Miracle-dz/UniFAR-FTUs huggingface dataset:
A new subgenus and new species of the genus Mixotrephes Papáček, Štys & Tonner, 1989 (Heteroptera: Helotrephidae: Limnotrephini)

Abstract A new subgenus Thermotrephes of the genus Mixotrephes from Southeast Asia is established. Mixotrephes freitagi sp. nov. from Central Nepal is described and illustrated. A character analysis sets M. freitagi sp. nov. close to M. thermophilus (Papáček & Kovac 2001); M. thermophilus is designated as the type species of the new subgenus. M. thermophilus is recorded from Myanmar for the first time; new records from Thailand are also presented. A new record of M. kumaonis (Polhemus 1990) is presented from Central Nepal. Both morphological and ecological characters of Mixotrephes species are compared, some of which are discussed from the taxonomic and phylogenetic point of view.
Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type algorithms

Although the performance of popular optimization algorithms such as the Douglas-Rachford splitting (DRS) and ADMM is satisfactory in small and well-scaled problems, ill conditioning and problem size pose a severe obstacle to their reliable employment. Expanding on recent convergence results for DRS and ADMM applied to nonconvex problems, we propose two linesearch algorithms to enhance and robustify these methods by means of quasi-Newton directions. The proposed algorithms are suited for nonconvex problems, require the same black-box oracle of DRS and ADMM, and maintain their (subsequential) convergence properties. Numerical evidence shows that the employment of L-BFGS in the proposed framework greatly improves convergence of DRS and ADMM, making them robust to ill conditioning. Under regularity and nondegeneracy assumptions at the limit point, superlinear convergence is shown when quasi-Newton Broyden directions are adopted.
Positive emotions help rank negative reviews in e-commerce

Negative reviews, the poor ratings in postpurchase evaluation, play an indispensable role in e-commerce, especially in shaping future sales and firm equities. However, extant studies seldom examine their potential value for sellers and producers in enhancing capabilities of providing better services and products. For those who exploited the helpfulness of reviews in the view of e-commerce keepers, the ranking approaches were developed for customers instead. To fill this gap, in terms of combining description texts and emotion polarities, the aim of the ranking method in this study is to provide the most helpful negative reviews under a certain product attribute for online sellers and producers. By applying a more reasonable evaluating procedure, experts with related backgrounds are hired to vote for the ranking approaches. Our ranking method turns out to be more reliable for ranking negative reviews for sellers and producers, demonstrating a better performance than the baselines like BM25 with a result of 8% higher. In this paper, we also enrich the previous understandings of emotions in valuing reviews. Specifically, it is surprisingly found that positive emotions are more helpful rather than negative emotions in ranking negative reviews. The unexpected strengthening from positive emotions in ranking suggests that less polarized reviews on negative experience in fact offer more rational feedbacks and thus more helpfulness to the sellers and producers. The presented ranking method could provide e-commerce practitioners with an efficient and effective way to leverage negative reviews from online consumers.
Fungicidal control of rust of French prune caused by Tranzschelia discolor

An orchard spray trial indicated that the dithiocarbamates mancozeb and zineb, the latter with or without the addition of white oil as a sticker, gave superior control of prune rust to dithianon, captafol or triforine. Further orchard studies involving artificial inoculation supported this result, with mancozeb and zineb giving 12-13 weeks of complete protection in one instance. In these studies chlorothalonil and bitertanol were also superior to dithianon and captafol, but appeared to be less persistent than the dithiocarbamates. Propiconazole and RH-3866 were relatively ineffective protectants.The efficacies and persistence of fungicides appeared to be less the later they were applied in spring and summer. Rain within a day or two of spraying appeared to reduce fungicide efficacy and persistence, but when it occurred some days or weeks later there was little effect, even a heavy fall (53 mm) not causing an observable reduction in efficacy. The implications of these findings for the development of control strategies is discussed.
BERTweet: A pre-trained language model for English Tweets

We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. We release BERTweet under the MIT License to facilitate future research and applications on Tweet data. Our BERTweet is available at https://github.com/VinAIResearch/BERTweet
The Inverse G‐Wishart distribution and variational message passing

Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily large graphical models. The notion of a factor graph fragment allows for compartmentalisation of algebra and computer code. We show that the Inverse G‐Wishart family of distributions enables fundamental variational message passing factor graph fragments to be expressed elegantly and succinctly. Such fragments arise in models for which approximate inference concerning covariance matrix or variance parameters is made, and are ubiquitous in contemporary statistics and machine learning.
Effect of Supplementing Synthetic Amino Acids in Low-protein Diet and Subsequent Re-feeding on Growth Performance, Serum Lipid Profile and Chemical Body Composition of Broiler Chickens

This study was conducted to evaluate the effects of supplementing methionine (Met) and lysine (Lys) in low protein (Low-CP) diet during 1-21 days of age, and subsequent re-feeding with conventional diet during 22-42 days of age on growth performance, serum lipid profile, chemical body composition and carcass quality of broiler chickens. During 1-21 days of age (starter period), 480 male broiler chicks (Ross 308) were divided into three treatments and given the following diets: 1) conventional diet group (all nutrients met the requirements of the strain), 2) Low-CP diet without Met and Lys supplementation and 3) the Low-CP diet supplemented with Met and Lys (Low-CP + Met + Lys). During the finisher period (22-42 days of age), all groups were fed a diet containing the same nutrients in accordance with the recommendations of the strain. At 21 days of age, Low-CP + Met + Lys diet showed significantly better growth performance and breast meat yields than those of the Low-CP diet group. Feed and protein intake of the chicks fed conventional diet was significantly higher than both of the other groups (P<0.01), whereas Low-CP + Met + Lys diet clearly improved protein efficiency (P<0.01). Feeding Low-CP diet increased abdominal fat content and body energy content (P<0.05), while the supplementing synthetic amino acids slightly decreased the fat content. Triglyceride, very low density lipoprotein (VLDL) and T3 hormone in blood were significantly increased in Low-CP +Met + Lys diet group compared to the conventional diet (P<0.05). After the re-feeding phase, feeding Low-CP diet groups were unable to compensate body weight equal to the conventional diet, although a compensation of FCR was observed. Feeding Low-CP + Met + Lys diet showed the same breast meat yield compared to the conventional diet, but abdominal fat, triglyceride and VLDL in blood were significantly increased (p<0.05). In conclusion, supplementing Met + Lys in Low-CP diet improved performance production, but was still inferior to the conventional diet.
Tessellated Wasserstein Auto-Encoders

Non-adversarial generative models such as variational auto-encoder (VAE), Wasserstein auto-encoders with maximum mean discrepancy (WAE-MMD), sliced-Wasserstein auto-encoder (SWAE) are relatively easy to train and have less mode collapse compared to Wasserstein auto-encoder with generative adversarial network (WAE-GAN). However, they are not very accurate in approximating the target distribution in the latent space because they don't have a discriminator to detect the minor difference between real and fake. To this end, we develop a novel non-adversarial framework called Tessellated Wasserstein Auto-encoders (TWAE) to tessellate the support of the target distribution into a given number of regions by the centroidal Voronoi tessellation (CVT) technique and design batches of data according to the tessellation instead of random shuffling for accurate computation of discrepancy. Theoretically, we demonstrate that the error of estimate to the discrepancy decreases when the numbers of samples $n$ and regions $m$ of the tessellation become larger with rates of $\mathcal{O}(\frac{1}{\sqrt{n}})$ and $\mathcal{O}(\frac{1}{\sqrt{m}})$, respectively. Given fixed $n$ and $m$, a necessary condition for the upper bound of measurement error to be minimized is that the tessellation is the one determined by CVT. TWAE is very flexible to different non-adversarial metrics and can substantially enhance their generative performance in terms of Frechet inception distance (FID) compared to VAE, WAE-MMD, SWAE. Moreover, numerical results indeed demonstrate that TWAE is competitive to the adversarial model WAE-GAN, demonstrating its powerful generative ability.
Importance of metal-binding proteins in the partitioning of Cd and Zn as trophically available metal (TAM) in the brine shrimp Artemia franciscana

Studies have shown that the trophic transfer of certain metals in aquatic systems may be controlled by the internal distribution of metal within prey and that this distribution may be influenced by detoxification mechanisms (i.e. metals bound to metal-binding proteins [metallothioneins, MT] may be more available to predators than metals associated with insoluble cellular constituents). The purpose of this investigation was to examine the interactive effects of Cd and Zn exposure on the accumulation and subcellular distribution of these metals in the brine shrimp Artemia franciscana. Particular attention was given to the partitioning of metals to a subcellular compartment containing heat-stable proteins (HSP) (e.g. MT), heat-denatured proteins (HDP) (e.g. 'enzymes') and organelles, here considered as trophically available metal (TAM). Adult A. franciscana were exposed for 3 d to Cd (control, 1, 89 or 445 μM and Zn-control) or Zn (control, 1, 89 or 445 μM and Cd-control) through solution using radioisotopes ( 109 Cd and 65 Zn) as tracers of stable metals. Following exposure, various operationally defined subcellular fractions were obtained. Increased binding of Cd to HSP resulted in increased partitioning of Cd to the TAM compartment (i.e. TAM-Cd% increased from 57 to 80% over the range of Cd exposures). This increase in TAM was greater than proportional at the 1 μM Cd exposure concentration and could result in a 'bioenhancement' of Cd trophic transfer to predators. Exposure to elevated concentrations of Cd (89 and 445 μM) also resulted in a ∼50% suppression in Zn accumulation by A. franciscana. Upon increasing Zn exposure, a shift in the subcellular partitioning of Zn among TAM fractions (from HSP to HDP and organelles) maintained a TAM-Zn% of ∼65%. As a verification of the TAM concept, a direct relationship was observed between the partitioning of Cd and Zn to the TAM compartment of A. franciscana exposed to the Cd-control/Zn-control treatment and absorption of these metals by the grass shrimp Palaemonetes pugio.
Fluctuation spectroscopy of giant unilamellar vesicles using confocal and phase contrast microscopy

A widely used method to measure the bending rigidity of bilayer membranes is fluctuation spectroscopy, which analyses the thermally-driven membrane undulations of giant unilamellar vesicles recorded with either phase-contrast or confocal microscopy. Here, we analyze the fluctuations of the same vesicle using both techniques and obtain consistent values for the bending modulus. We discuss the factors that may lead to discrepancies.
Synthesis of 4-Nitrophenyl 2-Acetamido-2-deoxy-β-D-mannopyranoside and 4-Nitrophenyl 2-Acetamido-2-deoxy-α-D-mannopyranoside

The title compounds were synthesized by the selective reduction of the azido group in 4-nitrophenyl 3,4,6-tri-O-acetyl-2-azido-2-deoxy-α-D-mannopyranoside (8) and 4-nitrophenyl 3,4,6-tri-O-acetyl-2-azido-2-deoxy-β-D-mannopyranoside (11), and by subsequent acetylation. Compound 8 was prepared by opening of the epoxide ring in methyl 2,3-anhydro-4,6-O-benzylidene-α-D-glucopyranoside (1) with sodium azide, followed by inversion of the configuration at C-3 in the resulting altropyranoside and glycosidation with 4-nitrophenol.
Inference on the Change Point in High Dimensional Dynamic Graphical Models

We propose a new estimator for the change point parameter in a dynamic high dimensional graphical model setting. We show that the proposed estimator retains sufficient adaptivity against plugin estimates of the edge structure of the underlying graphical models, in order to yield an $O(\psi^{-2})$ rate of convergence of the change point estimator in the integer scale. This rate is preserved while allowing high dimensionality as well as a diminishing jump size $\psi,$ provided $s\log^{3/2}(p\vee T)=o\big(\surd(Tl_T)\big). $ Here $s,p,T$ and $l_T$ represent a sparsity parameter, model dimension, sampling period and the separation of the change point from its parametric boundary, respectively. Moreover, since the rate of convergence is free of $s,p$ and logarithmic terms of $T,$ it allows the existence of a limiting distribution valid in the high dimensional setting, which is then derived. The method does not assume an underlying Gaussian distribution. Theoretical results are supported numerically with monte carlo simulations.
p-Norm Flow Diffusion for Local Graph Clustering

Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference. Prior work on local graph clustering mostly falls into two categories with numerical and combinatorial roots respectively. In this work, we draw inspiration from both fields and propose a family of convex optimization formulations based on the idea of diffusion with p-norm network flow for $p\in (1,\infty)$. In the context of local clustering, we characterize the optimal solutions for these optimization problems and show their usefulness in finding low conductance cuts around input seed set. In particular, we achieve quadratic approximation of conductance in the case of $p=2$ similar to the Cheeger-type bounds of spectral methods, constant factor approximation when $p\rightarrow\infty$ similar to max-flow based methods, and a smooth transition for general $p$ values in between. Thus, our optimization formulation can be viewed as bridging the numerical and combinatorial approaches, and we can achieve the best of both worlds in terms of speed and noise robustness. We show that the proposed problem can be solved in strongly local running time for $p\ge 2$ and conduct empirical evaluations on both synthetic and real-world graphs to illustrate our approach compares favorably with existing methods.
Data-driven Derivation of Stellar Properties from Photometric Time Series Data Using Convolutional Neural Networks

Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar observations with stellar physics and to understand the distribution of stellar populations within the context of galaxy formation. Numerous ongoing and upcoming missions are charting brightness fluctuations of stars over time, which encode information about physical processes such as the rotation period, evolutionary state (such as effective temperature and surface gravity), and mass (via asteroseismic parameters). Here, we explore how well we can predict these stellar properties, across different evolutionary states, using only photometric time-series data. To do this, we implement a convolutional neural network, and with data-driven modeling we predict stellar properties from light curves of various baselines and cadences. Based on a single quarter of Kepler data, we recover the stellar properties, including the surface gravity for red giant stars (with an uncertainty of ≲0.06 dex) and rotation period for main-sequence stars (with an uncertainty of ≲5.2 days, and unbiased from ≈5 to 40 days). Shortening the Kepler data to a 27 days Transiting Exoplanet Survey Satellite–like baseline, we recover the stellar properties with a small decrease in precision, ∼0.07 for log g and ∼5.5 days for P rot, unbiased from ≈5 to 35 days. Our flexible data-driven approach leverages the full information content of the data, requires minimal or no feature engineering, and can be generalized to other surveys and data sets. This has the potential to provide stellar property estimates for many millions of stars in current and future surveys.
Non-toxic control techniques for Microtus spp. in apple orchards1

Alternative control techniques to the use of endrin ground spray were investigated in apple orchards in Washington (US). Non-toxic control strategies using cultural practices proved highly successful in reducing high populations of Microtus montanus to low levels. Early spring raking and mowing followed by the application of herbicides to a strip along the tree line appeared to have the greatest effect. Regular mowing between the tree rows and the maintenance of bare ground under the trees prevented significant build-up of the population. Similar cultural work also substantially improved the efficacy of subsequent rodenticide applications. The economic impact of the vole damage on apple orchards, even those composed of mature trees, is substantial and the expense of intensive cultural work is justified where high vole populations are possible.
Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection

This paper presents a novel 3D object detection framework that processes LiDAR data directly on a representation of the sensor's native range images. When operating in the range image view, one faces learning challenges, including occlusion and considerable scale variation, limiting the obtainable accuracy. To address these challenges, a range-conditioned dilated block (RCD) is proposed to dynamically adjust a continuous dilation rate as a function of the measured range, achieving scale invariance. Furthermore, soft range gating helps mitigate the effect of occlusion. An end-to-end trained box-refinement network brings additional performance improvements in occluded areas, and produces more accurate bounding box predictions. On the Waymo Open Dataset, currently the largest and most diverse publicly released autonomous driving dataset, our improved range-based detector outperforms state of the art at long range detection. Our framework is superior to prior multiview, voxel-based methods over all ranges, setting a new baseline for range-based 3D detection on this large scale public dataset.
Fingerprinting encrypted voice traffic on smart speakers with deep learning

This paper investigates the privacy leakage of smart speakers under an encrypted traffic analysis attack, referred to as voice command fingerprinting. In this attack, an adversary can eavesdrop both outgoing and incoming encrypted voice traffic of a smart speaker, and infers which voice command a user says over encrypted traffic. We first built an automatic voice traffic collection tool and collected two large-scale datasets on two smart speakers, Amazon Echo and Google Home. Then, we implemented proof-of-concept attacks by leveraging deep learning. Our experimental results over the two datasets indicate disturbing privacy concerns. Specifically, compared to 1% accuracy with random guess, our attacks can correctly infer voice commands over encrypted traffic with 92.89% accuracy on Amazon Echo. Despite variances that human voices may cause on outgoing traffic, our proof-of-concept attacks remain effective even only leveraging incoming traffic (i.e., the traffic from the server). This is because the AI-based voice services running on the server side response commands in the same voice and with a deterministic or predictable manner in text, which leave distinguishable pattern over encrypted traffic. We also built a proof-of-concept defense to obfuscate encrypted traffic. Our results show that the defense can effectively mitigate attack accuracy on Amazon Echo to 32.18%.
Conditioned olfactory responses of a predatory mite, Neoseiulus womersleyi, to volatiles from prey‐infested plants

To clarify the prey‐finding behavior of the predatory mite Neoseiulus womersleyi (Schicha) (Acari: Phytoseiidae), we studied its olfactory responses to volatiles from the prey‐infested plant on which the mites had been collected. We used a local N. womersleyi population called Kanaya collected from tea (Camellia sinensis L.) (Theaceae) plants infested by Tetranychus kanzawai Kishida (Acari: Tetranychidae) in Kanaya City, Japan. Neoseiulus womersleyi (Kanaya population) were more attracted to volatiles from tea plants infested with five female T. kanzawai per leaf for 7 days than to intact tea leaves in a Y‐tube olfactometer. Tetranychus kanzawai‐induced tea leaf volatiles were identified as (E)‐β‐ocimene, (E)‐4,8‐dimethyl‐1,3,7‐nonatriene, and (E,E)‐α‐farnesene. As olfactory responses are known to differ among local populations of N. womersleyi, we compared the responses of the Kanaya population with those of a Kikugawa population collected from tea plants infested by T. kanzawai in Kikugawa City. To test the influence of previous predation experience, we reared the two populations on tea plants infested by T. kanzawai or on kidney bean plants (Phaseolus vulgaris) infested by Tetranychus urticae Koch. The Kanaya population was more attracted to the volatiles from infested plants on which they had been reared. Because the Kanaya population was not attracted to the plant volatiles they had not previously experienced, the positive response to previously experienced volatiles might be the result of learning. By contrast, the Kikugawa population showed no preference for previously experienced volatiles from infested plants. The implications of this flexibility in foraging behavior are discussed.
Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo

With the rapid development of COVID-19, people are asked to maintain "social distance" and "stay at home". In this scenario, more and more social interactions move online, especially on social media like Twitter and Weibo. People post tweets to share information, express opinions and seek help during the pandemic, and these tweets on social media are valuable for studies against COVID19, such as early warning and outbreaks detection. Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020. Moreover, the field information of the dataset is very rich, including basic tweets information, interactive information, location information and retweet network. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and faster researches to suppress the spread of this disease.