Miracle-dz/UniFAR-FTUs huggingface dataset:
Topological transformation of fractional optical vortex beams using computer generated holograms

Optical vortex beams with fractional topological charges (TCs) are generated by the diffraction of a Gaussian beam using computer generated holograms embedded with mixed screw-edge dislocations. When the input Gaussian beam has a finite wave-front curvature, the generated fractional vortex beams show distinct topological transformations in comparison to the integer charge optical vortices. The topological transformations at different fractional TCs are investigated through the birth and evolution of the points of phase singularity, the azimuthal momentum transformation, occurrence of critical points in the transverse momentum and the vorticity around the singular points. This study is helpful to achieve better control in optical micro-manipulation applications.
Tracing Actual Causes (CMU-CyLab-16-004)

We study the problem of tracing actual causes, i.e. given an event e, we seek to fully explain why that event happened. This problem was articulated by David Lewis in his work on causal explanations [Lewis, 1986a]. We address the problem by defining the causal history of the explanandum event. Informally, the causal history traces the immediate causes of the explanandum event, the causes of those causes, and so on to produce a full explanation. While related, this problem differs from the problem of determining actual causes where the focus is on identifying individual events that contributed to causing the explanandum event. The central technical contributions of this paper are (a) a formal definition of causal history in the structural equations model; and (b) a theorem that establishes the complexity of the decision problem for causal histories. In addition, we show that our definition cleanly explains a battery of problematic examples from the actual cause literature.
GarNet : A Two-stream Network for Fast and Accurate 3 D Cloth Draping

Using the same notation as in the paper, our PBS ground truth was obtained as follows: We use the SMPL framework [4] to generate the body meshes. We also use the SMPL skeleton information and Blender to rig the mesh. For the garment mesh, we rig it with the same skeleton as that of the body and compute the rigging weights using Blender. Given the template garment mesh M, and the body mesh B, we obtain the ground-truth draping result G of M onto B via physics simulation. The physics engine is based on NvCloth [5] and incorporates a number of physical properties and forces acting on the garment, such as stretching, bending, gravity and dragging, while preventing garment-body interpenetration. Each garment type has a different set of values for these parameters depending on their material properties. As M is usually modeled in the A-pose, shown in Fig. 1, while B can have an arbitrary pose, we first warp B to the same pose asM. This pre-processing step is necessary for a good initialization of the physics engine. The warping process is achieved via Dual Quaternion Skinning (DQS) [3], which requires the skeleton and the blending weights of the body meshes. We automatically compute the skeletons and blending weights for each body mesh B and template garment meshM using the Pinocchio Auto Rig framework [1]. After warping B to match the pose of M, we gradually unwarp B to the original body pose. The garment is deformed accordingly during this process. Depending on the difference between the poses, this process can take from 100 to 200 iterations. To speed up the simulation, we use the GPU solver of NvCloth, which can only handle 500 triangles. Therefore, we first simulate the garment on a decimated 500-facet version of the body mesh on the GPU and then improve the draping results at full mesh resolution on the CPU. It should also be noted that the topology of the Figure 1: Template garmentM in the A-pose.
Statistical Functional Yield Estimation and Enhancement of CNFET-Based VLSI Circuits

Carbon nanotube field effect transistors (CNFETs) show great promise as extensions to silicon CMOS. However, imperfections, which are mainly related to carbon nanotubes (CNTs) growth process, result in metallic and nonuniform CNTs leading to significant functional yield reduction. This paper presents a comprehensive technique for statistical functional yield estimation and enhancement of CNFET-based VLSI circuits. Based on experimental data extracted from aligned CNTs, we propose a compact statistical model to estimate the failure probability of a CNFET. Using the proposed failure model, we show that enhancing the CNT synthesis process alone cannot achieve acceptable functional yield for upcoming CNFET-based VLSI circuits. We propose a technique which is based on replacing each transistor by series-parallel transistor structures to reduce the failure probability of CNFETs in the presence of metallic and nonuniform CNTs. The technique is adapted to use single directional independence, which is inherent in aligned CNTs, to enhance the functional yield as validated by theoretical analysis and simulation results. Tradeoffs between failure probability reduction and design overheads such as area and current drive are explored. As demonstrated by extensive simulation results, the proposed technique achieves 80% functional yield in CNFET technology at the cost of 7.5X area and 34% current drive overheads if the CNT density and the fraction of semiconducting CNTs are improved to 200 CNTs per μm and 99.99%, respectively.
Design and test of centrifugal disc type sowing device for unmanned helicopter

In China, it is difficult for manned aircraft to sow seeds in small and scattered plots, especially in areas including hills, swamps, telegraph poles, windbreaks, and residential areas; in such terrain, the sowing machinery cannot function properly. However, unmanned helicopters (UHs) are flexible enough to control and adapt to the complex environments that are not easily accessible by terrain sowing machinery and large agricultural aircraft, which have been widely used in agriculture. In this study, a centrifugal disc-type sowing device for an unmanned helicopter (CDTSDUH) was designed. The factors influencing the seed velocity when the seeds move away from the disc were explored by analyzing the forces of the seed acting on the sowing disc and the wind field of the UHs. The influential factors include the distance from the falling mouth to the center of the disc, the offset angle of the falling mouth, and the rotation speed of the disc. An orthogonal test was designed with the sowing width and the curvature of the sowing area as the indexes. The test results showed that the three factors mentioned above had a greater impact on the sowing width than the curvature of the seeding area. Moreover, the superior parameters of the disc were determined. It was also suggested that the above factor levels had little effect on the offset width of sowing. The results of the test conducted for studying the sowing uniformity of the CDTSDUH indicated that the maximum and minimum difference values among the number of particles of the sampling points in the forward direction was 11 and 8, and the coefficient of variation of the number of particles in each row was more than 20%, indicating less uniformity was achieved when sowing in the lateral (perpendicular to the forward direction) direction, as compared to that in the forward direction. This study determined the ideal values for the radius of particles position, the offsetting angle, and the disc speed of the sowing machinery designed. Furthermore, considering that there are so many factors that influence the manner in which a UH functions, more analysis results and test data of influencing factors need to be acquired by experiments. The uniformity of sowing needs to be further improved. The results provided some theoretical and experimental references for the technology research on the centrifugal disc-type sowing device for UHs. Keywords: sowing, unmanned helicopter, aircraft sowing, centrifugal disc, uniformity, sowing disc DOI: 10.25165/j.ijabe. 20181102.3757 Citation: Song C C, Zhou Z Y, Luo X W, Lan Y B, He X G, Ming R, et al. Design and test of centrifugal disc type sowing device for unmanned helicopter. Int J Agric & Biol Eng, 2018; 11(2): 55–61.
D-optimal Designs for Multiple Poisson Regression Model with Random Coefficients

The majority of the Optimal design research has focused on linear models and binary data models. However, a couple of researches have recently called attention to Poisson regression models with random effects. In the present paper, we theoretically and numerically discuss the optimal designs for multiple Poisson regression model with random coefficients and two explanatory variables. Since there is no closed form for the information matrix, the quasiinformation approach is applied in order to find the optimal designs in this study. Some special cases are illustrated and a new version of equivalence theorem is obtained.
Electron beamline of a Linac-based injector applied to a compact free electron laser-terahertz radiation source

Required by high-power, continuously tunable terahertz (THz) radiation from a room-scale source, a Linac-based beam injector was developed, which aimed at achieving a tradeoff between performance and cost efficiency. Under the overall consideration of compactness, design choices for the injector involving short bunch generation, energy boosting, as well as performance improvement were described in detail. A testing bench with online diagnostic elements was refitted from a prototype of such a Linac-driven THz radiation source, and measurement results indicated that such an injector showed an outstanding advantage in terms of system miniaturization without negatively affecting beam performance.
Designing grid services for multimedia streaming in an e-learning environment

Next generation e-learning platforms should support cooperative use of geographically distributed computing and educational resources as an aggregated environment to provide new levels of flexibility and extensibility. In this overall framework, our activity addresses the definition and implementation of advanced multimedia services for an aggregated grid-based e-learning environment, as well as the design and experimentation of a content distribution and multimedia streaming infrastructure in light of edge device heterogeneity, mobility, content adaptation and scalability. In this paper we initially present the general objectives and requirements that we are taking into account in the development of a multimedia access service for an e-learning platform. Then we describe a partial system prototype which capitalizes upon traditional features of grid computing like providing access to heterogeneous resources and services of different administrative domains in a transparent and secure way. Moreover, our system takes advantage of recent proposals by the Global Grid Forum (GGF) aiming at a standard for service-oriented architectures based on the concept of grid service.
Butterfly in the Quantum World: The Story of the Most Fascinating Quantum Fractal

"Butterfly in the Quantum World" by Indu Satija, with contributions by Douglas Hofstadter, is the first book ever to tell the story of the "Hofstadter butterfly", a beautiful and fascinating graph lying at the heart of the quantum theory of matter. The butterfly came out of a simple-sounding question: What happens if you immerse a crystal in a magnetic field? What energies can the electrons take on? From 1930 onwards, physicists struggled to answer this question, until 1974, when graduate student Douglas Hofstadter discovered that the answer was a graph consisting of nothing but copies of itself nested down infinitely many times. This wild mathematical object caught the physics world totally by surprise, and it continues to mesmerize physicists and mathematicians today. The butterfly plot is intimately related to many other important phenomena in number theory and physics, including Apollonian gaskets, the Foucault pendulum, quasicrystals, the quantum Hall effect, and many more. Its story reflects the magic, the mystery, and the simplicity of the laws of nature, and Indu Satija, in a wonderfully personal style, relates this story, enriching it with a vast number of lively historical anecdotes, many photographs, beautiful visual images, and even poems, making her book a great feast, for the eyes, for the mind and for the soul.
The Large Scale Dynamical Response of Clouds to Aerosol Forcing

AbstractRadiative kernels are used to quantify the instantaneous radiative forcing of aerosols and the aerosol-mediated cloud response in coupled ocean–atmosphere model simulations under both historical and future emission scenarios. The method is evaluated using matching pairs of historical climate change experiments with and without aerosol forcing and accurately captures the spatial pattern and global-mean effects of aerosol forcing. It is shown that aerosol-driven changes in the atmospheric circulation induce additional cloud changes. Thus, the total aerosol-mediated cloud response consists of both local microphysical changes and nonlocal dynamical changes that are driven by hemispheric asymmetries in aerosol forcing. By comparing coupled and fixed sea surface temperature (SST) simulations with identical aerosol forcing, the relative contributions of these two components are isolated, exploiting the ability of prescribed SSTs to also suppress changes in the atmospheric circulation. The radiative impac.
Classifier 1 ... Classifier 2 Classifier n Dense local activations Aggregation Attention map

Recent advances in fine-grained recognition utilize attention maps to localize objects of interest. Although there are many ways to generate attention maps, most of them rely on sophisticated loss functions or complex training processes. In this work, we propose a simple and straightforward attention generation model based on the output activations of classifiers. The advantage of our model is that it can be easily trained with image level labels and softmax loss functions. More specifically, multiple linear local classifiers are firstly adopted to perform fine-grained classification at each location of high level CNN feature maps. The attention map is generated by aggregating and max-pooling the output activations. Then the attention map serves as a surrogate target object mask to train those local classifiers, similar to training models for semantic segmentation. Our model achieves state-of-the-art results on three heavily benchmarked datasets, i.e. 87.9% on CUB-200-2011 dataset, 94.1% on Stanford Cars dataset and 92.1% on FGVC-Aircraft dataset, demonstrating its effectiveness on fine-grained recognition tasks.
TrIMS : Transparent and Isolated Model Sharing for Low Latency Deep Learning Inference in Function as a Service Environments

Deep neural networks (DNNs) have become pervasive within low latency Function as a Service (FaaS) prediction pipelines, but suffers from two major sources of latency overhead: 1) the round-trip network latency between FaaS container and a remote model serving process; 2) Deep Learning (DL) framework runtime instantiation and model loading from storage to CPU or GPU memory. While models servers process solves the latter, they do so by eternally persisting models within memory — introduces resource waste and increases cost. With FaaS environments, models are frequently shared: image recognition, object detection, NLP, and speech synthesis for example. We propose TrIMS, a multi-tier software caching layer on top of FaaS worker machines to solve this problem. Our solution consists of a managing model within caches that span GPUs, CPUs, local and cloud storage through a resource management service. This enables sharing models across user processes within a system while guaranteeing isolation, a succinct set of APIs and container technologies for easy and transparent integration with FaaS, DL frameworks and user code. Moreover, we show that by adopting this technique, we are able to oversubscribe the system without degrading the baseline latency. We evaluate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24× speedup in latency for image classification models. Preprint. Work in progress. 58 . 7 3 15 . 3 7 50 . 6 3 45 . 8 8 10 . 4 5 58 . 7 5 9. 83 7. 63 7. 74 7. 04 7. 2 12 . 8 5 8. 24 56 . 9 2 58 . 0 4 51 . 2 6 11 . 5 5 1 2 3 4 7 8 9 14 15 16 29 30 31 32 33 35 36 XNet CPU XNet GPU affe CPU affe GPU affe2 CPU affe2 GPU TF CPU TF GPU 8. 45 12 . 9 9. 67 9. 85 8. 87 7. 07 3. 47 10 . 6 3 11 . 2 2 8. 29 13 . 3 3 14 . 8 14 . 3 25 . 1 26 . 8 1 25 . 2 12 . 1 8 1. 36 2. 24 1. 03 1. 17 3. 33 5. 17 0. 89 2. 65 2. 64 3. 34 1. 74 5. 82 3. 44 5. 71 2. 19 5. 63 2. 33 0. 77 0. 61 0. 79 0. 75 1. 16 1. 3 1. 03 1. 25 1. 23 1. 39 1. 14 0. 41 0. 41 0. 88 0. 89 0. 93 1. 1 Model Loading Input Processing Compute Figure 1: Percentage of time spent in model (with ids corresponding to Table 2) loading, inference computation, and image preprocessing for “cold start” online DL inference (batchsize = 1) using CPU and GPU for MXNet, Caffe, Caffe2, and TensorFlow on an IBM S822LC with Pascal GPUs. The speedup of using GPU over CPU for the inference compute alone is shown between the pie charts. Inference time for all frameworks is dominated by model loading except for small models, such as SqueezeNet, where the model size is a few megabytes. For TensorFlow, high GPU initialization overhead impacts the end-to-end time and the achieved speedup.
Web Service Recommendation via Exploiting Location and QoS Information

Web services are integrated software components for the support of interoperable machine-to-machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems (e.g., ill-suited performance) to the resulting applications. In this paper, we propose a novel collaborative filtering-based Web service recommender system to help users select services with optimal Quality-of-Service (QoS) performance. Our recommender system employs the location information and QoS values to cluster users and services, and makes personalized service recommendation for users based on the clustering results. Compared with existing service recommendation methods, our approach achieves considerable improvement on the recommendation accuracy. Comprehensive experiments are conducted involving more than 1.5 million QoS records of real-world Web services to demonstrate the effectiveness of our approach.
Clean Images Adversarial images Config 1 ... Config K Config 1 ... Config K Config 1 ... Config K ... ... Attack Method 1 Attack Method N VAE Reconstr

Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serous threat to their applications in security-sensitive systems. In this paper, we propose a simple yet effective defense algorithm Defense-VAE that uses variational autoencoder (VAE) to purge adversarial perturbations from contaminated images. The proposed method is generic and can defend whitebox and black-box attacks without the need of retraining the original CNN classifiers, and can further strengthen the defense by retraining CNN or end-to-end finetuning the whole pipeline. In addition, the proposed method is very efficient compared to the optimization-based alternatives, such as Defense-GAN, since no iterative optimization is needed for online prediction. Extensive experiments on MNIST, Fashion-MNIST, CelebA and CIFAR-10 demonstrate the superior defense accuracy of Defense-VAE compared to Defense-GAN, while being 50x faster than the latter. This makes Defense-VAE widely deployable in real-time security-sensitive systems. We plan to open source our implementation to facilitate the research in this area.
Overview of Undulator Concepts for Attosecond Single-Cycle Light

The production of intense attosecond light pulses is an active area in accelerator research, motivated by the stringent demands of attosecond science: (i) short pulse duration for resolving the fast dynamics of electrons in atoms and molecules; (ii) high photon flux for probing and controlling such dynamics with high precision. While the free-electron laser (FEL) can deliver the highest brilliance amongst laboratory x-ray sources today, the pulse duration is typically 10–100 femtoseconds. A major obstacle to attaining attosecond duration is that the number of optical cycles increases with every undulator period. Hence, an FEL pulse typically contains tens or hundreds of cycles. In recent years, several novel concepts have been proposed to shift this paradigm, providing the basis for single-cycle pulses and paving the way towards high-brilliance attosecond light sources. This article gives an overview of these concepts.
d-DeVIS : A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model

The widespread usability of Deep Learning algorithms to solve various machine 1 learning problems demands transparent decision making policy. Deep Learning 2 algorithms are considered as a black box type learning model and they are too 3 complex to understand. Moreover, the learning models trained on sequential data, 4 such as audio data, has intricate reasoning process due to their complex distribution 5 of features. Thus, a visual simulator might be helpful to trace the internal decision 6 making mechanisms in response of adversarial input data, and thus, helps to debug 7 and design appropriate deep learning models. However, interpreting the reasoning 8 of the hidden layer is not well studied in the literature. Hence, in this work, we 9 have developed a visual interactive web application, namely d-DeVIS, which helps 10 to visualize the internal reasoning of a learning model which is trained on the audio 11 data. d-DeVIS allows to perceive the behavior as well as to debug the model by 12 interactively generating adversarial audio data point. A demo video description of 13 d-DeVIS can be found here: https://youtu.be/cHuNhppYrf8 14
“ PatternVal ” “ computer ” “ able ” “ bring ” “ SpeBrand ” “ fruit ” mod ifier modifier Sense

Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but not necessarily atomic semantic units. Inspired by HowNet, we use sememes, the minimum semantic units in human languages, to represent the implicit semantics behind words for language modeling, named Sememe-Driven Language Model (SDLM). More specifically, to predict the next word, SDLM first estimates the sememe distribution given textual context. Afterwards, it regards each sememe as a distinct semantic expert, and these experts jointly identify the most probable senses and the corresponding word. In this way, SDLM enables language models to work beyond word-level manipulation to fine-grained sememe-level semantics, and offers us more powerful tools to fine-tune language models and improve the interpretability as well as the robustness of language models. Experiments on language modeling and the downstream application of headline generation demonstrate the significant effectiveness of SDLM. Source code and data used in the experiments can be accessed at https:// github.com/thunlp/SDLM-pytorch.