Miracle-dz/UniFAR-FTUs huggingface dataset:
Sharing the Agrarian Knowledge with Archaeology: First Evidence of the Dimorphism of Vitis Pollen from the Middle Bronze Age of N Italy (Terramara Santa Rosa di Poviglio)

The recovery of inaperturate pollen from functionally female flowers in archaeological layers opens the question of a possible pollen-based discrimination between wild and domesticated Vitis vinifera in prehistoric times. Pollen analysis applied to archaeology has not routinely considered the existence of pollen dimorphism in Vitis, a well-known trait in the field of agrarian studies. Therefore, the inaperturate shape of grapevine pollen is ignored by studies on the archaeobotanical history of viticulture. In this paper we investigate pollen morphology of the domesticated and wild subspecies of V. vinifera, and report the first evidence of inaperturate Vitis pollen from an archaeological site. We studied exemplar cases of plants with hermaphroditic flowers, belonging to the subspecies vinifera with fully developed male and female organs, cases of dioecious plants with male or female flowers, belonging to the wild subspecies sylvestris and cases of V. vinifera subsp. vinifera with morphologically hermaphroditic but functionally female flowers. The pollen produced by hermaphroditic and male flowers is usually trizonocolporate; the pollen produced by female flowers is inaperturate. This paper reports on the inaperturate pollen of Vitis found in an archeological site of the Po Plain, Northern Italy. The site dated to the Bronze Age, which is known to have been a critical age for the use of this plant with a transition from wild to domesticated Vitis in central Mediterranean. Can the inaperturate Vitis pollen be a marker of wild Vitis vinifera in prehistoric times? Palynology suggests a possible new investigation strategy on the ancient history of the wild and cultivated grapevine. The pollen dimorphism also implies a different production and dispersal of pollen of the wild and the domesticated subspecies. Grapevine plants are palynologically different from the other Mediterranean “cultural trees”. In fact, Olea, Juglans and Castanea, which are included in the OJC index, have the same pollen morphology and the same pollen dispersal, in wild and domesticated plants. In contrast, the signal of Vitis pollen in past records may be different depending on the hermaphroditic or dioecious subspecies.
On-line intelligent evaluation of the fatigue state of a composite conveyor belt

Fatigue failure of a composite conveyor belt occurs frequently, which will finally lead to an accident. An adaptive segmentation model is established to identify the rubber surface’s fatigue failure and to evaluate the belt fatigue level by using the machine vision technology and the artificial bee colony algorithm. The results show that the damage rate of rubber surface can be correctly calculated and the fatigue failure state can be objectively evaluated.
Scrub Typhus and Molecular Characterization of Orientia tsutsugamushi from Central Nepal

Scrub typhus is a vector-borne, acute febrile illness caused by Orientia tsutsugamushi. Scrub typhus continues to be an important but neglected tropical disease in Nepal. Information on this pathogen in Nepal is limited to serological surveys with little information available on molecular methods to detect O. tsutsugamushi. Limited information exists on the genetic diversity of this pathogen. A total of 282 blood samples were obtained from patients with suspected scrub typhus from central Nepal and 84 (30%) were positive for O. tsutsugamushi by 16S rRNA qPCR. Positive samples were further subjected to 56 kDa and 47 kDa molecular typing and molecularly compared to other O. tsutsugamushi strains. Phylogenetic analysis revealed that Nepalese O. tsutsugamushi strains largely cluster together and cluster away from other O. tsutsugamushi strains from Asia and elsewhere. One exception was the sample of Nepal_1, with its partial 56 kDa sequence clustering more closely with non-Nepalese O. tsutsugamushi 56 kDa sequences, potentially indicating that homologous recombination may influence the genetic diversity of strains in this region. Knowledge on the circulating strains in Nepal is important to the development of diagnostic tests and vaccines to support public health measures to control scrub typhus in this country.
Barriers to Information Communication Technology (ICT) adoption in Indian SME’s

The purpose of this research is to become attentive about crucial barriers to the implementation of ICT in Indian SME’s and to investigation the relations among them in order to simplify the implementation of ICT adoption in SME’s and find out supportive activity to facilitated the ICT adoption. Through simple random sampling, a questionnaire was circulated to gather the responses of 100 SME’s. The most significant barriers that were highlighted by the results of the study. The literature review emphasized that challenges of ICT adoption as a technical, managerial, administrative, supporting challenges. and also made classification of barriers in ICT adaptation in economical, legal, social, organizational, technical barriers. The most significant barriers that were highlighted by the results of the study are the lack of Internet Security as well as the lack of Internet Security, Cost of Internet and ICT Tools and lack of qualified staff. This study will support SMEs to become more successful by overcome barriers of ICT adoption, as they play a vibrant role in the Indian economy.
BANDUNG CREATIVE MOVEMENT 2016 | Multidisciplinary Design: Harmonizing design in today’s society, technology and business

Ground Zero Cafe Bandung is one of the cafes in Bandung that have a unique concept. Based on the results of initial observations of researchers, servicescape at Ground Zero Cafe Bandung get unfavorable response from consumers. This unfavorable respons impacts mismatches between perceptions and expectations received by consumers. It is important for the company to improve the quality of servicescape in order to create customer satisfaction. The purpose of this study was to determine how and how much the influence of servicescape on consumer satisfaction in Ground Zero Cafe Bandung. This study used a sample of 100 respondents. This type of research is descriptive analysis with quantitative approach, the analysis used is simple linear regression. The sampling technique used in this study is nonprobability sampling with incidental sampling method, using SPSS 22.00. The results of this study indicate that the servicescape variable earn a percentage of 69.4% in good categories, while customer satisfaction earn a percentage of 68.9% also in good categories. The influence of servicescape on customer satisfaction is 55.5%, while the remaining 44.5% is influenced by other variables not examined in this study. Servicescape variable significantly influence on consumer satisfaction variable by 55.5%
Towards Automated Acceptance testing for industrial robots

Industrial robots are important machines applied in numerous modern industries that execute repetitive tasks with high accuracy, replacing or supporting dangerous jobs. In this kind of system, with increased complexity in which cost is related to the time the system keeps working, the system must operate with a minimum number of failures. In other words, a quality aspect important in industry is reliability. We hypothesize that Automated Acceptance Testing improves reliability for industrial robot program. We present the research question, the motivation for this study, our hypothesis and future research efforts.
Time Series Forecasting via Learning Convolutionally Low-Rank Models

Recently, Liu and Zhang studied the rather challenging problem of time series forecasting from the perspective of compressed sensing. They proposed a no-learning method, named Convolution Nuclear Norm Minimization (CNNM), and proved that CNNM can exactly recover the future part of a series from its observed part, provided that the series is convolutionally low-rank. While impressive, the convolutional low-rankness condition may not be satisfied whenever the series is far from being seasonal, and is in fact brittle to the presence of trends and dynamics. This paper tries to approach the issues by integrating a learnable, orthonormal transformation into CNNM, with the purpose for converting the series of involute structures into regular signals of convolutionally low-rank. We prove that the resultant model, termed Learning-Based CNNM (LbCNNM), strictly succeeds in identifying the future part of a series, as long as the transform of the series is convolutionally low-rank. To learn proper transformations that may meet the required success conditions, we devise an interpretable method based on Principal Component Pursuit (PCP). Equipped with this learning method and some elaborate data argumentation skills, LbCNNM not only can handle well the major components of time series (including trends, seasonality and dynamics), but also can make use of the forecasts provided by some other forecasting methods; this means LbCNNM can be used as a general tool for model combination. Extensive experiments on 100,452 real-world time series from Time Series Data Library (TSDL) and M4 Competition (M4) demonstrate the superior performance of LbCNNM.
Discrete Painlevé transcendent solutions to the multiplicative-type discrete KdV equations

Hirota’s discrete Korteweg–de Vries (KdV) equation is an integrable partial difference equation on [Formula: see text], which approaches the KdV equation in a continuum limit. In this paper, we show that its multiplicative-discrete versions have the special solutions given by the solutions of q-Painlevé equations of types [Formula: see text].
APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization

Query categorization is an essential part of query intent understanding in e-commerce search. A common query categorization task is to select the relevant fine-grained product categories in a product taxonomy. For frequent queries, rich customer behavior (e.g., click-through data) can be used to infer the relevant product categories. However, for more rare queries, which cover a large volume of search traffic, relying solely on customer behavior may not suffice due to the lack of this signal. To improve categorization of rare queries, we adapt the Pseudo-Relevance Feedback (PRF) approach to utilize the latent knowledge embedded in semantically or lexically similar product documents to enrich the representation of the more rare queries. To this end, we propose a novel deep neural model named Attentive Pseudo Relevance Feedback Network (APRF-Net) to enhance the representation of rare queries for query categorization. To demonstrate the effectiveness of our approach, we collect search queries from a large commercial search engine, and compare APRF-Net to state-of-the-art deep learning models for text classification. Our results show that the APRF-Net significantly improves query categorization by 5.9% on F1@1 score over the baselines, which increases to 8.2% improvement for the rare (tail) queries. The findings of this paper can be leveraged for further improvements in search query representation and understanding.
Software-Defined Edge Computing: A New Architecture Paradigm to Support IoT Data Analysis

The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the current IoT architectures by exploring their inherent nature of communication and computing to support smart IoT data process and analysis. We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions. Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis. We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis. Results show that our method is effective and feasible.
Manipulating topology in tailored artificial graphene nanoribbons

Topological phases of matter give rise to exotic physics that can be leveraged for next generation quantum computation and spintronic devices. Thus, the search for topological phases and the quantum states that they exhibit have become the subject of a massive research effort in condensed matter physics. Topologically protected states have been produced in a variety of systems, including artificial lattices, graphene nanoribbons (GNRs) and bismuth bilayers. Despite these advances, the real-time manipulation of individual topological states and their relative coupling, a necessary feature for the realization of topological qubits, remains elusive. Guided by first-principles calculations, we spatially manipulate robust, zero-dimensional topological states by altering the topological invariants of quasi-one-dimensional artificial graphene nanostructures. This is achieved by positioning carbon monoxide molecules on a copper surface to confine its
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems

Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles. Given a graph representation of the transportation network - one where, for example, nodes represent areas of the city, and edges the connectivity between them - we argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. In this paper, we propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networks. Crucially, we demonstrate that graph neural networks enable reinforcement learning agents to recover behavior policies that are significantly more transferable, generalizable, and scalable than policies learned through other approaches. Empirically, we show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks such as inter-city generalization, service area expansion, and adaptation to potentially complex urban topologies.
Collaborative Information Sharing for ML-Based Threat Detection

Recently, coordinated attack campaigns started to become more widespread on the Internet. In May 2017, WannaCry infected more than 300,000 machines in 150 countries in a few days and had a large impact on critical infrastructure. Existing threat sharing platforms cannot easily adapt to emerging attack patterns. At the same time, enterprises started to adopt machine learning-based threat detection tools in their local networks. In this paper, we pose the question: \emph{What information can defenders share across multiple networks to help machine learning-based threat detection adapt to new coordinated attacks? } We propose three information sharing methods across two networks, and show how the shared information can be used in a machine-learning network-traffic model to significantly improve its ability of detecting evasive self-propagating malware.
Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug Discovery

In the past several months, COVID-19 has spread over the globe and caused severe damage to the people and the society. In the context of this severe situation, an effective drug discovery method to generate potential drugs is extremely meaningful. In this paper, we provide a methodology of discovering potential drugs for the treatment of Severe Acute Respiratory Syndrome Corona-Virus 2 (commonly known as SARS-CoV-2). We proposed a new model called Genetic Constrained Graph Variational Autoencoder (GCGVAE) to solve this problem. We trained our model based on the data of various viruses' protein structure, including that of the SARS, HIV, Hep3, and MERS, and used it to generate possible drugs for SARS-CoV-2. Several optimization algorithms, including valency masking and genetic algorithm, are deployed to fine tune our model. According to the simulation, our generated molecules have great effectiveness in inhibiting SARS-CoV-2. We quantitatively calculated the scores of our generated molecules and compared it with the scores of existing drugs, and the result shows our generated molecules scores much better than those existing drugs. Moreover, our model can be also applied to generate effective drugs for treating other viruses given their protein structure, which could be used to generate drugs for future viruses.
Beyond Voice Activity Detection: Hybrid Audio Segmentation for Direct Speech Translation

The audio segmentation mismatch between training data and those seen at run-time is a major problem in direct speech translation. Indeed, while systems are usually trained on manually segmented corpora, in real use cases they are often presented with continuous audio requiring automatic (and sub-optimal) segmentation. After comparing existing techniques (VAD-based, fixed-length and hybrid segmentation methods), in this paper we propose enhanced hybrid solutions to produce better results without sacrificing latency. Through experiments on different domains and language pairs, we show that our methods outperform all the other techniques, reducing by at least 30% the gap between the traditional VAD-based approach and optimal manual segmentation.
Towards Offloadable and Migratable Microservices on Disaggregated Architectures: Vision, Challenges, and Research Roadmap

Microservice and serverless computing systems open up massive versatility and opportunity to distributed and datacenter-scale computing. In the meantime, the deployments of modern datacenter resources are moving to disaggregated architectures. With the flourishing growths from both sides, we believe this is high time to write this vision paper to propose a potential research agenda to achieve efficient deployments, management, and executions of next-generation microservices on top of the emerging disaggregated datacenter architectures. In particular, we envision a critical systems research direction of designing and developing offloadable and migratable microservices on disaggregated architectures. With this vision, we have surveyed the recent related work to demonstrate the importance and necessity of researching it. We also outline the fundamental challenges that distributed systems and datacenter-scale computing research may encounter. We further propose a research roadmap to achieve our envisioned objectives in a promising way. Within the roadmap, we identify potential techniques and methods that can be leveraged.
The observable supernova rate in galaxy–galaxy lensing systems with the TESS satellite

The Transiting Exoplanet Survey Satellite (TESS) is the latest observational effort to find exoplanets and map bright transient optical phenomena. Supernovae (SN) are particularly interesting as cosmological standard candles for cosmological distance measures. The limiting magnitude of TESS strongly constrains supernova detection to the very nearby Universe (m ∼ 19, z < 0.05). We explore the possibility that more distant supernovae that are gravitationally lensed and magnified by a foreground galaxy can be detected by TESS, an opportunity to measure the time delay between light paths and constrain the Hubble constant independently. We estimate the rate of occurrence of such systems, assuming reasonable distributions of magnification, host dust attenuation and redshift. There are approximately 16 type Ia and 43 core-collapse SN (SNcc) expected to be observable with TESS each year, which translates to 18% and 43% chance of detection per year, respectively. Monitoring the largest collections of known strong galaxy-galaxy lenses from Petrillo et al., this translates into 0.6% and 1.3% chances of a SNIa and SNcc per year. The TESS all-sky detection rates are lower than those of the Zwicky Transient Facility (ZTF) and Vera Rubin Observatory (VRO). However, on the ecliptic poles, TESS performs almost as well as its all-sky search thanks to its continuous coverage: 2 and 4% chance of an observed SN (Ia or cc) each year. These rates argue for timely processing of full-frame TESS imaging to facilitate follow-up and should motivate further searches for low-redshift lensing system.
Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms

Wavelet scattering networks, which are convolutional neural networks (CNNs) with fixed filters and weights, are promising tools for image analysis. Imposing symmetry on image statistics can improve human interpretability, aid in generalization, and provide dimension reduction. In this work, we introduce a fast-to-compute, translationally invariant and rotationally equivariant wavelet scattering network (EqWS) and filter bank of wavelets (triglets). We demonstrate the interpretability and quantify the invariance/equivariance of the coefficients, briefly commenting on difficulties with implementing scale equivariance. On MNIST, we show that training on a rotationally invariant reduction of the coefficients maintains rotational invariance when generalized to test data and visualize residual symmetry breaking terms. Rotation equivariance is leveraged to estimate the rotation angle of digits and reconstruct the full rotation dependence of each coefficient from a single angle. We benchmark EqWS with linear classifiers on EMNIST and CIFAR-10/100, introducing a new second-order, cross-color channel coupling for the color images. We conclude by comparing the performance of an isotropic reduction of the scattering coefficients and RWST, a previous coefficient reduction, on an isotropic classification of magnetohydrodynamic simulations with astrophysical relevance.
Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer

—Text classiﬁcation has seen an increased use in both academic and industry settings. Though rule based methods have been fairly successful, supervised machine learning has been shown to be most successful for most languages, where most research was done on English. In this article, the success of lexicon analysis, support vector machines, and extreme gradient boosting for the task of text classiﬁcation(and sentiment analysis) are evaluated in Turkish and a pretrained transformer based classiﬁer is proposed, outperforming previous methods for Turkish text classiﬁcation. In the context of text classiﬁcation, all machine learning models proposed in the article are domain-independent and dont require any task-speciﬁc modiﬁcations.
A Congruence-Based Perspective on Finite Tree Automata

We provide new insights on the determinization and minimization of tree automata using congruences on trees. From this perspective, we study a Brzozowski’s style minimization algorithm for tree automata. First, we prove correct this method relying on the following fact: when the automata-based and the language-based congruences coincide, determinizing the automaton yields the minimal one. Such automata-based congruences, in the case of word automata, are defined using pre and post operators. Now we extend these operators to tree automata, a task that is particularly challenging due to the reduced expressive power of deterministic top-down (or equivalently co-deterministic bottom-up) automata. We leverage further our framework to offer an extension of the original result by Brzozowski for word automata.