Miracle-dz/UniFAR-FTUs huggingface dataset:
Explorer A Novel Piecewise Linear Recursive Convolution Approach for Dispersive Media Using the Finite-Difference Time-Domain Method

Two novel methods for implementing recursively the convolution between the electric field and a time dependent electric susceptibility function in the finite-difference time domain (FDTD) method are presented. Both resulting algorithms are straightforward to implement and employ an inclusive susceptibility function which holds as special cases the Lorentz, Debye, and Drude media relaxations. The accuracy of the new proposed algorithms is found to be systematically improved when compared to existing standard piecewise linear recursive convolution (PLRC) approaches, it is conjectured that the reason for this improvement is that the new proposed algorithms do not make any assumptions about the time variation of the polarization density in each time interval; no finite difference or semi-implicit schemes are used for the calculation of the polarization density. The only assumption that these two new methods make is that the first time derivative of the electric field is constant within each FDTD time interval.
Effects of Exogenous Amylase in Transition Dairy Cows Fed Low-Starch Diets: 2. Total Tract Digestibility and Blood Urea Nitrogen.

The objective of this trial was to determine the effect of exogenous amylase during the transition period on total tract digestibility, rumen pH and blood urea nitrogen in lactating dairy cows. The effect of exogenous dietary amylase supplementation on lactation diets with low starch concentration (19.5% of dry matter) and dry period diets with moderate starch concentration was evaluated (15.5% of dry matter). A total of 30 multiparus Holstein cows were randomly assigned to two groups with amylase (n=15) or control (n=15). Three cows from each group were randomly selected and ruminally cannulated for digestibility trials. The research was conducted starting at 21 d prepartum until 84 d postpartum. Digestibility of dry matter, organic matter, neutral detergent fiber, starch, and crude protein remained unaffected by treatment in postpartum. Average preand postpartum rumen pH concentrations were 6.25 and 6.15, respectively, and did not differ between treatments. Blood urea nitrogen (BUN) concentrations were lower in cows fed amylase supplemented diet compared to those fed diet without amylase in both preand postpartum periods (P<0.001). In conclusion, the dietary supplementation of amylase did may not affect the digestibility of nutrients, however, it may decrease the BUN concentration in preand postpartum period for cows fed amylase. Therefore, it may offer potential for improving nitrogen efficiency in dairy cows.
How Medical Crowdfunding Helps People? A Large-scale Case Study on Waterdrop Fundraising

While online medical crowdfunding achieved tremendous success, quantitative study about whether and how medical crowdfunding helps people remains little explored. In this paper, we empirically study how online medical crowdfunding helps people using more than 27,000 fundraising cases in Waterdrop Fundraising, one of the most popular online medical crowdfunding platforms in China. We find that the amount of money obtained by fundraisers is broadly distributed, i.e., a majority of lowly donated cases coexist with a handful of very successful cases. We further investigate the factors that potentially correlate with the success of medical fundraising cases. Profile information of fundraising cases, e.g., geographic information of fundraisers, affects the donated amounts, since detailed description may increase the credibility of a fundraising case. One prominent finding lies in the effect of social network on the success of fundraising cases: the spread of fundraising information along social network is a key factor of fundraising success, and the social capital of fundraisers play an important role in fundraising. Finally, we conduct prediction of donations using machine learning models, verifying the effect of potential factors on the success of medical crowdfunding. Altogether, this work presents a data-driven view of medical fundraising on the web and opens a door to understanding medical crowdfunding.
ShapeFlow: Dynamic Shape Interpreter for TensorFlow

We present ShapeFlow, a dynamic abstract interpreter for TensorFlow which quickly catches tensor shape incompatibility errors, one of the most common bugs in deep learning code. ShapeFlow shares the same APIs as TensorFlow but only captures and emits tensor shapes, its abstract domain. ShapeFlow constructs a custom shape computational graph, similar to the computational graph used by TensorFlow. ShapeFlow requires no code annotation or code modification by the programmer, and therefore is convenient to use. We evaluate ShapeFlow on 52 programs collected by prior empirical studies to show how fast and accurately it can catch shape incompatibility errors compared to TensorFlow. We use two baselines: a worst-case training dataset size and a more realistic dataset size. ShapeFlow detects shape incompatibility errors highly accurately -- with no false positives and a single false negative -- and highly efficiently -- with an average speed-up of 499X and 24X for the first and second baseline, respectively. We believe ShapeFlow is a practical tool that benefits machine learning developers. We will open-source ShapeFlow on GitHub to make it publicly available to both the developer and research communities.
Fourier duality in the Brascamp–Lieb inequality

Abstract It was observed recently in work of Bez, Buschenhenke, Cowling, Flock and the first author, that the euclidean Brascamp–Lieb inequality satisfies a natural and useful Fourier duality property. The purpose of this paper is to establish an appropriate discrete analogue of this. Our main result identifies the Brascamp–Lieb constants on (finitely-generated) discrete abelian groups with Brascamp–Lieb constants on their (Pontryagin) duals. As will become apparent, the natural setting for this duality principle is that of locally compact abelian groups, and this raises basic questions about Brascamp–Lieb constants formulated in this generality.
Trajectory planning for multiple autonomous underwater vehicles with safety guarantees

This paper addresses the safe trajectory planning problem for multiple autonomous underwater vehicles (AUVs) in strong waves that can disturb the AUVs’ trajectory tracking abilities and cause vehicle-obstacle and inter-vehicle collisions. A novel trajectory planning method based on the Hamilton-Jacobi (HJ) differential game formulation and model predictive control (MPC) is proposed to generate safety-guaranteed trajectories for AUV systems considering wave disturbance that is either time-varying state-independent or time-varying state-dependent. Further, this method allows for the consideration of AUVs with different shapes by incorporating nonlinear smooth collision avoidance constraints obtained via strong duality.
Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning

Chinese medical question-answer matching is more challenging than the open-domain questionanswer matching in English. Even though the deep learning method has performed well in improving the performance of question-answer matching, these methods only focus on the semantic information inside sentences, while ignoring the semantic association between questions and answers, thus resulting in performance deficits. In this paper, we design a series of interactive sentence representation learning models to tackle this problem. To better adapt to Chinese medical question-answer matching and take the advantages of different neural network structures, we propose the Crossed BERT network to extract the deep semantic information inside the sentence and the semantic association between question and answer, and then combine with the multi-scale CNNs network or BiGRU network to take the advantage of different structure of neural networks to learn more semantic features into the sentence representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show that our model significantly outperforms all the existing state-of-the-art models of Chinese medical question answer matching.
Spin-polarized vortices with reversed circulation

We present the analysis of the structure of vortices with the spin-polarized core from a weak coupling limit to the unitary regime of the BCS-BEC crossover. We show the mechanism for the generation of the reversed circulation in the vortex core induced by an excess of majority spin particles. We introduce the classification of the polarized vortices based on the number of Fermi circles where the minigap vanishes. This provides a unique description of the vortex as one cannot smoothly map wave functions into one another corresponding to vortices differing by the number of Fermi circles. The effective mass of quasiparticles along the vortex core is analyzed and its role in the propagation of spin-polarization along the vortex line is discussed.
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient

Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does not exploit the structural information of the unlabeled data. This leads to a sampling bias in the batch active learning setting, which selects several samples at once. In this work, we demonstrate that the amount of labeled training data can be reduced using active learning when it incorporates both uncertainty and diversity in the sequence labeling task. We examined the effects of our sequence-based approach by selecting weighted diverse in the gradient embedding approach across multiple tasks, datasets, models, and consistently outperform classic uncertainty-based sampling and diversity-based sampling.
Correlation of plastic events with local structure in jammed packings across spatial dimensions

Significance Mean-field theories, exact in the limit of infinite spatial dimensions, succeed in describing many features of glasses and amorphous solids in low dimensions, leading to considerable effort to understand how behavior evolves with dimension. Until now, all evidence has supported a picture in which “localized physics,” responsible for deviations from mean-field behavior in low dimensions, fades away with rising dimension. Our work shows that rearrangements, in which particles change relative positions leading to fluid-like response, reveal a different picture of dimensional cross-over. We find that rearrangements, which are localized in two- and three-dimensional systems and correlated with local structure, remain just as correlated with local structure up to five dimensions, suggesting that local structure is important even in high dimensions.
Fine-Grained Re-Identification

Research into the task of re-identification (ReID) is picking up momentum in computer vision for its many use cases and zero-shot learning nature. This paper proposes a computationally efficient fine-grained ReID model, FGReID, which is among the first models to unify image and video ReID while keeping the number of training parameters minimal. FGReID takes advantage of video-based pre-training and spatial feature attention to improve performance on both video and image ReID tasks. FGReID achieves state-of-the-art (SOTA) on MARS, iLIDS-VID, and PRID-2011 video person ReID benchmarks. Eliminating temporal pooling yields an image ReID model that surpasses SOTA on CUHK01 and Market1501 image person ReID benchmarks. The FGReID achieves near SOTA performance on the vehicle ReID dataset VeRi as well, demonstrating its ability to generalize. Additionally we do an ablation study analyzing the key features influencing model performance on ReID tasks. Finally, we discuss the moral dilemmas related to ReID tasks, including the potential for misuse. Code for this work is publicly available at https: //github.com/ppriyank/Fine-grained-ReIdentification.
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

System operators are faced with increasingly volatile operating conditions. In order to manage system reliability in a cost-effective manner, control room operators are turning to computerised decision support tools based on AI and machine learning. Specifically, Reinforcement Learning (RL) is a promising technique to train agents that suggest grid control actions to operators. In this paper, a simple baseline approach is presented using RL to represent an artificial control room operator that can operate a IEEE 14-bus test case for a duration of 1 week. This agent takes topological switching actions to control power flows on the grid, and is trained on only a single well-chosen scenario. The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios. The type and variability of topologies suggested by the agent are analysed across the test scenarios, demonstrating efficient and diverse agent behaviour.
Improving RNN Transducer with Target Speaker Extraction and Neural Uncertainty Estimation

Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers. This work presents a joint framework that combines time-domain target-speaker speech extraction and Recurrent Neural Network Transducer (RNN-T). To stabilize the joint-training, we propose a multi-stage training strategy that pre-trains and fine-tunes each module in the system before joint-training. Meanwhile, speaker identity and speech enhancement uncertainty measures are proposed to compensate for residual noise and artifacts from the target speech extraction module. Compared to a recognizer fine-tuned with a target speech extraction model, our experiments show that adding the neural uncertainty module significantly reduces 17% relative Character Error Rate (CER) on multi-speaker signals with background noise. The multi-condition experiments indicate that our method can achieve 9% relative performance gain in the noisy condition while maintaining the performance in the clean condition.
ML-MOC: Machine Learning based Membership Determination for Open Clusters.

The existing open cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present, ML-MOC, a new machine learning based approach to identify likely members of open clusters using the Gaia DR2 data, and no a priori information about the clusters. We use the k-Nearest Neighbours algorithm and the Gaussian Mixture Model on the high-precision proper motions and parallax measurements from Gaia DR2 data to determine the membership probabilities of individual sources down to G ~20 mag. To validate the developed method, we apply it on thirteen open clusters: M67, NGC 2099, NGC 2141, NGC 2243, NGC 2539, NGC 6253, NGC 6405, NGC 6791, NGC 7044, NGC 7142, NGC 752, Berkeley 18, and IC 4651. These clusters differ in terms of their ages, distances, metallicities, extinctions and cover a wide parameter space in proper motions and parallaxes with respect to the field population. The extracted members produce clean colour-magnitude diagrams and our astrometric parameters of the clusters are in good agreement with the values derived by the previous works. The degree of contamination in the extracted members range between 2% and 12%. The results show that ML-MOC is a reliable and scalable approach to segregate the open cluster members from the field stars.
Robust Autonomous Landing of UAV in Non-Cooperative Environments based on Dynamic Time Camera-LiDAR Fusion

Selecting safe landing sites in non-cooperative environments is a key step towards the full autonomy of UAVs. However, the existing methods have the common problems of poor generalization ability and robustness. Their performance in unknown environments is significantly degraded and the error cannot be self-detected and corrected. In this paper, we construct a UAV system equipped with low-cost LiDAR and binocular cameras to realize autonomous landing in non-cooperative environments by detecting the flat and safe ground area. Taking advantage of the non-repetitive scanning and high FOV coverage characteristics of LiDAR, we come up with a dynamic time depth completion algorithm. In conjunction with the proposed self-evaluation method of the depth map, our model can dynamically select the LiDAR accumulation time at the inference phase to ensure an accurate prediction result. Based on the depth map, the high-level terrain information such as slope, roughness, and the size of the safe area are derived. We have conducted extensive autonomous landing experiments in a variety of familiar or completely unknown environments, verifying that our model can adaptively balance the accuracy and speed, and the UAV can robustly select a safe landing site.
Hampshire SO 2 4 JH , all of United Kingdom Primary Examiner

54) OPTICAL FIBRE AND OPTICAL FIBRE 5,740,297 4/1998 Onishi et al. . 385/127 GRATING 5,852,690 12/1998 Haggans et al. . 385/37 5,881,197 3/1999 Dong et al. . 385/127 76 Inventors: Liang Dong, 6 Randalls Crescent, OTHER PUBLICATIONS Leatherhead, Surrey KT22 7NP; David Neil Payne, 13 Sylan Lane, Hamble, Dong et al., Optical Fibers with Depressed Claddings for Southampton, Hampshire SO3 5QB; Suppression of Coupling into Cladding Modes in Fiber Laurence Reekie, Pinewood, Norris Bragg Gratings, IEEE Photonics Technology Letters, V 9, Hill, Bitterne Park, Southampton, No. 1, pp. 64–66, Jan. 1997. Hampshire SO2 4JH, all of United Kingdom Primary Examiner John D. Lee ASSistant Examiner Juliana K. Kang
Classification of High-resolution Solar Hα Spectra Using t-distributed Stochastic Neighbor Embedding

The Hα spectral line is a well-studied absorption line revealing properties of the highly structured and dynamic solar chromosphere. Typical features with distinct spectral signatures in Hα include filaments and prominences, bright active-region plages, superpenumbrae around sunspots, surges, flares, Ellerman bombs, filigree, and mottles and rosettes, among others. This study is based on high-spectral resolution Hα spectra obtained with the Echelle spectrograph of the Vacuum Tower Telescope (VTT) located at Observatorio del Teide, Tenerife, Spain. The t-distributed stochastic neighbor embedding (t-SNE) is a machine-learning algorithm, which is used for nonlinear dimensionality reduction. In this application, it projects Hα spectra onto a two-dimensional map, where it becomes possible to classify the spectra according to results of cloud model (CM) inversions. The CM parameters optical depth, Doppler width, line-of-sight velocity, and source function describe properties of the cloud material. Initial results of t-SNE indicate its strong discriminatory power to separate quiet-Sun and plage profiles from those that are suitable for CM inversions. In addition, a detailed study of various t-SNE parameters is conducted, the impact of seeing conditions on the classification is assessed, results for various types of input data are compared, and the identified clusters are linked to chromospheric features. Although t-SNE proves to be efficient in clustering high-dimensional data, human inference is required at each step to interpret the results. This exploratory study provides a framework and ideas on how to tailor a classification scheme toward specific spectral data and science questions.
Locally repairable codes with availability and hierarchy: matroid theory via examples

Recent research on distributed storage systems (DSSs) has revealed interesting connections between matroid theory and locally repairable codes (LRCs). The goal of this paper is to illustrate these as well as some new — rather technical in nature — results via simple examples. The examples embed all the essential features of LRCs, namely locality, availability, and hierarchy alongside with related generalized Singleton bounds.
Efficacy of an Internet-Based Intervention to Promote a Healthy Lifestyle on the Reproductive Parameters of Overweight and Obese Women: Study Protocol for a Randomised Controlled Trial

Background: Infertility is estimated to affect 15% of couples of reproductive age. Weight management problems (being obese or overweight) are among the problems that produce infertility, both in women seeking spontaneous pregnancy and in those undergoing assisted reproduction techniques. Over the last few decades, the prevalence of obesity has increased alarmingly in our society and is now considered one of the most important public health problems. The combination of diet and exercise to achieve weight loss are currently considered an effective intervention for the improvement of reproductive parameters in overweight or obese infertile women. In other population groups, it has been shown that Internet-based interventions are just as effective as traditional ones, and these cover a larger population with a good cost–benefit ratio. However, to the best of our knowledge, no studies so far have analysed any specific online interventions for this group of infertile women. Thus, the objective of this project will be to evaluate the effectiveness of an online program to promote a healthy lifestyle among women who are overweight or obese who also have a diagnosis of infertility and are on the waiting list for in vitro fertilisation treatment. Methods: This will be a randomised controlled clinical trial conducted in 94 women which will compare a self-administered Internet-based intervention promoting a healthy lifestyle in terms of diet and exercise (n = 47) to a control group that will receive standard medical care. The online program will comprise nine modules, will last for 3 months, and will be monitored every 3 months after the intervention until the final follow-up at 12 months. The main outcome will be the spontaneous pregnancy rate. Secondary outcomes will include changes in body composition, dietary and physical exercise habits, glycaemic profiles, lipid profiles, hormonal profiles, and patient quality of life related to their fertility problems. The data analysis will be done on an intention-to-treat basis. Discussion: The aim of this study is to increase our knowledge of the effectiveness of online interventions specifically adapted to infertile women who are overweight or obese in the promotion of healthy lifestyles.
Deep orthogonal linear networks are shallow

We consider the problem of training a deep orthogonal linear network, which consists of a product of orthogonal matrices, with no non-linearity in-between. We show that training the weights with Riemannian gradient descent is equivalent to training the whole factorization by gradient descent. This means that there is no effect of overparametrization and implicit bias at all in this setting: training such a deep, overparametrized, network is perfectly equivalent to training a one-layer shallow network.
3 THE GLOBAL CARBON CYCLE : GEOLOGICAL PROCESSES

In this chapter, we will discuss the geological carbon cycle including all processes controlling the distribution of carbon between the interior of the Earth, the lithosphere, the hydrosphere and the atmosphere (Fig. 3.1, Table 3.1). Our discussion focuses on the geochemical evolution of carbon inventories and fluxes on a multimillion year timescale. We will first address the magnitude of carbon fluxes in the pre-human Holocene and then over the last million years. We next examine the feedbacks stabilizing the distribution of carbon on our planet and present a balanced geological carbon cycle. Subsequently, we explore proxies and models to explain how carbon cycling may have changed over the Earth’s geological history in parallel with its biological and geological evolution.