The paper studies a class of variational problems, modeling optimal shapes for tree roots. Given a measure $mu$ describing the distribution of root hair cells, we seek to maximize a harvest functional $mathcal{H}$, computing the total amount of water
and nutrients gathered by the roots, subject to a cost for transporting these nutrients from the roots to the trunk. Earlier papers had established the existence of an optimal measure, and a priori bounds. Here we derive necessary conditions for optimality. Moreover, in space dimension $d=2$, we prove that the support of an optimal measure is nowhere dense.
Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain. In this paper, we focus on clinical domain where only limited data is accessible and interpretability is important. Recent adv
ancement in technology and the acceleration of clinical trials has resulted in the discovery of new drugs, procedures as well as medical conditions. These factors motivate towards building robust zero-shot NER systems which can quickly adapt to new medical terminology. We propose an auxiliary gazetteer model and fuse it with an NER system, which results in better robustness and interpretability across different clinical datasets. Our gazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains on the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on novel entity mentions never presented during training. Moreover, our fusion model is able to quickly adapt to new mentions in gazetteers without re-training and the gains from the proposed fusion model are transferable to related datasets.
Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to its under-constrained nature. While templat
e-based approaches, such as parametric shape models, have achieved great success in modeling the closed world of known object categories, they cannot well handle the open-world of novel object categories or outlier shapes. In this work, we introduce a template-free approach to learn 3D shapes from a single video. It adopts an analysis-by-synthesis strategy that forward-renders object silhouette, optical flow, and pixel values to compare with video observations, which generates gradients to adjust the camera, shape and motion parameters. Without using a category-specific shape template, our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes. Code will be available at lasr-google.github.io .
Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render tra
ining data for optical flow that optimizes the performance of a model on a target dataset. AutoFlow takes a layered approach to render synthetic data, where the motion, shape, and appearance of each layer are controlled by learnable hyperparameters. Experimental results show that AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and RAFT. Our code and data are available at https://autoflow-google.github.io .
Deposition of perovskite thin films by antisolvent engineering is one of the most common methods employed in perovskite photovoltaics research. Herein, we report on a general method that allows the fabrication of highly efficient perovskite solar cel
ls by any antisolvent via the manipulation of the antisolvent application rate. Through a detailed structural, compositional and microstructural characterization of perovskite layers fabricated by 14 different antisolvents, we identify two key factors that influence the quality of the perovskite active layer: the solubility of the organic precursors in the antisolvent and its miscibility with the host solvent(s) of the perovskite precursor solution. Depending on these two factors, each antisolvent can be utilized to produce high performance devices reaching power conversion efficiencies (PCEs) that exceed 21%. Moreover, we demonstrate that by employing the optimal antisolvent application procedure, highly efficient solar cells can be fabricated from a broad range of precursor stoichiometries, with either a significant excess or deficiency of organic iodides.
Radio Access Networks (RAN) tends to be more distributed in the 5G and beyond, in order to provide low latency and flexible on-demanding services. In this paper, Blockchain-enabled Radio Access Networks (BE-RAN) is proposed as a novel decentralized R
AN architecture to facilitate enhanced security and privacy on identification and authentication. It can offer user-centric identity management for User Equipment (UE) and RAN elements, and enable mutual authentication to all entities while enabling on-demand point-to-point communication with accountable billing service add-on on public network. Also, a potential operating model with thorough decentralization of RAN is envisioned. The paper also proposed a distributed privacy-preserving P2P communication approach, as one of the core use cases for future mobile networks, is presented as an essential complement to the existing core network-based security and privacy management. The results show that BE-RAN significantly improves communication and computation overheads compared to the existing communication authentication protocols.
We use the superposition of the Levy processes to optimize the classic BN-S model. Considering the frequent fluctuations of price parameters difficult to accurately estimate in the model, we preprocess the price data based on fuzzy theory. The price
of S&P500 stock index options in the past ten years are analyzed, and the deterministic fluctuations are captured by machine learning methods. The results show that the new model in a fuzzy environment solves the long-term dependence problem of the classic model with fewer parameter changes, and effectively analyzes the random dynamic characteristics of stock index option price time series.
DETR is a recently proposed Transformer-based method which views object detection as a set prediction problem and achieves state-of-the-art performance but demands extra-long training time to converge. In this paper, we investigate the causes of the
optimization difficulty in the training of DETR. Our examinations reveal several factors contributing to the slow convergence of DETR, primarily the issues with the Hungarian loss and the Transformer cross attention mechanism. To overcome these issues we propose two solutions, namely, TSP-FCOS (Transformer-based Set Prediction with FCOS) and TSP-RCNN (Transformer-based Set Prediction with RCNN). Experimental results show that the proposed methods not only converge much faster than the original DETR, but also significantly outperform DETR and other baselines in terms of detection accuracy.
This paper investigates the thermodynamic driving force of transient negative capacitance (NC) in the series circuit of the resistor and ferroelectric capacitor (R-FEC). We find that the widely used Landau-Khalatnikov (L-K) theory, that is, the minim
um of the Gibbs free energy, is inapplicable to explain the transient NC. The thermodynamic driving force of the transient NC phenomenon is the minimum of the difference between the elastic Gibbs free energy and the electric polarization work. The appearance of the transient NC phenomenon is not due to the widely accepted view that the ferroelectric polarization goes through the negative curvature region of elastic Gibbs free energy landscape (Ga). Instead, the transient NC phenomenon appears when the energy barrier of Ga disappears. The transient NC is dependent on both the intrinsic ferroelectric material parameters and extrinsic factors in the R-FEC circuit.
DGA-based botnet, which uses Domain Generation Algorithms (DGAs) to evade supervision, has become a part of the most destructive threats to network security. Over the past decades, a wealth of defense mechanisms focusing on domain features have emerg
ed to address the problem. Nonetheless, DGA detection remains a daunting and challenging task due to the big data nature of Internet traffic and the potential fact that the linguistic features extracted only from the domain names are insufficient and the enemies could easily forge them to disturb detection. In this paper, we propose a novel DGA detection system which employs an incremental word-embeddings method to capture the interactions between end hosts and domains, characterize time-series patterns of DNS queries for each IP address and therefore explore temporal similarities between domains. We carefully modify the Word2Vec algorithm and leverage it to automatically learn dynamic and discriminative feature representations for over 1.9 million domains, and develop an simple classifier for distinguishing malicious domains from the benign. Given the ability to identify temporal patterns of domains and update models incrementally, the proposed scheme makes the progress towards adapting to the changing and evolving strategies of DGA domains. Our system is evaluated and compared with the state-of-art system FANCI and two deep-learning methods CNN and LSTM, with data from a large universitys network named TUNET. The results suggest that our system outperforms the strong competitors by a large margin on multiple metrics and meanwhile achieves a remarkable speed-up on model updating.
