Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinación de la pubescencia de cuatro genotipos de habichuela, Phaseolus vulgaris L.

Se estudió la pubescencia de cuatro genotipos de Phaseolus vulgaris L. que bajo condiciones de campo demostraron diferencias en la reacción al virus del mosaico dorado. Las hojas de los genotipos A-429, DOR-303, 27-R y PC-50 mostraron sólo dos tipos de tricomas sin glándulas. Estos tricomas son aciculares (en forma de aguja) y unciformes (en forma de gancho). Entre el haz y envés de las hojas de los cuatro genotipos evaluados hubo diferencias en densidad y tamaño de los dos tipos de tricomas. Los tricomas en las hojas de cada uno do los genotipos varían tanto en número como en tamaño según el tipo de hoja. La línea A-429 fue la de hojas menos pubescentes y con menos densidad de tricomas por centímetro cuadrado. La línea DOR-303 mostró un mayor densidad de tricomas unciformes, pero de menor tamaño. En la variedad PC-50 hubo más tricomas aciculares en el envés que tricomas unciformes. En términos generales, la variedad 27-R fue la de mayor densidad de tricomas y en la que con mayor regularidad se encontraron los dos tipos de tricomas.
Multimodal transport mode co-organized optimization model based on the environment

Considering the impact of the environment pollution,energy consumption and security factors on multimodal transport sustainable development,the concept of multimodal social total cost was considered.Then based on the target of minimizing the multimodal social total cost,the multimodal transportation coordination optimization model was establised.A variety of cost data and transport data of transport pollution energy and security were collected and disposed respectively,and then the fuzzy comprehensive evapuation method was used to confirm the cost weight.Finally,the lingo software was used to solve the model and the results were evaluated.The data of transport,pollution,energy and security cost of transport modes were provided.The results show that the multimodal transport social costs of railway transport and water transport are low,and they have little effects on environment.In the formulation of multimodal decisions,to realize green development of the multimodal transport,multimodal influence factors should be synthetically considered,and then choose a reasonable transport mode.
A New Formulation for Strigolactone Suicidal Germination Agents, towards Successful Striga Management

Striga hermonthica, a member of the Orobanchaceae family, is an obligate root parasite of staple cereal crops, which poses a tremendous threat to food security, contributing to malnutrition and poverty in many African countries. Depleting Striga seed reservoirs from infested soils is one of the crucial approaches to minimize subterranean damage to crops. The dependency of Striga germination on the host-released strigolactones (SLs) has prompted the development of the “Suicidal Germination” strategy to reduce the accumulated seed bank of Striga. The success of aforementioned strategy depends not only on the activity of the applied SL analogs, but also requires suitable application protocol with simple, efficient, and handy formulation for rain-fed African agriculture. Here, we developed a new formulation “Emulsifiable Concentration (EC)” for the two previously field-assessed SL analogs Methyl phenlactonoate 3 (MP3) and Nijmegen-1. The new EC formulation was evaluated for biological activities under lab, greenhouse, mini-field, and field conditions in comparison to the previously used Atlas G-1086 formulation. The EC formulation of SL analogs showed better activities on Striga germination with lower EC50 and high stability under Lab conditions. Moreover, EC formulated SL analogs at 1.0 µM concentrations reduced 89–99% Striga emergence in greenhouse. The two EC formulated SL analogs showed also a considerable reduction in Striga emergence in mini-field and field experiments. In conclusion, we have successfully developed a desired formulation for applying SL analogs as suicidal agents for large-scale field application. The encouraging results presented in this study pave the way for integrating the suicidal germination approach in sustainable Striga management strategies for African agriculture.
Molecular Dynamics Study of the Diffusion between Virgin and Aged Asphalt Binder

The diffusion between the virgin and aged asphalt binder in the recycled asphalt mixture is a crucial factor affecting its macro-mechanical performance. In this study, a combined model of the virgin-aged layered asphalt structure was assembled based on the molecular dynamics (MD) method. A four-component and twelve-category molecule were used to model the asphalt. The diffusion behaviors of the virgin and aged asphalt were characterized by mean square displacement (MSD), diffusion coefficient, relative concentration and cohesive energy density (CED). Results indicated that at the same temperature, the diffusion coefficient of the virgin asphalt was the largest, followed by the virgin-aged asphalt and the aged asphalt. As the temperature increased, the relative concentration on both sides of the virgin-aged asphalt overlapped to a certain extent. The covered lengths of the virgin asphalt were larger than those of the aged asphalt, indicating the diffusion between the virgin asphalt and aged asphalt was mainly manifested as the diffusion from the virgin asphalt to the aged asphalt. The development of CED and the fraction of free volume (FFV) indicated the mutual attractive interactions among the molecules in virgin and aged asphalt layers became strong and the cohesion properties inside the model became better.
High Specificity of Single Inertial Sensor-Supplemented Timed Up and Go Test for Assessing Fall Risk in Elderly Nursing Home Residents

The Timed Up and Go test (TUG) is commonly used to estimate the fall risk in the elderly. Several ways to improve the predictive accuracy of TUG (cameras, multiple sensors, other clinical tests) have already been proposed. Here, we added a single wearable inertial measurement unit (IMU) to capture the residents’ body center-of-mass kinematics in view of improving TUG’s predictive accuracy. The aim is to find out which kinematic variables and residents’ characteristics are relevant for distinguishing faller from non-faller patients. Data were collected in 73 nursing home residents with the IMU placed on the lower back. Acceleration and angular velocity time series were analyzed during different subtasks of the TUG. Multiple logistic regressions showed that total time required, maximum angular velocity at the first half-turn, gender, and use of a walking aid were the parameters leading to the best predictive abilities of fall risk. The predictive accuracy of the proposed new test, called i + TUG, reached a value of 74.0%, with a specificity of 95.9% and a sensitivity of 29.2%. By adding a single wearable IMU to TUG, an accurate and highly specific test is therefore obtained. This method is quick, easy to perform and inexpensive. We recommend to integrate it into daily clinical practice in nursing homes.
Are Roma People Descended from the Punjab Region of Pakistan: A Y-Chromosomal Perspective

Gypsies are a separate ethnic group living in Pakistan and some other countries as well. They are mostly known as ‘Roma’ and ‘untouchables’. They have different types of lifestyles as compared to other common people, as they always keep migrating from one place to another. They do not have proper houses; they live in tent houses and most probably work on daily wages to earn their living. Gypsies cannot be specified according to the place of residence and can only be classified according to their migration route. Previous historical and linguistic research showed the north Indian origin of Roma people. The present study collected 285 unrelated Roma individuals living in Punjab and typed with the Goldeneye Y20 system. Allelic frequencies ranged between 0.0035 and 0.5266, with haplotype diversity (HD) of 0.9999 and discrimination capacity (DC) of 0.8790. Gene diversity (GD) ranged from 0.6489 (DYS391) to 0.9764 (DYS391) (DY385ab). A total of 223 unique alleles were observed. Interestingly, the haplogroup R accounted for 40.56% and J for 22.06%. In MDS analysis, Pakistani Roma formed a close cluster with Roma from Constanta, Romania. The migration pattern of the Roma population from Pakistan, India and Europe was inferred using coalescence theory in the Migrate-n program. Overlapping Y-STR data were used to test different migration models. These migration models showed us the dominant gene flow from Pakistan to India and Europe to Pakistan. The results of our study showed that Y STRs provided substantially stronger discriminatory power in the Pakistani Roma population.
Incremental Machine Learning to Detect Fake New Using Support Vector Machine

: As one of the biggest online market worldwide, 97.4% of internet users in Indonesia are active on social media [1]. Facebook, one of the most common social media in Indonesia, provides a platform to spread information throughout its user. However, fact-based information is not the only one circulate on the Internet. The number of fake news shared throughout the Internet, especially social media, is concerning. Investigating fake news requires considerably longer time in collecting the data to compare. In addition, humans naturally are not very good at differentiating between real and fake news [3]. It makes machine learning becomes advantageous in dealing to this problem. However, the rapid changes of news throughout time requires machine learning to be able to train its model dynamically. Incremental machine learning is proposed to solve this problem. As much as 6757 labeled data containing both fake and factual news provided by George McIntire, Politifact, and Buzzfeed are set to be primary data in this study. In addition, over 30.000 crawled news from various reliable sources are prepared to observe the most efficient data ratio to train the model. Based on model-selection approach, Support Vector Machine outperformed the other models with the initial accuracy of 0.889. Along with feature extraction, parameter tuning, and feature selection, performance of the incremental machine learning can reach over 96% accuracy.
KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings

Knowledge-grounded dialogue systems utilise external knowledge such as knowledge graphs to generate informative and appropriate responses. A crucial challenge of such systems is to select facts from a knowledge graph pertinent to the dialogue context for response generation. This fact selection can be formulated as path traversal over a knowledge graph conditioned on the dialogue context. Such paths can originate from facts mentioned in the dialogue history and terminate at the facts to be mentioned in the response. These walks, in turn, provide an explanation of the flow of the conversation. This work proposes KG-CRuSE, a simple, yet effective LSTM based decoder that utilises the semantic information in the dialogue history and the knowledge graph elements to generate such paths for effective conversation explanation. Extensive evaluations showed that our model outperforms the state-of-the-art models on the OpenDialKG dataset on multiple metrics.
STT: Soft Template Tuning for Few-Shot Learning

With the rapid expansion of large pre-trained 001 language models, fine-tuning all the model pa-002 rameters for downstream tasks is becoming 003 computationally prohibitive. The recently de-004 veloped prompt-based methods freeze the en-005 tire model parameters and only update the so-006 called prompt parameters appended to the in-007 puts, significantly reducing the burden of fully 008 fine-tuning. However, standard prompt-based 009 methods mainly consider the case where suf-010 ficient data of downstream tasks are available. 011 It is still unclear whether the advantage can be 012 transferred to the few-shot regime, where only 013 limited data are available for each downstream 014 task. Our empirical studies suggest there is 015 still a gap between prompt tuning and fully 016 fine-tuning for few-shot learning. We propose 017 a new prompt-tuning framework, called Soft 018 Template Tuning (STT), to bridge the gap. STT 019 combines manual prompts and auto-prompts, 020 and treats downstream classification tasks as a 021 masked language modeling task. STT can close 022 the gap between fine-tuning and prompt-based 023 methods without introducing additional param-024 eters. Importantly, it can even outperform 025 the time-and resource-consuming fine-tuning 026 method on sentiment classification tasks. 027
Hyntrospect: a fuzzer for Hyper-V devices

Hypervisors are complex software which may require the reimplementation of legacy stacks. On Microsoft Hyper-V virtual machines (generation 1), some devices are emulated in the userland of its root partition. To explore this attack surface, a speciﬁcally crafted open source toolchain called Hyntrospect has been developed. It aims at helping ﬁnd vulnerabilities in a pragmatic way: by taking beneﬁts of existing Hyper-V and Windows capabilities and tools to perform coverage-guided fuzzing on Hyper-V closed-source binaries. That approach was inspired by previous experiences with libFuzzer, a publication by Microsoft on their fuzzing campaign, and other research conducted on the topic. The speciﬁcity of that tool is to rely on debugging and as a consequence to run in a real environment. It was also written in the perspective of putting together techniques that could be ported in the future to other Hyper-V root partition’s userland targets. After covering Hyper-V and the state of the art on its instrumentation and research, this paper introduces Hyntrospect and its associated design choices, and ﬁnally describes the outcome of the ﬁrst runs and future endeavors.
lar networks learn other tasks (e.g. past tense

With a vast expansion in available computa002 tional power, as well as the (re)discovery of 003 some architectural and mathematical tricks, 004 connectionist, viz. neural network-based, mod005 eling of (morpho)phonological phenomena 006 has seen a resurgence, with questions and ap007 proaches from past decades being rediscov008 ered, revisited, and revised (Alderete and Tup009 per, 2017; Kirov and Cotterell, 2018; Prickett, 01
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis

Pre-trained language models (PLMs) have gained increasing popularity due to their compelling prediction performance in diverse natural language processing (NLP) tasks. When formulating a PLM-based prediction pipeline for NLP tasks, it is also crucial for the pipeline to minimize the calibration error, especially in safety-critical applications. That is, the pipeline should reliably indicate when we can trust its predictions. In particular, there are various considerations behind the pipeline: (1) the choice and (2) the size of PLM, (3) the choice of uncertainty quantifier, (4) the choice of fine-tuning loss, and many more. Although prior work has looked into some of these considerations, they usually draw conclusions based on a limited scope of empirical studies. There still lacks a holistic analysis on how to compose a well-calibrated PLM-based prediction pipeline. To fill this void, we compare a wide range of popular options for each consideration based on three prevalent NLP classification tasks and the setting of domain shift. In response, we recommend the following: (1) use ELECTRA for PLM encoding, (2) use larger PLMs if possible, (3) use Temp Scaling as the uncertainty quantifier, and (4) use Focal Loss for fine-tuning.
The Mind’s Design: The Neuroscience of Stress and Resilience

Resilience and the ability to manage stress contribute to life satisfaction and success. Resiliency contributes to academic and professional success, healthy interpersonal relationships, confidence in oneself, and is a positive predictor of life satisfaction. The body’s stress response is a highly orchestrated biological action that can help or hinder resiliency. Understanding both the psychological and neuroscience components of stress and resiliency aids in understanding the “resilient person” prototype and offers insights into what interventions might increase the highly desired characteristic of resiliency.
Private measurement of nonlinear correlations between features hosted across multiple parties

We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. We provide utility guarantees of our private estimator. Ours is the first such private estimator of nonlinear correlations within a multi-party setup. The important measure of nonlinear correlation that we privatize is that of distance correlation . Several methods use distance correlation, or a post-processing of distance correlation for feature screening, independence testing, k-sample testing, causal direction estimation, copula based data synthesis in addition to exploratory data analysis. Thereby a private measurement of distance correlation between data variables hosted on two parties, is key in order to perform these applications in a privatized manner in distributed setups. With correlations being fundamental to statistics and data sciences, this work thereby has a multitude of downstream applications beyond the above mentioned examples. Code access: A link to publicly access the code is provided in the appendix.
Distribution Efficiency Analysis of Battery Swapping Station Considering Road Cost

It is essential to take the cost of electric vehicles on the road into account in the distribution program for swapping stations,as irrational distribution of centralized swapping stations would increase the cost,reduce the efficiency of power plants and electric vehicles and cause problems like deviation from distribution network planning,investment waste of transformers and other equipment.Thus,this paper proposes an efficiency analysis through two indicators,the effective service rate and the average load based on the road cost analysis for the distribution program for swapping stations.With this method,the efficiency analysis of the distribution program in a region will be made through behavioral simulation of indicators calculated by dividing the region and developing behavioral models for electric vehicles.
Limits on gestural reorganization following vowel deletion: The case of Tokyo Japanese

The coordination of gestures in consonant clusters differs across languages and hence must be a learned aspect of linguistic knowledge. Precisely pinning down the coordination relation used in a particular language, or for a particular consonant cluster type, has been facilitated by recent research showing that coordination relations structure kinematic variation in unique ways. We ap-ply these methods to a hitherto under-explored topic, the coordination of consonant clusters created via vowel deletion. Our case study involves fricative-fricative and fricative-stop consonant clusters resulting from the variable deletion of devoiced vowels in Tokyo Japanese. Examination of articulatory data obtained by Electromagnetic Articulography (EMA) show that some consonant clusters, i.e., fricative-stop clusters, show gestural reorganization whereas other cluster types, i.e., fricative-fricative sequences, behave as if a vowel remains in place, despite the fact that the tongue dorsum movement for the vowel is absent from the articulatory record. We discuss several theoretical possibilities to account for the differential effects of vowel deletion on gestural re-organization in these environments.
Development and investigation of a supercapacitor based on an aqueous electrolyte

The article is devoted to the development and research of a supercapacitor based on an aqueous electrolyte. The review of the current state in the field of energy storage is carried out. The processes occurring in the supercapacitor are considered. Sheets of paper with different carbon concentrations have been created. The design of the housing has been developed with the possibility of replacing the plates and electrolyte. This is an advantage in the process of conducting experiments. According to the results of the study, a prototype supercapacitor based on NaCl electrolyte with a capacity of 9F and a maximum output voltage of 1.2 V is presented.
Horseshoe Regularization for Machine Learning in Complex and Deep Models.

Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation, and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; see Bhadra et al. (2019b) for a systematic survey. The purpose of the current article is to demonstrate that the horseshoe regularization is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularization in nonlinear and non-Gaussian models; multivariate models; and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
Robot Learning and Execution of Collaborative Manipulation Plans from YouTube Cooking Videos

—People often watch videos on the web to learn how to cook new recipes, assemble furniture or repair a computer. We wish to enable robots with the very same capability. This is challenging; there is a large variation in manipulation actions and some videos even involve multiple persons, who collaborate by sharing and exchanging objects and tools. Furthermore, the learned representations need to be general enough to be transferable to robotic systems. On the other hand, previous work has shown that the space of human manipulation actions has a linguistic, hierarchical structure that relates actions to manipulated objects and tools. Building upon this theory of language for action, we propose a framework for understanding and executing demonstrated action sequences from full-length, unconstrained cooking videos on the web. The framework takes as input a cooking video annotated with object labels and bounding boxes, and outputs a collaborative manipulation action plan for one or more robotic arms. We demonstrate performance of the system in a standardized dataset of 100 YouTube cooking videos, as well as in three full-length Youtube videos that include collaborative actions between two participants. We additionally propose an open-source platform for executing the learned plans in a simulation environment as well as with an actual robotic arm.
Non Existence of Manifold Structures on Topological constructions

We show the non existence of manifold structure on certain topological constructions. The main results of this paper are: (1) The wedge of manifold with itself is not a manifold. (2) The cone of a manifold is not a manifold. The main tools for the main results are homological algebra and algebraic topology.