bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust Federated Learning for execution time-based device model identification under label-flipping attack
Authors: Sánchez Sánchez Pedro Miguel , Huertas Celdrán Alberto , Buendía Rubio José Rafael , Bovet Gérôme , Martínez Pérez Gregorio 
Published: January 2023

AbstractThe computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks and threats. Among them, device spoofing and impersonation cyberattacks stand out due to their impact and, usually, low complexity required to be launched. To solve this issue, several solutions have emerged to identify device models and types based on the combination of behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques. However, these solutions are not appropriate for scenarios where data privacy and protection are a must, as they require data centralization for processing. In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup. The present work analyzes and compares the device model identification performance of a centralized DL model with an FL one while using execution time-based events. For experimental purposes, a dataset containing execution-time features of 55 Raspberry Pis belonging to four different models has been collected and published. Using this dataset, the proposed solution achieved 0.9999 accuracy in both setups, centralized and federated, showing no performance decrease while preserving data privacy. Later, the impact of a label-flipping attack during the federated model training is evaluated using several aggregation mechanisms as countermeasures. Zeno and coordinate-wise median aggregation show the best performance, although their performance greatly degrades when the percentage of fully malicious clients (all training samples poisoned) grows over 50%.
The elements of resilience in the food system and means to enhance the stability of the food supply
Authors: Rimhanen Karoliina , Aakkula Jyrki , Aro Kalle , Rikkonen Pasi 
Published: January 2023

AbstractFood systems are increasingly exposed to disruptions and shocks, and they are projected to increase in the future. Most recently, the war in Ukraine and Covid-19 pandemic has increased concerns about the ability to secure the availability of food at stable prices. This article presents a food system resilience framework to promote a national foresight system to better prepare for shocks and disruptions. Our study identified four key elements of resilience: system thinking through science and communication; redundancy of activities and networks; diversity of production and partners; and buffering strategies. Three national means to enhance resilience in the Finnish food system included domestic protein crop production, renewable energy production, and job creation measures. Primary production was perceived as the cornerstone for food system resilience, and the shocks and disruptions that it confronts therefore call for a sufficient and diverse domestic production volume, supported by the available domestic renewable energy. A dialogue between different actors in the food system was highlighted to format a situational picture and enable a rapid response. Our study suggests that to a certain point, concentration and interdependence in the food system increase dialogue and cooperation. For critical resources, sufficient reserve stocks buffer disruptions over a short period in the event of unexpected production or market disruptions. Introducing and strengthening the identified resilience elements and means to the food system call for the preparation of a more holistic and coherent food system policy that acknowledges and emphasises resilience alongside efficiency.
Surface Tension of Ethylene Glycol-Based Nanofluids Containing Three Types of Oxides: Zinc Oxide (ZnO), Magnesium Oxide (MgO) and Indium Oxide (In$$_2$$O$$_3$$)
Authors: Traciak Julian , Żyła Gaweł 
Published: January 2023

AbstractThe paper presents the results of an experimental study of the density and surface tension of ethylene glycol-based nanofluids containing zinc oxide, magnesium oxide and indium oxide. Du Noüy ring method was employed to determine the values of the surface tension of these nanofluids. Samples were prepared in various mass fractions from 0.01 to 0.05 with 0.01 step. Examination was performed in the temperature range from 283.15 K up to 318.15 K. The averaged surface tension values for the studied ZnO14-EG, ZnO25-EG, MgO-EG, In$$_2$$

2

O$$_3$$

3

-EG nanofluids at 298.15 K are 48.685, 48.471, 48.335, and 48.462 mN·m$$^{-1}$$

-
1

, respectively. It was presented that surface tension value could be consider as constant within the examined mass fraction of the particles, and the explanation of such behaviour was provided.
Leveraging an open source serverless framework for high energy physics computing
Authors: Padulano Vincenzo Eduardo , Oliver Cortés Pablo , Alonso-Jordá Pedro , Tejedor Saavedra Enric , Risco Sebastián , Moltó Germán 
Published: January 2023

AbstractCERN (Centre Europeen pour la Recherce Nucleaire) is the largest research centre for high energy physics (HEP). It offers unique computational challenges as a result of the large amount of data generated by the large hadron collider. CERN has developed and supports a software called ROOT, which is the de facto standard for HEP data analysis. This framework offers a high-level and easy-to-use interface called RDataFrame, which allows managing and processing large data sets. In recent years, its functionality has been extended to take advantage of distributed computing capabilities. Thanks to its declarative programming model, the user-facing API can be decoupled from the actual execution backend. This decoupling allows physical analysis to scale automatically to thousands of computational cores over various types of distributed resources. In fact, the distributed RDataFrame module already supports the use of established general industry engines such as Apache Spark or Dask. Notwithstanding the foregoing, these current solutions will not be sufficient to meet future requirements in terms of the amount of data that the new projected accelerators will generate. It is of interest, for this reason, to investigate a different approach, the one offered by serverless computing. Based on a first prototype using AWS Lambda, this work presents the creation of a new backend for RDataFrame distributed over the OSCAR tool, an open source framework that supports serverless computing. The implementation introduces new ways, relative to the AWS Lambda-based prototype, to synchronize the work of functions.
Evaluation of total protein, peroxidase, and nutrients measured by pXRF for the determination of tissue rejuvenation/reinvigoration of Eucalyptus microcorys
Authors: Faria Júlio Cézar Tannure , Ribeiro-Kumara Caius , Delarmelina William Macedo , Namorato Filipe Aiura , Momolli Dione Richer , José Anderson Cleiton , Konzen Enéas Ricardo , de Carvalho Dulcinéia , Brondani Gilvano Ebling 
Published: January 2023

AbstractOntogenetic aging of tissues and the gradual decrease of adventitious rooting are known challenges for the clonal propagation of woody species, hampering clonal forestry programs. This study examined possible signatures of tissue rejuvenation/reinvigoration in different propagated materials of Eucalyptus microcorys by analyzing the total protein profile, peroxidase activity, macro- and micronutrient contents, and adventitious rooting of mini cuttings. The analyses were performed on E. microcorys shoots which were successfully obtained by seminal and grafting propagation, micropropagation with epicormic shoots, and indirect organogenesis. Among four mature trees used in the propagation, tissues from the one with the best propagation results were investigated for signs of tissue rejuvenation and/or reinvigoration. Five individuals from each technique were randomly selected and transferred to a semi-hydroponic “channel” system. After four weeks in the seedbed, the total protein, peroxidase activity, nutrient content and rooting of the mini cuttings were evaluated. SDS-PAGE enabled the differentiation of leaf samples obtained by grafting from the other propagation techniques, as revealed by two distinct bands. Materials obtained by micropropagation with epicormic shoots showed the highest peroxidase activity, while those obtained by seminal propagation and from the selected mature tree showed the lowest peroxidase activity. A portable X-ray fluorescence spectroscope (pXRF) identified adequate nutrient content in most of the nutrients tested in materials obtained by seminal and grafting propagation, and by indirect organogenesis. The analysis of adventitious rooting showed that the highest rooting percentage was observed in mini cuttings from seminal propagation (75%) followed by indirect organogenesis (35%). Based on principal component analysis, it was concluded that rooting of mini cuttings from both seminal propagation and indirect organogenesis was associated with phosphorous, sulphur, and potassium contents, which suggests a higher level of tissue rejuvenation/reinvigoration in these propagated plants. Further studies are recommended to search for other methods that present similarities with the responses to adventitious rooting in forest species and thus optimize the rescue and propagation of plants with distinct ontogenetic stages.
A phase I first-in-man study to investigate the pharmacokinetics and safety of liposomal dexamethasone in patients with progressive multiple myeloma
Authors: Metselaar Josbert , Lammers Twan , Boquoi Amelie , Fenk Roland , Testaquadra Fabio , Schemionek Mirle , Kiessling Fabian , Isfort Susanne , Wilop Stefan , Crysandt Martina 
Published: January 2023

AbstractDespite the introduction of multiple new drugs and combination therapies, conventional dexamethasone remains a cornerstone in the treatment of multiple myeloma (MM). Its application is, however, limited by frequent adverse effects of which the increased infection rate may have the strongest clinical impact. The efficacy-safety ratio of dexamethasone in MM may be increased by encapsulation in long-circulating PEG-liposomes, thereby both enhancing drug delivery to MM lesions and reducing systemic corticosteroid exposure. We evaluated the preliminary safety and feasibility of a single intravenous (i.v.) infusion of pegylated liposomal dexamethasone phosphate (Dex-PL) in heavily pretreated relapsing or progressive symptomatic MM patients within a phase I open-label non-comparative interventional trial at two dose levels. In the 7 patients that were enrolled (prior to having to close the study prematurely due to slow recruitment), Dex-PL was found to be well tolerated and, as compared to conventional dexamethasone, no new or unexpected adverse events were detected. Pharmacokinetic analysis showed high and persisting concentrations of dexamethasone in the circulation for over a week after i.v. administration, likely caused by the long-circulation half-life of the liposomes that retain dexamethasone as the inactive phosphate prodrug form, something which could significantly limit systemic exposure to the active parent drug. Thus, despite the limitations of this small first-in-man trial, Dex-PL seems safe and well tolerated without severe side effects. Follow-up studies are needed to confirm this in a larger patient cohort and to evaluate if i.v. Dex-PL can provide a safer and more efficacious dexamethasone treatment option for MM.
Graphical Abstract
A bidirectional trajectory contrastive learning model for driving intention prediction
Authors: Zhou Yi , Wang Huxiao , Ning Nianwen , Wang Zhangyun , Zhang Yanyu , Liu Fuqiang 
Published: January 2023

AbstractDriving intention prediction with trajectory data of surrounding vehicles is critical to advanced driver assistance system for improving the accuracy of decision-making. Previous works mostly focused on trajectory representation based on supervised manners. However, learning generalized and high-quality representations from unlabeled data remains a very challenging task. In this paper, we propose a self-supervised bidirectional trajectory contrastive learning (BTCL) model that learns generalized trajectory representation to improve the performance of the driving intention prediction task. Different trajectory data augmentation strategies and a cross-view trajectory prediction task are constructed jointly as pretext task of contrastive learning. The pretext task can maximize the similarity among different augmentations of the same sample while minimizing similarity among augmentations of different samples. It can not only learn the high-quality representation of trajectory without labeled information but also improve the adversarial attacks on BTCL. Moreover, considering the vehicle trajectory forward and backward follows the same social norms and driving behavior constraints. A bidirectional trajectory contrastive learning module is built to gain more positive samples that further increasing the prediction accuracy in downstream tasks and transfer ability of the model. Experimental results demonstrate that BTCL is competitive with the state-of-the-art, especially for adversarial attack and transfer learning tasks, on real-world HighD and NGSIM datasets.
DAACO: adaptive dynamic quantity of ant ACO algorithm to solve the traveling salesman problem
Authors: Liu Huijun , Lee Ao , Lee Wenshi , Guo Ping 
Published: January 2023

AbstractThe traveling salesman problem (TSP) is an NP-hard problem. Thus far, a large number of researchers have proposed different ant colony optimization (ACO) algorithms to solve the TSP. These algorithms inevitably encounter problems such as long convergence time and the tendency to easily fall into local optima. On the basis of the ACO algorithm, this study proposes a dynamic adaptive ACO algorithm (DAACO). DAACO realizes the diversity of initialization of the ACO algorithm by dynamically determining the number of ants to be prevented from falling into local optimization. DAACO also adopts a hybrid local selection strategy to increase the quality of ant optimization and reduce the optimization time. Among the 20 instances of the TSPLIB dataset, the DAACO algorithm obtains 19 optimal values, and the solutions of 10 instances are better than those of other algorithms. The experimental results on the TSPLIB dataset show that the DAACO algorithm has obvious advantages in terms of convergence time, solution quality, and average value relative to existing state-of-the-art ACO algorithms.
A Communication Efficient ADMM-based Distributed Algorithm Using Two-Dimensional Torus Grouping AllReduce
Authors: Wang Guozheng , Lei Yongmei , Zhang Zeyu , Peng Cunlu 
Published: January 2023

AbstractLarge-scale distributed training mainly consists of sub-model parallel training and parameter synchronization. With the expansion of training workers, the efficiency of parameter synchronization will be affected. To tackle this problem, we first propose 2D-TGA, a grouping AllReduce method based on the two-dimensional torus topology. This method synchronizes the model parameters by grouping and makes full use of bandwidth. Secondly, we propose a distributed algorithm, 2D-TGA-ADMM, which combines the 2D-TGA with the alternating direction method of multipliers (ADMM). It focuses on sub-model training and reduces the wait time among workers in the synchronization process. Finally, experimental results on the Tianhe-2 supercomputing platform show that compared with the $${\mathtt {MPI\_Allreduce}}$$

MPI
_
Allreduce

, the 2D-TGA could shorten the synchronization wait time by $$33\%$$

33
%

.
Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event
Authors: Liao Zenghua , Yang Zongqiang , Huang Peixin , Pang Ning , Zhao Xiang 
Published: January 2023

AbstractIn recent years, Coronavirus disease 2019 (COVID-19) has become a global epidemic, and some efforts have been devoted to tracking and controlling its spread. Extracting structured knowledge from involved epidemic case reports can inform the surveillance system, which is important for controlling the spread of outbreaks. Therefore, in this paper, we focus on the task of Chinese epidemic event extraction (EE), which is defined as the detection of epidemic-related events and corresponding arguments in the texts of epidemic case reports. To facilitate the research of this task, we first define the epidemic-related event types and argument roles. Then we manually annotate a Chinese COVID-19 epidemic dataset, named COVID-19 Case Report (CCR). We also propose a novel hierarchical EE architecture, named multi-model fusion-based hierarchical event extraction (MFHEE). In MFHEE, we introduce a multi-model fusion strategy to tackle the issue of recognition bias of previous EE models. The experimental results on CCR dataset show that our method can effectively extract epidemic events and outperforms other baselines on this dataset. The comparative experiments results on other generic datasets show that our method has good scalability and portability. The ablation studies also show that the proposed hierarchical structure and multi-model fusion strategy contribute to the precision of our model.
Quantized fields induced topological features in Harper-Hofstadter model
Authors: Han Xue , Li Fude , Qiu De-Xiu , Xue Kang , Yi X. X. 
Published: January 2023

AbstractClassical magnetic fields might change the properties of topological insulators such as the time reversal symmetry protected topological edge states. This poses a question that whether quantized fields would change differently the feature of topological materials with respect to the classical one. In this paper, we propose a model to describe topological insulators (ultracold atoms in square optical lattices with magnetic field) coupled to a tunable single-mode quantized field, and discuss the topological features of the system. We find that the quantized field can induce topological quantum phase transitions in a different way. To be specific, for fixed gauge magnetic flux ratio, we calculate the energy bands for different coupling constants between the systems and the fields in both open and periodic boundary conditions. We find that the Hofstadter butterfly graph is divided into a pair for continuous gauge magnetic flux ratio, which is different from the one without single-mode quantized field. In addition, we plot topological phase diagrams characterized by Chern number as a function of the momentum of the single-mode quantized field and obtain a quantized structure with non-zero filling factor.
Underestimated Passive Volcanic Sulfur Degassing Implies Overestimated Anthropogenic Aerosol Forcing
Authors: Jongebloed U. A. , Schauer A. J. , Cole‐Dai J. , Larrick C. G. , Wood R. , Fischer T. P. , Carn S. A. , Salimi S. , Edouard S. R. , Zhai S. , Geng L. , Alexander B. 
Published: January 2023

AbstractThe Arctic is warming at almost four times the global rate. An estimated sixty percent of greenhouse‐gas‐induced Arctic warming has been offset by anthropogenic aerosols, but the contribution of aerosols to radiative forcing (RF) represents the largest uncertainty in estimating total RF, largely due to unknown preindustrial aerosol abundance. Here, sulfur isotope measurements in a Greenland ice core show that passive volcanic degassing contributes up to 66 ± 10% of preindustrial ice core sulfate in years without major eruptions. A state‐of‐the‐art model indicates passive volcanic sulfur emissions influencing the Arctic are underestimated by up to a factor of three, possibly because many volcanic inventories do not include hydrogen sulfide emissions. Higher preindustrial volcanic sulfur emissions reduce modeled anthropogenic Arctic aerosol cooling by up to a factor of two (+0.11 to +0.29 W m−2), suggesting that underestimating passive volcanic sulfur emissions has significant implications for anthropogenic‐induced Arctic climate change.
SCGN deficiency is a risk factor for autism spectrum disorder
Authors: Liu Zhe , Tan Shuai , Zhou Lianyu , Chen Li , Liu Mingfeng , Wang Wang , Tang Yingying , Yang Qin , Chi Sensen , Jiang Peiyan , Zhang Yue , Cui Yonghua , Qin Junhong , Hu Xiao , Li Shenglong , Liu Qi , Chen Lu , Li Song , Burstein Ezra , Li Wei , Zhang Xiaohu , Mo Xianming , Jia Da 
Published: January 2023

AbstractAutism spectrum disorder (ASD) affects 1–2% of all children and poses a great social and economic challenge for the globe. As a highly heterogeneous neurodevelopmental disorder, the development of its treatment is extremely challenging. Multiple pathways have been linked to the pathogenesis of ASD, including signaling involved in synaptic function, oxytocinergic activities, immune homeostasis, chromatin modifications, and mitochondrial functions. Here, we identify secretagogin (SCGN), a regulator of synaptic transmission, as a new risk gene for ASD. Two heterozygous loss-of-function mutations in SCGN are presented in ASD probands. Deletion of Scgn in zebrafish or mice leads to autism-like behaviors and impairs brain development. Mechanistically, Scgn deficiency disrupts the oxytocin signaling and abnormally activates inflammation in both animal models. Both ASD probands carrying Scgn mutations also show reduced oxytocin levels. Importantly, we demonstrate that the administration of oxytocin and anti-inflammatory drugs can attenuate ASD-associated defects caused by SCGN deficiency. Altogether, we identify a convergence between a potential autism genetic risk factor SCGN, and the pathological deregulation in oxytocinergic signaling and immune responses, providing potential treatment for ASD patients suffering from SCGN deficiency. Our study also indicates that it is critical to identify and stratify ASD patient populations based on their disease mechanisms, which could greatly enhance therapeutic success.
Targeting integrin pathways: mechanisms and advances in therapy
Authors: Pang Xiaocong , He Xu , Qiu Zhiwei , Zhang Hanxu , Xie Ran , Liu Zhiyan , Gu Yanlun , Zhao Nan , Xiang Qian , Cui Yimin 
Published: January 2023

AbstractIntegrins are considered the main cell-adhesion transmembrane receptors that play multifaceted roles as extracellular matrix (ECM)-cytoskeletal linkers and transducers in biochemical and mechanical signals between cells and their environment in a wide range of states in health and diseases. Integrin functions are dependable on a delicate balance between active and inactive status via multiple mechanisms, including protein-protein interactions, conformational changes, and trafficking. Due to their exposure on the cell surface and sensitivity to the molecular blockade, integrins have been investigated as pharmacological targets for nearly 40 years, but given the complexity of integrins and sometimes opposite characteristics, targeting integrin therapeutics has been a challenge. To date, only seven drugs targeting integrins have been successfully marketed, including abciximab, eptifibatide, tirofiban, natalizumab, vedolizumab, lifitegrast, and carotegrast. Currently, there are approximately 90 kinds of integrin-based therapeutic drugs or imaging agents in clinical studies, including small molecules, antibodies, synthetic mimic peptides, antibody–drug conjugates (ADCs), chimeric antigen receptor (CAR) T-cell therapy, imaging agents, etc. A serious lesson from past integrin drug discovery and research efforts is that successes rely on both a deep understanding of integrin-regulatory mechanisms and unmet clinical needs. Herein, we provide a systematic and complete review of all integrin family members and integrin-mediated downstream signal transduction to highlight ongoing efforts to develop new therapies/diagnoses from bench to clinic. In addition, we further discuss the trend of drug development, how to improve the success rate of clinical trials targeting integrin therapies, and the key points for clinical research, basic research, and translational research.