iJTyper: An Iterative Type Inference Framework for Java by Integrating Constraint- and Statistically-based Methods

Inferring the types of API elements in incomplete code snippets (e.g., those on Q&A forums) is a prepositive step required to work with the code snippets. Existing type inference methods can be mainly categorized as constraint-based or statistically-based. The former imposes higher requirements on code syntax and often suffers from low recall due to the syntactic limitation of code snippets. The latter relies on the statistical regularities learned from a training corpus and does not take full advantage of the type constraints in code snippets, which may lead to low precision. In this paper, we propose an iterative type inference framework for Java, called iJTyper, by integrating the strengths of both constraint- and statistically-based methods. For a code snippet, iJTyper first applies a constraint-based method and augments the code context with the inferred types of API elements. iJTyper then applies a statistically-based method to the augmented code snippet. The predicted candidate types of API elements are further used to improve the constraint-based method by reducing its pre-built knowledge base. iJTyper iteratively executes both methods and performs code context augmentation and knowledge base reduction until a termination condition is satisfied. Finally, the final inference results are obtained by combining the results of both methods. We evaluated iJTyper on two open-source datasets. Results show that 1) iJTyper achieves high average precision/recall of 97.31% and 92.52% on both datasets; 2) iJTyper significantly improves the recall of two state-of-the-art baselines, SnR and MLMTyper, by at least 7.31% and 27.44%, respectively; and 3) iJTyper improves the average precision/recall of the popular language model, ChatGPT, by 3.25% and 0.51% on both datasets

Paclitaxel-loaded phosphonated calixarene nanovesicles as a modular drug delivery platform

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/A modular p-phosphonated calix[4]arene vesicle (PCV) loaded with paclitaxel (PTX) and conjugated with folic acid as a cancer targeting ligand has been prepared using a thin film-sonication method. It has a pH-responsive capacity to trigger the release of the encapsulated PTX payload under mildly acidic conditions. PTX-loaded PCV conjugated with alkyne-modified PEG-folic acid ligands prepared via click ligation (fP-PCVPTX) has enhanced potency against folate receptor (FR)-positive SKOV-3 ovarian tumour cells over FR-negative A549 lung tumour cells. Moreover, fP-PCVPTX is also four times more potent than the non-targeting PCVPTX platform towards SKOV-3 cells. Overall, as a delivery platform the PCVs have the potential to enhance efficacy of anticancer drugs by targeting a chemotherapeutic payload specifically to tumours and triggering the release of the encapsulated drug in the vicinity of cancer cells

Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network

Accurate temporal action proposals play an important role in detecting actions from untrimmed videos. The existing approaches have difficulties in capturing global contextual information and simultaneously localizing actions with different durations. To this end, we propose a Relation-aware pyramid Network (RapNet) to generate highly accurate temporal action proposals. In RapNet, a novel relation-aware module is introduced to exploit bi-directional long-range relations between local features for context distilling. This embedded module enhances the RapNet in terms of its multi-granularity temporal proposal generation ability, given predefined anchor boxes. We further introduce a two-stage adjustment scheme to refine the proposal boundaries and measure their confidence in containing an action with snippet-level actionness. Extensive experiments on the challenging ActivityNet and THUMOS14 benchmarks demonstrate our RapNet generates superior accurate proposals over the existing state-of-the-art methods.Comment: accepted by AAAI-2

Path following control of unmanned quadrotor helicopter with obstacle avoidance capability

International audienceThis paper proposes a new path following methodology combining with an obstacle avoidance scheme for unmanned quadrotor helicopter (UQH) capable of working in the cluttered and hazardous environments. A new cross-track error prediction based mechanism, where the cross-track error is estimated by utilizing the extend Kalman filter (EKF), is first developed for the path following scheme. Then, the UQH is equipped with obstacle avoidance capability employing a light-computational approach, the visibility graph algorithm. The priority of UQH is to switch to obstacles avoidance maneuvering in the presence of obstacles, and continue to execute the assigned mission after avoiding all hazardous objects blocking the desired path. The control system developed for attitude and position control of UQH is also introduced. Finally, extensive simulation studies on a nonlinear model of UQH with a series of dangerous scenarios are conducted to demonstrate the effectiveness of the proposed methodology

Influence of substrate type on transport properties of superconducting FeSe0.5Te0.5 thin films

FeSe0.5Te0.5 thin films were grown by pulsed laser deposition on CaF2, LaAlO3 and MgO substrates and structurally and electro-magnetically characterized in order to study the influence of the substrate on their transport properties. The in-plane lattice mismatch between FeSe0.5Te0.5 bulk and the substrates shows no influence on the lattice parameters of the films, whereas the type of substrates affects the crystalline quality of the films and, therefore, the superconducting properties. The film on MgO showed an extra peak in the angular dependence of critical current density Jc({\theta}) at {\theta} = 180{\deg} (H || c), which arises from c-axis defects as confirmed by transmission electron microscopy. In contrast, no Jc({\theta}) peaks for H || c were observed in films on CaF2 and LaAlO3. Jc({\theta}) can be scaled successfully for both films without c-axis correlated defects by the anisotropic Ginzburg-Landau (AGL) approach with appropriate anisotropy ratio {\gamma}J. The scaling parameter {\gamma}J is decreasing with decreasing temperature, which is different from what we observed in FeSe0.5Te0.5 films on Fe-buffered MgO substrates.Comment: accepted for publication in SUS

Phenylpropanoid amides from Solanum rostratum and their phytotoxic activities against Arabidopsis thaliana

IntroductionSolanum rostratum, an annual malignant weed, has seriously damaged the ecological environment and biodiversity of invasion area. This alien plant gains a competitive advantage by producing some new phytotoxic substances to inhibit the growth of native plants, thus achieving successful invasion. However, the chemical structures, inhibitory functions and action mechanisms of phytotoxic substances of S. rostratum remain unclear.MethodsIn this study, to clarify the chemical structures of phytotoxic substances from S. rostratum, we isolated phenylpropanoid amides from the plant. Their structures were identified by comprehensive HR-ESIMS, NMR and ECD data. And the inhibitory functions of isolated phenylpropanoid amides on one model plant (Arabidopsis thaliana) were also investigated. In addition, the action mechanisms of active phenylpropanoid amides were revealed by antioxidant-related enzymes [Catalase (CAT), Peroxidase (POD), Superoxide dismutase (SOD)] activities and corresponding molecular docking analyses.Results and DiscussionPhytochemical research on the whole plant of S. rostratum led to the isolation and identification of four new phenylpropanoid amides (1−4), together with two known analogues (5−6). All the compounds showed phytotoxic effects with varying levels on the seed germination and root elongation of one model plant (Arabidopsis thaliana), especially compound 2 and 4. Likewise, compounds 2 and 4 displayed potent inhibitory effects on antioxidant-related enzyme (POD). In addition, compounds 2 and 4 formed common conventional hydrogen bonds with residues Ala34 and Ser35 in POD revealed by molecular docking analyses. These findings not only helped to reveal the invasion mechanism of S. rostratum from the perspective of “novel weapons hypothesis”, but also opened up new ways for the exploitation and utilization of S. rostratum

Universal scaling behavior of the upper critical field in strained FeSe0.7Te0.3 thin films

open15Revealing the universal behaviors of iron-based superconductors (FBS) is important to elucidate the microscopic theory of superconductivity. In this work, we investigate the effect of in-plane strain on the slope of the upper critical field H c2 at the superconducting transition temperature T c (i.e. -dH c2/dT) for FeSe0.7Te0.3 thin films. The in-plane strain tunes T c in a broad range, while the composition and disorder are almost unchanged. We show that -dH c2/dT scales linearly with T c, indicating that FeSe0.7Te0.3 follows the same universal behavior as observed for pnictide FBS. The observed behavior is consistent with a multiband superconductivity paired by interband interaction such as sign change s ± superconductivity.openYuan, Feifei; Grinenko, Vadim; Iida, Kazumasa; Richter, Stefan; Pukenas, Aurimas; Skrotzki, Werner; Sakoda, Masahito; Naito, Michio; Sala, Alberto; Putti, Marina; Yamashita, Aichi; Takano, Yoshihiko; Shi, Zhixiang; Nielsch, Kornelius; Hühne, RubenYuan, Feifei; Grinenko, Vadim; Iida, Kazumasa; Richter, Stefan; Pukenas, Aurimas; Skrotzki, Werner; Sakoda, Masahito; Naito, Michio; Sala, Alberto; Putti, Marina; Yamashita, Aichi; Takano, Yoshihiko; Shi, Zhixiang; Nielsch, Kornelius; Hühne, Rube