up RAS and waste treatment technology for striped catfish to the production volumes handled in outdoor ponds without raising production costs.
Re-Examining Human Annotations for Interpretable NLP

Explanation methods in Interpretable NLP often explain the model's decision by extracting evidence (rationale) from the input texts supporting the decision. Benchmark datasets for rationales have been released to evaluate how good the rationale is. The ground truth rationales in these datasets are often human annotations obtained via crowd-sourced websites. Valuable as these datasets are, the details on how those human annotations are obtained are often not clearly specified. We conduct comprehensive controlled experiments using crowd-sourced websites on two widely used datasets in Interpretable NLP to understand how those unsaid details can affect the annotation results. Specifically, we compare the annotation results obtained from recruiting workers satisfying different levels of qualification. We also provide high-quality workers with different instructions for completing the same underlying tasks. Our results reveal that the annotation quality is highly subject to the workers' qualification, and workers can be guided to provide certain annotations by the instructions. We further show that specific explanation methods perform better when evaluated using the ground truth rationales obtained by particular instructions. Based on these observations, we highlight the importance of providing complete details of the annotation process and call for careful interpretation of any experiment results obtained using those annotations.
When is Good Good Enough? Context Factors for Good Remote Work of Agile Software Development Teams. The Otto Case

lukas.linke@otto.de Abstract —The Covid-19 pandemic led to several challenges in everybody working life. Many companies worldwide enabled comprehensive remote work settings for their employees. Agile Software Development Teams are affected by the switch to remote work as agile methods setting communication and collaboration in focus. The well-being and motivation of software engineers and developers, which impacting their performance, are influenced by specific context factors. This paper aims to analyze identify specific context factors for a good remote work setting. We designed a single case study at a German ecommerce company and conducted an experiment using a gamification approach including eight semi-structured interviews. Our results show, that the agile software development team members to their health. Furthermore, most the team members value the gamification approach to put more focus on physical activities and the health well-being. We discuss several practical implications and provide recommendations for other teams and
WiP: AABAC - Automated Attribute Based Access Control for Genomics Data

The COVID-19 crisis and the subsequent vaccine development have demonstrated the potential of cutting-edge genomics research. However, the privacy of these sensitive pieces of information is an area of significant concern for genomics researchers. The current security models make it difficult to create flexible, automated, and secure data-sharing frameworks. These models also increase the complexity of adding or revoking access. In this work, we investigate an automated attribute-based access control (AABAC) model for genomics data over Named Data Networking (NDN). AABAC secures the data itself rather than the storage location or transmission channel, provides automated data invalidation, and automates key retrieval and data validation while maintaining the ability to control access. When combined with NDN, we show that AABAC offers a secure and flexible access control framework.