Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobility-aware dynamic service placement for edge computing

Edge computing provides capabilities similar to traditional cloud computing at network edge closer to users. Offloading applications from the cloud to the edge of the network can effectively diminish the latency of users waiting for applications to improve their quality of service(QoS). Due to the user mobility, however, the distance between the user and the edge device is constantly increasing, resulting in a degradation of the user’s QoS. To satisfy the QoS of mobile users, applications should be dynamically migrated to follow the user mobility.In this paper, we propose a mobility-aware dynamic service placement policy that reduces the number of virtual machine migrations and diminishes user-perceived latency simultaneously. The proposed scheme filters out the invalid migration that the user handoffs to another access point before the migration is completed. The simulation trials demonstrate that our solution improves the QoS of users and reduces the number of application migrations. Received on 30 May 2019; accepted on 01 July 2019; published on 29 July 2019
ESTIMATING MARKET POWER IN A DYNAMIC FRAMEWORK: THE CASE OF THE ITALIAN PDO CHEESE MARKET

In this paper, we evaluate the role of market power by retailers within the supply chain of Parmigiano Reggiano and Grana Padano, the two most famous Italian quality cheeses. Market power is analysed in the context of a dynamic imperfect competition model of the supply chain, in which retailers are allowed to exert market power both downstream and upstream. We jointly estimate market power parameters together with supply and demand elasticities, by means of a structural system of demand, supply and price transmission equations, estimated using the Generalised Method of Moments (GMM). We find evidences of downstream market power by retailers (toward final consumers) only for Parmigiano Reggiano, and no evidences of upstream market power (toward processors/ripeners). These results may be justified by the structure of the supply chain and by the peculiar characteristics of the two cheeses.
CENTRE FOR CO-OPERATION WITH NON-MEMBERS DIRECTORATE FOR FINANCIAL, FISCAL AND ENTERPRISE AFFAIRS

Between 1990 and 1996, competition laws have been adopted in 22 of the 26 transition economies of central and eastern Europe and the former Soviet Union. Yet there is very little systematic evidence about implementation experience, and none regarding its impact on intensity of economy-wide competition. The novelty of this paper is twofold. First, it defines a range of competition policy implementation criteria relevant for transition and developing economies along the three main dimensions of enforcement, competition advocacy and institutional effectiveness. These classification criteria go significantly beyond the traditional emphasis on abuse of dominance, agreement and merger cases. Second, it provides an assessment of the effectiveness of competition policy implementation across 18 countries along these criteria, based on data from each country’s competition authorities and supplemented with assessments by legal practitioners. The relationship between competition policy and intensity of competition is explored, with the latter captured by a measure of economy-wide enterprise mobility that reflects selection effects. We find a robust positive relationship between effective competition policy implementation and expansion of more efficient private firms. JEL Classification Number L4, K21, P2.
Conspecific brood parasitism in the house sparrow

-We studied 94 clutches of the House Sparrow (Passer domesticus) in Newark, Delaware, to determine: (1) the frequency of apparent conspecific brood parasitism (CBP); (2) if the laying of eggs in a conspecific's nest is truly a parasitic interaction with the recipient; and (3) that indeterminate laying can predispose a species to CBP. Eight clutches yielded evidence of CBP; electrophoresis of egg albumin detected a genetically odd egg in 4 of 42 clutches, while 4 other clutches provided indirect evidence of CBP-visibly odd eggs in other than the ultimate position and two eggs laid in one day. An egg-removal experiment suggested that House Sparrows are indeterminate layers before the third egg is laid; thus a female can increase her fecundity by laying one or more parasitic eggs before completing her own clutch of normal size. A complementary egg-addition experiment showed that a recipient female may reduce her contribution to her own clutch by one egg if an egg is inserted before she lays her third one. Insertion after the third egg can result in an enlarged clutch to the potential detriment of both host and parasite. Received 19 May 1987, accepted 10 Sept. 1987. Females of over 50 species of birds are known to lay eggs in the nests of conspecifics (Yom-Tov 1980a, Andersson 1984). This behavior, variously termed intraspecific (or conspecific) nest (or brood) parasitism (e.g., Yom-Tov 1980a, Andersson 1984, Emlen and Wrege 1986), has been recognized since about 1900 (Weller 1959, Yom-Tov 1980a), but until recently it was treated largely as incidental, aberrant behavior. Its brief treatment under "dump nesting" and its exclusion from "brood parasitism" by Terres (1980) and Campbell and Lack (1985) are evidence of past perspectives. Our terminology throughout will refer to parasitism, even though the purist would object that we have not demonstrated cost and benefit of the behavior to the recipient and donor, respectively. Several recent authors have hypothesized which individuals should be parasitic and under what circumstances (Yom-Tov 1 980a, Andersson and Eriksson 1982, Andersson 1984, Emlen and Wrege 1986, Gibbons 1986). We summarize these as the: (1) "last resort hypothesis"-a female lays parasitically because, as a result of inexperience, social status, or accident, she lacks a nest of her own; (2) "enhancement hypothesis"-a female lays a clutch in her own nest and increases her fecundity by laying one or more eggs in other nests; (3) "pure parasite hypothesis"-a female lays all her ' Order of authorship determined by coin-toss. 2 Myrtle Bell Lane Lab. , Dept. of Medicine, Duke Univ. Medical Center, Durham, North Carolina 27710. 3Dept. of Entomology and Applied Ecology, Univ. of Delaware, Newark, Delaware 19717-1303.
Effect of transportation distance and crating density on preslaughter losses and blood biochemical profile in broilers during hot and humid weather

Transportation of broilers from different localities to the processing plants is a critical step in the farm to fork chain, and any disturbance in this chain may result in worst consequences. Keeping this scenario in view, a study was conducted to determine the live losses at different transportation distances ~80, 160, 240 km and crating densities 10, 12, 15 birds/crate during hot and humid summer. The uniform weight birds were picked from a farm and placed in plastic crates 0.91 m × 0.55 m . The results showed a significant increase in body weight loss and dead on arrival % with the increase in transportation distance and crating density in broiler. The long journey 240 km was also associated with significantly lower carcass and breast yield, and significantly higher catalase activity and uric acid concentration. An increase in crating density above 10 birds/crate resulted in a significant decrease in carcass and thigh yield in broiler. Similarly, placing 15 birds/crate showed significantly higher serum catalase activity and protein levels. Physical injuries, bruises, serum triiodothyronine T3 and thyroxine T4 remained unaffected from either transportation distance or crating density. Thus, transportation below 80 km and crating density below 12 birds/crate during hot and humid weather may be considered as less stressful for broilers.
Functional outcome of metaphyseal and diaphyseal fractures of tibia treated with expert tibial interlocking nail: A prospective study

Introduction: Tibia is the most commonly fractured bone due to road traffic accidents and other high-energy trauma and surgical management is required for most of these fractures, with either internal or external fixation. Locked intramedullary nailing has been widely accepted as a satisfactory treatment of tibial fracture. The indications of their use have been extended to fractures closer to proximal / distal fragments with introduction of expert tibial nail. It is a new kind of low, multidirectional locking tibial intramedullary nail developed to attain increased angular stability and to enhance the axial and lateral stability of fracture fragments.Aim: To study the functional outcome and duration of union of metaphyseal and diaphyseal fractures of tibia treated with expert tibial interlocking nail.Methodology: Study was done in 30 patients with fresh tibial metaphyseal/diaphyseal fractures treated surgically with expert tibial nail between October 2016- March 2018. Patients were followed up at 6 weeks, 3 months, and 6 months. The intra-operative blood loss, duration of surgery, intra operative complications, post-operative complication, and duration of hospital stay were studied. Functional outcome was assessed based on Johner and Wruh's Criteria.Results: Among the 30 patients treated with expert tibia nail for metaphyseal/diaphyseal tibial fractures, 25 were male and 5 were females. Most of patients were in the age group ranging from 20-30 years with a mean age group of 40.1 years. The mode of injury for 24 patients was road traffic accidents which accounted for about 80 %, 5 were due to fall which accounted for 16.67 % and 1 was due to assault which accounted for 3.33 %. 16 cases sustained right side tibia fracture and 14 cases sustained left side tibia fracture. The average period of commencement partial weight bearing was 7 weeks and full weight bearing was 13.43 weeks. Excellent results were seen in 43.33% cases, Good results were seen in 33.33%, Fair results in 16.67 %, poor results in 6.67% according to Johner and Wruh's Criteria.Conclusion: Our results with expert tibial interlocking nailing are encouraging and demonstrate the benefits of new nailing system. Changes in the design of the nail for improved proximal and distal locking enables it to use in metaphyseal/diaphyseal fractures of tibia. A better stabilization of small fragments has been achieved by the availability of locking option in three planes, thus providing a higher stability of the bone implant construct. Complications were comparable to other studies.
Computationally efficient compact model for ferroelectric field-effect transistors to simulate the online training of neural networks

In this paper, a compact drain current formulation that is simple and adequately computationally efficient for the simulation of neural network online training was developed for the ferroelectric memory transistor. Tri-gate ferroelectric field-effect transistors (FETs) with Hf0.5Zr0. 5O2 gate insulators were fabricated with a gate-first high-k metal gate CMOS process. Ferroelectric switching was confirmed with double sweep and pulse programming and erasure measurements. Novel characterization scheme for drain current was proposed with minimal alteration of ferroelectric state in subthreshold for accurate threshold voltage measurements. The resultant threshold voltage exhibited highly linear and symmetric across multilevel states. The proposed compact formulation accurately captured the FET gate-bias dependence by considering the effects of series resistance, Coulomb scattering, and vertical field dependent mobility degradation.
On lattice-ordered $t_{r}$-norms for type-2 fuzzy sets

In this paper, it is proved that, for the truth value algebra of interval-valued fuzzy sets, the distributive laws do not imply the monotonicity condition for the set inclusion operation. Then, a lattice-ordered $t_{r}$-norm, which is not the convolution of $t$-norms on $[0, 1]$, is obtained. These results negatively answer two open problems posed by Walker and Walker in [15].
Learning 3D Semantic Scene Graphs From 3D Indoor Reconstructions

Scene understanding has been of high interest in computer vision. It encompasses not only identifying objects in a scene, but also their relationships within the given context. With this goal, a recent line of works tackles 3D semantic segmentation and scene layout prediction. In our work we focus on scene graphs, a data structure that organizes the entities of a scene in a graph, where objects are nodes and their relationships modeled as edges. We leverage inference on scene graphs as a way to carry out 3D scene understanding, mapping objects and their relationships. In particular, we propose a learned method that regresses a scene graph from the point cloud of a scene. Our novel architecture is based on PointNet and Graph Convolutional Networks (GCN). In addition, we introduce 3DSSG, a semiautomatically generated dataset, that contains semantically rich scene graphs of 3D scenes. We show the application of our method in a domain-agnostic retrieval task, where graphs serve as an intermediate representation for 3D-3D and 2D-3D matching.
Is the Variance Swap Rate Affine in the Spot Variance? Evidence from S&P500 Data

ABSTRACT We empirically investigate the functional link between the variance swap rate and the spot variance. Using S&P500 data over the period 2006–2018, we find overwhelming empirical evidence supporting the affine link implied by exponential affine stochastic volatility models. Tests on yearly subsamples suggest that exponential mean-reverting variance models provide a good fit during periods of extreme volatility, while polynomial modelsare suited for years characterized by more frequent price jumps.
Dynamic Data Selection and Weighting for Iterative Back-Translation

Back-translation has proven to be an effective method to utilize monolingual data in neural machine translation (NMT), and iteratively conducting back-translation can further improve the model performance. Selecting which monolingual data to back-translate is crucial, as we require that the resulting synthetic data are of high quality \textit{and} reflect the target domain. To achieve these two goals, data selection and weighting strategies have been proposed, with a common practice being to select samples close to the target domain but also dissimilar to the average general-domain text. In this paper, we provide insights into this commonly used approach and generalize it to a dynamic curriculum learning strategy, which is applied to iterative back-translation models. In addition, we propose weighting strategies based on both the current quality of the sentence and its improvement over the previous iteration. We evaluate our models on domain adaptation, low-resource, and high-resource MT settings and on two language pairs. Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines.
Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction

This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multihead attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction.
X-ray variability analysis of a large series of XMM–Newton +NuSTAR observations of NGC 3227

We present a series of X-ray variability results from a long XMM–Newton + NuSTAR campaign on the bright, variable AGN NGC 3227. We present an analysis of the light curves, showing that the source displays typically softer-when-brighter behaviour, although also undergoes significant spectral hardening during one observation which we interpret as due to an occultation event by a cloud of absorbing gas. We spectrally decompose the data and show that the bulk of the variability is continuum-driven and, through rms variability analysis, strongly enhanced in the soft band. We show that the source largely conforms to linear rms-flux behaviour and we compute X-ray power spectra, detecting moderate evidence for a bend in the power spectrum, consistent with existing scaling relations. Additionally, we compute X-ray Fourier time lags using both the XMM–Newton and – through maximum-likelihood methods – NuSTAR data, revealing a strong low-frequency hard lag and evidence for a soft lag at higher frequencies, which we discuss in terms of reverberation models.
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employs structured dropout convolutional blocks instead of the original convolutional blocks of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that the proposed SA-UNet achieves state-of-the-art performance on both datasets. The implementation and the trained networks are available on Github11https://github.com/clguo/SA-UNet
A Polynomial Neural Network with Controllable Precision and Human-Readable Topology for Prediction and System Identification

Although artificial neural networks (ANNs) are successful, there is still a concern among many over their "black box" nature. Why do they work? Could we design a "transparent" network? This paper presents a controllable and readable polynomial neural network (CR-PNN) for approximation, prediction, and system identification. CR-PNN is simple enough to be described as one "small" formula, so that we can control the approximation precision and explain the internal structure of the network. CR-PNN, in fact, essentially is the fascinating Taylor expansion in the form of network. The number of layers represents precision. Derivatives in Taylor expansion are exactly imitated by error back-propagation algorithm. Firstly, we demonstrated that CR-PNN shows excellent analysis performance to the "black box" system through ten synthetic data with noise. Also, the results were compared with synthetic data to substantiate its search easily towards the global optimum. Secondly, it was verified, by ten real-world applications, that CR-PNN brought better generalization capability relative to the typical ANNs that approximate depended on the nonlinear activation function. Finally, 200,000 repeated experiments, with 4898 samples, demonstrated that CR-PNN is five times more efficient than typical ANN for one epoch and ten times more efficient than typical ANN for one forward-propagation. In short, compared with the traditional neural networks, the novelties and advantages of CR-PNN include readability of the internal structure, easy to find global optimal solution, lower computational complexity, and likely better robustness to real-world approximation. (We're strong believers in Open Source, and provide CR-PNN code for others. GitHub: this https URL)
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural topic models have shown improvements in overall coherence. Concurrently, contextual embeddings have advanced the state of the art of neural models in general. In this paper, we combine contextualized representations with neural topic models. We find that our approach produces more meaningful and coherent topics than traditional bag-of-words topic models and recent neural models. Our results indicate that future improvements in language models will translate into better topic models.
Min-max harmonic maps and a new characterization of conformal eigenvalues

Given a surface $M$ and a fixed conformal class $c$ one defines $\Lambda_k(M,c)$ to be the supremum of the $k$-th nontrivial Laplacian eigenvalue over all metrics $g\in c$ of unit volume. It has been observed by Nadirashvili that the metrics achieving $\Lambda_k(M,c)$ are closely related to harmonic maps to spheres. In the present paper, we identify $\Lambda_1(M,c)$ and $\Lambda_2(M,c)$ with min-max quantities associated to the energy functional for sphere-valued maps. As an application, we obtain several new eigenvalue bounds, including a sharp isoperimetric inequality for the first two Steklov eigenvalues. This characterization also yields an alternative proof of the existence of maximal metrics realizing $\Lambda_1(M,c)$, $\Lambda_2(M,c)$ and, moreover, allows us to obtain a regularity theorem for maximal Radon measures satisfying a natural compactness condition.
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method

We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains “4,372” images with “1.51 million” annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations, making it a very challenging dataset. Additionally, the dataset consists of a rich set of annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset. The dataset can be downloaded from http://www.crowd-counting.com. Furthermore, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements In errors.