Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditioning of metal surfaces enhances Shewanella chilikensis adhesion

Abstract Microbiologically influenced corrosion and biofouling of steels depend on the adsorption of a conditioning film and subsequent attachment of bacteria. Extracellular deoxyribonucleic acid (eDNA) and amino acids are biologically critical nutrient sources and are ubiquitous in marine environments. However, little is known about their role as conditioning film molecules in early biofilm formation on metallic surfaces. The present study evaluated the capacity for eDNA and amino acids to form a conditioning film on carbon steel (CS), and subsequently, the influence of these conditioning films on bacterial attachment using a marine bacterial strain. Conditioning films of eDNA or amino acids were formed on CS through physical adsorption. Biochemical and microscopic analysis of eDNA conditioning, amino acid conditioning and control CS surfaces demonstrated that organic conditioning surfaces promoted bacterial attachment. The results highlight the importance of conditioning the surface in initial bacterial attachment to steel.
The Impact of Virtual Reality Hypnosis on Pain and Anxiety Caused by Trauma: Lessons Learned from a Clinical Trial

ABSTRACT This randomized, controlled trial tested the impact that hypnosis delivered through immersive virtual reality technology on background pain, anxiety, opioid use, and hospital length of stay in a sample of patients hospitalized for trauma. Participants were randomly assigned to receive either virtual-reality-induced hypnosis, virtual reality for distraction, or usual care during the course of their hospitalization. Mean number of treatment sessions was 3. A total of 153 patients participated in the study. Results indicated no significant differences between the experimental and control conditions on any outcome measures. This study used an early version of virtual reality technology to induce hypnosis and highlighted several important lessons about the challenges of implementation of this technology and how to improve its use in clinical settings.
Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization

Abstract Objective Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease’s heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features. Methods After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation. Results Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%. Conclusion Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features. Supplemental data for this article is available online at at www.tandfonline. com/ijas .
HPVMD-C: a disease-based mutation database of human papillomavirus in China

Abstract Human papillomavirus (HPV) can cause condyloma acuminatum and cervical cancer. Some mutations of these viruses are closely related to the persistent infection of cervical cancer and are ideal cancer vaccine targets. Several databases have been developed to collect HPV sequences, but no HPV mutation database has been published. This paper reports a Chinese HPV mutation database (HPVMD-C), which contains 149 HPV genotypes, 468 HPV mutations, 3409 protein sequences, 4727 domains and 236 epitopes. We analyzed the mutation distribution among HPV genotypes, domains and epitopes. We designed a visualization tool to display these mutations, domains and epitopes and provided more detailed information about the disease, region and related literature. We also proposed an HPV genotype prediction tool, which can predict HPV carcinogenic or non-carcinogenic risk genotypes. We expect that HPVMD-C will complement the existing database and provide valuable resources for HPV vaccine research and cervical cancer treatment. HPVMD-C is freely available at Database URL: http://bioinfo.zstu. edu.cn/hpv.
Ultraprocessed food consumption and kidney function decline in a population-based cohort in the Netherlands

ABSTRACT Background Ultraprocessing makes food products more convenient, appealing, and profitable. Recent studies show that high ultraprocessed food (UPF) intake is associated with cardiometabolic diseases. Objectives The aim of this study is to investigate the association between UPF consumption and risks of kidney function decline in the general population. Methods In a prospective, general population–based Lifelines cohort from Northern Netherlands, 78,346 participants free of chronic kidney disease (CKD) at baseline responded to a 110-item FFQ. We used a multivariable regression analysis to study the associations of the proportion (in grams/day) of UPFs in the total diet with a composite kidney outcome [incident CKD or a ≥30% estimated glomerular filtration rate (eGFR) decline relative to baseline] and annual change in eGFR. Results On average, 37.7% of total food intake came from UPFs. After 3.6 ± 0.9 years of follow-up, 2470 participants (3.2%) reached the composite kidney outcome. Participants in the highest quartile of UPF consumption were associated with a higher risk of the composite kidney outcome (OR, 1.27; 95% CI, 1.09–1.47; P = 0.003) compared with those in the lowest quartile, regardless of their macro- or micronutrient intake or diet quality. Participants in the highest quartile had a more rapid eGFR decline (β, −0.17; 95% CI, −0.23 to −0.11; P < 0.001) compared with those in the lowest quartile. Associations were generally consistent across different subgroups. Conclusions Higher UPF consumption was associated with a higher risk of a composite kidney outcome (incident CKD or ≥30% eGFR decline) and a more rapid eGFR decline in the general population, independent of confounders and other dietary indices.
Information criteria for sparse methods in causal inference

For propensity score analysis and sparse estimation, we develop an information criterion for determining the regularization parameters needed in variable selection. First, for Gaussian distribution-based causal inference models, we extend Stein’s unbiased risk estimation theory, which leads to a generalized Cp criterion that has almost no weakness in conventional sparse estimation, and derive an inverse-probability-weighted sparse estimation version of the criterion without resorting to asymptotics. Next, for general causal inference models that are not necessarily Gaussian distribution-based, we extend the asymptotic theory on LASSO for propensity score analysis, with the intention of implementing doubly robust sparse estimation. From the asymptotic theory, an AIC-type information criterion for inverse-probability-weighted sparse estimation is given, and then a criterion with double robustness in itself is derived for doubly robust sparse estimation. Numerical experiments compare the proposed criterion with the existing criterion derived from a formal argument and verify that the proposed criterion is superior in almost all cases, that the diﬀerence is not negligible in many cases, and that the results of variable selection diﬀer signiﬁcantly. Real data analysis conﬁrms that the diﬀerence between variable selection and estimation by these criteria is actually large. Finally, generalizations to general sparse estimation using group LASSO, elastic net, and non-convex regularization are made in order to indicate that the proposed criterion is highly extensible.
LiDAR Snowfall Simulation for Robust 3D Object Detection

3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather. In this paper, we address the problem of LiDAR-based 3D object detection under snow-fall. Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds. Our method samples snow particles in 2D space for each LiDAR line and uses the in-duced geometry to modify the measurement for each LiDAR beam accordingly. Moreover, as snowfall often causes wet-ness on the ground, we also simulate ground wetness on LiDAR point clouds. We use our simulation to generate par-tially synthetic snowy LiDAR data and leverage these data for training 3D object detection models that are robust to snowfall. We conduct an extensive evaluation using several state-of-the-art 3D object detection methods and show that our simulation consistently yields significant performance gains on the real snowy STF dataset compared to clear-weather baselines and competing simulation approaches, while not sacrificing performance in clear weather. Our code is available at github.com/SysCV/LiDAR_snow_sim.
syslrn: learning what to monitor for efficient anomaly detection

While monitoring system behavior to detect anomalies and failures is important, existing methods based on log-analysis can only be as good as the information contained in the logs, and other approaches that look at the OS-level software state introduce high overheads. We tackle the problem with syslrn, a system that first builds an understanding of a target system offline, and then tailors the online monitoring instrumentation based on the learned identifiers of normal behavior. While our syslrn prototype is still preliminary and lacks many features, we show in a case study for the monitoring of OpenStack failures that it can outperform state-of-the-art log-analysis systems with little overhead.
Filler Word Detection and Classification: A Dataset and Benchmark

Filler words such as ‘uh’ or ‘um’ are sounds or words people use to signal they are pausing to think. Finding and removing ﬁller words from recordings is a common and tedious task in media editing. Automatically detecting and classifying ﬁller words could greatly aid in this task, but few studies have been published on this problem. A key reason is the absence of a dataset with annotated ﬁller words for training and evaluation. In this work, we present a novel speech dataset, PodcastFillers, with 35K annotated ﬁller words and 50K annotations of other sounds that commonly occur in podcasts such as breaths, laugh-ter, and word repetitions. We propose a pipeline that leverages VAD and ASR to detect ﬁller candidates and a classiﬁer to distinguish between ﬁller word types. We evaluate our proposed pipeline on PodcastFillers, compare to several baselines, and present a detailed ablation study. In particular, we evaluate the importance of using ASR and how it compares to a transcription-free approach resembling keyword spotting. We show that our pipeline obtains state-of-the-art results, and that leveraging ASR strongly outperforms a keyword spotting approach. We make PodcastFillers publicly available, and hope our work serves as a benchmark for future research. Using PodcastFillers, we train a ﬁller classiﬁer similar to a keyword spotting approach, and present an end-to-end pipeline that leverages VAD, ASR, and the classiﬁer to perform ﬁller word detection and classiﬁcation. We compare our proposed pipeline to two baselines and show that it yields state-of the-art results. We hope it serves as a robust benchmark for future research.
Speech Emotion Recognition with Co-Attention Based Multi-Level Acoustic Information

Speech Emotion Recognition (SER) aims to help the machine to understand human’s subjective emotion from only audio in-formation. However, extracting and utilizing comprehensive in-depth audio information is still a challenging task. In this paper, we propose an end-to-end speech emotion recognition system using multi-level acoustic information with a newly designed co-attention module. We firstly extract multi-level acoustic information, including MFCC, spectrogram, and the embedded high-level acoustic information with CNN, BiL-STM and wav2vec2, respectively. Then these extracted features are treated as multimodal inputs and fused by the pro-posed co-attention mechanism. Experiments are carried on the IEMOCAP dataset, and our model achieves competitive performance with two different speaker-independent cross-validation strategies. Our code is available on GitHub.
Why Do Banks Find Business Process Compliance So Challenging? An Australian Case Study

Banks play an intrinsic role in any modern economy, recycling capital from savers to borrowers. They are heavily regulated and there have been a signiﬁcant number of well publicized compliance failings in recent years. This is despite Business Process Compliance (BPC) being both a well researched domain in academia and one where signiﬁcant progress has been made. This study seeks to determine why Australian banks ﬁnd BPC so challenging. We interviewed 22 senior managers from a range of functions within the four major Australian banks to identify the key challenges. Not every process in every bank is facing the same issues, but in processes where a bank is particularly challenged to meet its compliance requirements, the same themes emerge. The compliance requirement load they bear is excessive, dynamic and complex. Fulﬁlling these requirements relies on impenetrable spaghetti processes, and the case for sustainable change remains elusive, locking banks into a fail-ﬁx cycle that increases the underlying complexity. This paper proposes a conceptual framework that identiﬁes and aggregates the challenges, and a circuit-breaker approach as an “off ramp” to the fail-ﬁx cycle.
Comparative Evaluations of Visualization Onboarding Methods

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non- experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of a visualization.
Simultaneous Communication and Tracking in Arbitrary Trajectories via Beam-Space Processing

In this paper, we develop a beam tracking scheme for an orthogonal frequency division multiplexing (OFDM) Integrated Sensing and Communication (ISAC) system with a hybrid digital analog (HDA) architecture operating in the millimeter wave (mmWave) band. Our tracking method consists of an estimation step inspired by radar signal processing techniques, and a prediction step based on simple kinematic equations. The hybrid architecture exploits the predicted state information to focus only on the directions of interest, trading off beamforming gain, hardware complexity and multistream processing capabilities. Our extensive simulations in arbitrary trajectories show that the proposed method can outperform state of the art beam tracking methods in terms of prediction accuracy and consequently achievable communication rate, and is fully capable of dealing with highly non-linear dynamic motion patterns.
Safe Active Learning for Multi-Output Gaussian Processes

Multi-output regression problems are commonly encountered in science and engineering. In particular, multi-output Gaussian processes have been emerged as a prom-ising tool for modeling these complex systems since they can exploit the inherent correlations and provide reliable uncertainty es-timates. In many applications, however, acquiring the data is expensive and safety concerns might arise (e.g. robotics, engineer-ing). We propose a safe active learning approach for multi-output Gaussian process regression. This approach queries the most informative data or output taking the related-ness between the regressors and safety constraints into account. We prove the eﬀect-iveness of our approach by providing theoretical analysis and by demonstrating empirical results on simulated datasets and on a real-world engineering dataset. On all datasets, our approach shows improved convergence compared to its competitors.
A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps

Climate control of buildings makes up a significant portion of global energy consumption, with groundwater heat pumps providing a suitable alternative. To prevent possibly negative interactions between heat pumps throughout a city, city planners have to optimize their layouts in the future. We develop a novel data-driven approach for building small-scale surrogates for modelling the thermal plumes generated by groundwater heat pumps in the surrounding subsurface water. Building on a data set generated from 2D numerical simulations, we train a convolutional neural network for predicting steady-state subsurface temperature fields from a given subsurface velocity field. We show that compared to existing models ours can capture more complex dynamics while still being quick to compute. The resulting surrogate is thus well-suited for interactive design tools by city planners.
Attributable Visual Similarity Learning

This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and ex-plainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplain-ability by mapping each sample to a single point in the em-bedding space with a distance metric (e.g., Mahalanobis distance, Euclidean distance). Motivated by the human se-mantic similarity cognition, we propose a generalized simi-larity learning paradigm to represent the similarity between two images with a graph and then infer the overall simi-larity accordingly. Furthermore, we establish a bottom-up similarity construction and top-down similarity inference framework to infer the similarity based on semantic hier-archy consistency. We first identify unreliable higher-level similarity nodes and then correct them using the most co-herent adjacent lower-level similarity nodes, which simulta-neously preserve traces for similarity attribution. Extensive experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improve-ments over existing deep similarity learning methods and verify the interpretability of our framework.11Code: https://github.com/zbr17/AVSL.
Dynamic-Subarray With Fixed Phase Shifters for Energy-Efficient Terahertz Hybrid Beamforming Under Partial CSI

Terahertz (THz) communications are regarded as a pillar technology for the 6G systems, by offering multi-ten-GHz bandwidth. To overcome the huge propagation loss, THz ultra-massive MIMO systems with hybrid beamforming are proposed to offer high array gain. Notably, the adjustable phase shifters considered in most existing hybrid beamforming studies are power-hungry and difficult to realize in the THz band. Moreover, due to the ultra-massive antennas, full channel-state-information (CSI) is challenging to obtain. To address these practical concerns, in this paper, an energy-efficient dynamic-subarray with fixed phase shifters (DS-FPS) architecture is proposed for THz hybrid beamforming. To compensate for the spectral efficiency loss caused by the fixed phase of FPS, a switch network is inserted to enable dynamic connections. In addition, by considering the partial CSI, we propose a row-successive-decomposition (RSD) algorithm to design the hybrid beamforming matrices for DS-FPS. A row-by-row (RBR) algorithm is further proposed to reduce the computational complexity. Extensive simulation results show that, the proposed DS-FPS architecture with the RSD and RBR algorithms achieves much higher energy efficiency than the existing architectures. Moreover, the spectral efficiency of the DS-FPS architecture with the proposed algorithms is robust to the CSI error.