Miracle-dz/UniFAR-FTUs huggingface dataset:
Deformation and dewetting of liquid films under gas jets

Abstract We study the deformation and dewetting of liquid films under impinging gas jets using experimental, analytical and numerical techniques. We first derive a reduced-order model (a thin-film equation) based on the long-wave assumption and on appropriate decoupling of the gas problem from that for the liquid. The model not only provides insight into relevant flow regimes, but is also used in conjunction with experimental data to guide more computationally prohibitive direct numerical simulations of the full governing equations. A unique feature of our modelling solution is the use of an efficient iterative procedure in order to update the interfacial deformation based on stresses originating from computational data. We show that both gas normal and tangential stresses are equally important for achieving accurate predictions. The interplay between these techniques allows us to study previously unreported flow features. These include finite-size effects of the host geometry, with consequences for flow and vortex formation inside the liquid, as well as the specific individual contributions from the non-trivial gas flow components on interfacial deformation. Dewetting phenomena are found to depend on either a dominant gas flow or contact line motion, with the observed behaviour (including healing effects) being explained using a bifurcation diagram of steady-state solutions in the absence of the gas flow.
The gap between theory and practice in function approximation with deep neural networks

Deep learning (DL) is transforming industry as decision-making processes are being automated by deep neural networks (DNNs) trained on real-world data. Driven partly by rapidly-expanding literature on DNN approximation theory showing they can approximate a rich variety of functions, such tools are increasingly being considered for problems in scientific computing. Yet, unlike traditional algorithms in this field, little is known about DNNs from the principles of numerical analysis, e.g., stability, accuracy, computational efficiency and sample complexity. In this paper we introduce a computational framework for examining DNNs in practice, and use it to study empirical performance with regard to these issues. We study performance of DNNs of different widths & depths on test functions in various dimensions, including smooth and piecewise smooth functions. We also compare DL against best-in-class methods for smooth function approx. based on compressed sensing (CS). Our main conclusion from these experiments is that there is a crucial gap between the approximation theory of DNNs and their practical performance, with trained DNNs performing relatively poorly on functions for which there are strong approximation results (e.g. smooth functions), yet performing well in comparison to best-in-class methods for other functions. To analyze this gap further, we provide some theoretical insights. We establish a practical existence theorem, asserting existence of a DNN architecture and training procedure that offers the same performance as CS. This establishes a key theoretical benchmark, showing the gap can be closed, albeit via a strategy guaranteed to perform as well as, but no better than, current best-in-class schemes. Nevertheless, it demonstrates the promise of practical DNN approx., by highlighting potential for better schemes through careful design of DNN architectures and training strategies.
Multi-factorial Optimization for Large-scale Virtual Machine Placement in Cloud Computing

The placement scheme of virtual machines (VMs) to physical servers (PSs) is crucial to lowering operational cost for cloud providers. Evolutionary algorithms (EAs) have been performed promising-solving on virtual machine placement (VMP) problems in the past. However, as growing demand for cloud services, the existing EAs fail to implement in large-scale virtual machine placement (LVMP) problem due to the high time complexity and poor scalability. Recently, the multi-factorial optimization (MFO) technology has surfaced as a new search paradigm in evolutionary computing. It offers the ability to evolve multiple optimization tasks simultaneously during the evolutionary process. This paper aims to apply the MFO technology to the LVMP problem in heterogeneous environment. Firstly, we formulate a deployment cost based VMP problem in the form of the MFO problem. Then, a multi-factorial evolutionary algorithm (MFEA) embedded with greedy-based allocation operator is developed to address the established MFO problem. After that, a re-migration and merge operator is designed to offer the integrated solution of the LVMP problem from the solutions of MFO problem. To assess the effectiveness of our proposed method, the simulation experiments are carried on large-scale and extra large-scale VMs test data sets. The results show that compared with various heuristic methods, our method could shorten optimization time significantly and offer a competitive placement solution for the LVMP problem in heterogeneous environment.
Caching at Base Stations With Multi-Cluster Multicast Wireless Backhaul via Accelerated First-Order Algorithms

Cloud radio access network (C-RAN) has been recognized as a promising architecture for next-generation wireless systems to support the rapidly increasing demand for higher data rate. However, the performance of C-RAN is limited by the backhaul capacities, especially for the wireless deployment. While C-RAN with fixed BS caching has been demonstrated to reduce backhaul consumption, it is more challenging to further optimize the cache allocation at BSs with multi-cluster multicast backhaul, where the inter-cluster interference induces additional non-convexity to the cache optimization problem. Despite the challenges, we propose an accelerated first-order algorithm, which achieves much higher content downloading sum-rate than a second-order algorithm running for the same amount of time. Simulation results demonstrate that, by simultaneously delivering the required contents to different multicast clusters, the proposed algorithm achieves significantly higher downloading sum-rate than those of time-division single-cluster transmission schemes. Moreover, it is found that the proposed algorithm allocates larger cache sizes to the farther BSs within the nearer clusters, which provides insight to the superiority of the proposed cache allocation.
Research Progress of Establishment of Animal Model for Lumbar Intervertebral Disc Degeneration

The article has been to sum up the progress on research of establishment of animal model for lumbar intervertebral disc degener-ation.The main 3 preparative methods include:(1)Spontaneous model:the natural lumbar intervertebral disc degeneration and the degeneration which is accelerated by special methods. (2)Model of inducibility:to change the biomechanics construction of vertebrae and histochemistry comstr-uction of nucleus pulposus by methods of biochemical and physics,then induce the degeneration. (3)Model of imitation: to imitate the lumbar intervertebral disc degeneration in vivo by methods of operation.Conclusion: most scholars choose needle acupuncture as the main method,because the method is convenient,less-invasive,velocity,cost effective and it cans imtata the low back pain so it has the high dependability with human.
A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels

Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task. We address this with a simple model enabling portable and fast predictions among different GPUs using only hardware-independent features. This model is built based on random forests using 189 individual compute kernels from benchmarks such as Parboil, Rodinia, Polybench-GPU, and SHOC. Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 8.86–52.0% for time and 1.84–2.94% for power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 ms.
An IoT Platform-as-a-Service for NFV-Based Hybrid Cloud/Fog Systems

Cloud computing, despite its inherent advantages (e.g., resource efficiency), still faces several challenges. The wide area network used to connect the cloud to end users could cause high latency, which may not be tolerable for some applications, especially Internet-of-Things (IoT) applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. Provisioning such applications requires novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this article, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case driven, and its applications’ components are implemented as virtual network functions (VNFs) with execution sequences modeled as graphs with substructures, such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when the PaaS modules and application components are placed in clouds and fogs in different geographical locations.
MOEA/D with Random Partial Update Strategy

Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work, we investigate a new, more straightforward partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D-DE using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D-DE with relative improvement-based resource allocation. The results indicate that using MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced.
Batchboost: Regularization for Stabilizing Training with Resistance to Underfitting & Overfitting

Overfitting & underfitting and stable training are an important challenges in machine learning. Current approaches for these issues are mixup, SamplePairing and BC learning. In our work, we state the hypothesis that mixing many images together can be more effective than just two. Batchboost pipeline has three stages: (a) pairing: method of selecting two samples. (b) mixing: how to create a new one from two samples. (c) feeding: combining mixed samples with new ones from dataset into batch (with ratio $\gamma$). Note that sample that appears in our batch propagates with subsequent iterations with less and less importance until the end of training. Pairing stage calculates the error per sample, sorts the samples and pairs with strategy: hardest with easiest one, than mixing stage merges two samples using mixup, $x_1 + (1-\lambda)x_2$. Finally, feeding stage combines new samples with mixed by ratio 1:1. Batchboost has 0.5-3% better accuracy than the current state-of-the-art mixup regularization on CIFAR-10 & Fashion-MNIST. Our method is slightly better than SamplePairing technique on small datasets (up to 5%). Batchboost provides stable training on not tuned parameters (like weight decay), thus its a good method to test performance of different architectures. Source code is at: this https URL
Text-to-Image Generation with Attention Based Recurrent Neural Networks

Conditional image modeling based on textual descriptions is a relatively new domain in unsupervised learning. Previous approaches use a latent variable model and generative adversarial networks. While the formers are approximated by using variational auto-encoders and rely on the intractable inference that can hamper their performance, the latter is unstable to train due to Nash equilibrium based objective function. We develop a tractable and stable caption-based image generation model. The model uses an attention-based encoder to learn word-to-pixel dependencies. A conditional autoregressive based decoder is used for learning pixel-to-pixel dependencies and generating images. Experimentations are performed on Microsoft COCO, and MNIST-with-captions datasets and performance is evaluated by using the Structural Similarity Index. Results show that the proposed model performs better than contemporary approaches and generate better quality images. Keywords: Generative image modeling, autoregressive image modeling, caption-based image generation, neural attention, recurrent neural networks.
Positive Impacts on Student Self-Efficacy from an Introductory Physics for Life Science Course Using the Team-Based Learning Pedagogy

We present the impact on student self-efficacy of an introductory physics for life-science students course taught using a Team-Based Learning pedagogy. We measured self-efficacy using the validated quantitative Sources of Self-Efficacy in Science Courses - Physics, SOSESC-P, survey developed by Fencl and Scheel. Data were collected both at the beginning and end of the semester to evaluate the impact of shifts in individual self-efficacy. After describing the key features of the pedagogy, we find that the Team-Based Learning system at University of Massachusetts Amherst, results in significant improvements for she-identifying individuals from three of the four sources of self-efficacy identified by Bandura as well as in three of four investigated attributes of the course. We also investigated the predictive power of self-efficacy on individual student performance using logarithmic regression. For our course, the shift in self-efficacy between the beginning and end of the semester is more important that a student's pronouns in predicting attaining at least a B on individual assignments.
SQuINTing at VQA Models: Interrogating VQA Models with Sub-Questions

Existing VQA datasets contain questions with varying levels of complexity. While the majority of questions in these datasets require perception for recognizing existence, properties, and spatial relationships of entities, a significant portion of questions pose challenges that correspond to reasoning tasks -- tasks that can only be answered through a synthesis of perception and knowledge about the world, logic and / or reasoning. This distinction allows us to notice when existing VQA models have consistency issues -- they answer the reasoning question correctly but fail on associated low-level perception questions. For example, models answer the complex reasoning question"Is the banana ripe enough to eat? "correctly, but fail on the associated perception question"Are the bananas mostly green or yellow? "indicating that the model likely answered the reasoning question correctly but for the wrong reason. We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting Sub-VQA, a new dataset consisting of 200K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split. Additionally, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend do the same parts of the image when answering the reasoning question and the perception sub questions. We show that SQuINT improves model consistency by 7.8%, also marginally improving its performance on the Reasoning questions in VQA, while also displaying qualitatively better attention maps.
Numerical computation of spin-transfer torques for antiferromagnetic domain walls

We numerically compute current-induced spin-transfer torques for antiferromagnetic domain walls, based on a linear response theory in a tight-binding model. We find that, unlike for ferromagnetic domain-wall motion, the contribution of adiabatic spin torque to antiferromagnetic domain-wall motion is negligible, consistent with previous theories. As a result, the nonadiabatic spin-transfer torque is a main driving torque for antiferromagnetic domain-wall motion. Moreover, the nonadiabatic spin-transfer torque for narrower antiferromagnetic domain walls increases more rapidly than that for ferromagnetic domain walls, which is attributed to the enhanced spin mistracking process for antiferromagnetic domain walls.
Towards flexible wireless charging for medical implants using distributed antenna system

This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming power, even when the implant moves around inside the human body. To accomplish this, we exploit the unique energy ball pattern of distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile, the power levels on other body parts stay in low level, reducing the risk of overheating. We proto-type In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Extensive experiments demonstrate that In-N-Out achieves 0.37 mW average charging power inside a 10 cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4X-18.1X power gain when the implant is stationary, and 5.3X-7.4X power gain when the implant is in motion.
BARNet: Bilinear Attention Network with Adaptive Receptive Field for Surgical Instrument Segmentation

Surgical instrument segmentation is crucial for computer-assisted surgery. Different from common object segmentation, it is more challenging due to the large illumination variation and scale variation in the surgical scenes. In this paper, we propose a bilinear attention network with adaptive receptive fields to address these two issues. To deal with the illumination variation, the bilinear attention module models global contexts and semantic dependencies between pixels by capturing second-order statistics. With them, semantic features in challenging areas can be inferred from their neighbors, and the distinction of various semantics can be boosted. To adapt to the scale variation, our adaptive receptive field module aggregates multi-scale features and selects receptive fields adaptively. Specifically, it models the semantic relationships between channels to choose feature maps with appropriate scales, changing the receptive field of subsequent convolutions. The proposed network achieves the best performance 97.47% mean IoU on Cata7. It also takes the first place on EndoVis 2017, exceeding the second place by 10.10% mean IoU.
Regular and chaotic behavior of collective atomic motion in two-component Bose-Einstein condensates

We theoretically study binary Bose-Einstein condensates trapped in a single-well harmonic potential to probe the dynamics of collective atomic motion. The idea is to choose tunable scattering lengths through Feshbach resonances such that the ground-state wave function for two types of the condensates are spatially immiscible where one of the condensates, located at the center of the potential trap, can be effectively treated as a potential barrier between bilateral condensates of the second type of atoms. In the case of small wave function overlap between bilateral condensates, one can parametrize their spatial part of the wave functions in the two-mode approximation together with the time-dependent population imbalance $z$ and the phase difference $\phi$ between two wave functions. The condensate in the middle can be approximated by a Gaussian wave function with the displacement of the condensate center $\xi$. As driven by the time-dependent displacement of the central condensate, we find the Josephson oscillations of the collective atomic motion between bilateral condensates as well as their anharmonic generalization of macroscopic self-trapping effects. In addition, with the increase in the wave function overlap of bilateral condensates by properly choosing tunable atomic scattering lengths, the chaotic oscillations are found if the system departs from the state of a fixed point. The Melnikov approach with a homoclinic solution of the derived $z,\,\phi$, and $\xi$ equations can successfully justify the existence of chaos. All results are consistent with the numerical solutions of the full time-dependent Gross-Pitaevskii equations.
Analysis of Water Used For Haemodialysis In Dialysis Centers, South East Nigeria – How Adequate?

Background: Standard water purity is one of the essential ingredients in achieving the goals of haemodialysis. However, water purity, though cardinal to the outcome of haemodialysis is probably the most neglected aspect of renal replacement therapy with haemodialysis. Methods: A total of eight haemodialysis centers were studied. Water samples were analysed from three points A) water storage tank B) an outlet in the piping connection between the water storage tank and reverse osmosis machine and C) an outlet piping between the reverse osmosis machine and haemodialysis machine. Samples from A and B were referred to as pretreated water while samples from C were referred to as post treated water. These samples were tested for aluminum, calcium and magnesium using colorimeter; potassium and sodium using flame photometer; chloramines, nitrate and free chlorine using colometric method. Water samples were also cultured in tryptone glucose extra agar at 37 C for 48 hours. Endotoxin analysis was done using limulus Amaeboctye assay. Results: Borehole was the commonest source of water for haemodialysis, 63%. Treated water was tested for chemical and bacteriological contaminations every three months in 50% of the centers, every six months in 25% and rarely in 12.5%. One centre never tested their water. Combination methods were used in all the centers for water treatment. Mean concentration of aluminum (0.35 ± 0.06), chloramines (0.84 ± 0.88) and nitrate (2.54 ± 2.07) exceeded the Association for Advancement of Medical Instrumentation (AAMI) recommendation. The microbial counts were within AAMI recommendation level. Conclusion: Water purification in our environment is not optimal. This calls for serious concern.