Miracle-dz/UniFAR-FTUs huggingface dataset:
TOXICITY OF INSECTICIDES TO FIRST-INSTAR LARVAE OF THE SPRUCE BUDMOTH, ZEIRAPHERA CANADENSIS MUT. AND FREE. (LEPIDOPTERA: TORTRICIDAE): LABORATORY AND FIELD STUDIES

Abstract Laboratory tests with selected carbamate, organophosphorus, and pyrethroid insecticides demonstrated that the pyrethroid permethrin has the best potential for controlling newly hatched larvae of Zeiraphera canadensis Mut. and Free. Permethrin possessed high crawling contact toxicity (toxicity of insecticide deposits on foliage when contacted by crawling larvae) and direct contact toxicity to first-instar larvae and exhibited long residual effectiveness on potted, white spruce trees. Chlorpyrifos, fenitrothion, mexacarbate, and methomyl had high crawling contact toxicity but short residual activity. Azinphos-methyl appeared to possess long residual effectiveness but relatively low crawling contact toxicity. Aminocarb and thiodicarb exhibited short residual effectiveness and relatively low toxicity. In field trials, an aerial application of permethrin (70 g/ha) at egg hatch resulted in an 81% population reduction and limited the destruction of tree leaders to 9%. Leader destruction was greater than 19% after treatments of permethrin at 35 g AI/ha or aminocarb at 180 g AI/ha or aminocarb twice at 90 g AI/ha. Leader destruction in an untreated plantation was 51%. Résumé Des essais réalisés en laboratoire avec des insecticides ont démontré que parmi les carbamates, les organophosphates, et les pyréthroides testés, la perméthrine qui est un pyréthroide était le candidat le plus prometteur pour le contrôle sur le terrain des larves de Zeiraphera canadensis Mut. et Free, lors de la période d’éclosion. La toxicité de la perméthrine appliquée directement sur les larves ou sur du feuillage sur lequel on a permis à des larves du premier stade de se déplacer librement avant de s’établir sous les bourgeons fut très élevé. Le taux de mortalité des larves déposées sur le feuillage d’épinettes blanches en pots, 5 jours après l’application de la perméthrine fut similaire à celui obtenu pour les larves déposées 1 h après l’application, indiquant ainsi une longue rémanence. La toxicité du chlorpyrifos, du fénitrothion, du mexacarbate et du méthomyle via le feuillage fut élevée, mais leur rémanence sur le feuillage des épinettes blanches en pot fut de courte durée. Bien que la rémanence de l’azinphos-méthyl sembla de longue durée, sa toxicité via le feuillage fut relativement faible. L’aminocarbe et le thiodicarbe ont démontré une rémanence courte et une toxicité via le feuillage faible. Un épandage aérien de perméthrine réalisé au Nouveau Brunswick, à raison de 70 g IA/ha au début de la période d’éclosion des larves, a réduit la population de Z. canadensis de 81%, et a limité à 9% la destruction des flèches apicales des épinettes blanches. Pour les placettes traitées avec de la perméthrine à raison de 35 g IA/ha, avec de l’aminocarbe à raison de 180 g IA/ha ou avec deux applications d’aminocarbe à 90 g IA/ha, la destruction des flèches fut supérieure à 19%. Dans la placette témoin, la proportion des flèches apicales détruites atteignit 51%.
Threshy: supporting safe usage of intelligent web services

Increased popularity of ‘intelligent’ web services provides end-users with machine-learnt functionality at little effort to developers. However, these services require a decision threshold to be set which is dependent on problem-specific data. Developers lack a systematic approach for evaluating intelligent services and existing evaluation tools are predominantly targeted at data scientists for pre-development evaluation. This paper presents a workflow and supporting tool, Threshy, to help software developers select a decision threshold suited to their problem domain. Unlike existing tools, Threshy is designed to operate in multiple workflows including pre-development, pre-release, and support. Threshy is designed for tuning the confidence scores returned by intelligent web services and does not deal with hyper-parameter optimisation used in ML models. Additionally, it considers the financial impacts of false positives. Threshold configuration files exported by Threshy can be integrated into client applications and monitoring infrastructure. Demo: https://bit.ly/2YKeYhE.
Physically Constrained Transfer Learning Through Shared Abundance Space for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., the source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pretraining in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and nonnegligible computational resources are required to retrain the whole network. In this article, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied to the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state of the art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios.
Use of molluscs to monitor bacteria in water

Discussion sur les techniques de controle bacterien selectionnees a partir d'etudes de laboratoire. Preparation des suspensions de tissu de mollusques. Isolement des bacteries. Surveillance des modifications dans l'environnement. Autres applications specifiques (production de mollusques comestibles, traceurs bacteriens des mouvements de l'eau, evaluation des rejets d'eau usee)
Series Expansions and Direct Inversion for the Heston Model

Efficient sampling for the conditional time integrated variance process in the Heston stochastic volatility model is key to the simulation of the stock price based on its exact distribution. We construct a new series expansion for this integral in terms of double infinite weighted sums of particular independent random variables through a change of measure and the decomposition of squared Bessel bridges. When approximated by series truncations, this representation has exponentially decaying truncation errors. We propose feasible strategies to largely reduce the implementation of the new series to simulations of simple random variables that are independent of any model parameters. We further develop direct inversion algorithms to generate samples for such random variables based on Chebyshev polynomial approximations for their inverse distribution functions. These approximations can be used under any market conditions. Thus, we establish a strong, efficient and almost exact sampling scheme for the Heston model.
ROSINA ion zoo at Comet 67P

Context. The Rosetta spacecraft escorted Comet 67P/Churyumov-Gerasimenko for 2 yr along its journey through the Solar System between 3.8 and 1.24 au. Thanks to the high resolution mass spectrometer on board Rosetta, the detailed ion composition within a coma has been accurately assessed in situ for the very first time. Aims. Previous cometary missions, such as Giotto, did not have the instrumental capabilities to identify the exact nature of the plasma in a coma because the mass resolution of the spectrometers onboard was too low to separate ion species with similar masses. In contrast, the Double Focusing Mass Spectrometer (DFMS), part of the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis on board Rosetta (ROSINA), with its high mass resolution mode, outperformed all of them, revealing the diversity of cometary ions. Methods. We calibrated and analysed the set of spectra acquired by DFMS in ion mode from October 2014 to April 2016. In particular, we focused on the range from 13–39 u q−1. The high mass resolution of DFMS allows for accurate identifications of ions with quasi-similar masses, separating 13C+ from CH+, for instance. Results. We confirm the presence in situ of predicted cations at comets, such as CHm+ (m = 1−4), HnO+ (n = 1−3), O+, Na+, and several ionised and protonated molecules. Prior to Rosetta, only a fraction of them had been confirmed from Earth-based observations. In addition, we report for the first time the unambiguous presence of a molecular dication in the gas envelope of a Solar System body, namely CO2++.
Full-sky photon simulation of clusters and active galactic nuclei in the soft X-rays for eROSITA

The eROSITA X-ray telescope on board the Spectrum-Roentgen-Gamma (SRG) mission will measure the position and properties of about 100,000 clusters of galaxies and 3 million active galactic nuclei over the full sky. To study the statistical properties of this ongoing survey, it is key to estimate the selection function accurately. We create a set of full sky light-cones using the MultiDark and UNIT dark matter only N-body simulations. We present a novel method to predict the X-ray emission of galaxy clusters. Given a set of dark matter halo properties (mass, redshift, ellipticity, offset parameter), we construct an X-ray emissivity profile and image for each halo in the light-cone. We follow the eROSITA scanning strategy to produce a list of X-ray photons on the full sky. We predict scaling relations for the model clusters, which are in good agreement with the literature. The predicted number density of clusters as a function of flux also agrees with previous measurements. Finally, we obtain a scatter of 0.21 (0.07, 0.25) for the X-ray luminosity -- mass (temperature -- mass, luminosity -- temperature) model scaling relations. We provide catalogues with the model photons emitted by clusters and active galactic nuclei. These catalogues will aid the eROSITA end to end simulation flow analysis and in particular the source detection process and cataloguing methods.
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization

Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function. However, softmax function suffers in retaining high-rank representations for complex visual or sequential information, which is known as the Softmax Bottleneck problem. In this paper, we propose a novel framework named Dual Mixture Attention (DMASum) model with Meta Learning for video summarization that tackles the softmax bottleneck problem, where the Mixture of Attention layer (MoA) effectively increases the model capacity by employing twice self-query attention that can capture the second-order changes in addition to the initial query-key attention, and a novel Single Frame Meta Learning rule is then introduced to achieve more generalization to small datasets with limited training sources. Furthermore, the DMASum significantly exploits both visual and sequential attention that connects local key-frame and global attention in an accumulative way. We adopt the new evaluation protocol on two public datasets, SumMe, and TVSum. Both qualitative and quantitative experiments manifest significant improvements over the state-of-the-art methods.
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers (such as, convolutional and fully-connected), our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.
Relationship between Pyrethroid Resistance and Attacking Persimmon in the Onion Thrips, Thrips tabaci (Thysanoptera: Thripidae)

To detect biological differences between pyrethroid-resistant strains and pyrethroid-susceptible strains of the onion thrips, Thrips tabaci, developmental and ovipositional characteristics on the leaves of 5 varieties of persimmon and green bean were investigated by rearing trials using isofemale lines. In addition, suitability of 2 varieties of persimmon fruits as foods for thrips larvae was compared between a pyrethroid-resistant strain and a pyrethroid-susceptible strain of onion thrips. Irrespective of susceptibility levels to pyrethroid, while T. tabaci adults deposited eggs into persimmon leaves, few individuals completed larval development on the leaves, suggesting that T. tabaci does not develop on persimmon leaf throughout reproductive seasons. Both strains of T. tabaci successfully molted into adults when they fed on persimmon fruits, but survival rates in immature stages differed among varieties and developmental stages of the fruits. These differences may cause inter-specific and seasonal variation in injury levels by thrips attacking persimmon orchards. The pyrethroid-resistant strains showed lower fecundities compared to the pyrethroid-susceptible strains of onion thrips on green bean leaf. In conclusion, there was no evidence that unique characteristics other than susceptibility levels to pyrethroid insecticide contribute to pestilent levels of the onion thrips in commercial persimmon orchards.
Invariant states of hydrodynamic limits of randomized load balancing networks

Randomized load-balancing algorithms play an important role in improving performance in large-scale networks at relatively low computational cost. A common model of such a system is a network of $N$ parallel queues in which incoming jobs with independent and identically distributed service times are routed on arrival using the join-the-shortest-of-$d$-queues routing algorithm. Under fairly general conditions, it was shown by Aghajani and Ramanan that as $N\rightarrow\infty$, the state dynamics converges to the unique solution of a countable system of coupled deterministic measure-valued equations called the hydrodynamic equations. In this article, a characterization of invariant states of these hydrodynamic equations is obtained and, when $d=2$, used to construct a numerical algorithm to compute the queue length distribution and mean virtual waiting time in the invariant state. Additionally, it is also shown that under a suitable tail condition on the service distribution, the queue length distribution of the invariant state exhibits a doubly exponential tail decay, thus demonstrating a vast improvement in performance over the case $d=1$, which corresponds to random routing, when the tail decay could even be polynomial. Furthermore, numerical evidence is provided to support the conjecture that the invariant state is the limit of the steady-state distributions of the $N$-server models. The proof methodology, which entails analysis of a coupled system of measure-valued equations, can potentially be applied to other many-server systems with general service distributions, where measure-valued representations are useful.
Assessing the Effectiveness of Using Live Interactions and Feedback to Increase Engagement in Online Learning

In-person instruction for professional development or other types of workplace training provides a social environment and immediate feedback mechanisms that typically ensure all participants are successful. Online, self-paced instruction lacks these mechanisms and relies on the motivation and persistence of each individual learner, often resulting in low completion rates. In this study, we studied the effect of introducing enabling tools and live feedback into an online learning experience on learner performance in the course, persistence in the course, and election to complete supplemental readings and assignments. The findings from our experiments show positive correlations with strong statistical significance between live interactions and all performance measures studied. Research funded by the National Science Foundation, award number #1843391.
Magneto-Optical Properties of InSb for Spectral Filtering in the Far-Infrared

We present measurements of the Faraday effect in n-type InSb. The Verdet coefficient was determined for a range of carrier concentrations near $10^{17}$ $cm^{-3}$ in the $\lambda$ = 8 $\mu$m - 12 $\mu$m far-infrared regime. The absorption coefficient was measured and a figure of merit calculated for each sample. From these measurements, we calculated the carrier effective mass and illustrate the variation of the figure of merit with wavelength. A method for creating a tunable bandpass filter via the Faraday rotation is discussed along with preliminary results from a prototype device.
Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication

In hepatocellular carcinoma (HCC), more than one third of patients were accompanied by macrovascular invasion (MaVI) during diagnosis and treatment. HCCs with MaVI presented with aggressive tumor behavior and poor survival. Early identification of HCCs at high risk of MaVI would promote adequate preoperative treatment strategy making, so as to prolong the patient survival. Thus, we aimed to develop a computed tomography (CT)-based radiomics model to preoperatively predict MaVI status in HCC, meanwhile explore the prognostic prediction power of the radiomics model. A cohort of 452 patients diagnosed with HCC was collected from 5 hospitals in China with complete CT images, clinical data, and follow-ups. 15 out of 708 radiomic features were selected for MaVI prediction using LASSO regression modeling. A radiomics signature was constructed by support vector machine based on the 15 selected features. To evaluate the prognostic power of the signature, Kaplan-Meier curves with log-rank test were plotted on MaVI occurrence time (MOT), progression free survival (PFS) and overall survival (OS). The radiomics signature showed satisfactory performance on MaVI prediction with area under curves of 0.885 and 0.770 on the training and external validation cohorts, respectively. Patients could successfully be divided into high- and low-risk groups on MOT and PFS with p-value of 0.0017 and 0.0013, respectively. Regarding to OS, the Kaplan-Meier curve did not present with significant difference which may be caused by non-uniform following treatments after disease progression. To conclude, the proposed radiomics model could facilitate MaVI prediction along with prognostic implication in HCC management.
International Journal of Approximate Reasoning

Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. They allow for a compact representation of joint probability mass functions deﬁned over sets of Boolean variables, that are also consistent with the logical constraints deﬁned by the circuit. The probabilities in such a model are usually “learned” from a set of observations. This leads to overconﬁdent and prior-dependent inferences when data are scarce, unreliable or conﬂicting. In this work, we develop the credal sentential decision diagrams , a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with (so-called credal ) sets of mass functions. These models induce a joint credal set over the set of Boolean variables, that sharply assigns probability zero to states inconsistent with the logical constraints. Three inference algorithms are derived for these models. These allow to compute: (i) the lower and upper probabilities of an observation for an arbitrary number of variables; (ii) the lower and upper conditional probabilities for the state of a single variable given an observation; (iii) whether or not all the probabilistic sentential decision diagrams compatible with the credal speciﬁcation have the same most probable explanation of a given set of variables given an observation of
Anchor-free Small-scale Multispectral Pedestrian Detection

Multispectral images consisting of aligned visual-optical (VIS) and thermal infrared (IR) image pairs are well-suited for practical applications like autonomous driving or visual surveillance. Such data can be used to increase the performance of pedestrian detection especially for weakly illuminated, small-scaled, or partially occluded instances. The current state-of-the-art is based on variants of Faster R-CNN and thus passes through two stages: a proposal generator network with handcrafted anchor boxes for object localization and a classification network for verifying the object category. In this paper we propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. In this way, we can both simplify the network architecture and achieve higher detection performance, especially for pedestrians under occlusion or at low object resolution. In addition, we provide a study on well-suited multispectral data augmentation techniques that improve the commonly used augmentations. The results show our method's effectiveness in detecting small-scaled pedestrians. We achieve 5.68% log-average miss rate in comparison to the best current state-of-the-art of 7.49% (25% improvement) on the challenging KAIST Multispectral Pedestrian Detection Benchmark. Code: this https URL