Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Warp-Based Motion Magnification Method to Reveal Subtle Changes in Video

Video motion magnification can amplify subtle motions and even reveal small color changes in video. Ordinary methods analyze the signal change at each pixel over time at different spatial scales and orientations. These methods inevitably amplify the noise and cause ringing artifacts in video. State-of-the-art methods relying on filters via learning also produce excessive blurring in images. In this paper, we present a warp-based video motion magnification method in which only one-frame latency is maintained. We propose a Lagrangian motion magnification method, which involves image deformation and optical flow techniques. Motion magnification is achieved by warping the video frame. The method is guided by feature points and only uses previous motion, while simultaneously maintaining the original video details without noise amplification. With this approach, the proposed method can work online in real time. Experimental results show that our method can achieve high-quality results and significantly reduce artifacts, compared with state-of-the-art techniques
Employee Perception of Enforced Telework in the Context of the Covid-19 Pandemic

Natural catastrophes, human-made disasters, or as most recently pandemics can force governments to impose telework for their citizen. With Twitter data from the Covid-19 time, where telework was required in Great Britain for certain periods, we look at employees' experience of telework. Our preliminary results indicate a more positive perception of telework while it was enforced due to Covid-19 restrictions, compared to a regular telework period. In our future research, we plan to deepen the understanding of the employees' topics of concern and to develop a model explaining how enforcement affects their perception of telework.
Urban Path Planning Based on Improved Model-based Reinforcement Learning Algorithm

With the development of the urban economy, and the continuous expansion of vehicle scale, trafﬁc congestion has become the most serious problem affecting contemporary urban development. Using advanced road network information perception and transmission technologies, path planning under real-time road conditions has become an important means to solve this problem. Previously, our proposed model-based reinforcement learning multipath planning algorithm realized the rapid response of the path planning result, alleviating congestion drift to a certain extent. However, further research shows that the model performs poorly in extreme road network environments (the road network trafﬁc pressure is 0) and cannot explore the complete path, the main reason is that the effect of model hyperparameters on the convergence of the algorithm was ignored. to solve this problems, we explore the hyperparameters in detail, especially discuss the discount factor γ and the finalReward to the model convergence by using Shenzhen road network data. the results show that when the discount factor γ and the finalReward value satisfy certain conditions, which is obtained in this study, the improved model-based method can guarantee the convergence stability of the algorithm under extreme road network environments. This paper reveals the importance of the design of hyperparameters γ and finalReward as well as their interrelationship on the convergence of reinforcement learning algorithms and we hope to give some insights in the field which explore hyperparameters of reinforcement learning algorithm.
An Attention GRU-XGBoost Model for Stock Market Prediction Strategies

Predicting stock prices and market indices is very difficult, and the associated prices and indices have too much uncertainty. There are already many deep neural networks for stock price prediction, which predict future stock prices based on historical stock price data. In this paper, a GRU-XGBoost model with attention is proposed to deal with heterogeneous data with various information in stock price prediction. The GRU model is used to solve the gradient problem, and the attention mechanism and XGBoost are used to save the context and process local optimal solutions. question. The experimental results show that the proposed method has better RMSE evaluation results.
Performance Bug Analysis and Detection for Distributed Storage and Computing Systems

This article systematically studies 99 distributed performance bugs from five widely deployed distributed storage and computing systems (Cassandra, HBase, HDFS, Hadoop MapReduce and ZooKeeper). We present the TaxPerf database, which collectively organizes the analysis results as over 400 classification labels and over 2,500 lines of bug re-description. TaxPerf is classified into six bug categories (and 18 bug subcategories) by their root causes; resource, blocking, synchronization, optimization, configuration, and logic. TaxPerf can be used as a benchmark for performance bug studies and debug tool designs. Although it is impractical to automatically detect all categories of performance bugs in TaxPerf, we find that an important category of blocking bugs can be effectively solved by analysis tools. We analyze the cascading nature of blocking bugs and design an automatic detection tool called PCatch, which (i) performs program analysis to identify code regions whose execution time can potentially increase dramatically with the workload size; (ii) adapts the traditional happens-before model to reason about software resource contention and performance dependency relationship; and (iii) uses dynamic tracking to identify whether the slowdown propagation is contained in one job. Evaluation shows that PCatch can accurately detect blocking bugs of representative distributed storage and computing systems by observing system executions under small-scale workloads.
Information Overload and Presented Lifestyle in Social Media: A Stress-Perspective on the Effects on Mental Health

To counteract the increasing number of people showing symptoms of depressive disorders since the outbreak of COVID-19, among others, the WHO suggests using social media more intensively to stay in contact and receive positive messages. However, existing literature indicates that this can have the opposite effect. Based on social comparison theory and technostress literature, we examine the impact of the constant confrontation with the overly optimistic presented lifestyle of social media influencers on Instagram on depression. Our quantitative study (N=191) based on structural equation modeling indicates that influencers' presented lifestyle has a substantial positive indirect effect on depression mediated through negative emotions. In contrast, positive self-esteem can help to reduce depression. We contribute to technostress literature, research on social comparison, and mental health research.1
EVALUATION OF POWER OF TESTS INVOLVING COVARIATES UNDER NONCONSTANT VARIANCE

In many experiments where pre-treatment and post-treatment measurements are taken, investigators wish to determine if there is a difference between two treatment groups. For this type of data, the post-treatment variable is used as the primary comparison variable and the pre-treatment variable is used as a covariate. Although most of the discussion in this paper is written with the pre-treatment variable as the covariate the results are applicable to other choices of a covariate. Tests based on residuals have been proposed as alternatives to the usual covariance methods. Our objective is to investigate how the powers of these tests are affected when the conditional variance of the post-treatment variable depends on the magnitude of the pre-treatment variable. In particular, we investigate two cases. [1] The conditional variance of the post-treatment variable gradually increases as the magnitude of the pre-treatment variable increases. (In many biological models this is the case.) [2] The conditional variance of the post-treatment variable is dependent upon natural or imposed subgroups contained within the pre-treatment variable. Power comparisons are made using Monte Carlo techniques.
A Fast bearing Fault diagnosis method based on lightweight Neural Network RepVGG

In view of the shortcomings of existing deep learning methods in rolling bearing fault diagnosis, such as large number of training parameters and complex network, a fast rolling bearing fault diagnosis method based on lightweight neural network RepVGG was proposed. Firstly, the vibration signal is converted into three-channel time-frequency image by the combination of short-time Fourier transform (STFT) and pseudo-color processing technology, then the time-frequency image is inputted into the RepVGG network model for training. and the experiment is carried out on the case Western Reserve University (CWRU) data set. The accuracy is 99.62% and the training time is obviously lower than other popular fault diagnosis algorithm models based on deep learning. Finally, using the open source framework ncnn to deploy the RepVGG network model to the edge computing node Raspberry Pi, the average test accuracy is 95%, and the running efficiency is good.
Social Sustainability Approaches for a Sustainable Software Product

Sustainability is the use of existing resources without compromising them for future generations. Resources must be guaranteed in the environmental, economic, and social dimensions. Sustainable Software Engineering is an emerging research field aiming to minimize software development's negative impacts on society. Furthermore, software sustainability can be defined as a way of keeping something running at a certain level of quality in relation to these dimensions. From the social dimension perspective, it is necessary to confirm software's trade-offs, benefits, and impacts on society since it lacks empirical evidence of its achievements in software. In this research thesis, we will explore how the literature addresses the aspect of social sustainability during software development and understand how social sustainability approaches can be integrated into the context of agile software development. In the end, we expect to have a set of guidelines, activities, and practices that can be adopted by agile teams.
An Improvement to Holospectrum Based Field Balancing Method by Reselection of Balancing Object

In this paper, the holospectrum technique based balancing method has been improved by reselection of the balancing object. Current holospectrum based balancing technique uses the initial phase vector (IPV) as the balancing object. Theoretical analysis of the precession behavior of an unbalanced rotor with anisotropic stiffness shows that, compared with the IPV, its forward precession component vector is much better in describing the rotor balance state and is more suitable to be used as the measurement of unbalance response. After the backward precession component is removed, the impact of probe orientation on the balancing analysis and calculation is completely eliminated and the computational procedure is greatly simplified without sacrificing the balancing precision. The experiments and field application cases verify the accuracy and effectiveness of this method.
GSN gene frameshift mutations in Alzheimer’s disease

Background The pathogenic missense mutations of the gelsolin (GSN) gene lead to familial amyloidosis of the Finnish type (FAF); however, our previous study identified GSN frameshift mutations existed in patients with Alzheimer’s disease (AD). The GSN genotype–phenotype heterogeneity and the role of GSN frameshift mutations in patients with AD are unclear. Method In total, 1192 patients with AD and 1403 controls were screened through whole genome sequencing, and 884 patients with AD were enrolled for validation. Effects of GSN mutations were evaluated in vitro. GSN, Aβ42, Aβ40 and Aβ42/40 were detected in both plasma and cerebrospinal fluid (CSF). Results Six patients with AD with GSN P3fs and K346fs mutations (0.50%, 6/1192) were identified, who were diagnosed with AD but not FAF. In addition, 13 patients with AD with GSN frameshift mutations were found in the validation cohort (1.47%, 13/884). Further in vitro experiments showed that both K346fs and P3fs mutations led to the GSN loss of function in inhibiting Aβ-induced toxicity. Moreover, a higher level of plasma (p=0.001) and CSF (p=0.005) GSN was observed in AD cases than controls, and a positive correlation was found between the CSF GSN and CSF Aβ42 (r=0.289, p=0.009). Besides, the GSN level was initially increasing and then decreasing with the disease course and cognitive decline. Conclusions GSN frameshift mutations may be associated with AD. An increase in plasma GSN is probably a compensatory reaction in AD, which is a potential biomarker for early AD.
Optimal robotic assembly sequence planning with tool integrated assembly interference matrix

Abstract Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible. Additionally, it is vital to test the feasibility of positioning and assembling components while employing robotic grippers and tools prior to their implementation. This paper presents a novel concept named by considering both part and tool geometry to propose “tool integrated assembly interference matrices” (TIAIMs) and a “tool integrated axis-aligned bounding box” (TIAABB) to generate practically feasible assembly sequence plans. Furthermore, the part-concatenation technique is used to determine the best assembly sequence plans for an actual mechanical component. The results show that the proposed approach effectively and efficiently deals with real-life industrial problems.
Effect of Inlet Turbulence and Premixer Length on Fuel Distribution in Swirling Gas-Turbine Premixer

The mixing characteristics of a production gas-turbine combustor premixer have been investigated using a planar Mie-scattering imaging technique. Speci cally, the effects of introducing controlled inlet grid turbulence (intensity and scale) and varying premixer length on large-scale mixing were independently studied. Baseline data acquired for a particular swirler con guration indicated a strong fuel concentration gradient in the annulus, with a maximum at the centerbody and decreasing outward. The measured fuel concentration, or equivalence ratio, also varied circumferentially, by as much as 45% over the entire premixer exit plane. The circumferential fuel concentration pro les showed that improvedmixingwith increased turbulence intensity was realized for most angularlocations.Premixer length extensionsof 2.5and5.0 cm, corresponding to 0.53and1. 06hydraulicdiameters, respectively, resulted in a considerable improvement in mixing, accompanied by signi cant structural changes in the scalar  eld. The circumferential variations were reduced up to 30% for the 5.0-cm extension case. Radial fuel concentration gradients were also reduced by 50%. Overall, increased residence time was a signi cantly more effective means of mixing enhancement than introducing moderate levels of freestream turbulence.
A boundary layer model for magnetospheric substorms

Magnetospheric substorm activity is presently understood as the combined effect of energy transport and dissipation which is directly driven through the solar-terrestrial interaction and of the unloading of energy stored in the magnetotail. Many studies of particle and field signatures of substorms in the magnetotail have led to the proposal that a neutral line forms close to the Earth at the time of expansive phase onset with the plasma sheet population on the anti-Earthward side of the neutral line ultimately being lost to the solar wind through the ejection of the plasmoid. While the near-Earth neutral line model has provided a useful working model for substorm phenomenology, it is not a unique description for understanding the observations.
Next-generation interaction proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions (HPIs) are pivotal in regulating establishment, progression, and outcome of an infection. Affinity-purification mass spectrometry has become instrumental for the characterization of HPIs, however the targeted nature of exogenously expressing individual viral proteins has limited its utility to the analysis of relatively small pathogens. Here we present the use of co-fractionation mass spectrometry (SEC-MS) for the high-throughput analysis of HPIs from native viral infections of two jumbophages (ϕKZ and ϕPA3) in Pseudomonas aeruginosa. This enabled the detection >6000 unique host-pathogen and >200 pathogen-pathogen interactions for each phage, encompassing >50% of the phage proteome. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. Prediction of novel ORFs revealed a ϕPA3 complex showing strong structural and sequence similarity to ϕKZ nvRNAp, suggesting ϕPA3 also possesses two RNA polymerases acting at different stages of the infection cycle. We further expanded our understanding on the molecular organization of the virion packaged and injected proteome by identifying 23 novel virion components and 5 novel injected proteins, as well as providing the first evidence for interactions between KZ-like phage proteins and the host ribosome. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction (https://phagemap.ucsf.edu/). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of hostpathogen interactomes and protein complex dynamics upon infection.
Comprehensive detection of structural variation and transposable element differences between wild type laboratory lineages of C. elegans

Genomic structural variations (SVs) and transposable elements (TEs) can be significant contributors to genome evolution, altered gene expression, and risk of genetic diseases. Recent advancements in long-read sequencing have greatly improved the quality of de novo genome assemblies and enhanced the detection of sequence variants at the scale of hundreds or thousands of bases. Comparisons between two diverged wild isolates of Caenorhabditis elegans, the Bristol and Hawaiian strains, have been widely utilized in the analysis of small genetic variations. Genetic drift, including SVs and rearrangements of repeated sequences such as TEs, can occur over time from long-term maintenance of wild type isolates within the laboratory. To comprehensively detect both large and small structural variations as well as TEs due to genetic drift, we generated de novo genome assemblies and annotations for each strain from our lab collection using both long- and short-read sequencing and compared our assemblies and annotations with that of other lab wild type strains. Within our lab assemblies, we annotate over 3.1Mb of sequence divergence between the Bristol and Hawaiian isolates: 337,584 SNPs, 94,503 small insertion-deletions (<50bp), and 4,334 structural variations (>50bp). Further, we define the location and movement of specific DNA TEs between N2 Bristol and CB4856 Hawaiian wild type isolates. Specifically, we find the N2 Bristol genome has 20.6% more TEs from the Tc1/mariner family than the CB4856 Hawaiian genome. Moreover, we identified Zator elements as the most abundant and mobile TE family in the genome. Using specific TE sequences with unique SNPs, we also identify 38 TEs that moved intrachromosomally and 9 TEs that moved interchromosomally between the N2 Bristol and CB4856 Hawaiian genomes. By comparing the de novo genome assembly of our lab collection Bristol isolate to the VC2010 Bristol assembly, we also reveal that lab lineages display over 2 Mb of total variation: 1,162 SNPs, 1,528 indels, and 897 SVs with 95% of the variation due to SVs. Overall, our work demonstrates the unique contribution of SVs and TEs to variation and genetic drift between wild type laboratory strains assumed to be isogenic despite growing evidence of genetic drift and phenotypic variation. Author Summary For multiple model organisms, propagation of wild type strains in independent labs can lead to multiple phenotypic differences over time. To assess recombination, map mutations, and understand genomic changes during speciation, Caenorhabditis elegans researchers primarily use the wild type isolates Bristol and Hawaiian. Here, we map structural variations, transposable elements, and sequence divergence between the Bristol and Hawaiian natural isolates and between genomes of different lab lineages of these same strains.
Region‐based epigenetic clock design improves RRBS‐based age prediction

Recent studies suggest that epigenetic rejuvenation can be achieved using drugs that mimic calorie restriction and techniques such as reprogramming induced rejuvenation. To effectively test rejuvenation in vivo, mouse models are the safest alternative. However, we have found that the recent epigenetic clocks developed for mouse reduced-representation bisulphite sequencing (RRBS) data have significantly poor performance when applied to external datasets. We show that the sites captured and the coverage of key CpGs required for age prediction vary greatly between datasets, which likely contributes to the lack of transferability in RRBS clocks. To mitigate these coverage issues in RRBS-based age prediction, we present two novel design strategies that use average methylation over large regions rather than individual CpGs, whereby regions are defined by sliding windows (e.g. 5 kb), or density-based clustering of CpGs. We observe improved correlation and error in our regional blood clocks (RegBCs) compared to published individual-CpG-based techniques when applied to external datasets. The RegBCs are also more robust when applied to low coverage data and detect a negative age acceleration in mice undergoing calorie restriction. Our RegBCs offer a proof of principle that age prediction of RRBS datasets can be improved by accounting for multiple CpGs over a region, which negates the lack of read depth currently hindering individual-CpG-based approaches.
Genome-wide screens identify SEL1L as an intracellular rheostat controlling collagen turnover

Accumulating evidence has implicated impaired extracellular matrix (ECM) clearance as a key factor in fibrotic disease. Despite decades of research elucidating the effectors of ECM clearance, relatively little is understood regarding the upstream regulation of this process. Collagen is the most abundant constituent of normal and fibrotic ECM in mammalian tissues. Its catabolism occurs through extracellular proteolysis and cell-mediated uptake of collagen fragments for intracellular degradation. Given the paucity of information regarding the regulation of this latter process, we executed unbiased genome-wide screens to understand the molecular underpinnings of cell-mediated collagen clearance. Using this approach, we discovered a previously unappreciated mechanism through which collagen biosynthesis is sensed by cells internally and directly regulates clearance of extracellular collagen. The sensing mechanism is dependent on endoplasmic reticulum-resident protein SEL1L and occurs via a noncanonical function of SEL1L. This pathway functions as a homeostatic negative feedback loop that limits collagen accumulation in tissues. In human fibrotic lung disease, the induction of this collagen clearance pathway by collagen synthesis is impaired, thereby contributing to the pathological accumulation of collagen in lung tissue. Thus cell-autonomous, rheostatic collagen clearance is a previously unidentified pathway of tissue homeostasis.