Miracle-dz/UniFAR-FTUs huggingface dataset:
Women at work: Changes in sexual harassment between September 2016 and September 2018

Over the last two years, awareness about the sexual mistreatment of women has stunned the world. According to analysis by the New York Times, the defeat of Hilary Clinton and election of Donald Trump spurred a women’s movement in the US that began in November of 2016 and resulted in protests across the country, including the largest single-day protest in history on January 21, 2017. Later that year, the #MeToo movement (starting in October 2017) and subsequent #TimesUp movement (starting in January 2018) galvanized women to unite against sexual assault and sexual harassment, which has become the hallmark of the current women’s movement. But has anything changed over this time period in regard to the sexual harassment of women? Using a repeat cross-sectional survey from over 500 women collected at two points in time (September 2016 and September 2018), we found reduced levels of the most egregious forms of sexual harassment (unwanted sexual attention and sexual coercion) but increased levels of gender harassment in 2018. More importantly, sexual harassment had a weaker relationship with women’s negative self-views (lower self-esteem, higher self-doubt) in 2018 compared to 2016. Qualitative interviews collected from women in the fall of 2016 and in the fall of 2018 from the same women, support the quantitative data. They suggest that the changes in sexual harassment are due to the increased scrutiny on the topic. The interviewees also emphasize that they feel better supported and empowered and are not ashamed to speak up about sexual harassment.
Impaired auditory processing and neural representation of speech in noise among symptomatic post-concussion adults

ABSTRACT Background: The purpose of the study was to examine auditory event-related potential (AERP) evidence of changes in earlier and later stages of auditory processing in individuals with long-term post-concussion problems compared to healthy controls, with a secondary aim of comparing AERPs by functional auditory behavioral outcomes. Methods: P1-N1-P2 complex and P300 components recorded to speech in quiet and background noise conditions were completed in individuals with ongoing post-concussion symptoms following mTBI and healthy controls. AERPs were also examined between sub-groups with normal or impaired auditory processing by behavioral tests. Results: Group differences were present for later stages of auditory processing (P300). Earlier components did not significantly differ by group overall but were more affected by noise in the mTBI group. P2 amplitude in noise differed between mTBI sub-groups with normal or impaired auditory processing. Conclusion: AERPs revealed differences between healthy controls and those with chronic post-concussion symptoms following mTBI at a later stage of auditory processing (P300). Neural processing at the earlier stage (P1-N1-P2) was more affected by noise in the mTBI group. Preliminary evidence suggested that it may be only the proportion of individuals with functional evidence of central auditory dysfunction with changes in AERPs at earlier stages of processing.
Validation of the caregiver oncology quality of life questionnaire in Portuguese caregivers of myeloma patients

Abstract Objective Cancer diagnosis affects patients, their families, and their caregivers in particular. This study focused on the validation of the CareGiver Oncology Quality of Life (CarGOQoL) questionnaire in Portuguese caregivers of patients with multiple myeloma, from the caregiver's point of view. Method This was a cross-sectional study with 146 caregivers of patients with multiple myeloma from outpatient medical oncology and clinical hematology consultations from five hospitals in north and central Portugal. Participants were assessed on quality of life (QoL), psychological morbidity and social support. Result The Portuguese version maintains 17 of the original 29 items version, maintaining general coherence and a dimensional structure that is clinically interpretable. Reliability findings indicated good internal consistency for the total scale (0.86) and respective subscales (0.75 to 0.88), which is in agreement with the alpha values from the previous CarGOQoL validation study for the corresponding subscales (0.74 to 0.89) and total scale (0.90). Significance of results The CarGOQoL is a reliable and valid tool for clinical trials and intervention programs to assess QoL in caregivers of myeloma patients. Future studies should validate the adapted version in caregivers of other types of cancer patients including other chronic diseases.
AVPR1b variation and the emergence of adaptive phenotypes in Platyrrhini primates

Platyrrhini (New World monkeys, NWm) are a group of primates characterized by behavioral and reproductive traits that are otherwise uncommon among primates, including social monogamy, direct paternal care, and twin births. As a consequence, the study of Platyrrhine primates is an invaluable tool for the discovery of the genetic repertoire underlying these taxon‐specific traits. Recently, high conservation of vasopressin (AVP) sequence, in contrast with high variability of oxytocin (OXT), has been described in NWm. AVP and OXT functions are possible due to interaction with their receptors: AVPR1a, AVPR1b, AVPR2, and OXTR; and the variability in this system is associated with the traits mentioned above. Understanding the variability in the receptors is thus fundamental to understand the function and evolution of the system as a whole. Here we describe the variability of AVPR1b coding region in 20 NWm species, which is well‐known to influence behavioral traits such as aggression, anxiety, and stress control in placental mammals. Our results indicate that 4% of AVPR1b sites may be under positive selection and a significant number of sites under relaxed selective constraint. Considering the known role of AVPR1b, we suggest that some of the changes described here for the Platyrrhini may be a part of the genetic repertoire connected with the complex network of neuroendocrine mechanisms of AVP–OXT system in the modulation of the HPA axis. Thus, these changes may have promoted the emergence of social behaviors such as direct paternal care in socially monogamous species that are also characterized by small body size and twin births.
Using Mixed Salt Electrolytes to Stabilize Silicon Anodes for Lithium-Ion Batteries via In situ Formation of Li-M-Si Ternaries (M=Mg, Zn, Al, Ca).

Replacing traditional graphite anode by Si anode can greatly improve the energy density of lithium-ion batteries. However, the large volume expansion and the formation of highly reactive lithium silicides during charging cause the continuous lithium and electrolyte consumption as well as the fast decay of Si anodes. In this work, by adding 0.1 M M(TFSI)x (M = Mg, Zn, and Al, and Ca) as a second salt into the electrolyte, we stabilize the anode chemistry through in situ formation of Li-M-Si ternary phases during the charging process. First, lithium silicides and magnesium lithium silicides were synthesized as model compounds to investigate the influence of metal doping on the reactivity of lithiated Si. Using solid-state nuclear magnetic resonance spectroscopy, we show that Mg doping can dramatically suppresses the chemical reactions between the lithium silicide compounds and common electrolyte solvents. New mixed-salt electrolytes were prepared containing M(TFSI)x as a second salt to LiPF6 and tested in commercially relevant electrodes, which show higher capacity, superior cyclability, and higher coulombic efficiencies in both half-cell and full-cell configurations (except for Zn) when compared with standard electrolytes. Post-electrochemistry characterizations demonstrate that adding M salts leads to the co-insertion of M cations along with Li into Si during the lithiation process, stabilizing silicon anions by forming more stable Li-M-Si ternaries, which fundamentally changes the traditional Li-Si binary chemistry while minimally affecting silicon electrochemical profiles and theoretical capacities. This study opens a new and simple way to stabilize silicon anodes to enable widespread application of Si anodes for lithium-ion batteries.
Novel Tools towards Magnetic Guidance of Neurite Growth: (I) Guidance of Magnetic Nanoparticles into Neurite Extensions of Induced Human Neurons and In Vitro Functionalization with RAS Regulating Proteins

Parkinson’s disease (PD) is a neurodegenerative disease associated with loss or dysfunction of dopaminergic neurons located in the substantia nigra (SN), and there is no cure available. An emerging new approach for treatment is to transplant human induced dopaminergic neurons directly into the denervated striatal brain target region. Unfortunately, neurons grafted into the substantia nigra are unable to grow axons into the striatum and thus do not allow recovery of the original connectivity. Towards overcoming this general limitation in guided neuronal regeneration, we develop here magnetic nanoparticles functionalized with proteins involved in the regulation of axonal growth. We show covalent binding of constitutive active human rat sarcoma (RAS) proteins or RAS guanine nucleotide exchange factor catalytic domain of son of sevenless (SOS) by fluorescence correlation spectroscopy and multiangle light scattering as well as the characterization of exchange factor activity. Human dopaminergic neurons were differentiated from neural precursor cells and characterized by electrophysiological and immune histochemical methods. Furthermore, we demonstrate magnetic translocation of cytoplasmic γ-Fe2O3@SiO2 core-shell nanoparticles into the neurite extensions of induced human neurons. Altogether, we developed tools towards remote control of directed neurite growth in human dopaminergic neurons. These results may have relevance for future therapeutic approaches of cell replacement therapy in Parkinson’s disease.
Is this my group or not? The role of ensemble coding of emotional expressions in group categorization.

When exposed to others' emotional responses, people often make rapid decisions as to whether these others are members of their group or not. These group categorization decisions have been shown to be extremely important to understanding group behavior. Yet, despite their prevalence and importance, we know very little about the attributes that shape these categorization decisions. To address this issue, we took inspiration from ensemble coding research and developed a task designed to reveal the influence of the mean and variance of group members' emotions on participants' group categorization. In Study 1, we verified that group categorization decreases when the group's mean emotion is different from the participant's own emotional response. In Study 2, we established that people identify a group's mean emotion more accurately when its variance is low rather than high. In Studies 3 and 4, we showed that participants were more likely to self-categorize as members of groups with low emotional variance, even if their own emotions fell outside of the range of group emotions they saw, and that this preference is seen for judgments of both positive and negative group emotions. In Study 5, we showed that this unique preference for low group emotional variance is special to group categorization and does not appear in a more basic face categorization task. Our studies reveal unexplored and important tendencies in group categorization based on group emotions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Effects of Nitroglycerine on Renal Ischemia-Reperfusion Injury in Adult Male Rats

Abstract Background Ischemia-reperfusion (I-R) leads to acute kidney injury (AKI). The present study investigated the effects of nitroglycerine (NG) on improving renal dysfunctions caused by I-R in rats. Methodology Twenty-four rats were equally divided into four groups: (1) the control group, (2) the sham group, (3) the I-R group, and (4) NG-treated groups.NG (50 μg/kg) was injected intraperitoneally after induction of IR. I-R was induced through clamping of the bilateral renal artery and vein of both kidneys for 20 min followed by 24 h of reperfusion. Results NG significantly increased the creatinine clearance levels and renal blood flow rate (which was reduced by I-R). NG also significantly improved serum electrolytes (sodium and potassium) that were disordered by I-R. In addition, NG significantly offset impaired antioxidant defense mechanism and inhibited lipid peroxidation. Conclusions The results show NG has a protective effect on renal tissue against AKI caused by I-R. These protective effects mediated through antioxidant activity and decrease of lipid peroxidation.
Vehicle Detection and Classification in Aerial Images

Background Nowadays the problem of vehicle detection and classification on aerial images received from UAV (Unmanned Aerial Vehicles) has become important because of development of UAV technology and image analysis methods. Methods The paper describes a multilevel detection of vehicles and their subsequent classification. This method can be used for search of moving and stationary vehicles. In this work we propose new approaches such as: image segmentation into superpixels by SEEDS method, trainable five-level cascade detector of combined superpixels-regions, which uses technology of artificial neutral networks. Characteristics of regions are built based on their geometric and texture features (HoG and LPB descriptors) and directly from the image patch using technology of nonlinear autoencoders. Additional cascade of the detector uses data of moving objects in the image. Similar responses of the detector are combined and classified by color and type of the vehicle. Findings For training these algorithms a largest image dataset was compiled from different sources.The results of tests of detection and classification showed high accuracy. Improvements Algorithm is fast enough to allow on-board usage. The proposed method can be applied for road traffic monitoring, analysis of parking lots occupancy and other similar tasks.
Artificial Bee Colony Algorithm and Its Application in Combinatorial Optimization

Artificial Bee Colony Algorithm(ABC) is a new kind of heuristic algorithm.By simulating natural bee colony foraging process,this algorithm can solve practical problems.In this algorithm,every bee can be regarded as an agent.These agents can search and optimize the objectives efficiently by mutual cooperation.A general approach of Artificial Bee Colony Algorithm to solve combinatorial optimization problems has been presented.The simulation to 0-1 Knapsack Problem proves that ABC is superior to Ant Colony Algorithm.
A Metadata Monitoring System for Ubiquitous Computing

In the highly dynamic context of ubiquitous systems, applications need to be continuously aware of QoS and QoC metadata to ensure their required level of quality. We present QoMonitor, a metadata monitoring system that receives synchronous and asynchronous requests from clients (a middleware system that supports ubiquitous applications), recovers metadata from several context providers, and sends them to the clients. We also present an evaluation of QoMonitor under a quantitative perspective, which aims to address the time for assessing QoS and QoC parameters and the time to completely reply to synchronous and asynchronous requests in the context of a health care application. The proposed monitoring system enables ubiquitous applications to focus on addressing the business requirements of the application and abstract away the burden of dealing with the complexities related to synchronous and asynchronous metadata monitoring.
Data analytics, interpretation and machine learning for environmental forensics using peak mapping methods

In this work our driving motivation is to develop mathematically robust and computationally efficient algorithms that will help chemists towards their goal of pattern matching. Environmental chemistry today broadly faces difficult computational and interpretational challenges for vast and ever-increasing data repositories. A driving factor behind these challenges are little known intricate relationships between constituent analytes that constitute complex mixtures spanning a range of target and non-target compounds. While the end of goal of different environment applications are diverse, computationally speaking, many data interpretation bottlenecks arise from lack of efficient algorithms and robust mathematical frameworks to identify, cluster and interpret compound peaks. There is a compelling need for compound-cognizant quantitative interpretation that accounts for the full informational range of gas chromatographic (and mass spectrometric) datasets. Traditional target-oriented analysis focus only on the dominant compounds of the chemical mixture, and thus are agnostic of the contribution of unknown non-target analytes. On the other extreme, statistical methods prevalent in chemometric interpretation ignore compound identity altogether and consider only the multivariate data statistics, and thus are agnostic of intrinsic relationships between the well-known target and unknown target analytes. Thus, both schools of thought (target-based or statistical) in current-day chemical data analysis and interpretation fall short of quantifying the complex interaction between major and minor compound peaks in molecular mixtures commonly encountered in
Robust and fast generation of top and side grasps for unknown objects

In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one. We demonstrate the effectiveness of our approach on a picking scenario on a real robot platform. Our approach has shown to be more reliable than another recent geometry-based method considered as baseline [7] in terms of grasp stability, by increasing the successful grasp attempts by a factor of six.
Querying Knowledge via Multi-Hop English Questions

Abstract The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.
Enhancement of Spectrum Detection in 5G Heterogeneous Network

Modern advancement of Long Term Evolution Advanced (LTE-A) in wireless communication industry is revolutionary to fulfill the high order expectations through extending the high system throughput and capacity. This is mainly due to the effects of the Orthogonal Frequency Division Multiplexing (OFDM), where both frequency and time division are employed in order to exploit the system capacity. However, in LTE-A Heterogeneous Network (HetNet), femtocells (HeNBs) are optimal choice to extend the coverage in indoor environment, whereas the deployment of such base station creates cross-tier and co-tier interferences with the macrocell users (MUE) becomes critical challenge. Therefore, the unutilized/idle spectrums reallocation is one of the solution to mitigate such challenges. However, the Enhanced Spectrum Detection (ESD) in such typical network is challenging. Trendy DSS networks, licensed secondary users (SUs) be able to gain assigned licensed spectrum band without any interference with primary users (PUs). This paper enhances the spectrum detection in LTE-A based Heterogeneous Networks (HetNets) using ESD mechanism. In ESD false alarms and missed detections are considered the performance metric. The result suggests that the performance of the spectrum detected is enhanced over existing method.
Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA

Future Square Kilometre Array (SKA) surveys are expected to generate huge data sets of 21 cm maps on cosmological scales from the Epoch of Reionization. We assess the viability of exploiting machine learning techniques, namely, convolutional neural networks (CNNs), to simultaneously estimate the astrophysical and cosmological parameters from 21 cm maps from seminumerical simulations. We further convert the simulated 21 cm maps into SKA-like mock maps using the detailed SKA antennae distribution, thermal noise, and a recipe for foreground cleaning. We successfully design two CNN architectures (VGGNet-like and ResNet-like) that are both efficiently able to extract simultaneously three astrophysical parameters, namely the photon escape fraction (fesc), the ionizing emissivity power dependence on halo mass (Cion), and the ionizing emissivity redshift evolution index (Dion), and three cosmological parameters, namely the matter density parameter (Ωm), the dimensionless Hubble constant (h), and the matter fluctuation amplitude (σ8), from 21 cm maps at several redshifts. With the presence of noise from SKA, our designed CNNs are still able to recover these astrophysical and cosmological parameters with great accuracy ($R^{2} \gt 92{{\ \rm per\ cent}}$), improving to $R^{2} \gt 99{{\ \rm per\ cent}}$ towards low-redshift and low neutral fraction values. Our results show that future 21 cm observations can play a key role to break degeneracy between models and tightly constrain the astrophysical and cosmological parameters, using only few frequency channels.
A Survey of Data Quality Measurement and Monitoring Tools

High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between data quality research and practical implementations with a detailed investigation on how data quality measurement and monitoring concepts are implemented in state-of-the-art tools. For the first time and in contrast to all existing data quality tool surveys, we conducted a systematic search, in which we identified 667 software tools dedicated to “data quality. ” To evaluate the tools, we compiled a requirements catalog with three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) automated data quality monitoring. Using a set of predefined exclusion criteria, we selected 13 tools (8 commercial and 5 open-source tools) that provide the investigated features and are not limited to a specific domain for detailed investigation. On the one hand, this survey allows a critical discussion of concepts that are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics. On the other hand, it reveals potential for functional enhancement of data quality tools and supports practitioners in the selection of appropriate tools for a given use case.