Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Parental Acceptance–Rejection in Emotional Instability During Adolescence

The present study focuses on analyzing the relationship between the parental acceptance–rejection perceived by adolescents and emotional instability from the early stages of adolescence. Special attention will be paid to potential differences between mothers and fathers. A total of 1181 students, aged 11–17, took part in the study. We used the factor of emotional instability in the Big Five Questionnaire (BFQ-NA) and an affect scale (EA-H) to measure parental acceptance–rejection. The analyses performed show a clear association between emotional instability with maternal/paternal criticism and rejection. Specifically, maternal criticism and rejection in early adolescence and paternal criticism and rejection in middle adolescence were associated with emotional instability, confirming the association between children’s and adolescents’ emotional adjustment and family dynamics. This study makes interesting contributions to understanding paternal and maternal rejection during the different stages of adolescence. These differences should be incorporated into the research on parental influence and its role in the development of personality among adolescents.
Predators attacking virtual prey reveal the costs and benefits of leadership

Significance Across a wide range of animals, it is assumed that leading from the front of a group exposes individuals to greater predation risk, generating a cost that explains variation between individuals in their tendencies to lead and follow. Remarkably, there is scant empirical evidence to support this, and there are no experimental tests. By presenting real predators with a simulation of collective behavior, we were able to exclude any correlation between social behavior and other traits that could confound effects on predation risk. We show that virtual prey leading others were preferentially attacked, but leaders were still safer than solitary prey. Leaders benefit from being followed, and they should act to maintain group cohesion and avoid splitting from their followers. A long-standing assumption in social behavior is that leadership incurs costs as well as benefits, and this tradeoff can result in diversified social roles in groups. The major cost of leadership in moving animal groups is assumed to be predation, with individuals leading from the front of groups being targeted more often by predators. Nevertheless, empirical evidence for this is limited, and experimental tests are entirely lacking. To avoid confounding effects associated with observational studies, we presented a simulation of virtual prey to real fish predators to directly assess the predation cost of leadership. Prey leading others are at greater risk than those in the middle of groups, confirming that any benefits of leading may be offset by predation costs. Importantly, however, followers confer a net safety benefit to leaders, as prey leading others were less likely to be attacked compared with solitary prey. We also find that the predators preferentially attacked when solitary individuals were more frequent, but this effect was relatively weak compared with the preference for attacking solitary prey during an attack. Using virtual prey, where the appearance and behavior of the prey can be manipulated and controlled exactly, we reveal a hierarchy of risk from solitary to leading to following social strategies. Our results suggest that goal-orientated individuals (i.e., potential leaders) are under selective pressure to maintain group cohesion, favoring effective leadership rather than group fragmentation. Our results have significant implications for understanding the evolution and maintenance of different social roles in groups.
Research on autonomous formation of Multi-UAV based on MADDPG algorithm

Multi-UAV autonomous formation task is one of the important research hotspots in UAV application. A typical mission scenario is constructed for the autonomous formation, maintenance and obstacle avoidance tasks of multi-UAV. Based on Multiple-Agents Deep Deterministic Policy Gradient (MADDPG) algorithm, we designed a hybrid reward distribution mechanism for autonomous formation task, which effectively solved the problem of uneven global reward distribution and individual "selfish strategy", and designed a dynamic communication strategy inside the UAV formation to reduce the computational complexity. It can effectively improve the communication efficiency between UAVs. After training, the UAV can effectively avoid the no-fly zone and efficiently perform autonomous formation flight task. The introduction of the hybrid reward mechanism improves the stability of the UAV autonomous formation task and has certain application prospects.
Resource Provisioning for Mitigating Edge DDoS Attacks in MEC-Enabled SDVN

Vehicular ad hoc network (VANET) has become an accessible technology for improving road safety and driving experience, the problems of heterogeneity and lack of resources it faces have also attracted widespread attention. With the development of software-defined networking (SDN) and multiaccess edge computing (MEC), a variety of resource allocation strategies in MEC-enabled software-defined networking-based VANET (SDVN) have been proposed to solve these problems. However, we note that few of these work involves the situation where SDVN is under Distributed Denial of Service (DDoS) attacks. Actually, Internet of Things (IoT) devices are extremely easy to be compromised by malicious users, and compromised IoT devices may be used to launch edge DDoS attacks against the MEC servers in MEC-enabled SDVN at any time. In this article, we propose a graph neural network (GNN)-based collaborative deep reinforcement learning (GCDRL) model to generate the resource provisioning and mitigating strategy. The model evaluates the trust value of the vehicles, formulates mitigation of edge DDoS attacks and resource provisioning strategies to ensure that the MEC servers can work normally under edge DDoS attacks. In addition, GNN is adopted in the DRL model to extract the structure feature of the graph composed of MEC servers, and help transfer computing tasks between MEC servers to alleviate the problem of resources imbalance between them. Experimental results show that the method of estimating the vehicular trust value is effective, and our method can make the average throughput of edge nodes more stable and lower down the average delay and the average energy consumption under the edge DDoS attack. Also, a real-world case study is conducted to verify our conclusion.
Nutrients for Prevention of Macular Degeneration and Eye-Related Diseases

The risk of macular degeneration can be reduced through the consumption of antioxidant-rich foods, supplements, and nutraceutical formulas. This review focuses on the antioxidants, vitamins, and minerals that have been reported for reducing the risk of macular degeneration and other eye-related diseases. Antioxidants including anthocyanins, carotenoids, flavonoids, and vitamins have been shown to reduce the risk of eye-related diseases. Anthocyanins extracted from berries are powerful antioxidants. Cyanidin, delphinidin, malvidin, pelargonidin, peonidin, and petunidin are anthocyanin aglycones detected in berries, currants, and other colored fruits and vegetables. β-Carotene, as well as xanthophyll lutein and zeaxanthin, have been reported to reduce the risk of macular degeneration. Flavonoids from plants help in the prevention of eye-related diseases through anti-inflammatory mechanisms. A combination of these antioxidants, vitamins, and minerals possess a synergistic effect on the prevention or risk reduction of macular degeneration. Formulas have been developed as dietary supplements to cater to the high demand from consumers and patients with eye problems. Many of the formulated dietary supplements that are sold in the market have been clinically proven for their efficacy to treat eye diseases. Although the bioactivities in the supplement capsules or tablets have been scientifically established for reducing risks of several diseases, which include macular degeneration and other eye-related diseases, knowledge on the right dosage, efficacy, and bioavailability of antioxidants, vitamins, and minerals is important for consumers. The information may help them make the best decision in choosing the right dietary supplements and nutraceuticals following the evidence-based recommended dosages and reference intakes for improving general health and preventing eye-related diseases. This review covers the potential causal factors involved in eye diseases, clinically proven treatments, and controversial findings on the antioxidants in the prevention of macular degeneration. Future studies should consider multiethnic and multicenter trials for eliminating potential bias in research.
Robust variable selection in semiparametric mixed effects longitudinal data models

Abstract In this paper, we present a robust joint variable selection procedure for fixed and random effects in semiparametric linear mixed effects model for longitudinal data. Our simultaneous selection method overcomes the defects of typical approaches which select separately each of the two effects components. Meanwhile, the proposed procedure performs better than nonrobust method when there are outliers in the data. This method is based on a robustified penalized joint likelihood of the reparameterized linear mixed effects model through B-splines approximation and Cholesky decomposition. It is further shown that the robust variable selection method enjoys the Oracle property. We demonstrate the performance of the method based on a simulation study in the end.
Life and death of circulating cell-free DNA

ABSTRACT Tumor-specific, circulating cell-free DNA in liquid biopsies is a promising source of biomarkers for minimally invasive serial monitoring of treatment responses in cancer management. We will review the current understanding of the origin of circulating cell-free DNA and different forms of DNA release (including various types of cell death and active secretion processes) and clearance routes. The dynamics of extracellular DNA in blood during therapy and the role of circulating DNA in pathophysiological processes (tumor-associated inflammation, NETosis, and pre-metastatic niche development) provide insights into the mechanisms that contribute to tumor development and metastases formation. Better knowledge of circulating tumor-specific cell-free DNA could facilitate the development of new therapeutic and diagnostic options for cancer management.
Transdisciplinary Interventions to Improve the Sustainability of Maize Agroecosystems: A Case Study from Mexico

The municipality of Ahuazotepec presented a complex food insecurity scenario, mainly because there are limitations regarding food supply/local maize production and the inhabitants so their access to food can be disrupted quite easily. A transdisciplinary approach and methodology were adapted to analyze, design, implement, and evaluate an agricultural intervention to improve agroecosystems in the region using agroecological practices. The experimental results indicated that the application of agroecological fertilization management improved soil fertility indicators, grain yield, benefit-to-cost ratio, and the resilience of the system. For the intervention, when a transdisciplinary scheme in which collaboration between actors was intensified, the benefits are greater than in a conventional scheme, where only economic or in-kind aid is provided, even when based on similar practices.
Planificación y estructuración del territorio desde la perspectiva de los sistemas de agua potable y saneamiento básico, las dimensiones ambiental y etnográfica

Partiendo del estado actual de los servicios de agua potable y saneamiento básico, se pretende comprender como podría y cuáles son las son las condiciones para planear el Pacifico Colombiano y específicamente el departamento del Choco. Entendiendo en primera instancia las características culturales de las comunidades negras que lo habitan y su forma de apropiación del territorio, se pueden plantear estrategias de planificación que respeten sus costumbre y ayuden a la conservación de los ecosistemas megadiversos que caracterizan esta región del país.
Detecting Unknown Threat Based on Continuous-Time Dynamic Heterogeneous Graph Network

Unknown threats have caused severe damage in critical infrastructures. To solve this issue, the graph-based methods have been proposed because of their ability for learning complex interaction patterns of network entities with discrete graph snapshots. However, such methods are challenged by the computer networking model characterized by the natural continuous-time dynamic heterogeneous graph (CDHG). In this paper, we propose a CDHG-based graph neural network model, namely, CDHGN, for unknown threat detection. It first constructs the CDHG using interaction relationships among network entities extracted from various log records. Then, it trains the detection model based on a heterogeneous attention network and performs streaming detection for live online network events. We implement a prototype and conduct extensive experiments on a comprehensive cybersecurity dataset with more than nine million records. Experimental result shows that the proposed method can achieve superior detection performance than the state-of-the-art methods.
Learning as program induction

This workshop will cover new work that casts human learning as program induction — i.e. learning of programs from data. The notion that the mind approximates rational (Bayesian) inference has had a strong influence on thinking in psychology since the 1950s. In constrained scenarios, typical of psychology experiments, people often behave in ways that approximate the dictates of probability theory. However, natural learning contexts are typically much more open-ended — there are often no clear limits on what is possible, and initial proposals often prove inadequate. This means that coming up with the right hypotheses and theories in the first place is often much harder than ruling among them. How do people, and how can machines, expand their hypothesis spaces to generate wholly new ideas, plans and solutions? Recent work has begun to shed light on this problem via the idea that many aspects of learning can be better understood through the mathematics of program induction (Chater & Oaksford, 2013; Lake, Salakhutdinov, & Tenenbaum, 2015). People are demonstrably able to compose hypotheses from parts (Goodman, Tenenbaum, Feldman, & Griffiths, 2008; Piantadosi, Tenenbaum, & Goodman, 2016; Schulz, Tenenbaum, Duvenaud, Speekenbrink, & Gershman, 2017) and incrementally grow and adapt their models of the world (Bramley, Dayan, Griffiths, & Lagnado, 2017). A number of recent studies has formalized these abilities as program induction, using algorithms that mix stochastic recombination of primitives with memoization and compression to explain data (Dechter, Malmaud, Adams, & Tenenbaum, 2013; Ellis, Dechter, & Tenenbaum, 2015; Romano, Salles, Amalric, Dehaene, Sigman, & Figueria, 2017), ask informative questions (Rothe, Lake, & Gureckis, 2017), and support oneand few-shot-inferences (Lake et al., 2015). Program induction is also proving to be an important notion for understanding development and learning through play (Sim & Xu, 2017) and the formation of geometric understanding about the physical world (Amalric, Wang, Pica, Figueira, Sigman, & Dehaene, 2017).
Dermal Penetration Studies of Potential Phenolic Compounds Ex Vivo and Their Antioxidant Activity In Vitro

Phenolic compounds with miscellaneous biological activities are an interesting component in dermatology and cosmetology practices. The aim of our study was to determine the phenolic compounds released from emulsion, emulgel, gel, ointment, and oleogel formulations penetration into human skin layers, both the epidermis and dermis, and estimate their antioxidant activity. The ex vivo penetration study was performed using Bronaugh type flow-through diffusion cells. Penetration studies revealed that, within 24 h, the chlorogenic acid released from the oleogel penetrated into skin layers to a depth of 2.0 ± 0.1 µg/mL in the epidermis and 1.5 ± 0.07 µg/mL in the dermis. The oleogel-released complex of phenolic compounds penetrating into epidermis showed the strongest DPPH free radical scavenging activity (281.8 ± 14.1 µM TE/L). The study estimated a strong positive correlation (r = 0.729) between the amount of quercetin penetrated into epidermis and the antioxidant activity detected in the epidermis extract. Plant based phenolic compounds demonstrated antioxidant activity and showed great permeability properties through the skin.
The effect of the COVID-19 pandemic period on the cases of acute cholecystitis

Aim: A gallstone is a stone formed within the gallbladder out of precipitated bile components. Risk factors for gallstones include a family history of gallstones, age, genetic and environmental factors, sedentary lifestyle, high-fat diet, obesity. In this study, it was aimed to determine the effects of the pandemic process on acute cholecystitis cases. Material and Method: Patients who applied to hospital between 1st January 2018 and 1st January 2022 and were diagnosed with Acute Cholecystitis were retrospectively screened. The demographic data as age and sex were recorded by dividing the patients into two groups, two years before and two years after the COVID-19 pandemic period. Results: The 2-year retrospective evaluation before and after the COVID-19 pandemic, a significant increase was found in cases of acute cholecystitis after the pandemic (p
A new semi-cryptic Filistata from caves in the Levant with comments on the limits of Filistata insidiatrix (Forsskål, 1775) (Arachnida: Araneae: Filistatidae)

We describe a new troglophilic species of Filistata, F. betarif sp. nov. , collected from two caves in central Israel, by using light and electron microscopy and by DNA barcoding of the cytochrome c oxidase subunit I (COI) gene. Sequences of this gene show more than 15% of divergence between the new species and its sibling, F. insidiatrix (Forsskål, 1775), which is widely distributed across the Mediterranean and the Middle East. Notwithstanding, the two species are diagnosed only by a minor morphological detail in the embolic keel of the male pedipalp; females of both species are not diagnosable based only on morphology. We also find that samples of F. insidiatrix from different localities have large genetic divergence values (larger than 15% in some cases), but their pedipalps are identical in males; this suggests that F. insidiatrix might hide an additional cryptic diversity. We take this opportunity to provide a dichotomous key for identifying the crevice-weavers (Filistatidae) of Israel and Palestine.
Robust RGB-D Visual Odometry Based on the Line Intersection Structure Feature in Low-Textured Scenes

Most methods of estimating camera motion for visual odometry are based on point features. Point-based algorithms often fail in low-texture scenes because it is difficult to build a large number of features. Instead, line segments are often very rich. However, the instability of the endpoints of line segments and the loss of line segment connectivity make the matching of the line segments difficult. This paper proposes a new odometry algorithm based on line intersection structure feature (LISF). LISFs are formed by the adjacent line pairs in 2D images that are coplanar in 3D, which include two line segments and their joint point. These LISFs are then described with the proposed combined descriptor consists of structure feature and gradient feature for matching. Also, we implement a RGB-D odometry system that utilizes LISFs by adopting a RANSAC-based motion estimation, followed by a g2o-based motion refinement. In experiments, using data sets in weak texture scenes, results show that proposed method can achieve high continuity and accuracy.
Image Enhancement Based on InfoGAN for Target Tracking in Natural Gas Gathering Station

Natural gas gathering station plays an important role in providing stable gas supply for downstream users. With the trend of the unattended station, the safety of the station becomes a major challenge. Intelligent video surveillance system, as an important part of unattended system, is mainly responsible for monitoring suspicious intruders. Target tracking technology is the key technology to achieve this goal. Off-line tracker is one of the main ways to achieve target tracking tasks. However, it requires a large number of samples for pre-training. Considering that human is the main monitoring object in intelligent video surveillance system, facial features can provide an effective basis for target tracking. Therefore, we introduce information maximizing generative adversarial nets as a generative model, which takes CelebA dataset as a benchmark to generate a large number of face samples with continuous change of feature attributes. Qualitative evaluation shows that the quality of synthetic face samples is high, which provides reliable data support for further training off-line tracker.
Development of an Automatic Meter Reading and Outage Management System using LoRaWAN Technology

Automatic Meter Reading (AMR) is an important part of the distribution system that can provide data from customers for utilities to monitor. With the help of communication technology, it can also be used for outage management. When outages occur, AMR may send outage notification to utilities for assessing the situation. AMR can also be polled at any time to determine power and status. However, it would not be efficient for utilities to poll every meter in an affected area of the network due to time and data constraints especially with Low-Power Wide-Area Network as LoRaWAN. LoRaWAN technology can provide very long-range transmission with low power consumption. However, it also has some restrictions including a limited payload, capacity and network size. This paper explains how LoRaWAN can be employed in an AMR setting, including for the on-demand reading feature and the polling algorithm for outage management system (OMS), which is used to identify system conditions.
On the Resource Allocation in HetNets with Massive MIMO Wireless Backhaul

This paper proposes a new transmission technique for heterogeneous networks with massive MIMO wireless back-haul with the objective of minimizing the power consumption cost. We assume that transmissions occur during two phases. During the first phase, the multi-antenna small-cell base stations (SBSs) receive signals from their associated users and from the macro-cell base station (MBS) thanks to MIMO spatial multiplexing. In the second phase, the SBSs transmit signals to the MBS and to the users. We study the problem of minimizing the sum SBS transmit power under minimum-rate constraints required at the users. We solve the formulated problem by deriving analytically the optimal time splitting parameter and the allocated transmit powers. Compared with the well-known reverse time division duplex (RTDD) with bandwidth splitting, considered as a benchmark, simulations show that the proposed transmission technique allows the SBSs to reduce considerably the power consumption.