Miracle-dz/UniFAR-FTUs huggingface dataset:
Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud

Nowadays, there are many approaches to acquire three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from three-dimensional (3D) point clouds remains challenging, especially for new emerging leaves that are very close and wrapped together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three main steps: skeleton extraction, coarse segmentation based on the skeleton, fine segmentation based on stem-leaf classification. The segmentation method was tested on 30 maize seedlings and compared with manually obtained ground truth. The mean precision, mean recall, mean micro F1 score and mean over accuracy of our segmentation algorithm were 0.964, 0.966, 0.963 and 0.969. Using the segmentation results, two applications were also developed in this paper, including phenotypic trait extraction and skeleton optimization. Six phenotypic parameters can be accurately and automatically measured, including plant height, crown diameter, stem height and diameter, leaf width and length. Furthermore, the values of R2 for the six phenotypic traits were all above 0.94. The results indicated that the proposed algorithm could automatically and precisely segment not only the fully expanded leaves, but also the new leaves wrapped together and close together. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction and dynamic growth animation. We released the source code and test data at the web site this https URL
Croke–Kleiner Admissible Groups: Property (QT) and Quasiconvexity

Croke-Kleiner admissible groups firstly introduced by Croke-Kleiner belong to a particular class of graph of groups which generalize fundamental groups of $3$--dimensional graph manifolds. In this paper, we show that if $G$ is a Croke-Kleiner admissible group, acting geometrically on a CAT(0) space $X$, then a finitely generated subgroup of $G$ has finite height if and only if it is strongly quasi-convex. We also show that if $G \curvearrowright X$ is a flip CKA action then $G$ is quasi-isometric embedded into a finite product of quasi-trees. With further assumption on the vertex groups of the flip CKA action $G \curvearrowright X$, we show that $G$ satisfies property (QT) that is introduced by Bestvina-Bromberg-Fujiwara.
Deep Sparse Light Field Refocusing

Light field photography enables to record 4D images, containing angular information alongside spatial information of the scene. One of the important applications of light field imaging is post-capture refocusing. Current methods require for this purpose a dense field of angle views; those can be acquired with a micro-lens system or with a compressive system. Both techniques have major drawbacks to consider, including bulky structures and angular-spatial resolution trade-off. We present a novel implementation of digital refocusing based on sparse angular information using neural networks. This allows recording high spatial resolution in favor of the angular resolution, thus, enabling to design compact and simple devices with improved hardware as well as better performance of compressive systems. We use a novel convolutional neural network whose relatively small structure enables fast reconstruction with low memory consumption. Moreover, it allows handling without re-training various refocusing ranges and noise levels. Results show major improvement compared to existing methods.
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks

Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms. However, it often degrades the model performance on normal images and the defense does not generalize well to novel attacks. Given the success of deep generative models such as GANs and VAEs in characterizing the underlying manifold of images, we investigate whether or not the aforementioned problems can be remedied by exploiting the underlying manifold information. To this end, we construct an "On-Manifold ImageNet" (OM-ImageNet) dataset by projecting the ImageNet samples onto the manifold learned by StyleGSN. For this dataset, the underlying manifold information is exact. Using OM-ImageNet, we first show that adversarial training in the latent space of images improves both standard accuracy and robustness to on-manifold attacks. However, since no out-of-manifold perturbations are realized, the defense can be broken by Lp adversarial attacks. We further propose Dual Manifold Adversarial Training (DMAT) where adversarial perturbations in both latent and image spaces are used in robustifying the model. Our DMAT improves performance on normal images, and achieves comparable robustness to the standard adversarial training against Lp attacks. In addition, we observe that models defended by DMAT achieve improved robustness against novel attacks which manipulate images by global color shifts or various types of image filtering. Interestingly, similar improvements are also achieved when the defended models are tested on out-of-manifold natural images. These results demonstrate the potential benefits of using manifold information in enhancing robustness of deep learning models against various types of novel adversarial attacks.
3D Room Layout Estimation Beyond the Manhattan World Assumption

Predicting 3D room layout from single image is a challenging task with many applications. In this paper, we propose a new training and post-processing method for 3D room layout estimation, built on a recent state-of-the-art 3D room layout estimation model. Experimental results show our method outperforms state-of-the-art approaches by a large margin in predicting visible room layout. Our method has obtained the 3rd place in 2020 Holistic Scene Structures for 3D Vision Workshop.
Antenna Selection for Improving Energy Efficiency in XL-MIMO Systems

We consider the recently proposed extra-large scale massive multiple-input multiple-output (XL-MIMO) systems, with some hundreds of antennas serving a smaller number of users. Since the array length is of the same order as the distance to the users, the long-term fading coefficients of a given user vary with the different antennas at the base station (BS). Thus, the signal transmitted by some antennas might reach the user with much more power than that transmitted by some others. From a green perspective, it is not effective to simultaneously activate hundreds or even thousands of antennas, since the power-hungry radio frequency (RF) chains of the active antennas increase significantly the total energy consumption. Besides, a larger number of selected antennas increases the power required by linear processing, such as precoding matrix computation, and short-term channel estimation. In this paper, we propose four antenna selection (AS) approaches to be deployed in XL-MIMO systems aiming at maximizing the total energy efficiency (EE). Besides, employing some simplifying assumptions, we derive a closed-form analytical expression for the EE of the XL-MIMO system, and propose a straightforward iterative method to determine the optimal number of selected antennas able to maximize it. The proposed AS schemes are based solely on long-term fading parameters, thus, the selected antennas set remains valid for a relatively large time/frequency intervals. Comparing the results, we find that the genetic-algorithm based AS scheme usually achieves the best EE performance, although our proposed highest normalized received power AS scheme also achieves very promising EE performance in a simple and straightforward way.
An extremely hot white dwarf with a rapidly rotating K-type subgiant companion: UCAC2 46706450

UCAC2 46706450 is a late-type star with an ultraviolet (UV) excess. It was considered a candidate to establish a sample of FGK stars with white dwarf (WD) companions that can be used to test binary evolution models. To verify the WD nature of the companion, UV spectroscopy was performed by Parsons et al. (2016). By a detailed model-atmosphere analysis we show that the UV source is an extremely hot WD with effective temperature $T_\mathrm{eff}$ = $105\,000\pm5000$ K, mass $M/M_\odot = 0.54\pm0.02$, radius $R/R_\odot = 0.040^{+0.005}_{-0.004}$, and luminosity $L/L_\odot= 176^{+55}_{-49}$, i.e., the compact object is just about to enter the WD cooling sequence. Investigating spectra of the cool star ($T_\mathrm{eff}$ = $4945\pm250$ K) we found that it is a K-type subgiant with $M/M_\odot = 0.8-2.4$, $R/R_\odot = 5.9^{+0.7}_{-0.5}$, and $L/L_\odot= 19^{+5}_{-5}$, that is rapidly rotating with $v \sin(i)=81$ km s$^{-1}$. Optical light curves reveal a period of two days and an o-band peak-to-peak amplitude of 0.06 mag. We suggest, that it is caused by stellar rotation in connection with star spots. With the radius we infer an extremely high rotational velocity of $v_{\mathrm{rot}}=151^{+18}_{-13}$ km s$^{-1}$, thus marking the star as one of the most rapidly rotating subgiants known. This explains chromospheric activity observed by H$\alpha$ emission and emission-line cores in CaII H and K as well as NUV flux excess. From equal and constant radial velocities of the WD and the K subgiant as well as from a fit to the spectral energy distribution we infer that they form a physical, wide though unresolved binary system. Both components exhibit similar metal abundances and show iron-group elements with slightly oversolar (up to 0.6 dex) abundance, meaning that atomic diffusion in the WD atmosphere is not yet active due to a residual, weak radiation-driven wind. (abridged)
Light Field View Synthesis via Aperture Flow and Propagation Confidence Map

This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images. A key challenge for this novel view synthesis arises from the reconstruction process, when the views from different input images may not be consistent due to obstruction in the light path. We overcome this by jointly modeling the epipolar property and occlusion in designing a convolutional neural network. We start by defining and computing an aperture flow map, which approximates the parallax and measures the pixel-wise shift between two views. While this relates to free-space rendering and can fail near the object boundaries, we further develop a propagation confidence map to address pixel occlusion in these challenging regions. The proposed method is evaluated on diverse real-world and synthetic light field scenes, and it shows outstanding performance over several state-of-the-art techniques.
Towards an Interoperable Data Protocol Aimed at Linking the Fashion Industry with AI Companies

The fashion industry is looking forward to use artificial intelligence technologies to enhance their processes, services, and applications. Although the amount of fashion data currently in use is increasing, there is a large gap in data exchange between the fashion industry and the related AI companies, not to mention the different structure used for each fashion dataset. As a result, AI companies are relying on manually annotated fashion data to build different applications. Furthermore, as of this writing, the terminology, vocabulary and methods of data representation used to denote fashion items are still ambiguous and confusing. Hence, it is clear that the fashion industry and AI companies will benefit from a protocol that allows them to exchange and organise fashion information in a unified way. To achieve this goal we aim (1) to define a protocol called DDOIF that will allow interoperability of fashion data; (2) for DDOIF to contain diverse entities including extensive information on clothing and accessories attributes in the form of text and various media formats; and (3)To design and implement an API that includes, among other things, functions for importing and exporting a file built according to the DDOIF protocol that stores all information about a single item of clothing. To this end, we identified over 1000 class and subclass names used to name fashion items and use them to build the DDOIF dictionary. We make DDOIF publicly available to all interested users and developers and look forward to engaging more collaborators to improve and enrich it.
Palettailor: Discriminable Colorization for Categorical Data

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.
Maurer–Cartan deformation of Lagrangians

The Maurer-Cartan algebra of a Lagrangian $L$ is the algebra that encodes the deformation of the Floer complex $CF(L,L;\Lambda)$ as an $A_\infty$-algebra. We identify the Maurer-Cartan algebra with the $0$-th cohomology of the Koszul dual dga of $CF(L,L;\Lambda)$. Making use of the identification, we prove that there exists a natural isomorphism between the Maurer-Cartan algebra of $L$ and a certain analytic completion of the wrapped Floer cohomology of another Lagrangian $G$ when $G$ is \emph{dual} to $L$ in the sense to be defined. In view of mirror symmetry, this can be understood as specifying a local chart associated with $L$ in the mirror rigid analytic space. We examine the idea by explicit calculation of the isomorphism for several interesting examples.
Changing Pattern of Plasma Cortisol Level Associated with Feeding in Sheep

ヒツジにおける血漿中コルチゾール濃度の日内での変動に対しす,給餌が関与するかうかについて検討した. 1日2回の給餌時刻,およびその内容を一定にし,その条件に慣らした4頭の去勢成熟ヒツジに対し,15分間隔の血液サンプリングを午前8時より12時間にわたり行ない,血漿中コルチゾール濃度を測定した. サンプリングは1週間ごとに3回繰返した,得られたデータに対して,給餌時刻を留意した2時間ごとの時間のグループを要因とする最小自乗分散分析法により,統計処理を行なった. その結果,この時間グループの要因は有意であった(P<0.01). 最小自乗平均値では,朝の給餌前のグループの値がもっとも大きいことが示された(P<0.01). また,朝夕の給餌直後のグループの値は,給餌前のグループと比べて,各々有意に小さいことが認められた(P<0.01). ヒツジにおける給餌に伴う血漿中コルチゾールと濃度の変動は,ラットにおいて認められているパターンに類似しているものと推察される.
Stability of a Viable Non-Minimal Bounce

The main difficulties in constructing a viable early Universe bouncing model are: to bypass the observational and theoretical no-go theorem, to construct a stable non-singular bouncing phase, and perhaps, the major concern of it is to construct a stable attractor solution which can evade the Belinsky–Khalatnikov–Lifshitz (BKL) instability as well. In this article, in the homogeneous and isotropic background, we extensively study the stability analysis of the recently proposed viable non-minimal bouncing theory in the presence of an additional barotropic fluid and show that, the bouncing solution remains stable and can evade BKL instability for a wide range of the model parameter. We provide the expressions that explain the behavior of the Universe in the vicinity of the required fixed point i.e., the bouncing solution and compare our results with the minimal theory and show that ekpyrosis is the most stable solution in any scenario.
HLSGD Hierarchical Local SGD With Stale Gradients Featuring

While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming data synchronizing between workers. To alleviate this problem, a novel Hierarchical Parallel SGD (HPSGD) strategy is proposed based on the observation that the data synchronization phase can be paralleled with the local training phase (i.e., Feed-forward and back-propagation). Furthermore, an improved model updating method is unitized to remedy the introduced stale gradients problem, which commits updates to the replica (i.e., a temporary model that has the same parameters as the global model) and then merges the average changes to the global model. Extensive experiments are conducted to demonstrate that the proposed HPSGD approach substantially boosts the distributed DNN training, reduces the disturbance of the stale gradients and achieves better accuracy in given fixed wall-time.
Tableau stabilization and lattice paths

If one attaches shifted copies of a skew tableau to the right of itself and rectifies, at a certain point the copies no longer experience vertical slides, a phenomenon called tableau stabilization. While tableau stabilization was originally developed to construct the sufficiently large rectangular tableaux fixed by given powers of promotion, the purpose of this paper is to improve the original bound on tableau stabilization to the number of rows of the skew tableau. In order to prove this bound, we encode increasing subsequences as lattice paths and show that various operations on these lattice paths weakly increase the maximum combined length of the increasing subsequences.
Deep Learning-Based Single-Ended Objective Quality Measures for Time-Scale Modified Audio

Objective evaluation of audio processed with Time-Scale Modification (TSM) is seeing a resurgence of interest. Recently, a labelled time-scaled audio dataset was used to train an objective measure for TSM evaluation. This DE measure was an extension of Perceptual Evaluation of Audio Quality, and required reference and test signals. In this paper, two single-ended objective quality measures for time-scaled audio are proposed that do not require a reference signal. Data driven features are created by either a convolutional neural network (CNN) or a bidirectional gated recurrent unit (BGRU) network and fed to a fully-connected network to predict subjective mean opinion scores. The proposed CNN and BGRU measures achieve an average Root Mean Squared Error of 0.608 and 0.576, and a mean Pearson correlation of 0.771 and 0.794, respectively. The proposed measures are used to evaluate TSM algorithms, and comparisons are provided for 16 TSM implementations. The objective measure is available at this https URL.
A Class of Optimal Structures for Node Computations in Message Passing Algorithms

Consider the computations at a node in a message passing algorithm. Assume that the node has incoming and outgoing messages <inline-formula> <tex-math notation="LaTeX">$\mathbf {x} = (x_{1}, x_{2}, \ldots, x_{n})$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mathbf {y} = (y_{1}, y_{2}, \ldots, y_{n})$ </tex-math></inline-formula>, respectively. In this paper, we investigate a class of structures that can be adopted by the node for computing <inline-formula> <tex-math notation="LaTeX">$\mathbf {y}$ </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">$\mathbf {x}$ </tex-math></inline-formula>, where each <inline-formula> <tex-math notation="LaTeX">$y_{j}, j = 1, 2, \ldots, n$ </tex-math></inline-formula> is computed via a binary tree with leaves <inline-formula> <tex-math notation="LaTeX">$\mathbf {x}$ </tex-math></inline-formula> excluding <inline-formula> <tex-math notation="LaTeX">$x_{j}$ </tex-math></inline-formula>. We make three main contributions regarding this class of structures. First, we prove that the minimum complexity of such a structure is <inline-formula> <tex-math notation="LaTeX">$3n - 6$ </tex-math></inline-formula>, and if a structure has such complexity, its minimum latency is <inline-formula> <tex-math notation="LaTeX">$\delta + \lceil \log (n-2^{\delta }) \rceil $ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$\delta = \lfloor \log (n/2) \rfloor $ </tex-math></inline-formula>, where the logarithm always takes base two. Second, we prove that the minimum latency of such a structure is <inline-formula> <tex-math notation="LaTeX">$\lceil \log (n-1) \rceil $ </tex-math></inline-formula>, and if a structure has such latency, its minimum complexity is <inline-formula> <tex-math notation="LaTeX">$n \log (n-1)$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$n-1$ </tex-math></inline-formula> is a power of two. Third, given <inline-formula> <tex-math notation="LaTeX">$(n, \tau)$ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$\tau \geq \lceil \log (n-1) \rceil $ </tex-math></inline-formula>, we propose a construction for a structure which we conjecture to have the minimum complexity among structures with latencies at most <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>. Our construction method runs in <inline-formula> <tex-math notation="LaTeX">$O(n^{3} \log ^{2}(n))$ </tex-math></inline-formula> time, and the obtained structure has complexity at most (generally much smaller than) <inline-formula> <tex-math notation="LaTeX">$n \lceil \log (n) \rceil - 2$ </tex-math></inline-formula>.
Chemically characterized Cymbopogon martinii (Roxb.) Wats. essential oil for shelf life enhancer of herbal raw materials based on antifungal, antiaflatoxigenic, antioxidant activity, and favorable safety profile

The study reports antifungal and antiaflatoxigenic efficacy of chemically characterized Cymbopogon martinii essential oil (CMEO) against aflatoxigenic Aspergillus flavus causing infestation to stored herbal raw materials. In addition, the antioxidant activity and safety profile of CMEO were also assessed to recommend it as ideal preservative for stored herbal raw materials. The GC–MS of CMEO showed nerol as the major component (79.91%). CMEO inhibited growth and aflatoxin secretion of A. flavus LHPA9 at 0.5 and 0.4 μl/ml respectively, showing better efficacy over synthetic antimicrobial Propineb 70. It also exhibited broad fungitoxic spectrum against fungi causing postharvest deterioration of herbal raw materials. The TEM analysis of CMEO-treated fungal cells showed disruption of plasma-membrane and deformed cell organelles. The EO also caused inhibition of ergosterol content emphasizing plasma membrane as active site during antimicrobial action. CMEO also exhibited pronounced antioxidant activity (IC50 = 49 μl/ml) better than nerol, the major component of CMEO. The LD50 of CMEO, determined through oral administration on mice, was calculated as 2569.16 mg/kg body weight indicating its favorable safety profile as preservative. CMEO may thus be recommended as postharvest preservative in enhancement of shelf life of herbal raw materials against storage fungi, mycotoxins, and oxidative deterioration.