Bio-inspired functional surface fabricated by electrically assisted micro-embossing of AZ31 magnesium alloy

Developing bio-inspired functional surfaces on engineering metals is of extreme importance, involving different industrial sectors, like automotive or aeronautics. In particular, micro-embossing is one of the efficient and large-scale processes for manufacturing bio-inspired textures on metallic surfaces. However, this process faces some problems, such as filling defects and die breakage due tocsize effect, which restrict this technology for some components. Electrically assisted micro-forming has demonstrated the ability of reducing size effects, improving formability and decreasing flow stress, making it a promising hybrid process to control the filling quality of micro-scale features. This research focuses on the use of different current densities to perform embossed micro-channels of 7 um and sharklet patterns of 10 um in textured bulk metallic glass dies. These dies are prepared by thermoplastic forming based on the compression of photolithographic silicon molds. The results show that large areas of bio-inspired textures could be fabricated on magnesium alloy when current densities higher than 6 A/mm2 (threshold) are used. The optimal surface quality scenario is obtained for a current density of 13 A/mm2. Additionally, filling depth and depth–width ratio nonlinearly increases when higher current densities are used, where the temperature is a key parameter to control, keeping it below the temperature of the glass transition to avoid melting or an early breakage of the die.Peer ReviewedPostprint (published version

A Margin-based MLE for Crowdsourced Partial Ranking

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a \emph{margin-based Maximum Likelihood Estimate} (MLE) method. We prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-of-the-art algorithms.Comment: 9 pages, Accepted by ACM Multimedia 2018 as a full pape

CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control

This paper presents a novel solution for UAV control in cooperative multi-robot systems, which can be used in various scenarios such as leader-following, landing on a moving base, or specific relative motion with a target. Unlike classical methods that tackle UAV control in the world frame, we directly control the UAV in the target coordinate frame, without making motion assumptions about the target. In detail, we formulate a non-linear model predictive controller of a UAV, referred to as the agent, within a non-inertial frame (i.e., the target frame). The system requires the relative states (pose and velocity), the angular velocity and the accelerations of the target, which can be obtained by relative localization methods and ubiquitous MEMS IMU sensors, respectively. This framework eliminates dependencies that are vital in classical solutions, such as accurate state estimation for both the agent and target, prior knowledge of the target motion model, and continuous trajectory re-planning for some complex tasks. We have performed extensive simulations to investigate the control performance with varying motion characteristics of the target. Furthermore, we conducted real robot experiments, employing either simulated relative pose estimation from motion capture systems indoors or directly from our previous relative pose estimation devices outdoors, to validate the applicability and feasibility of the proposed approach

Automatic Context Pattern Generation for Entity Set Expansion

Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various NLP and IR downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing bootstrapping methods have achieved great progress, most of them still rely on manually pre-defined context patterns. A non-negligible shortcoming of the pre-defined context patterns is that they cannot be flexibly generalized to all kinds of semantic classes, and we call this phenomenon as "semantic sensitivity". To address this problem, we devise a context pattern generation module that utilizes autoregressive language models (e.g., GPT-2) to automatically generate high-quality context patterns for entities. In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities. Extensive experiments and detailed analyses on three widely used datasets demonstrate the effectiveness of our method. All the codes of our experiments will be available for reproducibility.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A genetic mouse model with postnatal Nf1 and p53 loss recapitulates the histology and transcriptome of human malignant peripheral nerve sheath tumor

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas. Somatic inactivation of METHODS: We combined 2 genetically modified alleles, an RESULTS: Postnatal CONCLUSIONS: The NP-Plp model recapitulates human MPNST genetically, histologically, and molecularly

A Survey on Transferability of Adversarial Examples across Deep Neural Networks

The emergence of Deep Neural Networks (DNNs) has revolutionized various domains, enabling the resolution of complex tasks spanning image recognition, natural language processing, and scientific problem-solving. However, this progress has also exposed a concerning vulnerability: adversarial examples. These crafted inputs, imperceptible to humans, can manipulate machine learning models into making erroneous predictions, raising concerns for safety-critical applications. An intriguing property of this phenomenon is the transferability of adversarial examples, where perturbations crafted for one model can deceive another, often with a different architecture. This intriguing property enables "black-box" attacks, circumventing the need for detailed knowledge of the target model. This survey explores the landscape of the adversarial transferability of adversarial examples. We categorize existing methodologies to enhance adversarial transferability and discuss the fundamental principles guiding each approach. While the predominant body of research primarily concentrates on image classification, we also extend our discussion to encompass other vision tasks and beyond. Challenges and future prospects are discussed, highlighting the importance of fortifying DNNs against adversarial vulnerabilities in an evolving landscape

YAP and TAZ maintain PROX1 expression in the developing lymphatic and lymphovenous valves in response to VEGF-C signaling

Lymphatic vasculature is an integral part of digestive, immune and circulatory systems. The homeobox transcription factor PROX1 is necessary for the development of lymphatic vessels, lymphatic valves (LVs) and lymphovenous valves (LVVs). We and others previously reported a feedback loop between PROX1 and vascular endothelial growth factor-C (VEGF-C) signaling. PROX1 promotes the expression of the VEGF-C receptor VEGFR3 in lymphatic endothelial cells (LECs). In turn, VEGF-C signaling maintains PROX1 expression in LECs. However, the mechanisms of PROX1/VEGF-C feedback loop remain poorly understood. Whether VEGF-C signaling is necessary for LV and LVV development is also unknown. Here, we report for the first time that VEGF-C signaling is necessary for valve morphogenesis. We have also discovered that the transcriptional co-activators YAP and TAZ are required to maintain PROX1 expression in LVs and LVVs in response to VEGF-C signaling. Deletion o