Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Self-Assembled Structures from Diblock Copolymer Mixtures by Competitive Hydrogen Bonding Strength

In this work we prepared poly(styrene–b–vinylphenol) (PS-b-PVPh) by sequential anionic living polymerization and poly(ethylene oxide-b-4-vinylpyridine) (PEO-b-P4VP) by reversible addition fragmentation chain transfer polymerization (RAFT) by using poly(ethylene oxide) 4-cyano-4-(phenylcarbonothioylthio)pentanoate (PEO-SC(S)Ph) as a macroinitiator with two hydrogen bonded acceptor groups. When blending with disordered PEO-b-P4VP diblock copolymer, we found the order-order self-assembled structure transition from lamellar structure for pure PS-b-PVPh to cylindrical, worm-like, and finally to PEO crystalline lamellar structures. Taking the advantage of the ΔK effect from competitive hydrogen bonding strengths between PVPh/P4VP and PVPh/PEO domains, it could form the hierarchical self-assembled morphologies such as core–shell cylindrical nanostructure.
HPC Multiscale Simulation of Transport and Aggregation of Volcanic Particles

Since volcanic eruptions are events that can greatly endanger human lives and disrupt human activities such as air traffic, it is of major interest to simulate such phenomena. Volcanic ash transport and dispersion models typically describe particle motion in a turbulent velocity field. Volcanic particles (known as tephra) are advected inside this field from the moment they leave the vent of the volcano until they deposit on the ground. We developed a flexible simulation tool called TETRAS (TEphra TRAnsport Simulator) based on a hybrid Eulerian-Lagrangian model. As this kind of model needs computationally intensive simulations, a parallelization on a distributed memory architecture was developed. Volcanic eruptions involve phenomena occurring at multiple temporal and spatial scales, such as fast rising of particles inside the volcanic column, transport of fine particles over very long range or aggregation of fine particles into larger ones. Thus, we developed a multiscale implementation of the software using MMSF (Multiscale Modeling and Simulation Framework) techniques and tools. This implementation combines short and long-range transport of particles as well as particle aggregation. In this paper, we show the multiscale aspect of the design and implementation. Then, we tackle two load balancing problems that arise from this model: first at the transport code level, where unequal distribution of particles inside the domain directly leads to different load for processors if a naive work distribution is chosen; second at the multiscale level, where the resource allocation for each submodel must be chosen wisely to achieve optimal performances.
Expander Flows, Geometric Embeddings and Graph Partitioning

We give a O(√ log n)-approximation algorithm for spars-est cut, balanced separator, and graph conduc-tance problems. This improves the O(log n)-approximation of Leighton and Rao (1988). We use a well-known semidefinite relaxation with triangle inequality constraints. Central to our analysis is a geometric theorem about projections of point sets in ℜ d , whose proof makes essential use of a phenomenon called measure concentration. We also describe an interesting and natural " certificate " for a graph's expansion, by embedding an n-node expander in it with appropriate dilation and congestion. We call this an expander flow. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
Full-endoscopic uniportal retropharyngeal odontoidectomy for anterior craniocervical infection

Abstract Background: Infections of the anterior craniocervical junction may require surgery. There are various techniques with individual advantages and disadvantages. This study evaluates the full-endoscopic uniportal technique via the anterior retropharyngeal approach for odontoidectomy, decompression, and debridement. Material and methods: Three patients with an infection of the anterior craniocervical junction with retrodental involvement were operated on between 2014 and 2016 using the full-endoscopic uniportal technique. Posterior stabilization was also performed with the same procedure for all patients. Results: The operation was technically satisfactory in all cases. No problems due to swelling of the pharyngeal soft tissue occurred. No other complications were observed. All patients had a satisfactory outcome with stable regression of the myelopathy symptoms and/or complete healing of the infection. The follow-up images showed sufficient decompression of bone and soft tissues in all cases. Conclusions: The full-endoscopic uniportal technique with an anterior retropharyngeal approach can be an adequate and minimally invasive surgical technique for odontoidectomy, decompression, and debridement in infections of the craniocervical junction and can reduce access-related problems. The transoral, transnasal, and retropharyngeal approaches have different surgical fields due to the access trajectories, which must be taken into consideration depending on the anatomy and pathology when selecting a suitable technique.
A Resource Identity Management Strategy for Combined Fog-to-Cloud Systems

Fog-to-Cloud (F2C) is an emerging architecture intended to manage the resources continuum from far datacenters up to the near edge, putting together the cloud and fog concepts. It aims to obtain both, an efficient utilization of the entire set of available resources and an optimal execution of highly demanding services, supported by the deployment of highly skilled IoT devices at the edge. Many research efforts are pushing for the creation of a widely accepted F2C framework architecture aimed at controlling and managing the resources integrating the F2C network in a hierarchical and distributed fashion. To that end however, many research challenges remain unsolved. This paper puts the focus on one of these challenges, particularly the identity management and proposes a novel strategy that, based on fragmenting the full resource identifier, uses portions of the full resource name to identify the resources in the network without losing the uniqueness property. The presented results show that by using fragments instead of the full identifier, the lookup time and database size in the F2C aggregator nodes are both notably reduced, what undoubtedly enables a fast resource identification.
Head Pose Estimation for an Omnidirectional Camera Using a Convolutional Neural Network

The human head pose provides insights on the activities or intentions of a given person. Head pose estimation techniques are thus often employed in intelligent surveillance camera systems for marketing analysis or security monitoring. Nowadays, omnidirectional cameras have become widely used in surveillance systems owing to their unique property of wide-range coverage. However, this property causes significant changes in visual appearance and distortions inside the image, and general approaches using a head image may fail in estimation. In this paper, we thus propose a method for head pose estimation using omnidirectional camera images. The proposed model employs both a head image and full body image for cases in which a face is self-occluded and the head image is thus almost useless. In addition, image attribute data are integrated into the network to learn the relation between the changes in appearance or distortion and locations inside the whole image. Experiments are conducted to compare the accuracy of the presented approach with those of ordinary methods. It is verified that the proposed method improves the accuracy by more than 19% over the baseline method.
A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series

Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-to-end deep learning framework named Time-distributed ConvLSTM model (TDConvLSTM) is proposed in the paper for machine health monitoring, which works directly on raw multi-sensor time series. In TDConvLSTM, the normalized multi-sensor data is first segmented into a collection of subsequences by a sliding window along the temporal dimension. Time-distributed local feature extractors are simultaneously applied to each subsequence to extract local spatiotemporal features. Then a holistic ConvLSTM layer is designed to extract holistic spatiotemporal features between subsequences. At last, a fully-connected layer and a supervised learning layer are stacked on the top of the model to obtain the target. TDConvLSTM can extract spatiotemporal features on different time scales without any handcrafted feature engineering. The proposed model can achieve better performance in both time series classification tasks and regression prediction tasks than some state-of-the-art models, which has been verified in the gearbox fault diagnosis experiment and the tool wear prediction experiment.
Lasagna: Programming Abstractions for End-to-End Slicing in Software-Defined WLANs

Current 802.11-based WLANs are asked to support an ever increasing number of services and applications, each of them characterized by a diverse set of requirements in terms of bitrate, latency, and reliability. Network virtualization and programmability are two emerging trends that can support the realization of such a vision in a cost-effective fashion. In this paper we introduce Lasagna, a novel end-to-end solution that enables flexible management of slices encompassing both the wired and the wireless segments of an Enterprise WLAN. Lasagna allows flexible management of network slices to meet their respective service requirements. An experimental evaluation carried out over a real-world testbed shows that Lasagna can ensure both functional and performance isolation between the different slices and efficient radio resource utilization. We release the entire implementation including the controller and the datapath under a permissive license for academic use.
Psychedelics, Meditation, and Self-Consciousness

In recent years, the scientific study of meditation and psychedelic drugs has seen remarkable developments. The increased focus on meditation in cognitive neuroscience has led to a cross-cultural classification of standard meditation styles validated by functional and structural neuroanatomical data. Meanwhile, the renaissance of psychedelic research has shed light on the neurophysiology of altered states of consciousness induced by classical psychedelics, such as psilocybin and LSD, whose effects are mainly mediated by agonism of serotonin receptors. Few attempts have been made at bridging these two domains of inquiry, despite intriguing evidence of overlap between the phenomenology and neurophysiology of meditation practice and psychedelic states. In particular, many contemplative traditions explicitly aim at dissolving the sense of self by eliciting altered states of consciousness through meditation, while classical psychedelics are known to produce significant disruptions of self-consciousness, a phenomenon known as drug-induced ego dissolution. In this article, we discuss available evidence regarding convergences and differences between phenomenological and neurophysiological data on meditation practice and psychedelic drug-induced states, with a particular emphasis on alterations of self-experience. While both meditation and psychedelics may disrupt self-consciousness and underlying neural processes, we emphasize that neither meditation nor psychedelic states can be conceived as simple, uniform categories. Moreover, we suggest that there are important phenomenological differences even between conscious states described as experiences of self-loss. As a result, we propose that self-consciousness may be best construed as a multidimensional construct, and that “self-loss,” far from being an unequivocal phenomenon, can take several forms. Indeed, various aspects of self-consciousness, including narrative aspects linked to autobiographical memory, self-related thoughts and mental time travel, and embodied aspects rooted in multisensory processes, may be differently affected by psychedelics and meditation practices. Finally, we consider long-term outcomes of experiences of self-loss induced by meditation and psychedelics on individual traits and prosocial behavior. We call for caution regarding the problematic conflation of temporary states of self-loss with “selflessness” as a behavioral or social trait, although there is preliminary evidence that correlations between short-term experiences of self-loss and long-term trait alterations may exist.
Weighted HOG for Thermal Pedestrian Detection

Pedestrian detection is an important topic in many applications. For the purpose of applications used around the clock, the work for detecting pedestrian based on thermal sensors has attracted significant attentions. Based on the observation that the histograms of four cells in HOG descriptor over the pedestrian contour are significantly different, this study proposes a new feature called weighted HOG (WHOG) that weights the traditional HOG by the difference pattern of the histograms of cells. In the experiment, four videos are used to validate the proposed WHOG and the results demonstrate that WHOG outperforms HOG in all cases.
Differences in Context-Specific Sedentary Behaviors According to Weight Status in Adolescents, Adults and Seniors: A Compositional Data Analysis

To develop effective sedentary behavior interventions aimed at people who are overweight/obese, detailed insight is needed into the contexts of sedentary behavior of these people. Therefore, the aims of this study were to describe the composition of sedentary behavior and to compare context-specific sedentary behaviors between different weight groups. Cross-sectional data were used from a study conducted in 2013–2014 among a Flemish sample of adolescents (n = 513), adults (n = 301), and seniors (n = 258). Sixteen context-specific sedentary behaviors were assessed using a validated questionnaire during the week and weekend. Compositional descriptive statistics were performed to determine the relative contribution of context-specific sedentary behaviors in the three age groups. Compositional multivariate analysis of covariance and pairwise comparisons were conducted to examine weight group differences in context-specific sedentary behaviors. The compositional means indicated that the highest proportion of sedentary time was spent at school, at work, and while watching television. Statistically significant differences were found in the composition of sedentary behaviors between healthy weight and overweight/obese participants. In all age groups, socially engaging sedentary behaviors were more prevalent in healthy weight people, whereas socially disengaging behaviors were more prevalent in overweight/obese people. Consequently, the findings of this study suggest that future overweight/obesity interventions should no longer focus on total sedentary time, as not all context-specific sedentary behaviors are associated with overweight/obesity. Instead, it might be better to target specific contexts of sedentary behaviors—preferably those less socially engaging—when aiming to reduce overweight/obesity.
Diagnostik blasenbildender Autoimmundermatosen

Blasenbildende Autoimmundermatosen (BAIDs) sind eine heterogene Gruppe seltener Erkrankungen, die klinisch durch Erosionen und/oder Blasen an Haut und Schleimhäuten charakterisiert sind. BAIDs können in zwei Gruppen eingeteilt werden: Pemphigus‐Erkrankungen, die durch intraepidermale Blasenbildung und Autoantikörper gegen desmosomale Proteine wie Desmoglein (Dsg) 1, Dsg3 und Mitglieder der Plakin‐Familie charakterisiert sind, sowie subepidermale BAIDs, die Pemphigoid‐Erkrankungen und die Dermatitis herpetiformis umfassen. Bei der Dermatitis herpetiformis greifen die Autoantikörper die Transglutaminasen 2 und 3 an, während sie bei Pemphigoid‐Erkrankungen gegen Strukturproteine der dermoepidermalen Junktionszone gerichtet sind. Die Analyse einer periläsionalen Biopsie mittels direkter Immunfluoreszenzmikroskopie (IF‐Mikroskopie) stellt zwar nach wie vor den diagnostischen Goldstandard dar, mittlerweile sind jedoch verschiedene Tests allgemein verfügbar, die eine serologische Diagnosefindung für den Großteil der Patienten ermöglichen. Zur serologischen Standarddiagnostik gehört die indirekte IF auf Affenösophagus und NaCl‐separierter Spalthaut. Die Spezifität der Autoantikörper kann mit ELISA‐Systemen, die auf rekombinanten Formen der immundominanten Regionen der Zielantigene basieren, sowie indirekter IF‐Mikroskopie anhand multivarianter Tests mit mehreren multivariate näher charakterisiert werden. Diese serologischen Tests werden durch verschiedene hauseigene Immunoblotting‐ und ELISA‐Systeme ergänzt, die nur in spezialisierten Laboratorien verfügbar sind. In diesem Review geben wir einen Überblick über neue Entwicklungen bei der Diagnose von BAIDs und beschreiben moderne Diagnoseverfahren für diese Krankheitsgruppe.
Revealing Clusters of Connected Pathways Through Multisource Data Integration in Huntington's Disease and Spastic Ataxia

The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of –omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary “connectors” to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.
Discovering Periodic Patterns for Large Scale Mobile Traffic Data: Method and Applications

Understanding the temporal traffic load profile of cellular networks is extremely valuable to many network operation tasks in large mobile networks. Such knowledge is useful for network planning, improving network performance, designing better load balancing schemes, testing handoff algorithms, and proposing new charging mechanisms. This paper proposes a simple yet powerful method to model the temporal traffic profile by a large 3G/LTE cellular network dataset in a metropolitan area, consisting of 9 thousand base stations and 3.5 million subscribers. Specifically, using the spectrum-based analysis, we extract three major frequency components, which captures the weekly, daily, and hourly temporal patterns in the traffic load across base stations. By clustering the traffic utilizing the features extracted from spectrum-domain components, we find that urban scale cellular traffic can be classified into five groups, which maps to five types of geographic locations. Besides the comprehensive analysis, we apply this model to two applications: predicting the future traffic load, and designing a load based pricing scheme, where we demonstrate the usefulness of our model and analysis results.
Parallelism in C++ Using Sequential Communicating Processes

Programming parallelism with shared memory raises some technical difficulties in synchronization and accessing memory in a thread-safe way. Most of the time the result is a trade-off between application's source code understandability, maintainability and error-prone on one side, and performance on the other side. This trade-off is even tighter when using low-level languages such as C++. The paper presents an open-source library for C++ that provides a Sequential Communicating Processes method for communication and synchronization, and overall, aims to simplify the shared memory parallelism development, make it more predictable and less error-prone, without sacrificing performance. The efficiency of the solution is proven through a set of concrete examples and benchmarks.
Port-Controlled Hamiltonian and Sliding Mode Control of Gantry Robot Based on Induction Motor Drives

A port-controlled Hamiltonian (PCH) control approach is presented to solve the position tracking problem of gantry robot based on induction motor (IM) drives. First, a robot model is established. Second, a PCH controller is designed to realize accurate position tracking of a gantry robot. For IM drives, it is convenient to choose a direct torque control strategy based on the sliding mode control, which overcomes the higher ripples of torque and flux. Third, a voltage reconstruction technique is introduced to calculate the stator voltage of the IM, which replaces the stator voltage measurement of the IM. Finally, the load torque observer is developed to estimate an unknown load torque. The asymptotic stability of the robot system is proved by the Lyapunov stability theory. Simulation results indicate that the system has excellent position tracking performances and load disturbance attenuation ability.
Ttyh1 regulates embryonic neural stem cell properties by enhancing the Notch signaling pathway

Despite growing evidence linking Drosophila melanogaster tweety‐homologue 1 (Ttyh1) to normal mammalian brain development and cell proliferation, its exact role has not yet been determined. Here, we show that Ttyh1 is required for the maintenance of neural stem cell (NSC) properties as assessed by neurosphere formation and in vivo analyses of cell localization after in utero electroporation. We find that enhanced Ttyh1‐dependent stemness of NSCs is caused by enhanced γ‐secretase activity resulting in increased levels of Notch intracellular domain (NICD) production and activation of Notch targets. This is a unique function of Ttyh1 among all other Ttyh family members. Molecular analyses revealed that Ttyh1 binds to the regulator of γ‐secretase activity Rer1 in the endoplasmic reticulum and thereby destabilizes Rer1 protein levels. This is the key step for Ttyh1‐dependent enhancement of γ‐secretase activity, as Rer1 overexpression completely abolishes the effects of Ttyh1 on NSC maintenance. Taken together, these findings indicate that Ttyh1 plays an important role during mammalian brain development by positively regulating the Notch signaling pathway through the downregulation of Rer1.