Miracle-dz/UniFAR-FTUs huggingface dataset:
IPS: Instance Profile for Shapelet Discovery for Time Series Classification

Time series classification (TSC) has been one of the most fundamental problems of time series data. Time series shapelets (or simply, shapelets) are discriminative subsequences that have been recently found both effective and interpretable for solving TSC. However, shapelet discovery is known to be computationally costly. Meanwhile, matrix profile has been recently proposed for efficient motif discovery and anomaly detection. Our preliminary experiment shows that a direct adoption of the matrix profile on TSC does not bring superior classification accuracy. We have identified two main issues of such an adoption: 1) discords as “shapelets”, and 2) lack of shapelet diversity. In response to these issues, we propose instance profile for shapelets, called IPS, for shapelet discovery for TSC. The main challenge is to utilize the instance profile (IP) to capture the characteristics of shapelets in a robust manner and then to discover high-quality shapelets efficiently. First, we use our IP to generate abundant shapelet candidates. We next efficiently prune candidates that do not align with the definition of shapelets using a novel distribution-aware bloom filter (DABF). Three utility functions are proposed to measure the shapelet candidates and DABF is used to efficiently compute the functions. We have conducted comprehensive experiments on IPS with 12 competitive state-of-the-art methods using UCR Archive datasets. The efficiency is on average 25 times faster than that of BSPCOVER (the current state-of-the-art method). The accuracy of IPS is comparable to or higher than that of existing work. Furthermore, we select one case study to illustrate the interpretability of the shapelets.
Detection of Equus Caballus Papillomavirus Type-2 in Asymptomatic Italian Horses

Equine Papillomavirus 2 (EcPV2) is responsible for squamous cell carcinomas (eSCCs) of external genitalia of both male and female horses. However, few studies report the EcPV2 prevalence among healthy horses. Currently, the lack of these data does not permit identifying at-risk populations and, thus, developing screening protocols aimed at the early detection of the infection, as for humans. The aim of our study was to estimate the genoprevalence of EcPV2 in clinically healthy horses in Italy and to evaluate their innate immune response. For this purpose, penile and vulvar swabs of 234 healthy horses were collected through sampling with sterile cytobrushes. Nucleic acids were isolated and EcPV2-L1 presence (DNA) and gene expression (RNA) were checked by RT-qPCR. Our results showed EcPV2-L1 DNA presence in 30.3% of the samples and L1 expression in 48% of the positive samples. No statistically significant differences were found in genoprevalence in relation to sex, age, and origin, while, concerning breeds, the Thoroughbred had the highest risk of infection. Concerning specifically the mares, 40.2% of them resulted in being positive for EcPV2; our findings show a major positivity in pluriparous (p = 0.0111) and mares subjected to natural reproduction (p = 0.0037). Moreover, samples expressing L1 showed an increased expression of IL1B (p = 0.0139) and IL12p40 (p = 0.0133) and a decreased expression of RANKL (p = 0.0229) and TGFB (p = 0.0177). This finding suggests the presence of an effective immune response, which could explain the low incidence of SCCs in positive horses, despite a high EcPV2 genoprevalence (30%).
Therapeutic perspectives of exosomes in glucocorticoid-induced osteoarthrosis

Exosomes are widely involved in a variety of physiological and pathological processes. These important roles are also hidden in the physiological processes related to bone. Chondrocytes, osteoblasts, synovial fibroblasts, and bone marrow mesenchymal stem cells produce and secrete exosomes, thereby affecting the biology process of target cells. Furthermore, in the primary pathogenesis of osteoarthrosis induced by steroid hormones, mainly involve glucocorticoid (GC), the exosomes have also widely participated. Therefore, exosomes may also play an important role in glucocorticoid-induced osteoarthrosis and serve as a promising treatment for early intervention of osteoarthrosis in addition to playing a regulatory role in malignant tumors. This review summarizes the previous results on this direction, systematically combs the role and therapeutic potential of exosomes in GC-induced osteoarthrosis, discusses the potential role of exosomes in the treatment and prevention of GC-induced osteoarthrosis, and reveals the current challenges we confronted.
A Granular Computing Model Based on Tolerance relation

Granular computing is a new intelligent computing theory based on partition of problem concepts. It is an important problem in Rough Set theory to process incomplete information systems directly. In this paper, a granular computing model based on tolerance relation for processing incomplete information systems is developed. Furthermore, a criteria condition for attribution necessity is proposed in this model.
Bamboo Filters: Make Resizing Smooth

The approximate membership query (AMQ) data structure is a kind of space-efficient probabilistic data structure. It can approximately indicate whether an element exists in a set. The AMQ data structure has been widely used in database indexing, network security, IoT applications, etc. Resizing is an extensively utilized operation of the AMQ data structure, but it can lead to system performance degradation. We summarize two main problems that lead to such degradation. Specifically, one of them is that the resizing operation can block other operations, while the other is that the performance of AMQ structures will deteriorate after multiple resizing operations. However, existing related work cannot alleviate both of them. Therefore, we propose a novel AMQ data structure called bamboo filter, which can alleviate the two problems simultaneously. Bamboo filters can insert, search and delete an element in constant time. Moreover, bamboo filters can dynamically resize in a fine-grained way according to the number of contained elements. Experimental results show that bamboo filters significantly outperform state-of-the-art resizable AMQ data structures in insertion, lookup, and deletion operations. For example, bamboo filters achieve $\mathbf{2.46}\times$ lookup throughput of the dynamic cuckoo filter, on average.
Deep Popularity Prediction in Multi-Source Cascade with HERI-GCN

Popularity prediction is to predict the number of social network users involved in information diffusion. Recently, deep learning methods for popularity prediction advance traditional approaches that rely on hand-crafted features. However, existing approaches ignore the multi-source cascade that consists of multiple sub-cascades with different content but under the same topic. Different from single-source cascade, more cascading information can be observed from multi-source cascade and they are potentially correlated. How to correlate the diverse information and take advantage of them from both temporal and spatial aspects is critical for prediction. To this end, we propose a novel framework, called HEterogeneous Recurrent Integrated Graph Convolutional Neural Network (HERI-GCN). Specifically, we construct a heterogeneous cascade graph to model the multi-source cascade where time intervals are treated as heterogeneous time nodes. Besides, we propose a heterogeneous GCN to learn rich features from the multi-source cascade. RNN is organically integrated into the heterogeneous GCN to overcome the limited learning ability toward temporal and spatial data. We evaluate HERI-GCN through comparative experiments on three datasets. The experimental evaluation shows that HERI-GCN outperforms the state-of-the-art baseline methods.
Comparative transcriptome analysis of Lupinus polyphyllus Lindl. provides a rich molecular resource for research on coloration mechanism

Lupinus polyphyllus is rich in color, making it a well-known horticultural ornamental plant. However, little is known about the genes related to anthocyanin and carotenoid biosynthesis in L. polyphyllus. In this study, transcriptome sequencing was performed on eight different colors of L. polyphyllus. A total of 1.13 billion clean reads were obtained and assembled into 89,124 unigenes, which were then aligned with six databases, resulting in the identification of 54,823 annotated unigenes. Among these unigenes, 76 and 101 were involved in the biosynthetic pathway of carotenoids and anthocyanins, respectively. In addition, 505 transcription factors were revealed, which belonged to the MYB, R2R3-MYB, NAC, bHLH, and WD40 families. A total of 6,700 differentially expressed genes (DEGs) were obtained by comparative transcriptome analysis. Among them, 17 candidate unigenes (four carotenoid genes, seven anthocyanin genes, and six TFs) were specifically up-regulated for one or more colors of L. polyphyllus. Eight representative candidate unigenes were analyzed by qRT-PCR. The findings enrich the transcriptome database of lupine, and provide a rich molecular resource for research on the coloration mechanism of L. polyphyllus.
Highly Efficient Capacitive Galvanic Isolation for EV Charging Stations

This paper proposes an isolated Switched Capacitor (SC) power converter which provides galvanic isolation through Capacitive Power Transfer (CPT). The combination of these two technologies might answer for electrical and power requirements in different electrical mobility application fields, such as battery charging. Accordingly, due to the low conversion losses the combination of these two technologies can provide, compact, cost effective and highly efficient power converters can be derived, thus potentially answering the scalability requirements for the Electric Vehicles (EV) market. To assess the operation of the proposed circuital solution, a Full Bridge (FB) CPT isolated interfacing converter prototype has been designed and built. Since GaN switches were used for the primary bridge, by taking advantage of their low input capacitance and gate charge, a 500 kHz switching frequency was set, thus deriving a compact power converter. The prototype has been designed for applications up to 12 kW (600 V, 20 A), and tested close to 3 kW (up to 400 V or 15 A) for the purpose of this work to demonstrate its functionality. The measurements assessed a conversion efficiency above 90% with a peak of almost 95%.
Analog model of a Friedmann-Robertson-Walker universe in Bose-Einstein condensates: Application of the classical field method

Analog models of gravity have been motivated by the possibility of investigating phenomena not readily accessible in their cosmological counterparts. In this paper, we investigate the analog of cosmological particle creation in a Friedmann-Robertson-Walker universe by numerically simulating a Bose-Einstein condensate with a time-dependent scattering length. In particular, we focus on a two-dimensional homogeneous condensate using the classical field method via the truncated Wigner approximation. We show that for various forms of the scaling function the particle production is consistent with the underlying theory in the long wavelength limit. In this context, we further discuss the implications of modified dispersion relations that arise from the microscopic theory of a weakly interacting Bose gas.
The Malta cistern mapping project: Underwater robot mapping and localization within ancient tunnel systems

This paper documents the development of an underwater robot system enabled with several mapping and localization techniques applied to a particular archaeological expedition. The goal of the expedition was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. Such cisterns often consisted of several connected chambers, still containing water. A sonar-equipped remotely operated vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Six different mapping and localization techniques were employed, including (1) sonar image mosaics using stationary sonar scans, (2) sonar image mosaics using stationary sonar scans with Smart Tether position data, (3) simultaneous localization and mapping (SLAM) while the vehicle was in motion, (4) SLAM using stationary sonar scans, (5) localization using previously created maps, and (6) SLAM while the vehicle was in motion with Smart Tether position data. Top-down-view maps of 22 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C., and few records of their size, shape, and connectivity existed before the expedition. © 2010 Wiley Periodicals, Inc.
The Influence of Employability Skills toward Career Adaptability

Career adaptability is becoming increasingly critical in the rapidly changing business world as it assures organizations to sustain their competitiveness. Career adaptability is described as an individual’s willingness and ability to cope and adapt to work environment changes. Employability skills have become one of the factors that affect career adaptability as soft skills will contribute to the growth and success of an organization. Therefore, the purpose of this research is to investigate the relationship between communication skills, problem-solving skills, and teamwork skills towards career adaptability and to identify the most influential factors toward career adaptability in a Malaysian public university. This research is quantitative research which used a self-administered questionnaire as the instrument to collect data. A total of 136 respondents from two faculties responded to a set of electronic questionnaires. The data collected were then evaluated using the Statistical Package for the Social Science (SPSS) Version 23. The Pearson Correlation analysis revealed that there were positive significant relationships between problem solving and teamwork toward career adaptability. While communication skills revealed no significant relationship toward career adaptability. Regression analysis showed that problem-solving skills have become the most influential factor toward career adaptability.
Simultaneous registration and activation detection: overcoming activation-induced registration errors in functional mri

In the processing of functional magnetic resonance imaging (fMRI) data, motion correction is typically performed before activation detection. However, on high-field MR scanners (3 T and higher), the strength of the blood oxygen level dependent (BOLD) signal can cause registration algorithms to produce motion estimates that have stimulus-correlated errors. Motion compensation using these biased motion estimates can result in both false-positive and false-negative regions of activation. By formulating the registration and activation detection problems into a single least-squares problem, both the motion estimates and activation map can be solved for simultaneously. However, the solution is not unique and an additional constraint is used to find a solution that is appropriate. This constrained optimization problem can be solved efficiently, and two equivalent methods are proposed and demonstrated on both simulated and in vivo datasets.
Zero-Rating, One Big Mess: Analyzing Differential Pricing Practices of European Mobile Network Operators

Mobile cellular networks have become a major access technology to the public Internet. While roaming was expensive and sparsely used in the early days, free roaming agreements such as in the European Union have erased borders for end customers and let them perceive cellular service in an international and unified fashion. In addition, new tariff models that introduced zero-rating, where data of certain applications does not count towards a customers’ bill, have appeared within the mobile ecosystem. Roaming and zero-rating are significant to consumer protection and net neutrality. However, there is little insight into current practices and a lack of tools to independently audit network operators. To fill this gap, we introduce the MobileAtlas measurement platform. MobileAtlas is an open and extensible platform that focuses on fine-grained measurements and geographically decouples the SIM card from the cellular modem by tunneling the corresponding communication protocol over the Internet. Thus, it enables taking roaming measurements in a well scalable manner. We use the framework to analyze zero-rating at seven providers within three different European countries. We execute and evaluate more than 200 measurements within domestic and internationally roamed environments and identify potentially problematic practices at more than half of the investigated providers. Together with the results of our European market analysis, we provide a structured overview of the European zero-rating landscape and help to get more detailed insights into current provider practices.
The FAO data lab on statistical innovation and the use of big data for the production of international statistics

Data is an extremely important intangible good, but official data is not always available. It may be scarce for many reasons, among which: low statistical capacities, poor funding for data and statistics, weak data dissemination and use culture. A solution to fill data gaps needs to consider that there is data made available on the web, usually coming in an unstructured way, that can be combined with innovative methods to generate relevant information. National and international organizations need to engage with new sources of data and methods considering the crisis of traditional data collection systems that cause data gaps. In this light, FAO created in 2019 the “Data Lab for statistical innovation” to fill such gaps by modernising the Organization’s statistical business, which means improving the timeliness and granularity of data collection, providing automated analysis, and capturing early warning signals. It does so through the use of cutting-edge technologies (such as web scraping, text mining, geo-spatial data analysis and artificial intelligence) and by introducing nonconventional sources of data (social media, online newspaper articles). This article summarises the experience of the FAO Data Lab and how it has been useful for the Organization to fulfil its mandate.
A new nanohybrid with BODIPY and Fe3O4 modified graphdiyne oxide in theranostic

Graphdiyne (GDY) is a new 2D carbon material different from graphene, and its ideal drug carrying and photothermal therapy efficacy had been noticed recently. Graphdiyne-oxide (GDYO), which few phototherapy researches had been reported, has more oxygen functional groups than GDY. Metal-organic framework materials Fe3O4@UiO-66-NH2 (FU) can covalently connect to GDYO layers and obtain covalent-organic framework-like structures FUGYO. FU enlarged interlamellar space of GDYO, which avoided the aggregation of FUGYO multiple layers. At the same time, more acetylenic bonds and hydrophilic group were exposed, which brings out advantages like stability, hydrophily, high drug-loading, as well as magnetic targeting and better magnetic resonance imaging efficacy. To promote cancer treatment effect and water solubility, we loaded chemotherapy drug doxorubicin (DOX) in FUGYO, then encapsulated FUGYO/DOX in phosphatidylcholine and fluorescent organic polymer BODIPY-PEG (BP) to integrate image-guided and synergistic therapy in one nanoparticle (BPFG/DOX). Due to the unique structure of BPFG/DOX, the π–π interaction between GDYO and BP improved photodynamic therapy efficiency. About 10 μg ml−1 BPFG/DOX have nearly 90% cell lethality to MCF-7 (human breast cancer cell line) cells with laser irradiation. In vivo, BPFG/DOX possessed ideal magnetic targeting and effectively inhibited tumor growth in 6 d. In conclusion, BPFG/DOX not only acts as an excellent tumor theranostic agent but also provides a scheme for applying GDYO in the biomedical region.
Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

Electrical power system abnormalities may have several negative consequences on its stable operation. As a result, preserving its stability under such operational states has become an ongoing challenge for power engineers. PSSs are created as auxiliary controllers to address the instability issues produced upon disturbances. They dampen the oscillations induced by the disturbances by giving the system the necessary damping torque. This research aims at presenting a comprehensive study for the optimum tuning of power system stabilizer (PSS) of different structures. This aim is accomplished with the help of a novel modified optimization algorithm called Quantum Artificial Gorilla Troops Optimizer. The modified optimizer’s validation is first investigated with the well-known benchmark optimization functions and shows superiority over Gorilla Troops Optimizer and competitive algorithms. The research is extended to the application of the optimum tuning of various PSS structures of the single machine to the infinite bus model. The proposed optimization algorithm shows fast convergence over investigated optimization algorithms. Moreover, the Tilt-integral-derivative based PSS shows better performance characteristics in terms of lower settling time and lower maximum and undershoot values over the conventional lead-lag PSS, dual input PSS, and fractional-order proportional-integral-derivative based PSS.