Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a sweet pepper harvesting robot

This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB‐D camera, high‐end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end‐user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4‐week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.
Modern faces of filtration

The filtration method for proving decidability in a focused minimal manner is a highlight of modal logic, widely used, but also posing a bit of a challenge as to its scope and what makes it tick. In this paper, we bring together a number of modern perspectives on filtration, including model-theoretic and proof-theoretic ones. We also include a few more unusual recent connections with dynamic logics of model change and logics of questions and issues. Finally, we analyze where the filtration method fails in full first-order logic, and what it still has to say there.
Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary, multi‐scale approach

Abstract Retention forestry, which retains a portion of the original stand at the time of harvesting to maintain continuity of structural and compositional diversity, has been originally developed to mitigate the impacts of clear‐cutting. Retention of habitat trees and deadwood has since become common practice also in continuous‐cover forests of Central Europe. While the use of retention in these forests is plausible, the evidence base for its application is lacking, trade‐offs have not been quantified, it is not clear what support it receives from forest owners and other stakeholders and how it is best integrated into forest management practices. The Research Training Group ConFoBi (Conservation of Forest Biodiversity in Multiple‐use Landscapes of Central Europe) focusses on the effectiveness of retention forestry, combining ecological studies on forest biodiversity with social and economic studies of biodiversity conservation across multiple spatial scales. The aim of ConFoBi is to assess whether and how structural retention measures are appropriate for the conservation of forest biodiversity in uneven‐aged and selectively harvested continuous‐cover forests of temperate Europe. The study design is based on a pool of 135 plots (1 ha) distributed along gradients of forest connectivity and structure. The main objectives are (a) to investigate the effects of structural elements and landscape context on multiple taxa, including different trophic and functional groups, to evaluate the effectiveness of retention practices for biodiversity conservation; (b) to analyze how forest biodiversity conservation is perceived and practiced, and what costs and benefits it creates; and (c) to identify how biodiversity conservation can be effectively integrated in multi‐functional forest management. ConFoBi will quantify retention levels required across the landscape, as well as the socio‐economic prerequisites for their implementation by forest owners and managers. ConFoBi's research results will provide an evidence base for integrating biodiversity conservation into forest management in temperate forests.
Can Society Function Without Ethical Agents? An Informational Perspective

Many facts are learned through the intermediation of individuals with special access to information, such as law enforcement officers, officials with a security clearance, or experts with specific knowledge. This paper considers whether societies can learn about such facts when information is cheap to manipulate, produced sequentially, and these individuals are devoid of ethical motive. The answer depends on an "information attrition" condition pertaining to the amount of evidence available which distinguishes, for example, between reproducible scientific evidence and the evidence generated in a crime. Applications to institution enforcement, social cohesion, scientific progress, and historical revisionism are discussed.
Induced Currents and AC Losses Models for a Butt-Joint With Rutherfords Shunts

The ITER Central Solenoid (CS) has terminal butt-type joints called Coaxial joints. It was decided to study a design of this joint with rutherford shunts, and to build models for its resistive and inductive behaviors. In particular, the behavior of the joint under magnetic field transients is investigated with various analytical models that are compared with a FEM model. The key point of the study was to verify that the induced currents were reasonable and would not induce flux jumps in the rutherfords. To validate the model, a prototype with simplified geometry was tested in the CEA Josefa facility under various field ramps. The comparison between model and experimental results are presented and discussed.
Limited Climate Response of Very Large Volcanic Eruptions

Extremely large volcanic eruptions have been linked to global climate change, biotic turnover, and, for the Younger Toba Tuff eruption 74,000 years ago, near-extinction of modern humans. One of the largest uncertainties of the estimated climate effects in previous model studies involves the aerosol size distribution. A huge sulfate load causes higher collision rates, larger particle sizes, and rapid fall out, which in turn greatly affects radiative feedbacks. Incorporating aerosol microphysical processes into global model simulations results in a smaller climate effect than estimated before.
Global Warming Due to Albedo & Hydro-Hotspots Humidity Forcing A Lack of IPCC Albedo Goals

Understanding root causes is always needed to find proper solutions. In climate change, we must ask, what has historically changed? Besides CO2, we have a change in the specific and relative humidity, slight decrease in land albedo, and yearly growth of Hydro-HotSpots (HHS). We denote hydro-hotspot as water evaporation and bulk heating from low albedo manmade type roads and cities surfaces (often called urban heat islands), including cars and engine hoods. This includes both Highly Evaporating Surfaces (HES) and bulk warm waste Rain Water Management (RWM) where billions of gallons of water is into rivers and the ocean each year causing numerous concerns. This is Humidity Forcing (HF) related to albedo forcing and the creation of HHS. Most significant is land albedo forcing. Modeling provided are in agreement with other authors that albedo forcing due to cities and roads are a major effect on global warming. This also feeds most of the HHS. We show in this article that such surfaces, while seemingly covering only about 1% of the Earth, can have very large effective solar and evaporation areas many times the size of the HES and RWM area itself compared with higher albedo absorbing vegetative areas that also include transpiration. This is significant since water vapor is a potent GreenHouse (GH) gas. City surfaces can prove to be enormous when tall buildings are considered. In addition, active hydro-hotspots will decrease relative humidity while increasing specific humidity. We are able to estimate the large percentage of global warming contribution due to albedo and humidity HHS forcing compared to CO2 increase. This leads to the conclusion that changing the albedo of cities and roads is a main solution to global warming. This paper, then points to numerous concerns including the lack of IPCC albedo goals for cities and roads. Specifically, it is concluded that there is not enough proof that CO2 goals will be enough to stop global warming trends in light of the complex influences on global warming from Cities and Roads.
Novel Machine Learning Algorithms for Centrality and Cliques Detection in Youtube Social Networks

The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
Quasi-likelihood Estimation with R

We introduce the R package qle for simulation-based quasi-likelihood parameter estimation. We briefly summarise the basic theory of quasi-likelihood for our setting and outline the algorithmic framework of the proposed method. We focus on the general workflow using the package and present two introductory examples. Finally, we apply the method to an example data set from spatial statistics.
Recommendations for Promoting Patient Engagement in Chronic Care: A Consensus Conference Statement

1. EngageMinds Hub – Consumer, Food & Health Research Center, Università Cattolica del Sacro Cuore (Italy); 2. Department of Psychology, Catholic University of Milan (Italy); 3. Casa di Cura Privata del Policlinico, Milan (Italy); 4. Department of Public Health, Università Cattolica del Sacro Cuore, Fondazione Policlinico ‘Agostino Gemelli’, Rome (Italy) 5. Higher Institute of Health, Rome (Italy).
Capsule Network Performance With Autonomous Navigation

Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios. In real world navigation, rewards external to agents may be rare. In turn, reinforcement learning algorithms can struggle to form meaningful policy functions. This paper's approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage Actor Critic algorithm. Other approaches for navigating sparse environments require intrinsic reward generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM). Caps-EM uses a more compact architecture without need for intrinsic rewards. Tested using ViZDoom, the Caps-EM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and D-ACM, respectively, for converging to a policy function across "My Way Home" scenarios.
LANGUAGE MODELS MORE GROUNDED

Many vision and language models suffer from poor visual grounding – often falling back on easy-to-learn language priors rather than associating language with visual concepts. In this work, we propose a generic framework which we call Human Importance-aware Network Tuning (HINT) that effectively leverages human supervision to improve visual grounding. HINT constrains deep networks to be sensitive to the same input regions as humans. Crucially, our approach optimizes the alignment between human attention maps and gradient-based network importances, ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We demonstrate our approach on Visual Question Answering and Image Captioning tasks, achieving state-of-the-art for the VQA-CP dataset which penalizes overreliance on language priors.
Mucocele in paediatric patients: A case series with review

Mucocele is the most common lesion of the oral mucosa, which results from the accumulation of mucous secretion due to trauma and lip biting habits or alteration of minor salivary glands. It is the most common minor salivary gland lesion in children and young adults. Diagnosis is mostly based on clinical findings and history. Various treatment options include marsupialization, surgical excision, dissection, laser ablation, cryosurgery, electrocautery, intra-lesional steroid injections and irradiation. Here we are reporting two cases of mucocele with comprehensive review on its etiopathogenesis and treatment
Managerial Macro-Cognition and the Speed of Leverage Adjustment

This study takes advantage of the concept of “Managerial Macro-Cognition” (MMC) to explore how does the different macro cognition of executives will affect their capital structure decision. Drawing on a sample of Chinese listed firms from 2002 to 2018, I find that under the same conditions, the enterprise has executives with higher macro cognition can adjust its capital structure faster, the promote effect on the speed of the leverage adjustment caused by the macro cognition of executive is more obvious when cognition is about future. Besides, when the company is faced with financing constraints, tight monetary policy and low equity concentration, the influence of executives’ macro cognition on the adjustment speed is more obvious.
Treating Anxiety with a Beta Blocker - Antimuscarinic Combination: A Review of Compounded Atenolol - Scopolamine

A patented new class of anti-anxiety medications consists of a beta blocker plus an antimuscarinic agent to inhibit the sympathetic and parasympathetic symptoms of anxiety disorders, respectively. The PanX® medications are non-benzodiazepines to address the unmet medical need for fast-acting and effective anxiolytics, without using active ingredients known to produce dependence or addiction. The effects and side effects of compounded Atenolol - Scopolamine HBr were assessed in physician-sponsored studies in psychiatry and pain management settings. A total of 22 patients affected by Generalized Anxiety Disorder, Social Anxiety Disorder, Post-Traumatic Stress Disorder, and/or Panic Attack, or acute anxiety conditions (e.g., situational anxiety associated with a medical procedure or Substance Use Disorders) were treated prn with Atenolol 25 mg - Scopolamine HBr 0.2 mg. Eighteen patients (82 percent) were responders to this combination drug approach. The patients perceived a calming effect in less than 30 minutes via oral mucosal delivery or less than 60 minutes by oral ingestion, and the perceived calming effect lasted up to 8 hours. The calming effect was further substantiated using a 0-10 point subjective anxiety score and the “State” anxiety symptom severity was assessed using the Beck Anxiety Instrument (BAI-S). Heart rate and blood pressure were reduced at 1 hour and 4-5 hours, consistent with the effects of a beta blocker.
Numerical Simulation of Electron Amplification Process in Micro Channel Plate by Using Furman's Model

In measurement devices of very high-speed phenomena such as the framing camera and streak camera, the Micro Channel Plate (MCP) plays very important role for amplification of very small signals. It is necessary to understand the electron amplification process of the MCP for further improvement of these high-speed mesurement devices. This paper presents a numerical simulation method of the MCP based on the Monte Carlo calculation and Furman’s secondary electron emission model. It is shown that electron amplification phenomena by the secondary emission in the MCP can be simulated. Keywords— Micro Channel Plate (MCP), FDM, Monte Carlo Method, Furman's model, Lorentz equation of motion, Runge-Kutta method, Electrostatic field.
On Optimal Resource Allocation for Hybrid VLC/RF Networks With Common Backhaul

The synergy between visible light communication (VLC) and radio frequency (RF) networks has attracted a considerable amount of attention due to the envisioned improvements compared to conventional systems, mainly in terms of data rate and coverage. In this paper, we investigate for the first time the coexistence of VLC and RF networks, assuming that both networks are served by a common backhaul network, as well as both perfect and imperfect channel state information (CSI). In this context, we propose an optimal resource allocation scheme that maximizes the corresponding data rate, while also taking into account the fairness among the involved users. This is of paramount importance because in such heterogeneous networks, a standard rate maximization approach yields a severely degraded performance for the weaker users. In order to provide a tractable solution to the formulated problem, which is non-convex, we transform this into an equivalent convex one. Moreover, a simplified power allocation problem is solved, which provides comparable results with substantially lower complexity. Finally, extensive simulations illustrate the validity and effectiveness of the proposed analysis, and provide valuable insights on the impact of the imperfect CSI on the overall network performance.
Mining User-Generated Financial Content to Predict Stock Price Movements

Sentiment extraction from user-generated online content to predict stock price movements has become an active research field. This paper gives an overview of common approaches to this topic and analyzes the content generated by the financial social network Seekingalpha.com. The first finding is that a large proportion of users’ attention is focused on only a few stocks. Regarding these stocks it can be shown that sentiment is significantly driven by past abnormal performance. Only the sentiment of premium users contains some degree of predictive power. Generally, the users’ sentiment is consistent with a naïve momentum mentality.
Single implant supported fixed prosthesis in anterior zone: A review

The goal of modern dentistry is to restore the patient to normal contour, function, comfort, esthetics, speech, and health, whether restoring a single tooth with caries or replacing several teeth. A treatment option to replace a single missing tooth is a single-tooth implant. The replacement of a single tooth in the anterior region is challenging because of the highly specific soft and hard tissue criteria, in addition to all other esthetic, phonetic, functional, and occlusal requirements. Since 1993, single-tooth implant survival has demonstrated that this procedure is the most predictable method of tooth replacement. 100% survival rates of single tooth implants has been reported. Anterior single-tooth replacement is often a challenge, regardless of the experience and skill of the dentist. This review article comprehensively summarizes the therapeutic situation “single-tooth restoration in the esthetic region” and provides the clinician with relevant information and state-of-the-art knowledge on both surgical and prosthetic procedures.
PopPAnTe: Population and Pedigree Association Testing of Quantitative Data —— Users’ Guide

PopPAnTe is a user-friendly Java program that enables association studies of quantitative data in related samples. Relationships between individuals can be either described by known family structures of any size and complexity, or by genetic similarity matrices (GSM) inferred from genome-wide genetic data. This approach is particularly useful when some degree of hidden relatedness (including population stratification) is expected, but extensive genealogical information is missing or incomplete, thus facilitating the usage of biobank collections from population isolates.
Measuring gerrymandering by recovering individuals’ preferences and turnout costs

Legislative maps are often evaluated along dimensions of proportionality (the alignment between parties’ seat shares and their stateor nation-wide vote shares) and competitiveness (the fraction of contests with uncertain winners). Since a map is intended to be used for multiple elections, policymakers want to accurately predict how it will perform on these dimensions in the future. Doing this is difficult because future elections will differ from past ones due to changes in the demographic composition of the electorate and as a result of electoral shocks to preferences and turnout costs. Citing this uncertainty, the U.S. Supreme Court recently ruled that the judicial system is incapable of adjudicating claims of partisan gerrymandering. In this paper, I develop a method for predicting the uncertainty in a map’s performance due to these factors. The method relies on a structural voting model, which describes the preference and turnout decisions of a potential voter. The model decomposes an election into (i) a set of candidate qualities and (ii) individual-level utility parameters. I assess map performance in two steps. First, I examine the effect of electoral shocks by simulating alternative values of the candidate qualities and utility parameters. Second, I investigate the influence of demographic changes by re-running the simulations using different electorates. I apply the method to rich data from the 2008 to 2018 general elections in North Carolina and show that it allows credible and precise evaluations of maps. I also show that the method is better than existing approaches at predicting gerrymandering outcomes in excluded elections. ∗I thank my advisers, Miguel Urquiola, Suresh Naidu, and Cristian Pop-Eleches, for much guidance and support. I am also grateful to Doug Almond, Matt Backus, Michael Best, Alessandra Casella, Don Davis, Wojciech Kopczuk, Cameron LaPoint, Bentley Macleod, Andy Pham, Miikka Rokkanen, Tobias Salz, Jeffrey Shrader, and numerous seminar participants. All errors are my own.
Does ICT-Trade Openness ensure Energy and Environmental Sustainability? Empirical Evidence from selected South Asian Economies

Consumption of fossil fuels has triggered worldwide awareness to attain sustainability with respect to ensuring adequate energy access and mitigating environmental adversities, globally. Against this background, this paper aimed at investigating the impacts of enhancing ICT-trade openness on the transition from non-renewable to renewable energy use and carbon dioxide emissions in the context of six South Asian economies. The overall results from the econometric analyses confirm that greater openness to ICT-trade leads to greater consumption of renewable energy, reduces the intensity of energy-use and enhances the access to clean fuel and technology for cooking. However, although ICT trade is found to foster renewable energy consumption across South Asia, it fails to ensure renewable energy transition completely since greater openness to ICT-trade curbs the share of renewables in the aggregate energy consumption figures. Moreover, trade of ICT goods is found to reduce the levels of carbon emissions as well. Thus, these results impose key policy implications for the governments with respect to ensuring energy security alongside environmental sustainability across South Asia.