Miracle-dz/UniFAR-FTUs huggingface dataset:
Do farmers and scientists differ in their understanding and assessment of farm animal welfare?

In response to an increased public awareness regarding how livestock are reared, animal welfare scientists have attempted to develop new methods of welfare assessment at the farm level. Furthermore, in recent years they have increasingly moved away from the conventional approach of evaluating the provision of resources necessary to ensure good welfare, and have instead focused on the use of animal-based measures of welfare. In contrast, it is believed that farmers use mostly resource-based and management-based measures (eg the provision of food, water and housing) when assessing the welfare of their animals. They also seem to be driven more by economic and financial concerns than by the welfare of the animals per se, when it comes to the provision of animal welfare. Different approaches to the definition and assessment of farm animal welfare were explored in work carried out at Newcastle University as part of the Welfare Quality® project by both social and welfare scientists. Social scientists explored farmers’ perceptions and understanding of animal welfare, whilst welfare scientists developed animal-based measures of welfare for use in a prototype on-farm welfare monitoring system. Based on two separate surveys, this paper focuses on UK farmers’ perception and understanding of animal welfare and their criteria of assessment in contrast with those employed by welfare scientists, using a specific case study of pigs. Results show that, despite scientists being unaware of the findings from the farmer survey, they produced a set of measures to assess welfare which were very similar to those used by farmers. However, ‘instinctive’ terms used by farmers to describe (positive or negative) animal behaviour did not bear any relation to more objective welfare measures. Compared with conventional monitoring systems which focus more on the provision of resources to promote good welfare than on the animal itself, the prototype monitoring system may be more acceptable to farmers given that it uses similar animal-based measures to assess welfare to those they use themselves, and furthermore, the focus is on the animal.
Recent advances in minimally invasive surgery for gynecologic indications

Recently, an increasing interest in less invasive surgery has led to the advent of laparoendoscopic single-site surgery (LESS) and natural orifice transluminal endoscopic surgery (NOTES). LESS and NOTES could be technically challenging, but available literature has demonstrated the feasibility and safety of LESS for benign gynecologic diseases. However, the evidence is not strong enough to recommend the use of LESS over that of conventional multiport laparoscopic surgery (MLS). As per the results of the most recently published meta-analysis, the majority of surgical outcomes are equivalent between LESS and MLS, except for the longer operative time in LESS for both adnexal surgery and hysterectomy. Although an increasing number of studies have reported on robotic LESS, NOTES, and LESS for gynecologic malignancy, definite conclusions have not been drawn owing to the lack of sufficient information.
La cooperación: estrategia para la sostenibilidad de los recursos /

Surge una gran inquietud en relacion a cual debe ser el principio de la estructuracion de una organizacion, sino existe una base que subyace los cimientos de la estructura cuyo objetivo sea la sostenibilidad de los recursos. La idea no es simplemente definir la estructura corporativa, sino tambien buscar beneficios mutuos de forma colectiva que facilite la sostenibilidad y la optimizacion de los recursos de la organizacion, a partir de estrategias coherentes, pertinentes, viables y rigurosas. Para ello se plantean los postulados de criticos como Robert Axelrod, Thomas Hobbes, Michael Taylor, Thomas Schelling, Roy Behr y Elinor Ostrom, Garret Hardin, que sirvieron de base para la formulacion teorica de la evolucion en los sistemas de cooperacion relacionados con los recursos. Se exponen y analizan los principios de: definicion de limites, reglas y sanciones para los apropiadores de los recursos, arreglos de eleccion colectiva, y supervision. Producto de los planteamientos expuestos, se infiere que para obtener beneficios relevantes, se hace necesario desarrollar modelos de autoadministracion y autogestion colectiva.
An Analysis and Implementation of the FFDNet Image Denoising Method

FFDNet is a recent image denoising method based on a convolutional neural network architecture. In contrast to other existing neural network denoisers, FFDNet exhibits several desirable properties such as faster execution time and smaller memory footprint, and the ability to handle a wide range of noise levels with a single network model. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. In this paper we propose an open-source implementation of the method based on PyTorch, a popular machine learning library for Python. Code for the training of the network is also provided. We also discuss the characteristics of the architecture of this algorithm and we compare it to other similar methods.
Targeted treatment of severe head injury: Head injury is poorly classified for treatment and so its categorisation is not as useful as it could be to guide therapy

It has been increasingly appreciated in recent times that head injury is not a homogeneous concept and is poorly classified for the purposes of treatment. 1 The separation of patients into 3 categories of severity (mild, moderate and severe) remains a blunt measure used to guide therapy in individual patients. Patients with severe traumatic brain injury (TBI), i.e. a Glasgow Coma Score (GCS) ≤8, may have different pathologies, including an extradural haematoma, subdural haematoma, cerebral ischaemia, cerebral hyperaemia, vasospasm, diffuse axonal injury, and/or focal haemorrhagic contusions. Moreover, autoregulation of the links between cerebral blood flow (CBF) and blood pressure, carbon dioxide tension, and cerebral metabolic requirements may be variably impaired in individuals – all of which have major implications for treatment. Yet traditional management tends to treat all individuals similarly. For example, intracranial pressure (ICP) is usually treated in a standardised stepwise approach. Yet in individual patients, elevated ICP may be associated with cerebral hyperaemia or cerebral ischaemia, subclinical seizures, or impaired autoregulation (where
Throughput of Wireless-Powered Based Multiuser System with Large-Scale Distributed Antennas

This paper investigates energy harvesting based multiuser system with large-scale distributed antennas, where a large number of remote antenna units (RAUs) are evenly separated across multiple circles. An efficient wireless energy and information transmission protocol is proposed. To save the signaling and the radio frequency chains overheads, the RAU with the shortest distance towards a user equipment (UE) is employed for the downlink wireless energy transfer (WET). In the uplink phase, we analyze the probability of wireless information transmission (WIT) of UEs. Then, linear zero-forcing detection and minimum-mean-square error are used to separate the data information among all the UEs that satisfy the requirement of WIT. The asymptotic throughput for an arbitrary UE is derived in closed-form. The time fraction used for the WET is optimized through maximizing the asymptotic throughput. Numerical and simulation results are given to verify the theoretical analysis, and bring to light the time fraction planning and the RAUs deployment for the system.
Multi-Lane Detection via Multi-Task Network in Various Road Scenes

Lane detection is one of the most important technologies in the autonomous driving system. Conventional road segmentation-based or lane marking-based lane detection methods may fail in various complex scenes. To address the problem of lane detection in various road scenes, this paper proposes a multitask based approach. The approach simultaneously segments road and lane marking instances. The branches of the two tasks share the same encoder but employ different decoder. In order to train the network with different datasets, this paper devises a training strategy to make sure only one branch can be trained per iteration. Finally, a fusion algorithm is presented to fuse the results from road segmentation and lane marking instances segmentation. The multi-lane (ego-lane, left and right adjacent lanes) detection results are obtained. Tested with Cityscapes and TuSimple dataset, the accuracy of lane detection is satisfied for the road scenes with the lanes number from zero up to four. The experiments demonstrate that the proposed approach is accurate and robust for various road scenes.
Detecting Novel Structural Variants In Genomes By Leveraging Parent-Child Relatedness

Genomic variation shared by members of the same species that are longer than a single nucleotide are commonly called structural variants (SVs). Though relatively rare, they represent an increasingly important class of variation as SVs have been associated with diseases and susceptibility to some types of cancer. Common approaches to SV detection require the sequencing and mapping of fragments from a test genome to a high-quality reference genome. Candidate SVs correspond to fragments with discordant mapped configurations, but because errors in the sequencing and mapping will also create discordant arrangements, many of these predictions will be false. When sequencing coverage is low, distinguishing true SVs from errors is even more complicated. In recent work, we have developed SV detection methods that simultaneously consider the genomes of closely related individuals – parents and children. Our approaches control false positive SVs by requiring children inherit all SVs in their genome from a parent. However, in doing so we may have missed true novel variants acquired by the child. In this work, we generalize our previous approaches to allow the child to carry novel variants but enforce sparsity through an l1 penalty (since novel SVs in the child should be rare). We present results on both simulated genomes as well as two-sequenced parent-child trios from the 1000 Genomes Project.
An Efficient Implementation of a Subgraph Isomorphism Algorithm for GPUs.

The subgraph isomorphism problem is a computational task that applies to a wide range of today’s applications, ranging from the understanding of biological networks to the analysis of social networks. Even though different implementations for CPUs have been proposed to improve the efficiency of such a graph search algorithm, they have shown to be bounded by the intrinsic sequential nature of the algorithm. More recently, graphics processing units (GPUs) have become widespread platforms that provide massive parallelism at low cost. Nevertheless, parallelizing any efficient and optimized sequential algorithm for subgraph isomorphism on many-core architectures is a very challenging task. This article presents GRASS, a parallel implementation of the subgraph isomorphism algorithm for GPUs. Different strategies are implemented in GRASS to deal with the space complexity of the graph searching algorithm, the potential workload imbalance, and the thread divergence involved by the non-homogeneity of actual graphs. The paper presents the results obtained on several graphs of different sizes and characteristics to understand the efficiency of the proposed approach.
A Novel Trajectory Reproduction Method: Multi Virtual Spring-Dampers

In robot learning, compliance and generalization ability are two desired properties. To get compliance and generalization ability, virtual spring damper(VSD) [1] is applied. Conventional virtual spring damper is time variant, so it is limited in many application scenarios, such as human robot interaction. In this paper, we propose a novel interaction model called multi virtual spring-dampers(MVSD), it is time invariant, and can adjust the trajectory profile similarity as well as the approaching attractor at the same time.
Automatic Assessment of Human Personality Traits: A Step Towards Intelligent Human-Robot Interaction

Personality is nothing but individual differences in the way we tend to think, feel and behave. It is ingrained in our basic instincts which tend to answer the question of why people differ in behavioral aspects in our day-to-day life. The assessment of personality traits is highly significant in human-human interaction. However, the topic has not been studied extensively in the context of human-robot interaction. This study focuses on the significance of nonverbal cues with respect to personality traits. A supervised learning approach has been used to recognize 3 personality traits of the big five model namely extroversion, agreeableness and neuroticism traits. Nonverbal cues such as head gestures, postures, proxemics, facial expressions and bodily cues are used to construct a feature vector for classification. A humanoid robot, ROBIN, is used for the assessment of personality traits in different scenarios. Sequences are labeled with the help of a psychology expert. The system shows above 90 % accuracy in the automatic assessment of personality traits.
Stability of Properties on Magnetic Ribbon and Cores With Domestic Fe-Based Amorphous Alloy for HTS AMDT

Although the high-temperature superconductivity amorphous metal core distribution transformers (HTS AMDT) have the advantage of lower no-load losses over orientation silicon-steel distribution transformers (OSDT), a high cost level is inevitable because of lower saturation flux density compared with that of OSDT. In this paper, the static and dynamic characteristics of sample cores have been measured under different temperatures. The testing platform was set up to analyze the cores magnetic function at normal operation and limited temperature. The experimental results suggest that the magnetic characteristics are almost stable. All the testing results and analysis above are helpful to substitute imported amorphous alloy metal strip with the domestic product and reduce the manufacturing cost of HTS AMDT.
Formation Tracking Control for a Team of Networked Underwater Robot Systems with Uncertain Hydrodynamics

This paper studies time-varying formation tracking control for a group of AUVs (autonomous underwater vehicles). Due to the inherent nonlinearities, uncertain hydrodynamics, and unpredictable external disturbances, coordination of AUVs is challenging. Conventional linear controllers may not satisfy performance requirements, especially when changes in system and environment occur during underwater operations. We investigate robust formation tracking under a static communication network, where the states of AUVs form a predefined time-varying formation, while tracking a desired time-varying reference trajectory. A distributed controller using continuous control is proposed to solve this problem. A numerical example is provided to illustrate the effectiveness of the proposed method.
$\Delta-\Sigma$  Noise-Shaping in 2-D Space-Time for Wideband Antenna Array Receivers

This paper proposes multi-dimensional signal processing methods to extend <inline-formula> <tex-math notation="LaTeX">$\Delta-\Sigma $ </tex-math></inline-formula> modulation into the two-dimensional (2-D) space, time case. The proposed 2-D noise-shaping methods employ lossless discrete integrators (LDIs) to reduce the spectral overlap of the regions of support (ROSs) of array signals with that of both thermal and quantization noise in microwave and mm-wave array processing systems. Spatio–temporal low-pass filtering can then be used to improve the overall noise figure, linearity, and resolution of the resulting <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-port receiver. A proof-of-concept 65-port receiver that uses first-order <inline-formula> <tex-math notation="LaTeX">$\Delta-\Sigma $ </tex-math></inline-formula> within both the low-noise amplifier and analog-to-digital converter (ADC) has been designed in the UMC 65-nm CMOS process. Detailed circuit simulations with wideband RF inputs at a center frequency of 4 GHz confirm that the proposed 2-D noise-shaping approach provides significant improvements in noise, linearity, and resolution. Specifically, noise figure (NF) improves by 1.5 dB, IIP3 improves by 15 dB, and the effective number of bits (ENOB) improves by 3.1 bits. Experimental results from a low-frequency board-level implementation of a 64-port ADC with first-order noise shaping are also presented.
Performance Analysis of CPU and DRAM Power Constrained Systems with Magnetohydrodynamic Simulation Code

Presently the power consumption of supercomputer system becomes a critical issue to develop the exascale supercomputer system. On the other hand, the power consumption character of applications is not so considered by the applications developers because their main interest is how fast to run their applications. In this study, we examine and evaluate the power consumption behavior of our Magnetohydrodynamic simulation code which solves the planetary magnetosphere under the constrained power of CPU and DRAM on the x86 computer system. As the results, we found there are some regions in the simulation code which decrease the calculation performance or do not affect the performance under the power capping. This indicates the capability of power optimization without performance degradation using the dynamic power capping in running the application. In addition, we obtained the specific power consumption combinations between CPU and DRAM which greatly affect the calculation performance.
Electromagnetic metamaterial based sensor design for chemical discrimination

In this paper, a simple and efficient approach is presented for the design of a metamaterial based high sensitive chemical sensor. The sensor is designed by incorporating metamaterial split ring resonator (SRR) unit cell within a small square loop. A high Quality-factor of 419 is intensifying its suitability for the sensing application and applied for chemical discrimination. The experimental results for various liquid samples such as distilled water, alcohol and acetone are discussed. Also, the measurement of these three liquid mixtures is performed. The proposed metamaterial based sensor is small in size and compact in its shape.
VIA-QMI: A visualized data analytic tool for Quantitative Multiplex Co-Immunoprecipitation(QMI) Platform

We introduce a software program that facilitates the Quantitative Multiplex coimmunoprecipitation (QMI) platform. The QMI platform generates high-dimensional data that describe the protein-protein interaction network state of the cell. However, the current QMI statistical analysis pipeline has poor usability and lacks visualization and interactive interfaces. Currently, users must use three independent programs and transfer data between those programs manually. Here, we introduce a unified graphical user interface program built specifically for the analysis of QMI data, VIA-QMI (VIsualized data Analysis tool for QMI). VIA-QMI provides a streamlined workflow and an intuitive and interactive interface that integrates three separate analytic packages into a single program. We believe that VIA-QMI will greatly facilitate the use and dissemination of the QMI platform by simplifying the complex data analysis processes.
Collecting Cyber Threat Intelligence from Hacker Forums via a Two-Stage, Hybrid Process using Support Vector Machines and Latent Dirichlet Allocation

Traditional security controls, such as firewalls, anti-virus and IDS, are ill-equipped to help IT security and response teams keep pace with the rapid evolution of the cyber threat landscape. Cyber Threat Intelligence (CTI) can help remediate this problem by exploiting non-traditional information sources, such as hacker forums and "dark-web" social platforms. Security and response teams can use the collected intelligence to identify emerging threats. Unfortunately, when manual analysis is used to extract CTI from non-traditional sources, it is a time consuming, error-prone and resource intensive process. We address these issues by using a hybrid Machine Learning model that automatically searches through hacker forum posts, identifies the posts that are most relevant to cyber security and then clusters the relevant posts into estimations of the topics that the hackers are discussing. The first (identification) stage uses Support Vector Machines and the second (clustering) stage uses Latent Dirichlet Allocation. We tested our model, using data from an actual hacker forum, to automatically extract information about various threats such as leaked credentials, malicious proxy servers, malware that evades AV detection, etc. The results demonstrate our method is an effective means for quickly extracting relevant and actionable intelligence that can be integrated with traditional security controls to increase their effectiveness.
An Empirical Analysis on Expressibility of Vertex Centric Graph Processing Paradigm

We study the vertex-centric (VC) paradigm for distributed graph processing, popularized by Google’s Pregel system. Since then, there were several attempts to implement many graph algorithms in a vertex-centric framework, as well as efforts to design optimization techniques for improving their efficiency. Many follow up works experimentally compared the efficiency and scalability of existing VC systems. However, to the best of our knowledge, there has not been any systematic study to analyze the expressibility of the VC paradigm itself.Our work addresses this gap in the following ways. We consider multiple distributed algorithms for two important graph problems: single-source shortest path and betweenness centrality. We investigate, with thorough experiments, whether all these algorithms can be efficiently implemented in a VC framework. We find that all distributed algorithms (often the more efficient ones) cannot be effectively implemented in the VC paradigm. We conclude by discussing our recommendations on the road ahead.
The Momentum Equilibrium Principle: Foot Contact Stabilization with Relative Angular Momentum/Velocity

The spatial momentum relation of an underac-tuated articulated multibody system on a floating base is a dynamic equilibrium relation between its coupling and relative momenta. The relative momentum is the difference between the system momentum and the momentum of the composite-rigid-body (CRB) that is obtained when the joints are locked. This relation is referred to as the momentum equilibrium principle. The focus in this work is on the angular momentum component of the momentum equilibrium principle. It is clarified that the relative angular momentum component can be represented in terms of the so-called relative angular velocity that is used as a control input in a balance controller. The balance controller proposed here is a whole-body controller that has independent inputs for center of mass (CoM) velocity and base-link angular velocity control. In addition, the relative angular velocity control input endows the controller with the unique property of generating an appropriate upper-limb motion that can stabilize the system momentum. More specifically, it is shown that when the relative angular velocity is derived from the reaction null-space (RNS) of the system, it becomes possible to stabilize the unstable states with a rolling foot/feet. The formulation is simple and yet quite efficient — there is no need to modify the contact model to account for the transitions between the stable and unstable contact states. There is also no need to command the upper-limb motion directly. A few simulation examples are presented to demonstrate and discuss the properties of the controller.