Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-reconfigurable multilegged robot swarms collectively accomplish challenging terradynamic tasks

Multilegged swarm robots with self-reconfigurability can perform diverse tasks in variable terrain. Swarms of ground-based robots are presently limited to relatively simple environments, which we attribute in part to the lack of locomotor capabilities needed to traverse complex terrain. To advance the field of terradynamically capable swarming robotics, inspired by the capabilities of multilegged organisms, we hypothesize that legged robots consisting of reversibly chainable modular units with appropriate passive perturbation management mechanisms can perform diverse tasks in variable terrain without complex control and sensing. Here, we report a reconfigurable swarm of identical low-cost quadruped robots (with directionally flexible legs and tail) that can be linked on demand and autonomously. When tasks become terradynamically challenging for individuals to perform alone, the individuals suffer performance degradation. A systematic study of performance of linked units leads to new discoveries of the emergent obstacle navigation capabilities of multilegged robots. We also demonstrate the swarm capabilities through multirobot object transport. In summary, we argue that improvement capabilities of terrestrial swarms of robots can be achieved via the judicious interaction of relatively simple units.
TastifyNet: Leveraging adversarial examples for generating improved recipes

In the following work, we present TastifyNet: A deep learning-based recipe improvement algorithm. In contrast to existing work which focuses on identifying recipes or generating entirely new ones - we sought to improve existing culinary dishes. After training a neural network model on over 250,000 rated recipes, an adversarial example inspired alteration algorithm recommends improvements.
Computation Prediction of the Therapeutic Effect of Metal Stent Implantation for Coronary Bifurcation

A good understanding of the characteristics behavior of blood flow is significant for exploring the formation mechanism and process of atherosclerosis (AS), grasping the pathogenesis of AS, and providing effective theoretical guidance for clinical treatment of AS. To predict the influence of plaque on hemodynamics and the therapeutic effect of stent interventional treatment on stenosis artery, the coronary bifurcation models with/without stenosis and with stent implantation were constructed to analyze the hemodynamic characteristics in three cases using fluid-solid interaction method based on COMSOL software. The dynamic velocity, streamline, pressure, Von Mises stress, and displacement distributions in the coronary artery were obtained in these studies. Results show that the velocity of the plaque increased sharply, and the peripheral tissues of the plaque are more prone to the risk of injury because of the higher stress. The blood flow velocity tends to be stable in the stented artery and the velocity distribution is uniform, indicating that the blood supply of branch vessels is further improved, which is conducive to the normal blood supply and stable blood flow.
The Perceptual Belief Problem

The explainability of robotic systems depends on people’s ability to reliably attribute perceptual beliefs to robots, i.e., what robots know (or believe) about objects and events in the world based on their perception. However, the perceptual systems of robots are not necessarily well understood by the majority of people interacting with them. In this article, we explain why this is a significant, difficult, and unique problem in social robotics. The inability to judge what a robot knows (and does not know) about the physical environment it shares with people gives rise to a host of communicative and interactive issues, including difficulties to communicate about objects or adapt to events in the environment. The challenge faced by social robotics researchers or designers who want to facilitate appropriate attributions of perceptual beliefs to robots is to shape human–robot interactions so that people understand what robots know about objects and events in the environment. To meet this challenge, we argue, it is necessary to advance our knowledge of when and why people form incorrect or inadequate mental models of robots’ perceptual and cognitive mechanisms. We outline a general approach to studying this empirically and discuss potential solutions to the problem.
Enabling Service Cache in Edge Clouds

The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache, which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.
Integrated Hardware Garbage Collection

Garbage collected programming languages, such as Python and C#, have accelerated software development. These modern languages increase productivity and software reliability as they provide high-level data representation and control structures. Modern languages are widely used in software development for mobile, desktop, and server devices, but their adoption is limited in real-time embedded systems. There is clear interest in supporting modern languages in embedded devices as emerging markets, like the Internet of Things, demand ever smarter and more reliable products. Multiple commercial and open-source projects, such as Zerynth and MicroPython, are attempting to provide support. But these projects rely on software garbage collectors that impose high overheads and introduce unpredictable pauses, preventing their use in many embedded applications. These limitations arise from the unsuitability of conventional processors for performing efficient, predictable garbage collection. We propose the Integrated Hardware Garbage Collector (IHGC); a garbage collector tightly coupled with the processor that runs continuously in the background. Further, we introduce a static analysis technique to guarantee that real-time programs are never paused by the collector. Our design allocates a memory cycle to the collector when the processor is not using the memory. The IHGC achieves this by careful division of collection work into single-memory-access steps that are interleaved with the processor’s memory accesses. As a result, our collector eliminates run-time overheads and enables real-time program analysis. The principles behind the IHGC can be used in conjunction with existing architectures. For example, we simulated the IHGC alongside the ARMv6-M architecture. Compared to a conventional processor, our experiments indicate that the IHGC offers 1.5–7 times better performance for programs that rely on garbage collection. The IHGC delivers the benefits of garbage-collected languages with real-time performance but without the complexity and overheads inherent in software collectors.
How to Analyze, Preserve, and Communicate Leonardo's Drawing? A Solution to Visualize in RTR Fine Art Graphics Established from “the Best Sense”

Original hand drawings by Leonardo are astonishing collections of knowledge, superb representations of the artist's way of working, which proves the technical and cultural peak of the Renaissance era. However, due to their delicate and fragile nature, they are hard to manipulate and compulsory to preserve. To overcome this problem we developed, in a 10-year-long research program, a complete workflow to produce a system able to replace, investigate, describe and communicate ancient fine drawings through what Leonardo calls “the best sense” (i.e., the view), the so-called ISLe (InSightLeonardo). The outcoming visualization app is targeted to a wide audience made of museum visitors and, most importantly, art historians, scholars, conservators, and restorers. This article describes a specific feature of the workflow: the appearance modeling with the aim of an accurate Real-Time Rendering (RTR) visualization. This development is based on the direct observation of five among the most known Leonardo da Vinci's drawings, spanning his entire activity as a draftsman, and it is the result of an accurate analysis of drawing materials used by Leonardo, in which peculiarities of materials are digitally reproduced at the various scales exploiting solutions that favor the accuracy of perceived reproduction instead of the fidelity to the physical model and their ability to be efficiently implemented over a standard GPU-accelerated RTR pipeline. Results of the development are exemplified on five of Leonardo's drawings and multiple evaluations of the results, subjective and objective, are illustrated, aiming to assess potential and critical issues of the application.
Computational Design of Potential Binder Protein for SARS-CoV-2 Spike RBD through A Novel Deep Neural Network Based-Protein Outpainting Algorithm

COVID-19 caused by SARS-CoV-2 is seriously endangering the health of all human beings. There is an urgent need for drugs that can inhibit the replication and propagation of the virus. Traditional macromolecular drugs have long discovery and development cycles and high experimental costs, which can't give rapid response to new viruses. Through computational protein design method, scientists have designed binder proteins with high affinity for the RBD of SARS-CoV-2 spike protein which can effectively inhibit virus replication. However, traditional computational protein design methods rely heavily on human experience and domain knowledge of protein design, and the protein design workflow is too complicated to be widely accepted and used in academia and industry. Based on previous work in the field of deep neural network protein structure prediction and protein design, we developed a novel protein outpainting method that can generate the remaining part of the protein based on a given hot spot motif and complete the entire protein. This method can generate stable protein scaffold which can support the functional hot spot motif, resulting in a protein with excellent thermal stability and developability. We tested this method in a drug discovery project with the aim of designing new SARS-CoV-2 inhibitors. Several proteins are obtained which are predicted to be stable and may have high affinity for the RBD of the SARS-CoV-2 spike protein. Although they have not been verified by wet-lab experiments, we believe that these proteins have great potential to be developed into effective drugs for the treatment of COVID-19. The protein outpainting algorithm proposed in this paper has great advantages over traditional protein design methods. It can be applied to many fields that require the design of functional proteins, such as protein drug design, enzyme de novo design, vaccine design, etc. The method will play an important role in reducing the cost of experiments, shortening the research and development period, and improving the successful rate of biological research and development.
Facile Synthesis of (3,5-(CF3)2C6H3)2BX (X = H, OMe, F, Cl, Br): Reagents for the Introduction of a Strong Boryl Acceptor Unit

The reaction of (3,5-(CF3)2C6H3)Li ((Fxyl)Li) with BH3·SMe2 in Et2O furnishes Li[(Fxyl)BH3] in an essentially quantitative yield. Hydride abstraction with Me3SiCl followed by the addition of a second equivalent of (Fxyl)Li gives Li[(Fxyl)2BH2] in 71% yield. Treatment of Li[(Fxyl)2BH2] with 1 equiv of Me3SiCl and a subsequent targeted methanolysis provide access to the methoxyborane (Fxyl)2BOMe. The latter compound serves as starting material for the synthesis of the haloboranes (Fxyl)2BX (X = F, Cl, Br) through the reaction with KHF2/Me3SiCl, BCl3, and BBr3, respectively. All three haloboranes, as well as key synthesis intermediates, have been structurally characterized by X-ray crystallography.
“I See What You Did There”

Unpredictability in robot behaviour can cause difficulties in interacting with robots. However, for social interactions with robots, a degree of unpredictability in robot behaviour may be desirable for facilitating engagement and increasing the attribution of mental states to the robot. To generate a better conceptual understanding of predictability, we looked at two facets of predictability, namely, the ability to predict robot actions and the association of predictability as an attribute of the robot. We carried out a video human-robot interaction study where we manipulated whether participants could either see the cause of a robot’s responsive action or could not see this, because there was no cause, or because we obstructed the visual cues. Our results indicate that when the cause of the robot’s responsive actions was not visible, participants rated the robot as more unpredictable and less competent, compared to when it was visible. The relationship between seeing the cause of the responsive actions and the attribution of competence was partially mediated by the attribution of unpredictability to the robot. We argue that the effects of unpredictability may be mitigated when the robot identifies when a person may not be aware of what the robot wants to respond to and uses additional actions to make its response predictable.
NetGraph: An Intelligent Operated Digital Twin Platform for Data Center Networks

This paper presents a digital twin platform towards automatic and intelligent management for data center networks: NetGraph. It builds a virtual image of the physical network, which comprises the entire network elements and simplifies the workflows of network service management. NetGraph redefines network operation (including maintenance) model by integrating four functional blocks: unified digital twin model management, automatic configuration deployment and translation, Inventory search, and network elements validation. NetGraph has been deployed in Huawei's data center networks and served more than fifty thousand devices along with millions of network connectivity information (e.g., links and terminations points). Statistics in real network scenarios show that NetGraph has a wide coverage of network models and supports multiple functions.
Plant Robot for At-Home Behavioral Activation Therapy Reminders to Young Adults with Depression

Adolescents with depression who participate in behavioral activation therapy may find it hard to be motivated to perform tasks at home that their therapists recommend. We describe the initial design and usability evaluation of a home device (“PlantBot”) that could be used to remind young adults with depression at home of their behavioral activation therapy-related tasks. The prototype features electronics in a two-layer base, with a fake plant on top and supported using the Amazon Echo voice agent. We use an online panel study to evaluate the usability of our system with youth with past depression (N = 30). Initial findings highlight the device’s usability, potential benefit, and attractiveness of the plant component, as well as multiple improvements to be made.
Multimodal Optimization Evolutionary Algorithm for RNA Secondary Structure Prediction

Recent years, RNA secondary structure prediction has attracted much attention of many researchers, which is an important way to grasp the biochemical function of RNA. However, it is very difficult to predict the RNA secondary structure including pseudoknot, which has been identified to be an NP-complete problem. In this paper, a novel multimodal optimization evolutionary algorithm is proposed to optimize the decision space based on the minimum free energy to predict the secondary structure of RNA. Because there exist multiple equivalent secondary structures which represent the same minimum free energy, our algorithm maintain diversity in decision space to find multiple sets of secondary structure simultaneously. The performance of our algorithm is evaluated by PseudoBase instances and compared with some good prediction algorithms. The comparison results show that our method has higher accuracy in RNA secondary structure prediction.
Identification of Key Gene Modules and Hub Genes of Hypertension Based on WGCNA Algorithm

Background: Hypertension is a chronic disease with high morbidity and high mortality in the world. Its pathogenesis is complicated and its molecular mechanism has not been fully elucidated, which seriously threatens human life and health. The purpose of this paper was to the molecular study of hypertension, explore candidate biomarkers affecting the occurrence of hypertension from the perspective of weighted network, and provide the theoretical and practical basis for the prevention and treatment of hypertension. Materials and methods: The hypertension gene expression dataset of GSE75360 were downloaded from the Gene Expression Omnibus (GEO). The “limma” package of R was utilized to screen the differentially expressed genes (DEGs) between the sample group with and without high blood pressure. Next, Weight Gene co-expression Network Analysis (WGCNA) algorithm was used to establish a co-expression network of the DEGs and to detect hypertension-related gene modules. And DAVID was utilized to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we proposed the hierarchical fusion method to screen hub genes. Results: We identified 2 key gene modules that were significantly associated with hypertension, named Mlightcyan and Mgreenyellow. In addition, 18 hub genes (RPS28, LOC730288/RPS28P6, LOC645968/ RPS3AP25, LOC727826/RPS11P5, RPL21, LOC653079/ RPL36P14, LOC441743/RPL23AP5, LOC651453/RPL36P14, LPPR2, NSMCE4A, FKBP1A, RAB5C, MAN2B1, FURIN, TBXAS1, RPS6KA4, PARN, LOC642489/FKBP1C) relating to hypertension were identified form the two key gene modules. Conclusions: In this study, we identified two key gene modules and 18 hub genes, which were associated with the mechanisms of hypertension. These findings will provide references that improve the understanding of the pathogenesis of hypertension. The hub genes might can serve as therapeutic targets for diagnosis of hypertension in the future.
Synthesis of several bisabolane sesquiterpenoids from xanthorrhizol isolated from C. Xanthorrhiza

Xanthorrhizol was isolated from the essential oil of fresh rhizomes of C. xanthorrhiza in 20.2% yield by fractionation using vacuum liquid chromatography. Several bisabolane-type sesquiterpenoids have been synthesised from this xanthorrhizol. Both diastereomers of 10,11-dihydro-10,11-dihydroxyxanthorrhizols, sesquiterpenoids isolated from the Mexican medicinal plant, Iostephane heterophylla, have been prepared in three steps from xanthorrhizol via Sharpless asymmetric dihydroxylation as the key steps. Fremyâ€™s salt oxidation of xanthorrhizol gave curcuhydroquinone in 60% yield, which was successfully reduced with sodium dithionite to curcuhydroquinone in 100% yield. Sequential acetylation and Sharpless asymmetric dihydroxylation on curcuhydroquinone led to the diacetate derivative of helibisabonol A. Cleavage of the diacetate esters by reduction with lithium borohydride furnished helibisabonol A, an allelopathic agent isolated from Helianthus annuus (sunflowers). The unexpected difficulty in deprotection of helibisabonol A diacetate was due to acidic, basic and air-sensitive natures of helibisabonol A. An allylic alcohol derivative of O-methylxanthorrhizol, (3S,6R)-(3- methoxy-4-methylphenyl)-2-methylhept-1-en-3-ol, has been synthesised from xanthorrhizol in five steps via Sharpless asymmetric dihydroxylation as the key steps. Sharpless asymmetric dihydroxylation in all syntheses gave excellent enantioselectivity (ee > 98%). The enantiomeric excess and the absolute configuration of the diol was determined by the modified Mosherâ€™s method.
A variant of Clark’s theorem and its applications for nonsmooth functionals without the global symmetric condition

Abstract We give a new non-smooth Clark’s theorem without the global symmetric condition. The theorem can be applied to generalized quasi-linear elliptic equations with small continous perturbations. Our results improve the abstract results about a semi-linear elliptic equation in Kajikiya [10] and Li-Liu [11].
Qualitative Identification of Floral Origin and Adulteration of Honey by Near-infrared Spectroscopy

A new method for identification of floral origin and adulteration of honey was developed.Honey samples were scanned by Fourier tansform near infrared spectrometer(FT-NIR) in the region of 4000～12000 cm-1,and then transformed by 1st derivatives combined with multiplicative scatter correction and variable standards.In different spectral bands,models were established by principal component analysis combined with Mahalanobis distance for floral origin and adulteration identification of honey samples.Results showed 6100～5700 cm-1 was the best region;floral origin identification was over 90% and adulteration identification was 93.10%.
Caldendrin represses neurite regeneration via a sex-dependent mechanism in sensory neurons

Caldendrin is a calmodulin-like Ca2+ binding protein that is expressed primarily in neurons and regulates multiple effectors including Cav1 L-type Ca2+ channels. Here, we tested the hypothesis that caldendrin regulates Cav1-dependent pathways that repress neurite growth in dorsal root ganglion neurons (DRGNs). By immunofluorescence, caldendrin was localized in medium- and large-diameter DRGNs. Consistent with an inhibitory effect of caldendrin on neurite growth, neurite initiation and growth was enhanced in dissociated DRGNs from caldendrin knockout (KO) mice compared to those from wild type (WT) mice. In an in vitro axotomy assay, caldendrin KO DRGNs grew longer neurites via a mechanism that was more sensitive to inhibitors of transcription as compared to WT DRGNs. Strong depolarization, which normally represses neurite growth through activation of Cav1 channels, had no effect on neurite growth in DRGN cultures from female caldendrin KO mice. Remarkably, DRGNs from caldendrin KO males were no different from those of WT males in terms of depolarization-dependent neurite growth repression. We conclude that caldendrin opposes neurite regeneration and growth, and this involves coupling of Cav1 channels to growth-inhibitory pathways in DRGNs of females but not males. Our findings suggest that caldendrin KO mice represent an ideal model in which to interrogate the transcriptional pathways controlling neurite regeneration and how these pathways may differ in males and females.