Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Distributed Bitruss Decomposition on Bipartite Graphs

Mining cohesive subgraphs on bipartite graphs is an important task. The k -bitruss is one of many popular cohesive subgraph models, which is the maximal subgraph where each edge is contained in at least k butterflies. The bitruss decomposition problem is to find all k -bitrusses for k ≥ 0. Dealing with large graphs is often beyond the capability of a single machine due to its limited memory and computational power, leading to a need for efficiently processing large graphs in a distributed environment. However, all current solutions are for a single machine and a centralized environment, where processors can access the graph or auxiliary indexes randomly and globally. It is difficult to directly deploy such algorithms on a shared-nothing model. In this paper, we propose distributed algorithms for bitruss decomposition. We first propose SC-HBD as the baseline, which uses H -function to define bitruss numbers and computes them iteratively to a fix point in parallel. We then introduce a subgraph-centric peeling method SC-PBD, which peels edges in batches over different butterfly complete subgraphs. We then introduce local indexes on each fragment, study the butterfly-aware edge partition problem including its hardness, and propose an effective partitioner. Finally we present the bitruss butterfly-complete subgraph concept, and divide and conquer DC-BD method with optimization strategies. Extensive experiments show the proposed methods solve graphs with 30 trillion butterflies in 2.5 hours, while existing parallel methods under shared-memory model fail to scale to such large graphs.
Finetuning Variational Autoencoders for the Restoration of Sports Video Highlights

As time has passed, cameras and video recorders have improved with quality. For this reason, past video footage may not necessarily be the most pleasant for modern viewers due to outdated quality and less capable technology. This is particularly relevant for sports data, since present-day viewers may not be able to fully enjoy old footage of their favorite athletes, matches, and sporting triumphs. Our work shows that it is possible to enhance old sports highlights by finetuning an existing variational autoencoder framework for a specific category of sports video data. We perform data-based finetuning, hyperparameter optimization, and postprocessing with the goal of various visual features being crisper and more visible. Extra care is taken to ensure that important features such as ball location or player movement is not obscured by our model. We use Wan et. al’s publicly available pre-trained model as a foundation and baseline for our frame-by-frame restoration work of tennis video data from the Wimbledon Championships.
High-frequency random vibration analysis of a high-speed vehicle–track system with the frequency-dependent dynamic properties of rail pads using a hybrid SEM–SM method

ABSTRACT A hybrid Spectral Element Method (SEM)–Symplectic Method(SM) method for high-efficiency computation of the high-frequency random vibrations of a high-speed vehicle–track system with the frequency-dependent dynamic properties of rail pads is presented. First, the Williams-Landel-Ferry (WLF) formula and Fractional Derivative Zener (FDZ) model were, respectively, applied for prediction and representation of the frequency-dependent dynamic properties of Vossloh 300 rail pads frequently used in China's high-speed railway. Then, the proposed hybrid SEM–SM method was used to investigate the influence of the frequency-dependent dynamic performance of Vossloh 300 rail pads on the high-frequency random vibrations of high-speed vehicle–track systems at various train speeds or different levels of rail surface roughness. The experimental results indicate that the storage stiffness and loss factors of Vossloh 300 rail pad increase with the decrease in dynamic loads or the increase in preloads within 0.1–10,000 Hz at 20°C, and basically linearly increase with frequency in a logarithmic coordinate system. The results computed by the hybrid SEM–SM method demonstrate that the frequency-dependent viscous damping of Vossloh 300 rail pads, compared with its constant viscous damping and frequency-dependent stiffness, has a much more conspicuous influence on the medium-frequency (i.e. 20–63 Hz) random vibrations of car bodies and rail fasteners, and on the mid- (i.e. 20–63 Hz) and high-frequency (i.e. 630–1250 Hz) random vibrations of bogies, wheels and rails, especially with the increase in train speeds or the deterioration of rail surface roughness. The two sensitive frequency bands can also be validated by frequency response function (FRF) analysis of the proposed infinite rail–fastener model. The mid and high frequencies influenced by the frequency-dependent viscous damping of rail pads are exactly the dominant frequencies of ground vibration acceleration and wheel rolling noise caused by high-speed railways, respectively. Even though the existing time-domain (or frequency-domain) finite track models associated with the time-domain (or frequency-domain) fractional derivative viscoelastic (FDV) models of rail pads can also be used to reach the same conclusions, the hybrid SEM–SM method in which only one element is required to compute the high-order vibration modes of infinite rail is more appropriate for high-efficiency analysis of the high-frequency random vibrations of high-speed vehicle–track systems.
Cell Cycle Classification using Imaging Flow Cytometry and Deep Learning

Imaging flow cytometry (IFC) enables the characterization of morphology and protein expression of thousands of cells at a single-cell resolution. However, fluorescent staining protocols can alter cell state and lead to visual artifacts. Thus, staining-free methods to quantify cell char-acteristics using only bright-field (BF) images are needed. Previous work has focused on extracting morphological features from BF images in a deterministic manner, with moderate success. Indeed, a key flaw of this approach is the bias introduced by the pre-defined features extracted from each image. Hence, the automated and agnostic feature extraction provided by deep learning architectures has the potential to improve on these results and provide superior performance. In this work, I leveraged a dataset of 32,266 Jurkat cells in a proof-of-concept study to examine whether deep learning can classify cell cycle stage using only BF images. I separately trained a fully-connected (FC) 2-layer neural network, a 3-layer convolutional neural network (CNN), and a simple vision transformer (Simple ViT) and assessed their performance in a held-out test set. While the 2-layer FC architecture achieved the best balanced accuracy, the 3-layer CNN had the most interpretable embeddings. Thus, this work provides the first characterization of using deep learning for BF image analysis acquired using IFC.
Strava Route Art Sketch Image Classification

Inspired by image sketch classification, our project attempts to classify Strava route art into ImageNet classes. The Strava route art images are largely sparse and are unique, abstract representations of objects. Studying this problem can help us learn more about the general intelligence of computer vision models based on if they can interpret sketches. We take five models pre-trained on ImageNet and fine-tune these models on ImageNet-Sketch, a large sketch dataset that exhibits similar properties to our Strava art test dataset. All of our pre-trained models had some element of residual connections in their architecture, because we found that to be promising in our research of re-lated work. We saw significant improvement in test set top-1 and top-5 accuracy scores for 3 out of our 5 models after fine-tuning, which demonstrates that those models were effective were able to effectively transfer learn. However, the improved accuracy scores are still low and we were expecting to see improvement on all 5 models so there is clear room in this project for future improvement.
Computer Move Suggestions from Physical Game Images

In this paper, we discuss how we constructed computer-vision programs which are able to recognize the board state of physical versions of the games Set and Scrabble. The programs rely on a combination of traditional computer-vision techniques as well as convolutional neural networks. Once the board state is recognized, the programs are then able to analyze the game and suggest optimal moves. In order to construct our programs, we also developed our own datasets of images of Scrabble and Set games from a simulated, realistic looking environment through rendering on Blender. We demonstrated that CNNs can successfully recognize different areas of Set and Scrabble boards. We hope these programs will help people both save time when looking for computer assistance for their games as well as contribute to players’ improvement through ease of use in postgame analysis.
Terpenes of the Essential Oil from Ipomoea alba Leaf in Response to Herbivore and Mechanical Injuries

There is no doubt that the chemical composition of plants, including norvolatile and volatile compounds, is widely affected by abiotic and biotic stress. Plants are able to biosynthesize a variety of secondary metabolites against actions of natural enemies, such as herbivores, fungus, virus and bacteria. The present study revealed that the chemical compositions of leaf essential oils from Ipomoea alba underwent quantitative and qualitative alterations both when infested with the grasshopper Elaeochlora trilineata and mechanically damaged. Grasshopper attack and mechanical wounding induced the biosynthesis of nine volatile compounds in leaves of I. alba: cumene, α-ylangene, β-panasinsene, β-gurjunene aromadendrene, β-funebrene, spirolepechinene, cubenol and sclareolide. The amount of germacrene D (33.2% to 20.4%) decreased when the leaves were mechanically damaged; but when the leaves were attacked by a grasshopper, the germacrene D increased from 33.2% to 39.4%. The results showed that I. alba leaves clearly responded to abiotic and biotic stress and contribute to an understanding of plant responses to stress conditions.
Biomechanical Comparison between Isobar and Dynamic-Transitional Optima (DTO) Hybrid Lumbar Fixators: A Lumbosacral Finite Element and Intersegmental Motion Analysis

Biomechanical performance of longitudinal component in dynamic hybrid devices was evaluated to display the load-transfer effects of Dynesys cord spacer or Isobar damper-joint dynamic stabilizer on junctional problem based on various disc degenerations. The dynamic component was adapted at the mildly degenerative L3–L4 segment, and the static component was fixed at the moderately degenerative L4–L5 segment under a displacement-controlled mode for the finite element study. Furthermore, an intersegmental motion behavior was analyzed experimentally on the synthetic model under a load-controlled mode. Isobar or DTO hybrid fixator could reduce stress/motion at transition segment, but compensation was affected at the cephalic adjacent segment more than the caudal one. Within the trade-off region (as a motion-preserving balance between the transition and adjacent segments), the stiffness-related problem was reduced mostly in flexion by a flexible Dynesys cord. In contrast, Isobar damper afforded the effect of maximal allowable displacement (more than peak axial stiffness) to reduce stress within the pedicle and at facet joint. Pedicle-screw travel at transition level was related to the extent of disc degeneration in Isobar damper-joint (more than Dynesys cord spacer) attributing to the design effect of axial displacement and angular rotation under motion. In biomechanical characteristics relevant to clinical use, longitudinal cord/damper of dynamic hybrid lumbar fixators should be designed with less interface stress occurring at the screw-vertebral junction and facet joint to decrease pedicle screw loosening/breakage under various disc degenerations.
Reluctance Network Model of a Permanent Magnet Tubular Motor

Abstract The reluctance network model of a permanent magnet tubular motor (PMTM) has been presented in the paper. The reluctance values of the magnetic circuit have been calculated with using analytical expressions. The air gap reluctance has been determined with using both analytical expressions and the finite element method (FEM). Using the calculation model, the flux values coupled with the windings have been obtained and used in the calculations of force value. The calculated results have been compared with numerical and measured ones.
SAR image target recognition based on non-local operation

Synthetic aperture radar (SAR) target recognition is an important part of SAR image interpretation. It has been widely used in the field of national defense and national economy. At present, the feature extraction based on convolution is a local operation in space and time. The convolution kernel extracts the features in the local region with a certain step size. The global information of the picture can only be obtained by increasing the number of convolution layers. However, this method will not only increase the difficulty of model training but also makes the optimization of the network more difficult, and even leads to over fitting. Therefore, this paper proposes a SAR image target recognition algorithm based on GoogLeNet -NB, which combines accurate and effective GoogLeNet framework and non-local block(NB). By adding NB to GoogLeNet framework, we can capture more context information, enhance the correlation between pixels and regions, and improve the representation ability of the network. In order to verify the effectiveness of NB, NB is added in different positions of GoogLeNet framework for experimental comparison. Finally, MSTAR database is used to verify the algorithm. The experimental results show that the recognition effect of the GoogLeNet -NB algorithm model proposed in this paper is better than the traditional Alexnet algorithm and GoogLeNet algorithm. In the adding NB in different positions of GoogLeNet framework, adding NB in the front position of GoogLeNet can reduce the loss of information in the training process and obtain more global information. Therefore, GoogLeNet-preNB algorithm has certain advantages over GoogLeNet-postNB algorithm.
Studies of the impact of environmental conditions and varietal features of sweet cherry on the accumulation of vitamin C in fruits by using the regression analysis method

The accumulation of vitamin C in sweet cherry fruits depends on the variety and environmental conditions. The aim of our research was to substantiate the rate of impact of weather factors as well as of varietal features on vitamin C accumulation in sweet cherry fruits. The varieties ʹKazkaʹ and ʹZabutaʹ, ʹKordiaʹ and ʹMirazhʹ were chosen as the best ones from among 33 varieties of early, medium and late term of ripening (7.31–10.67 mg 100 g-1) according to the average content of vitamin C in sweet cherry fruits. The studies found that the environmental conditions of the research years had the largest impact on the vitamin C content in the fruits of late and early ripening varieties, and in the fruits of medium ripening variety the vitamin C amount depended on the varietal features. The practicability of forecasting of vitamin C content in sweet cherry fruits on the average indices for a group of early and late maturity varieties, but not separately for every pomological variety, has been proven. For the medium ripening variety this index can be forecasted within each pomological variety. The models of dependence of vitamins C accumulation on the impact of meteorological parameters were evaluated on the basis of the principle components analysis and the least square method.
Identifying and Managing Project Risk: Essential Tools for Failure-Proofing Your Project

Fusion Management: Harness ing th e Power of Six Sigma, Lean. ISO 9001:2000. Malcolm Baldrige, TQ~I and Other Quall~' Breakthroughs of the Past Century, Stanley A. Marash. with Paul Berman and Michael Flynn. 2003. Fairfax, \'a. : QSU Publishing Company. 179 pages. Stanley Marash applies the astrophysicist's approach to unite the fou r forces of nature into a grand unfed theoIy in his quest to bring themyriol 01 quality concepts that arebeingused todayintoa single theoretical construct Herefers to hisquest as "fusion management," which unites Six Sigma, lean enterprise. ISO 9001:2000, the Baldrige Award, total quality management, andother quali ty breakthroughs 01 the past centuryinto a singlequality concept In his defin ition of fusion, simpler elements combine to form more complex elements,which take on features and qualities that the constituent ingredients did not Jl'"S"S. (p. 8) However, rather than four forceselectromagnetic, weak, strong. andgraviry-c-that physicists deal with. ~1a rash has identified more than 70! Putting this in perspective he says: "In the 70·plus users since Shewhart's 1931 text,we can point 10 an average of about one . program per rear. " (p. 18) His analysis of the strengths and weaknesses ofthemajorones is,-et)' insightful. He makes a good point that many of them were not pro-grams at all. but rather techniques,
Bivariate random coefficient integer‐valued autoregressive models: Parameter estimation and change point test

This study examines a first‐order bivariate random coefficient integer‐valued autoregressive (BRCINAR) model and the inferential procedures of this model, such as the parameter estimation and parameter change test. We first introduce the BRCINAR model and investigate its probabilistic properties such as stationarity, ergodicity, and high moment conditions, and then propose estimation methods such as the conditional least squares (CLS), modified quasi‐likelihood (MQL), and exponential family quasi‐likelihood (EQL) methods. As an application, a parameter change test is considered. For this task, a residual‐based cumulative sum (CUSUM) test is employed. To evaluate the performances of the three estimation methods and the respective CUSUM tests, we conduct a Monte Carlo simulation study and demonstrate the adequacy of the proposed methods. A real data analysis is also carried out using syphilis data in the United States for illustration.
A COMPACT DUAL-ELEMENT UNIPLANAR ANTENNA FOR PORTABLE BROADBAND MIMO SYSTEMS

A printed dual-port coplanar waveguide (CPW)-fed antenna is proposed for wideband communication systems. The antenna includes two identical hybrid trapezoidal-elliptical radiating elements that are printed perpendicular to each other. Also, two orthogonal CPW lines are used to feed the antenna. In order to achieve broadband dual-polarized operation with a compact size, the geometrical parameters of the antenna are optimized by using Ansoft HFSS. The antenna was fabricated and tested. Reasonable agreement between the simulation and experimental results is obtained. The fabricated prototype with a small size of 25 × 53 mm2 can cover the wide operating frequency band from 2.4 to 18 GHz (reflection coefficient less than −10 dB) for both ports. The measured isolation is better than 25 dB over the entire operating bandwidth. Moreover, the measured results show that the proposed antenna can provide omnidirectional radiation patterns with a good orthogonal polarization operation, reasonable gain, high radiation efficiency, and constant group delay.
Wildfire Impacts on Snowpack Phenology in a Changing Climate Within the Western U.S.

Snowpack in the western U.S. is critical for water supply and is threatened by wildfires, which are becoming larger and more common. Numerous studies have examined impacts of wildfire on snow water equivalent (SWE), but many of these studies are limited in the number of observation locations, and they have sometimes produced conflicting results. The objective of this study is to distinguish the net effects of wildfires on snowpack from those of climate. We consider 45 burned sites from the Snow Telemetry network. For each burned site, unburned control sites are identified from the same level III ecoregion. Impacts of climate changes on snowpack are analyzed by comparing pre‐fire and post‐fire snow water equivalent at the unburned sites. Combined climate and wildfire effects are considered by comparing pre‐fire and post‐fire SWE at the burned sites. Wildfire impacts are then isolated by taking the difference between the burned and unburned sites. The wildfire‐induced changes in SWE are also compared to several possible controlling variables including burn severity, leaf‐area index change, dominant pre‐fire tree genus, years since the fire, and site elevation. On average, wildfires have advanced melt‐out dates 9 days and maximum SWE dates 6 days and reduced annual maximum SWE by 10% across the sites considered. On average, the combined effects of climate and wildfire have advanced melt‐out and maximum SWE dates approximately 14 and 10 days, respectively, while decreasing annual maximum SWE by approximately 10%.
Evaluating 5G uplink performance in low latency video streaming

In this paper, we evaluate the efficiency of low latency video streaming in a real standalone 5G test environment with a particular focus on uplink latency using UDP and TCP transport protocols. Furthermore, the evaluation comprise also congested scenario in which additional traffic is brought to the uplink reducing the network capacity, which is a common phenomenon in live mobile streaming from the field. Although 5G networking has brought higher capacity and reduced latency, the uplink is usually less prioritized in terms of network share. This limitation can generate a bottleneck in live video streaming use cases requiring low latency, such as delivering real time environmental data between vehicles in V2X scenarios or performing remote operations from a distance. The results gathered from the extensive set of evaluation with test cases indicate that 5G standalone with right uplink to downlink duration ratio has potential for low latency streaming and delay variation stays relatively satisfying level even in congested network scenarios.
Yeast Kinesin-5 Motor Protein CIN8 Promotes Accurate Chromosome Segregation

Accurate chromosome segregation depends on bipolar chromosome–microtubule attachment and tension generation on chromosomes. Incorrect chromosome attachment results in chromosome missegregation, which contributes to genome instability. The kinetochore is a protein complex that localizes at the centromere region of a chromosome and mediates chromosome–microtubule interaction. Incorrect chromosome attachment leads to checkpoint activation to prevent anaphase onset. Kinetochore detachment activates the spindle assembly checkpoint (SAC), while tensionless kinetochore attachment relies on both the SAC and tension checkpoint. In budding yeast Saccharomyces cerevisiae, kinesin-5 motor proteins Cin8 and Kip1 are needed to separate spindle pole bodies for spindle assembly, and deletion of CIN8 causes lethality in the absence of SAC. To study the function of Cin8 and Kip1 in chromosome segregation, we constructed an auxin-inducible degron (AID) mutant, cin8-AID. With this conditional mutant, we first confirmed that cin8-AID kip1∆ double mutants were lethal when Cin8 is depleted in the presence of auxin. These cells arrested in metaphase with unseparated spindle pole bodies and kinetochores. We further showed that the absence of either the SAC or tension checkpoint was sufficient to abolish the cell-cycle delay in cin8-AID mutants, causing chromosome missegregation and viability loss. The tension checkpoint-dependent phenotype in cells with depleted Cin8 suggests the presence of tensionless chromosome attachment. We speculate that the failed spindle pole body separation in cin8 mutants could increase the chance of tensionless syntelic chromosome attachments, which depends on functional tension checkpoint for survival.