Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain-Specific Language Facilitates Scheduling in Model Checking

A concurrent system consists of multiple processes that are run simultaneously. The execution orders of these processes are defined by a scheduler. In model checking techniques, the scheduling policy is closely related to a search algorithm that explores all of system states. To ensure the correctness of the system, the scheduling policy needs to be taken into account during the verification. Current approaches, which use fixed strategies, are only capable of limited kinds of policies and are difficult to extend to handle the variations of the schedulers. To address these problems, we propose a method using a domain-specific language (DSL) for the succinct specification of different scheduling policies. Necessary artifacts are automatically generated from the specification of the policy to analyze the system. We also propose a search algorithm for exploring the system states. Based on this method, we develop a tool to verify the system with different scheduling policies. Our experiments show that we could serve the variations of the schedulers easily and verify systems accurately.
Riemannian Motion Policies

This paper introduces a new mathematical object for modular motion generation called the Riemannian Motion Policy (RMP). An RMP is an acceleration field (dynamical system) coupled with a corresponding Riemannian metric defining directions of importance at each point, typically defined on a nonlinear task space. We derive operators for transforming RMPs through the network of nonlinear maps defining their task spaces and combining multiple RMPs residing at a given task space, and show that these operators optimally preserve the geometric defined by the metrics in a way strongly analogous to the covariant transformations of natural gradients. This framework enables us to bridge the literature by fusing motion policies together across differing motion generation paradigms, from Dynamical Systems and Dynamic Movement Primitives to Optimal Control and Model Predictive Control. RMPs are easy to implement and manipulate, simplify controller design, clarify a number of open questions around how to effectively combine existing techniques, and their properties of geometric consistency, for the first time, make feasible the generic application of a single smooth and reactive motion generation system across a range of robots with zero re-tuning.
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument’s position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our deep learning-based approach for robotic instrument segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several novel deep neural network architectures. It addressed the binary segmentation problem, where every pixel in an image is labeled as an instrument or background from the surgery video feed. In addition, we solve a multi-class segmentation problem, in which we distinguish between different instruments or different parts of an instrument from the background. In this setting, our approach outperforms other methods for automatic instrument segmentation thereby providing state-of-the-art results for these problems. The source code for our solution is made publicly available.
Training Deep Learning based Denoisers without Ground Truth Data

Recent deep learning based denoisers are trained to minimize the mean squared error (MSE) between the output of a network and the ground truth noiseless image in the training data. Thus, it is crucial to have high quality noiseless training data for high performance denoisers. Unfortunately, in some application areas such as medical imaging, it is expensive or even infeasible to acquire such a clean ground truth image. We propose a Stein's Unbiased Risk Estimator (SURE) based method for training deep learning based denoisers without ground truth data. We demonstrated that our SURE based method only with noisy input data was able to train CNN based denoising networks that yielded performance close to that of the original MSE based deep learning denoisers with ground truth data.
The Peculiarities of Robot Embodiment (EmCorp-Scale) : Development, Validation and Initial Test of the Embodiment and Corporeality of Artificial Agents Scale

We propose a new theoretical framework assuming that embodiment effects in HAI and HRI are mediated by users’ perceptions of an artificial entity’s body-related capabilities. To enable the application of our framework to foster more theoretical-driven research, we developed a new self-report measurement that assesses bodily- related perceptions of the embodiment and corporeality - which we reveal as not being a binary characteristic of artificial entities. For the development and validation of the new scale we conducted two surveys and one video-based experiment. Exploratory factor analysis reveal a four-factorial solution with good reliability (Study 2, n = 442), which was confirmed via confirmatory factor analysis (Study 3, n = 260). In addition, we present first insights into the explanatory power of the scale: We reveal that humans’ perceptions of an artificial entity’s capabilities vary between virtual and physical embodiments, and that the evaluation of the artificial counterpart can be explained through the perceived capabilities. Practical applications and future research lines are discussed.
Applying deep learning to the newsvendor problem

Abstract The newsvendor problem is one of the most basic and widely applied inventory models. If the probability distribution of the demand is known, the problem can be solved analytically. However, approximating the probability distribution is not easy and is prone to error; therefore, the resulting solution to the newsvendor problem may not be optimal. To address this issue, we propose an algorithm based on deep learning that optimizes the order quantities for all products based on features of the demand data. Our algorithm integrates the forecasting and inventory-optimization steps, rather than solving them separately, as is typically done, and does not require knowledge of the probability distributions of the demand. One can view the optimal order quantities as the labels in the deep neural network. However, unlike most deep learning applications, our model does not know the true labels (order quantities), but rather learns them during the training. Numerical experiments on real-world data suggest that our algorithm outperforms other approaches, including data-driven and machine learning approaches, especially for demands with high volatility. Finally, in order to show how this approach can be used for other inventory optimization problems, we provide an extension for (r, Q) policies.
Chronic Kidney Disease in Cats and the Risk of Total Hypercalcemia

Background Chronic kidney disease (CKD) is a common comorbidity in cats with hypercalcemia, but whether CKD is a risk factor for hypercalcemia is unclear. Hypercalcemia often is diagnosed based on total calcium concentration (tCa), which tends to underestimate the ionized calcium concentration (iCa) in cats. Objectives Assessment of the performance of tCa for the diagnosis of ionized hypercalcemia, and exploration of factors influencing the relationship between iCa and tCa. Determination of risk factors for incident total hypercalcemia (ie, the development of hypercalcemia based on tCa during follow‐up). Animals Records of a cross‐section (n = 477) and observational cohort (n = 367) of client‐owned cats with and without azotemic CKD from first opinion practice. Methods Retrospective cross‐sectional and retrospective cohort study. The diagnostic accuracy of tCa as an index test for ionized hypercalcemia was evaluated, and risk factors for underestimation were explored by binary logistic and linear regression in a cross‐section of cats with and without azotemic CKD. Chronic kidney disease and clinicopathological variables were assessed as predictors of incident total hypercalcemia by both time‐invariant and time‐dependent Cox regression in a cohort of cats. Results Specificity of tCa for identification of ionized hypercalcemia was high (100%), but sensitivity was low. Underestimation was associated with lower venous bicarbonate concentrations. Cats with CKD had increased risk for incident total hypercalcemia (hazard ratio, 4.29; 95% confidence interval, 1.96–9.37; P < .001). Higher tCa predicted incident total hypercalcemia in both azotemic and nonazotemic cats (P < .001). Conclusions and Clinical Importance Chronic kidney disease is a risk factor for incident total hypercalcemia, and most cats with increased tCa had concurrent ionized hypercalcemia. Higher baseline tCa predicts incident total hypercalcemia. Prospective studies assessing changes in iCa are warranted.
Low efficiency of nutrient translocation for enhancing oceanic uptake of carbon dioxide

Anthropogenic emissions of carbon dioxide (CO2) are steadily increasing the concentration of this greenhouse gas in the Earth's atmosphere. The possible long-term consequences of this elevated concentration have led to proposals for a number of large-scale geoengineering schemes that aim to enhance or augment natural sinks for CO2. One such scheme proposes deploying a large number of floating “pipes” in the ocean that act to translocate nutrient-rich seawater from below the mixed layer to the ocean's surface: the nutrient supplied should enhance the growth of phytoplankton and consequently the export of organic carbon to the deep ocean via the biological pump. Here we examine the practical consequences of this scheme in a global ocean general circulation model that includes a nitrogen-based ecosystem and the biogeochemical cycle of carbon. While primary production is generally enhanced by the modeled pipes, as expected, the effect on the uptake of CO2 from the atmosphere is much smaller, may be negative, and shows considerable spatiotemporal variability.
Exogenous GR24 Alleviates Cadmium Toxicity by Reducing Cadmium Uptake in Switchgrass (Panicum virgatum) Seedlings

Strigolactones (SLs) are classified into plant hormones, playing a key role as a mediator of plant growth in response to several abiotic stresses, including drought and salinity. However, the role of SLs in cadmium (Cd)-induced stress to plants is still unknown. The physiological responses of switchgrass (Panicum virgatum) stressed in 10 μmol L−1 Cd to exogenous synthetic SLs analog, GR24 were studied in hydroponics. The Cd stress significantly caused the adverse effects on plant growth and root morphology, inhibited photosynthesis, but boosted lipid peroxidation of Switchgrass seedlings. After treatment of 1 μmol L−1 GR24, the above adverse effects caused by Cd stress were significantly alleviated, mainly reflects in improvement of shoot biomass, relative water content, root development, chlorophyll contents, activities of typical antioxidant enzymes, nutrient uptake. The reason for exogenous GR24 alleviating cadmium toxicity might be owing to that exogenous GR24 promoted the content of endogenous SLs, increased some essential element Fe (iron), Zn (zinc), Mn (manganese) and Cu (copper) uptake and reduced cadmium uptake, accumulation and partition in shoot of switchgrass seedlings.
Aspergillus section Flavi and aflatoxins in dried figs and nuts in Algeria

ABSTRACT The presence of Aspergillus section Flavi and aflatoxin (AF) contamination was investigated in 112 samples of peanuts, almonds and dried figs collected in Algeria. The occurrence of aflatoxin B1 (AFB1), B2 (AFB2), G1 (AFG1) and G2 (AFG2) in different commodities has been determined with a sensitive method based on high performance liquid chromatography (HPLC) coupled with fluorescence detection with post-column photochemical derivatisation. Analytical results indicated that 28 samples of peanuts, 16 samples of almonds and 26 samples of dried figs contained detectable levels of AFs. A total of 69 samples (61.6%) were contaminated with AFB1 ranging from the limit of quantification to 174 µg kg−1. AFB2 was found in 12 samples (10.7%) and varied from 0.18 to 193 µg kg−1. Seven samples revealed AF concentrations lower than the limit of quantification. Eleven peanut and fourteen dried fig samples exceeded the European maximum limits for AFB1.
Energy-delay tradeoffs of virtual base stations with a computational-resource-aware energy consumption model

The next generation (5G) cellular network faces the challenges of efficiency, flexibility, and sustainability to support data traffic in the mobile Internet era. To tackle these challenges, cloud-based cellular architectures have been proposed where virtual base stations (VBSs) play a key role. VBSs bring further energy savings but also demands a new energy consumption model as well as the optimization of computational resources. This paper studies the energy-delay tradeoffs of VBSs with delay tolerant traffic. We propose a computational-resource-aware energy consumption model to capture the total energy consumption of a VBS and reflect the dynamic allocation of computational resources including the number of CPU cores and the CPU speed. Based on the model, we analyze the energy-delay tradeoffs of a VBS considering BS sleeping and state switching cost to minimize the weighted sum of power consumption and average delay. We derive the explicit form of the optimal data transmission rate and find the condition under which the energy optimal rate exists and is unique. Opportunities to reduce the average delay and achieve energy savings simultaneously are observed. We further propose an efficient algorithm to jointly optimize the data rate and the number of CPU cores. Numerical results validate our theoretical analyses and under a typical simulation setting we find more than 60% energy savings can be achieved by VBSs compared with conventional base stations under the EARTH model, which demonstrates the great potential of VBSs in 5G cellular systems.
The remarkable vocal anatomy of the koala (Phascolarctos cinereus): insights into low‐frequency sound production in a marsupial species

Koalas are characterised by a highly unusual vocal anatomy, with a descended larynx and velar vocal folds, allowing them to produce calls at disproportionately low frequencies. Here we use advanced imaging techniques, histological data, classical macroscopic dissection and behavioural observations to provide the first detailed description and interpretation of male and female koala vocal anatomy. We show that both males and females have an elongated pharynx and soft palate, resulting in a permanently descended larynx. In addition, the hyoid apparatus has a human‐like configuration in which paired dorsal, resilient ligaments suspend the hyoid apparatus from the skull, while the ventral parts tightly connect to the descended larynx. We also show that koalas can retract the larynx down into the thoracic inlet, facilitated by a dramatic evolutionary transformation of the ventral neck muscles. First, the usual retractors of the larynx and the hyoid have their origins deep in the thorax. Secondly, three hyoid muscles have lost their connection to the hyoid skeleton. Thirdly, the genioglossus and geniohyoid muscles are greatly increased in length. Finally, the digastric, omohyoid and sternohyoid muscles, connected by a common tendinous intersection, form a guiding channel for the dynamic down‐and‐up movements of the ventral hyoid parts and the larynx. We suggest that these features evolved to accommodate the low resting position of the larynx and assist in its retraction during call production. We also confirm that the edges of the intra‐pharyngeal ostium have specialised to form the novel, extra‐laryngeal velar vocal folds, which are much larger than the true intra‐laryngeal vocal folds in both sexes, but more developed and specialised for low frequency sound production in males than in females. Our findings illustrate that strong selection pressures on acoustic signalling not only lead to the specialisation of existing vocal organs but can also result in the evolution of novel vocal structures in both sexes.
Plasmodium P36 determines host cell receptor usage during sporozoite invasion

Plasmodium sporozoites, the mosquito-transmitted forms of the malaria parasite, first infect the liver for an initial round of replication before the emergence of pathogenic blood stages. Sporozoites represent attractive targets for antimalarial preventive strategies, yet the mechanisms of parasite entry into hepatocytes remain poorly understood. Here we show that the two main species causing malaria in humans, Plasmodium falciparum and Plasmodium vivax, rely on two distinct host cell surface proteins, CD81 and the Scavenger Receptor BI (SR-BI), respectively, to infect hepatocytes. By contrast, CD81 and SR-BI fulfil redundant functions during infection by the rodent parasite P. berghei. Genetic analysis of sporozoite factors reveals the 6-cysteine domain protein P36 as a major parasite determinant of host cell receptor usage. Our data provide molecular insights into the invasion pathways used by different malaria parasites to infect hepatocytes, and establish a functional link between a sporozoite putative ligand and host cell receptors. DOI: http://dx.doi. org/10.7554/eLife.25903. 001
Convolutional neural network on neural compute stick for voxelized point-clouds classification

2D Convolutional Neural Networks (CNNs) have enjoyed a surge in popularity over the last few years, mainly because they outperform traditional algorithms/methods in a myriad of computer vision (and other fields) tasks. On the other hand, the problem becomes more complex when dealing with 3D volumes. Lack of readily available training data, memory and computational requirements are just some of the factors hindering the progress of 3D CNNs. We propose a synthetic 3D voxelized point-clouds generation method containing object and scene in this paper. Furthermore, an efficient 3D volumetric representation called VOLA is applied. VOLA (Volumetric Accelerator) is a sexaquaternary (power-of-four subdivision) tree-based representation which aims to save significant memory for volumetric data. After training the model, it was deployed onto Movidius Neural Compute Stick which is a USB, containing a low-power processing unit as well as dedicated CNN hardware blocks. The trained model on NCS takes only ∼ 90 frames per second to perform inference on each 3D volume, with an average power consumption of 1.2W.
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures---arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance. A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively. We prove statistical error rates for three kinds of population loss functions: strongly convex, non-strongly convex, and smooth non-convex. In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses. To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round. For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.
Subsecond fear discrimination in rats: adult impairment in adolescent heavy alcohol drinkers

Discriminating safety from danger must be accurate and rapid. Yet, the rapidity with which fear discrimination emerges remains unknown. Rapid fear discrimination in adulthood may be susceptible to impairment by adolescent heavy alcohol drinking, which increases incidence of anxiety disorders. Rats were given voluntary, adolescent alcohol access, and heavy drinkers were identified. In adulthood, rapid fear discrimination of safety, uncertainty, and danger cues was assessed. Normal rats, but not heavy drinkers, showed discriminative fear <1 sec following cue onset. This provides the first demonstration of subsecond fear discrimination and its adult impairment in adolescent heavy alcohol drinkers.
Water Environment Assessment as an Ecological Red Line Management Tool for Marine Wetland Protection

A ‘red line’ was established, identifying an area requiring for ecological protection in Tianjin, China. Within the protected area of the red line area, the Qilihai wetland is an important ecotope with complex ecological functions, although the ecosystem is seriously disturbed due to anthropogenic activities in the surrounding areas. This study assesses the water quality status of the Qilihai wetlands to identify the pollution sources and potential improvements based on the ecological red line policy, to improve and protect the waters of the Qilihai wetlands. An indicator system was established to assess water quality status using single factor evaluation and a comprehensive evaluation method, supported by data from 2010 to 2013. Assessment results show that not all indicators met the requirement of the Environmental Quality Standards for Surface Water (GB3838-2002) and that overall, waters in the Qilihai wetland were seriously polluted. Based on these findings we propose restrictions on all polluting anthropogenic activities in the red line area and implementation of restoration projects to improve water quality.
Homocysteine and Stroke Risk: Modifying Effect of Methylenetetrahydrofolate Reductase C677T Polymorphism and Folic Acid Intervention

Background and Purpose— Elevated blood homocysteine concentration increases the risk of stroke, especially among hypertensive individuals. Homocysteine is largely affected by the methylenetetrahydrofolate reductase C677T polymorphism and folate status. Among hypertensive patients, we aimed to test the hypothesis that the association between homocysteine and stroke can be modified by the methylenetetrahydrofolate reductase C677T polymorphism and folic acid intervention. Methods— We analyzed the data of 20 424 hypertensive adults enrolled in the China Stroke Primary Prevention Trial. The participants, first stratified by methylenetetrahydrofolate reductase genotype, were randomly assigned to receive double-blind treatments of 10-mg enalapril and 0.8-mg folic acid or 10-mg enalapril only. The participants were followed up for a median of 4.5 years. Results— In the control group, baseline log-transformed homocysteine was associated with an increased risk of first stroke among participants with the CC/CT genotype (hazard ratio, 3.1; 1.1–9.2), but not among participants with the TT genotype (hazard ratio, 0.7; 0.2–2.1), indicating a significant gene–homocysteine interaction (P=0.008). In the folic acid intervention group, homocysteine showed no significant effect on stroke regardless of genotype. Consistently, folic acid intervention significantly reduced stroke risk in participants with CC/CT genotypes and high homocysteine levels (tertile 3; hazard ratio, 0.73; 0.55–0.97). Conclusions— In Chinese hypertensive patients, the effect of homocysteine on the first stroke was significantly modified by the methylenetetrahydrofolate reductase C677T genotype and folic acid supplementation. Such information may help to more precisely predict stroke risk and develop folic acid interventions tailored to individual genetic background and nutritional status. Clinical Trial Registration— URL: http://www.clinicaltrials. gov. Unique identifier: NCT00794885.