Miracle-dz/UniFAR-FTUs huggingface dataset:
Sim2Real Docs: Domain Randomization for Documents in Natural Scenes using Ray-traced Rendering

In the past, computer vision systems for digitized documents could rely on systematically captured, high-quality scans. Today, transactions involving digital documents are more likely to start as mobile phone photo uploads taken by non-professionals. As such, computer vision for document automation must now account for documents captured in natural scene contexts. An additional challenge is that task objectives for document processing can be highly use-case specific, which makes publicly-available datasets limited in their utility, while manual data labeling is also costly and poorly translates between use cases. To address these issues we created Sim2Real Docs - a framework for synthesizing datasets and performing domain randomization of documents in natural scenes. Sim2Real Docs enables programmatic 3D rendering of documents using Blender, an open source tool for 3D modeling and ray-traced rendering. By using rendering that simulates physical interactions of light, geometry, camera, and background, we synthesize datasets of documents in a natural scene context. Each render is paired with use-case specific ground truth data specifying latent characteristics of interest, producing unlimited fit-for-task training data. The role of machine learning models is then to solve the inverse problem posed by the rendering pipeline. Such models can be further iterated upon with real-world data by either fine tuning or making adjustments to domain randomization parameters.
Agriculture’s Nitrogen Legacy

Nitrogen pollution of waterways is a large global problem, especially in regions with intensive cropland agriculture such as the Mississippi River Basin that drains 40% of the continental United States. In contrast to prior studies, which mostly apply agronomic and hydrologic models, we collect detailed data from water quality monitors and use panel data econometric methods to estimate how land use affects nitrogen pollution. We find a strong positive effect of corn acreage on nitrogen concentration in nearby streams and rivers that is an order of magnitude smaller than those implied by the agronomic and hydrologic models. Our findings are consistent with a new line of research documenting accumulation of large amounts of nitrogen in subsurface soil and groundwater over several decades; this is excess nitrogen that was applied to fields but has yet to appear in waterways. This legacy nitrogen will eventually leach into streams and rivers exacerbating further nutrient pollution. In the presence of large amounts of legacy nitrogen, land retirement and other on-farm mitigation policies are uneconomic. Downstream off-farm practices, such as the creation and restoration of fluvial wetlands, which can remove both legacy and new nitrogen, however, are cost-effective.
Non-intrusive prediction of speech intelligibility for the ﬁrst Clarity Prediction Challenge (CPC1)

Most existing speech intelligibility measures are either designed for single-channel applications – hence unsuited to evaluate hearing aid algorithms –or intrusive, applicable only in simulated scenarios in which the clean signal is available. Non-intrusive speech intelligibility measures able to reliably predict speech intelligibility without knowledge of the clean signal are urgently needed. This paper proposes a non-intrusive measure that predicts speech intelligibility using only the processed signals and audiogram of the listener as input. The proposed measure relies on three steps, namely a hearing-loss model, a feature extractor and a predicting function. The hearing loss model uses the target signal and the listener’s audiogram as input while the feature extractor and the predicting function are trained on processed signals labeled in terms of speech intelligibility during a listening test. The evaluation is conducted using cross-validation on both tracks of the ﬁrst Clarity Prediction Challenge (CPC1).
On the Initiation of the 20 June 2015 Great Plains Low-Level Jet

AbstractExtensive measurements were made of the summertime Great Plains low-level jet (LLJ) in central Kansas during June and July 2015 as a component of the Plains Elevated Convection at Night (PECAN) field study. Here, the authors describe the early phase of the LLJ development on 20 June 2015. Half-hourly soundings were launched to monitor the progress of the jet. An airborne mission was also conducted using the University of Wyoming King Air research aircraft. Vertical sawtooth patterns were flown along a fixed track at 38.7°N between longitudes 98.9° and 100.3°W to document changes in the potential temperature and wind profiles. Ageostrophic winds during the LLJ formation were also assessed. In addition, a high-resolution numerical simulation of the 20 June 2015 LLJ case was conducted using the Weather Research and Forecasting Model. Observations and model results show that the early stage of development consisted of a rapid increase in wind speed in the hours just after sunset with less pronounced d.
Emergence of Implicit System Identification via Embodiment Randomization

Modern robots come in various shapes and sizes, and use different sensor suites (e.g., different cameras with different camera parameters) to observe their environment. By contrast, when training embodied agents (i.e., virtual robots) in simulation, these differences are typically ignored and a single robot embodiment (height, radius, camera parameters, etc.) is used. As a result, behavior policies learned in such simulations only work for the fixed embodiment used in training, and generalize poorly when even minor changes to the robot’s embodiment are introduced, such as changing the robot’s height or camera field-of-view. However, such variations are inevitable as new robots are developed or existing robots are updated and modified for new applications. How do we develop embodied agents that can adapt, as needed, to new embodiments during deployment? Recent work has proposed a number of techniques for addressing this embodiment generalization challenge [3, 8, 11,14]. One line of work trains “universal” navigation policies that are conditioned on a learned robot embedding vector [11]. Alternatively, Shah et al. [8] train a navigation policy using data from multiple robot embodiments by conditioning the policy on past observations (used to infer a robot’s embodiment) and using a normalized action space (to generalize actions across embodiments). This work studies a simpler alternative: embodiment randomization. Akin to domain randomization [10], in embodiment randomization the robot configuration is randomly sampled at the beginning of each training episode. In simulation, modifying a robot’s embodiment is trivial. Thus, embodiment randomization is an inexpensive and intuitive technique for addressing the embodiment generalization challenge. We empirically investigate embodiment randomization using the image-goal navigation (ImageNav) task [15]. We observe that, on this task, policies trained using a fixed embodiment catastrophically fail to generalize to new robot configurations. However, with embodiment randomization, agents recover a substantial portion of this lost performance. Furthermore, we discover that policies trained with embodiment randomization implicitly learn to perform system identification. Specifically, we find that many (but not all) configuration parameters can be predicted from intermediate representations within agents trained with embodiment randomization, allowing these agents to adapt to new embodiments during deployment. Our findings suggest that embodiment randomization is a simple but effective method for training navigation agents that are generalizable to new embodiments zero-shot.
Advanced AI-based approaches in Industry 4.0 of the University of Naples Federico II node of the CINI-AIIS Lab

This paper provides an overview of recent advances in the application of artificial intelligence (AI) in industrial contexts such as data center and the railway industries carried out at the University of Naples Federico II node of the CINI-AIIS Lab. We discuss some challenges and opportunities associated with the adoption of AI in these industries. In data centers, AI is being used to optimize resource utilization, reduce energy consumption, and ensure high availability of services. Despite the potential benefits of AI, there are also challenges associated with its adoption, including the need for high-quality data, reliability and interpretability of AI-based systems, and ethical and legal concerns related to privacy, security, and bias. From the other hand, in the railway industry, AI is being used to optimize train schedules, reduce delays, support predictive maintenance operations and improve passenger safety by predicting and preventing accidents. In this paper, we focus on Hard Disk Drive health status assessment task and on an overview of AI techniques for railway domain in the context H2020
Impact of Transition from Onsite to Online Education on Students Learning and Psychological Well-being: A Cross-sectional Study of King Faisal University, KSA

The COVID-19 pandemic has caused disruptions in a variety of industries, including education and way of life. The educational system has undergone significant transformation as some activities have abruptly moved from on-site to online. Researchers' curiosity about how the transitional period affects students' learning and well-being has grown during this time. This study intends to assess how King Faisal University students perceive the effects of switching from traditional classroom instruction to online learning on their academic performance and psychological well-being.
The Growth of Small Ice Crystals in Gases of High and Low Pressures

The habit and the growth feature of small ice crystals formed in a free fall state in highand low-pressure helium and argon gases at the temperatures of -7 and -15* were studied experimentally. It is found that the habit and the growth feature of small ice crystals in the range of about 20 to 50 *m depend not only on the temperature and the degree of supersaturation but also on the kind and the pressure of the carrier gas and the size of crystals when small ice crystals were formed at water saturation. It is found that the dependence of the habit of small ice crystals on temperature is more emphasized under a high pressure but has a tendency to vanish under a low pressure. In high-pressure gas, ice crystal shapes which have never been observed in the earth's atmospheres are found. Such ice crystals may form in the atmospheres of Jupiter and Saturn where pressures are higher than that of the earth.
A . 1 Bayesian Optimisation

The obtained posterior distribution represents our updated belief about the unknown objective function being modelled. There are two main aspects that must be taken into account for the Bayesian optimisation. First, the selection of priors to express our prior belief about the function being modelled. Gaussian processes are used in defining prior distributions for the unknown objective function. Second, the acquisition function to determine the next promising point for the function evaluation. Therefore, Bayesian optimisation consists of two main components: (i) a Gaussian process model [Williams and Rasmussen, 2006] of f , and (ii) acquisition functions [Kushner, 1964] to guide the search for optima.
UAVs Towards Sustainable Crop Production

One of the greatest challenges for humanity is to produce sufficient food, feed, fiber, and fuel for an ever-growing world population while simultaneously reducing the environmental footprint of agricultural production. Productive arable land is limited, and the input of agro-chemicals needs to be reduced to curb environmental pollution and halt the decline in biodiversity. Climate change poses additional constraints on crop farming. Achieving sustainable crop production with limited resources is, thus, a task of immense proportions. We believe that a major shift toward sustainable crop production can be achieved via two approaches: (1) multi-scale monitoring of plants and their environment using autonomous robots with automated and individualized intervention and big data analytics combined with machine learning to improve our understanding of the relation between input and output parameters of crop production, and (2) assessing, modeling, and optimizing the implications of the developed technical innovations in a systemic manner. The DFG-funded Cluster of Excellence “PhenoRob” addresses the hypothesis above and develops an approach that is characterized by the integration of robotics, digitalization, big data analytics, and machine learning on one hand, and modern methods of plant phenotyping, modeling, and crop production on the other, taking a technology-driven approach. Especially UAVs will play an important role for managing fields, reducing the environmental footprint of crop production, and maintaining the quality of soil and arable land. Our UAV-based in-field monitoring activities generates heterogeneous data on plants, crop stands, and fields. This data includes geometric and semantic models of individual plants, multispectral images at different scales using optical and non-optical sensors, distributions of plant species in and around the fields, the status of plant health, nutrient as well as water contents of soil. Besides several other scenarios of UAVs in agriculture, we focus on two central use cases for the integration of UAVs into crop production processes. First, UAVs will be a main source of information to identify the weed pressure in the field and in this way form the basis for robotdriven field management. The information will enable plant-specific treatments of individual plants at high accuracy, providing each plant the care it requires. Second, by combining the UAV data with predictive models of the agro-ecosystem on farm level, we can even identify nutrient deficiencies from UAV data. The discrepancies between the model prediction, weather models, and the UAV perception, form the basis for location-specific fertilization minimizing negative impacts on our ecosystem.
Dual pH- and Thermo-Sensitive Poly(N-isopropylacrylamide-co-allylamine) Nanogels for Curcumin Delivery: Swelling–Deswelling Behavior and Phase Transition Mechanism

Curcumin (Cur) is a beneficial ingredient with numerous bioactivities. However, due to its low solubility and poor bioavailability, its therapeutic application is limited. In this work, we prepared poly-N-isopropylacrylamide p(NIPAm) and polyallylamine p(Am)-based nanogel (p(NIPAm-co-Am)) NG for a dual pH- and temperature-sensitive copolymer system for drug delivery application. In this copolymer system, the p(NIPAm) segment was incorporated to introduce thermoresponsive behavior and the p(Am) segment was incorporated to introduce drug binding sites (amine groups) in the resulting (p(NIPAm-co-Am)) NG system. Various instrumental characterizations including 1H nuclear magnetic resonance (1H NMR) spectroscopy, Fourier transform infrared (FT-IR) analysis, scanning electron microscopy (SEM), zeta potential, and particle size analysis were performed to confirm the copolymer synthesis. Curcumin (Cur), an anticancer bioactive substance, was employed to assess the in vitro drug loading and release performance of the resulting copolymer nanogels system at varied pH levels (pH 7.2, 6.5, and 4.0) and temperatures (25 °C, 37 °C, and 42 °C). The cytocompatibility of the p(NIPAm-co-Am) NG sample was also tested on MDA-MB-231 cells at various sample concentrations. All the study results indicate that the p(NIPAm-co-Am) NG produced might be effective for drug loading and release under pH and temperature dual-stimuli conditions. As a result, the p(NIPAm-co-Am) NG system has the potential to be beneficial in the use of drug delivery applications in cancer therapy.
Length of the Longest Path and Diameter in Orientations of Graphs

We say that a parameter p of directed graphs has the interval property if for every graph G and orientations of G, p can take every value between its minimum and maximum values. Let λ be the length of the longest directed path. A question asked by C. Lin in [1] is equivalent to the question of whether λ has the interval property. In this note, we answer this question in the affirmative. We also show that the diameter of directed graphs does not have the interval property.
Exploring the effects of service quality on customer loyalty: An empirical study of Vinaphone

This study was conducted to determine the service quality factors affecting customer loyalty to Vinaphone mobile services in Vietnam. Based on the survey results of 250 customers who have participated in using Vinaphone's mobile services, the collected data is processed by the SPSS 25.0 software, including descriptive statistics, and the reliability test of the scale measured by Cronbach's Alpha method, exploratory factor analysis (EFA), analysis of multiple linear regression models. The results after analyzing the data identified five factors of service quality that affect customer loyalty in descending order: call quality, convenience, value-added services, customer support services, and reliability. Based on the research results, five management implications have been proposed to help Vinaphone’s network improve customer loyalty through improving service quality
High-temperature thermal conductivity of thermoelectric clathrates

We have carried out an evaluation of the lattice thermal conductivity for 19 different type-I intermetallic clathrate compositions at temperatures near or above their respective Debye temperatures. Although the low temperature data (T 300 K) appear to be relatively well described by a simple model that assumes only anharmonic three-phonon scattering processes. The analysis suggests the large number of atoms in the unit cell accompanied by a significant degree of anharmonicity and relatively low Debye temperatures produce low lattice thermal conductivities in these materials above 300 K, regardless of composition or the nature and influence of guest rattling behavior on thermal transport below room temperature. The dominant mechanism underlying the universally low lattice thermal conductivity of type-I intermetallic clathrates (ranging f.
The Impact of Social Media Relationships on e-WOM in Syria and Hungary

Cultural values play a crucial role in the formation of individuals’ behaviour. With the emergence of social networking sites, which have formed a parallel world to the real world, the behaviour of individuals and their motives to engage in electronic word of mouth (eWOM) on social media platforms has become extremely important for brands. Due to a relative lack in studies focusing on the cultural difference-based impact of social relationships on eWOM in social media, the present study seeks to address this gap as its broad objective is to investigate the effect of social relationship variables on eWOM in social media by comparing two different cultures, namely Syria and Hungary in order to explore whether social relationships exert a more significant impact on eWOM in a collectivistic society than in an individualistic one. An explanatory research design was adopted and the data was collected by means of a questionnaire survey. The final sample included 113 Syrian and 90 Hungarian respondents, all of them aged 35 or below. It was found that tie strength among young adults has a more positive impact on eWOM in Syria than in Hungary, while no homophily impact was found on eWOM in either. Regarding the other social relationship variables examined in the study (trust, normative and informational influences), the results showed that they do not have a more positive impact on eWOM in Syria than in Hungary. The research is believed to have contributed to previous investigations on the impact of social relationships on eWOM via social media by providing insights into the role of a cultural value dimension in determining the extent to which individuals are affected by their cultural background, whether collectivistic or individualistic, when they interact in social media platforms. At the same time, it is acknowledged that the study has limitations thus future examinations are necessary.
The Generic Action Principles of Conformal Supergravity

All N=4 conformal supergravities in four space-time dimensions are constructed. These are the only N=4 supergravity theories whose actions are invariant under off-shell supersymmetry. They are encoded in terms of a holomorphic function that is homogeneous of zeroth degree in scalar fields that parametrize an SU(1, 1)/U(1) coset space. When this function equals a constant the Lagrangian is invariant under continuous SU(1, 1) transformations. Based on the known non-linear transformation rules of the Weyl multiplet fields, the action of N = 4 conformal supergravity is constructed up to terms quadratic in the fermion fields. The bosonic sector corrects a recent result in the literature. The construction of these higher-derivative invariants also opens the door to various applications for non-conformal theories.
Poster: Local Differentially-Private Genomic Database Fingerprinting

—Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific GWAS statistics as it enables “do-it-yourself” calculations. Constructed genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus when sharing the databases with others, the curator (database owner) should be able to prevent unauthorized redistributions and protect individuals’ genomic data. As it becomes increasingly common for a same database be shared with multiple recipients, the shared database should also be robust against collusion attack, where malicious recipients combine their individual versions of received database to forge a pirated copy with the hope that none of them can be traced back. We propose to develop a novel steganographic scheme to achieve both copyright protection and privacy preservation guarantees for the shared genomic databases by inserting random fingerprint bit-strings (steganographic marks) into each shared copy. In a nutshell, the proposed scheme attains local differential privacy of fingerprinted genomic databases by leveraging the intrinsic randomness of the fingerprint marks. To defend against collusion attacks, we augment Gen-Scope with the Tardos codes, which is a powerful traitor tracing techniques to mitigate collusion attacks by identifying the colluders with high probability.
SOY ADVANCEMENT AND ADAPTATION STRATEGIES FOR BEEF CATTLE IN SOUTHERN BRAZIL

Mudanças nos sistemas produtivos são observadas em diferentes realidades rurais no Brasil, marcadamente identificadas por meio do crescente avanço do cultivo de monoculturas. Em resposta a essas dinâmicas, os atores locais adotam diversificadas estratégias de adaptação e reação frente às mudanças. Assim, diante de um contexto de transformações provocadas pelo avanço da soja em áreas típicas de pastagens naturais e tradição pecuária, o objetivo deste artigo foi identificar quais estratégias de adaptação vêm sendo adotadas por dois perfis distintos de pecuaristas de corte, Pecuaristas Tradicionais e Pecuaristas Sojicultores do Pampa brasileiro. Os principais resultados apontam para a diversidade de estratégias adotadas pelos pecuaristas entrevistados, que ora buscam se aproximar das dinâmicas do agronegócio por meio da expansão das lavouras de soja, caso principal dos Pecuaristas Sojicultores, ora parecem querer se distanciar da dependência do mercado global de commodities agrícolas por meio da criação pecuária de forma extensiva, caso especial dos Pecuaristas Tradicionais. Conclui-se que as estratégias adotadas para expandir as áreas cultivadas com soja no Pampa brasileiro representam ameaça à pecuária extensiva, atividade tradicional desse território