Miracle-dz/UniFAR-FTUs huggingface dataset:
New Routes and Opportunities for Modular Construction of Particulate Vaccines: Stick, Click, and Glue

Vaccines based on virus-like particles (VLPs) can induce potent B cell responses. Some non-chimeric VLP-based vaccines are highly successful licensed products (e.g., hepatitis B surface antigen VLPs as a hepatitis B virus vaccine). Chimeric VLPs are designed to take advantage of the VLP framework by decorating the VLP with a different antigen. Despite decades of effort, there have been few licensed chimeric VLP vaccines. Classic approaches to create chimeric VLPs are either genetic fusion or chemical conjugation, using cross-linkers from lysine on the VLP to cysteine on the antigen. We describe the principles that make these classic approaches challenging, in particular for complex, full-length antigens bearing multiple post-translational modifications. We then review recent advances in conjugation approaches for protein-based non-enveloped VLPs or nanoparticles, to overcome such challenges. This includes the use of strong non-covalent assembly methods (stick), unnatural amino acids for bio-orthogonal chemistry (click), and spontaneous isopeptide bond formation by SpyTag/SpyCatcher (glue). Existing applications of these methods are outlined and we critically consider the key practical issues, with particular insight on Tag/Catcher plug-and-display decoration. Finally, we highlight the potential for modular particle decoration to accelerate vaccine generation and prepare for pandemic threats in human and veterinary realms.
A high-voltage p-LDMOS with enhanced current capability comparable to double RESURF n-LDMOS

In this paper, a simple p-LDMOS structure with significantly improved performances based on a novel three dimensional concept is proposed. The hole current in the Ptop region of the signal region flows into the floating P+, then through the integrated resistor Rp formed by the Pbase region in the z-axis direction with a distance of W2+W3, into electrode D. A voltage drop (VGn) which controls the n-channel will be auto-generated across Rp during the on- and off-state of the hole current. Thus the p-LDMOS applies both hole and electron as majority carriers to conduct current. The proposed p-LDMOS, having only one external controlling signal (GP), has a current capability comparable to or even larger than that of an optimized double RESURF n-LDMOS implemented through the same process steps. The power loss can be reduced by 74.9% compared with the conventional p-LDMOS.
Follow the "Mastodon": Structure and Evolution of a Decentralized Online Social Network

In this paper we present a dataset containing both the network of the "follow" relationships and its growth in terms of new connections and users, all which we obtained by mining the decentralized online social network named Mastodon. The dataset is combined with usage statistics and meta-data (geographical location and allowed topics) about the servers comprising the platform's architecture. These server are called instances. The paper also analyzes the overall structure of the Mastodon social network, focusing on its diversity w.r. t. other commercial microblogging platforms such as Twitter. Finally, we investigate how the instance-like paradigm influences the connections among the users. The newest and fastest-growing microblogging platform, Mastodon is set to become a valid alternative to established platforms like Twitter. The interest in Mastodon is mainly motivated as follows: a) the platform adopts an advertisement and recommendation-free business model; b) the decentralized architecture makes it possible to shift the control over user contents and data from the platform to the users; c) it adopts a community-like paradigm from both user and architecture viewpoints. In fact, Mastodon is composed of interconnected communities, placed on different servers; in addition, each single instance, with specific topics and languages, is independently owned and moderated. The released dataset paves the way to a number of research activities, which range from classic social network analysis to the modeling of social network dynamics and platform adoption in the early stage of the service. This data would also enable community detection validation since each instance hinges on specific topics and, lastly, the study of the interplay between the physical architecture of the platform and the social network it supports.
Study on the adaptive scale of agricultural development and utilization in Mu Us Sandy Land based on NSGA-II

The balance of water and soil resources directly influences the ecological environment and the potential of social and economic development in water shortage areas. Especially in the Mu Us Sandy Land, where was suffering extreme lack of water, when all kinds of agricultural development and utilization mode had caused obvious pressure on local water resources, if the mode and the existing agricultural planting structure were still continuing, disorderly expanding the developing scale, will undoubtedly made more serious ecological problems. In this study, taking Yulin District in the southern margin of Mu Us Sandy Land as the research object, the multi-objective optimization model of adaptive scale of development and utilization of the sandy land agriculture was established with the constraints of limited water resources and goals of economic, social, ecological benefits. Based on the NSGA-II algorithm, typical schemes including unitary planting structure, binary planting structure and three-component planting structure were proposed. After summing up and analyzing, seven different schemes including diversified agricultural planting pattern were recommended, also the planting areas of diverse crops were given. The final optimized schemes could provide references for the development, harnessing and utilization of sandy land, and improve the local ecological environment, expanding the effective cultivated land area of Yuyang District and promoting the coordinated development of regional water and soil resources.
A study on the effect of emotional intelligence on retail investors' behavior

Article history: Received Feb 28, 2013 Received in revised format 19 September 2013 Accepted 23 October 2013 Available online November 28 2013 Investment decisions are normally accomplished based on fundamental or technical methods. However, there are many cases where investors make their investment decisions based on their emotions. This study investigates the effects of various factors including biases representation, mental accounting and risk aversion when an investment decision is executed. In other words, the study examines the effects of emotional intelligence components on retail investors’ investment strategies on Tehran Stock Exchange (TSE). The proposed study selects a sample of 270 investors who had some experiences on TSE randomly and using a questionnaire based survey detected that there was a positive and meaningful relationship between emotional intelligence and investment decisions.
anyAlign: An Intelligent and Interactive Text-Alignment Web-Application for Historical Document

Text alignment is an important step for analyzing historical archives. For analysis, it is important to align the text with their corresponding document images. It is a time and labor intensive work for many paleographers. In this paper, we have presented an end-to-end semi-automatic and interactive text alignment system for historical document. The presented system consists of five main sequential steps: binarization, automatic text-line extraction, interactive error correction in extracted text-line, automatic text alignment, and interactive error correction in aligned text. The anyOCR system [1] is used for the first three steps. Afterwards, text alignment is done automatically by the system using Oriented Fast and Rotated Brief (ORB) local image feature descriptors. The ORB features are matched by k-Nearest-Neighbor (KNN). Finally, the system provides an interactive user interface for rectifying wrong text alignment. The results are discussed in the evaluation section.
Implementing acoustic radiation force imaging on GPU using OpenCL

Acoustic Radiation Force Impulse (ARFI) imaging is a novel method of tissue stiffness estimation. By using radiation force to generate and measure the small displacement of tissue, it can detect and calculate the stiffness of tissues. Since tissue elasticity is known to be associated with pathological condition, ARFI can used to diagnose disease. Normally, massive computation is required in order to finish data processing, so it will be computationally expensive to implement with traditional platform and methods. In this paper, a new method of ARFI imaging based on OpenCL (Open Computing Language) parallel processing platform is introduced. Based on this platform and Loupas' two-dimensional (2-D) autocorrelation [1] algorithm, some optimization has been made and compared. The results of the comparison demonstrate the speedup using GPU and each time the optimization been made.
An Overview of Non-Destructive Testing Methods for Integrated Circuit Packaging Inspection

The article provides a review of the state-of-art non-destructive testing (NDT) methods used for evaluation of integrated circuit (IC) packaging. The review identifies various types of the defects and the capabilities of most common NDT methods employed for defect detection. The main aim of this paper is to provide a detailed review on the common NDT methods for IC packaging addressing their principles of operation, advantages, limitations and suggestions for improvement. The current methods such as, X-ray, scanning acoustic microscopy (SAM), infrared thermography (IRT), magnetic current imaging (MCI) and surface acoustic waves (SAW) are explicitly reviewed. The uniqueness of the paper lies in comprehensive comparison of the current NDT methods, recommendations for the improvements, and introduction of new candidate NDT technologies, which can be adopted for IC packaging.
Offline Arabic Handwriting Recognition Using BLSTMs Combination

We propose in this paper, an Arabic handwriting recognition system based on multiple BLSTM-CTC combination architectures. Given several feature sets, the low-level fusion consisted in projecting them into a unique feature space. Mid-level combination methods were performed using two techniques: the first one consists in averaging the a-posteriori probabilities of each individual BLSTM, and injecting them in the CTC decoding. The second is based on the training of a new BLSTM-CTC system using the sum of the a-posteriori probabilities generated by the individual systems. The high-level fusion is based on the combination of the individual decoding outputs. Lattice combination and ROVER strategies were evaluated in this context. The experiments conducted on the KHATT database showed that the high-level combination method significantly improves the recognition rate compared to the other fusion strategies.
Occupational health and safety of road haulage company employees

Background: This paper follows up on a project that was launched in 2008 and contributed to the development of the new Italian Society of Occupational Medicine (SIML) guidelines for the road haulage industry. Objective: To reach a better understanding of occupational illness amongst truck drivers, in order to define appropriate health monitoring protocols and promote a healthy life-style. Methods: We assessed 673 male drivers (mean age 43.85 years, SD 9.56; mean working seniority 27.28 years, SD 10.59), employed by 46 different companies. The drivers, who were gradually recruited in the study over the years, had a maximum of 8 re-assessments each, for a total of 2608 examinations. We applied a survey protocol consisting in a medical examination, questionnaires for the most common risks and instrumental and laboratory tests in compliance with SIML guidelines. Results: We identified a total of 44 work-related diseases: 22 cases of noise-induced hearing loss (NIHL) and 22 cases of lumbar degenerative disc disease. As regards metabolic disorders, we observed 28 cases of diabetes mellitus, in most cases (71.4%) as a first diagnosis or under poor therapeutic control. We observed poorly-controlled hypertension in 103 drivers, the majority of whom (54%) were diagnosed for the first time. Over 30% of the workers in our study were obese and approximately 40% were tobacco smokers. We identified just 9 individuals (1.3%) with a positive toxicological screening for use of recreational drugs. Our data confirm a high prevalence of occupational illness amongst truck drivers. Cardiovascular and metabolic conditions require close monitoring.
GPU-based PQGA algorithm for estimating parameters of Muskingum model

Accurate parameter estimation for the Muskingum method is important in its use for forecasting flood damage. Combining ideas from quantum computing and evolutionary computing, a quantum genetic algorithm (QGA) is presented for this purpose, which is shown to offer higher precision and greater robustness than eight typical methods. However, it requires longer computation time. In this paper, we therefore provide an implementation of a parallel quantum genetic algorithm (PQGA) in CCUDA for estimating the parameters of the Muskingum model. PQGA was tested on a classic river example from China. Experimental results show that the PQGA scales well with available computing resources and is substantially faster than QGA and the other methods.
Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatment. However, some methodologic challenges in the implementation of MR are particularly pertinent when applying this method to cancer etiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies. In this review, we provide an overview of the key principles and assumptions of MR, focusing on applications of this method to the study of cancer etiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared with conventional epidemiological studies. Finally, limitations of MR and recent methodologic developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer. Cancer Epidemiol Biomarkers Prev; 27(9); 995–1010. ©2018 AACR.
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards

The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effective exploration strategy to deal with sparse or misleading reward scenarios: if they do not experience any state with a positive reward during the initial random exploration, it is very unlikely to solve the problem. Here, we propose a novel model-based policy search algorithm, Multi-DEX, that leverages a learned dynamical model to efficiently explore the task space and solve tasks with sparse rewards in a few episodes. To achieve this, we frame the policy search problem as a multi-objective, model-based policy optimization problem with three objectives: (1) generate maximally novel state trajectories, (2) maximize the expected return and (3) keep the system in state-space regions for which the model is as accurate as possible. We then optimize these objectives using a Pareto-based multi-objective optimization algorithm. The experiments show that Multi-DEX is able to solve sparse reward scenarios (with a simulated robotic arm) in much lower interaction time than VIME, TRPO, GEP-PG, CMA-ES and Black-DROPS.
KNITIT: a computational tool for design, simulation, and fabrication of multiple structured knits

Highly specified digital control tools for the customization of knitted fabrics is an existing technology, widely used industrially by trained knitters. Some fabrication challenges inhibit the extent of use of digital customization for knitted products on a mass scale, specifically when complex multiple structured knits are involved. This is due, in part, to physical changes that occur in the overall fabric dimensions, when stitch combinations with different physical attributes are combined within the same fabric. Existing computational tools fail to assist in the simulation and prediction of these overall deformations and the industry as a whole relies on the expertise of highly trained individuals. The outline shape of the fabric is of specific importance as it is commonly preconfigured to a specific shape and dimension that must be reproducible. In this work, we propose a computational parametric tool for digitally designing and industrially producing knitted fabrics, creating a direct link between design and manufacturability. The tool includes an integrated physical simulation component for estimating the fabric deformation. It allows users to visualize and control compensations in the fabric structure, to better match between the initial graphic intent of the design and the actual physical knitted fabric outcome. Our approach aims to reduce the number of iteration cycles for knitting material samples, especially when knitting highly varied designs.
Resisting Unwanted Sexual and Social Advances: Perspectives of College Women and Men

This study examines college men’s (N = 635) and women’s (N = 650) reports of women’s resistance against unwanted sexual and social advances. Men completed surveys to assess (a) initiation of unwanted sexual contact with a female partner, (b) initiation of unwanted sexual intercourse with a female partner, (c) sexual situations where a female partner decided she did not want to proceed, (d) encouraging a woman to drink when they were aware that the woman did not want to, and (e) attempting to take a woman to an isolated location against her will. Women completed corresponding questionnaires regarding the receipt of such unwanted advances, the outcome of the situation, and the resistance tactics utilized. Many men (5%-46%) and women (15%-38%) initiated or experienced, respectively, at least one of the five unwanted sexual or social advances. Unwanted sexual and social advances most often stopped as a result of women’s verbal and physical resistance. Few advances (0%-8%) stopped as a result of bystander intervention. In addition to decreasing men’s likelihood of engaging in unwanted sexual and social advances, these data highlight the importance of educating women on the effectiveness of verbal and physical resistance tactics, and increasing the frequency of prosocial bystander action on college campuses.
"Ora sì ch'io son contenta: sembra fatto inver per me" Identifying User Requirements for Wearable Augmented Reality at Opera

The purpose of this study is to propose a User Experience (UX) model to support the design of a wearable Augmented Reality (AR) application for Opera. To this end, it implements the UX approach by Hassenzahl and Tractinsky [17] and User-Centered Design methods within a People Action Context Technologies (PACT) analysis. The preliminary results identify users’ requirements relating to People, Actions and Contexts for a visual AR interface at Opera. The study contributes to generate useful knowledge to support the design of visual AR interfaces for predominantly hedonic contexts, such as performative arts. Further, being among the first to investigate UX for wearable AR at Opera, this study provides a foundation for the implementation of wearable digital applications in this context.
Visual attention is not deployed at the endpoint of averaging saccades

The premotor theory of attention postulates that spatial attention arises from the activation of saccade areas and that the deployment of attention is the consequence of motor programming. Yet attentional and oculomotor processes have been shown to be dissociable at the neuronal level in covert attention tasks. To investigate a potential dissociation at the behavioral level, we instructed human participants to move their eyes (saccade) towards 1 of 2 nearby, competing saccade targets. The spatial distribution of visual attention was determined using oriented visual stimuli presented either at the target locations, between them, or at several other equidistant locations. Results demonstrate that accurate saccades towards one of the targets were associated with presaccadic enhancement of visual sensitivity at the respective saccade endpoint compared to the nonsaccaded target location. In contrast, averaging saccades, landing between the 2 targets, were not associated with attentional facilitation at the saccade endpoint. Rather, attention before averaging saccades was equally deployed at the 2 target locations. Taken together, our results reveal that visual attention is not obligatorily coupled to the endpoint of a subsequent saccade. Rather, our results suggest that the oculomotor program depends on the state of attentional selection before saccade onset and that saccade averaging arises from unresolved attentional selection.
A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets

This article describes how, recently, data mining has been in great use for extracting meaningful patterns from medical domain data sets, and these patterns are then applied for clinical diagnosis. Truly, any accurate, precise and reliable classification models significantly assist the medical practitioners to improve diagnosis, prognosis and treatment processes of individual diseases. However, numerous intelligent models have been proposed in this respect but still they have several drawbacks like, disease specificity, class imbalance, conflicting and lack adequacy for dimensionality of patient's data. The present study has attempted to design a hybrid prediction model for medical domain data sets by combining the decision tree based classifier (mainly C4.5) and the decision table based classifier (DT). The experimental results validate in favour of the claims.
Comparing Different Feedback Modalities in Assisted Transcription of Manuscripts

Transcription of handwritten text can be speed-up by using off-line Handwritten Text Recognition techniques, that allow the obtention of an initial draft transcription of an image with handwritten text. However, this draft transcription usually contains errors that must be amended by the transcriber by providing a feedback signal. The usual approach is post-edition, where each error is corrected without modifying the rest of the current transcription. A more sophisticated approach can employ the current modification to provide a new whole transcription, hopefully with less errors. Apart from that, feedback can be provided in different modalities: keyboard input, on-line handwritten text, or speech. Each of these modalities presents different features with respect to ambiguity, derived errors, and final transcription time. In this work we study how the different modalities behave in the assisted transcription of a historical handwritten text document in Spanish and we evaluate their transcription productivity.