Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Event-driven Neuromorphic Architecture for Deep Spiking Neural Networks

Deep Neural Networks (DNNs) have been successfully applied to various real-world machine learning applications. However, performing large DNN inference tasks in real-time remains a challenge due to its substantial computational costs. Recently, Spiking Neural Networks (SNNs) have emerged as an alternative way of processing DNN’fs task. Due to its eventbased, data-driven computation, SNN reduces both inference latency and complexity. With efficient conversion methods from traditional DNN, SNN exhibits similar accuracy, while leveraging many state-of-the-art network models and training methods. In this work, an efficient neuromorphic hardware architecture for image recognition task is presented. To preserve accuracy, the analog-to-spiking conversion algorithm is adopted. The system aims to minimize hardware area cost and power consumption, enabling neuromorphic hardware processing in edge devices. Simulation results have shown that, with the MNIST digit recognition task, the system has achieved $\times 20$ reduction in terms of core area cost compared to the state-of-the-art works, with an accuracy of 94.4%, core area of 15 $\mu m^{2}$ at a maximum frequency of 250 MHz.
PENGEMBANGAN MODUL ANALISIS RANGKAIAN LISTRIK BERBASIS INKUIRI

Penelitian ini bertujuan untuk 1) Mengetahui kualitas modul praktikum analisis rangkaian listrik berbasis inkuiri menurut ahli materi dan ahli media. 2) Mengetahui respon mahasiswa setelah menggunakan modul praktikum analisis rangkaian listrik berbasis inkuiri. Penelitian ini merupakan penelitian Research and Development (R & D) dengan menggunakan model ADDIE yang terdiri dari empat tahap: tahap analisis ( analysis ), tahap perancangan ( design ), tahap pengembangan dan produksi ( develop ), tahap penerapan ( implementation ), dan tahap evaluasi ( evaluation ). Subyek penelitian ini adalah mahasiswa semester 5 (Lima) yang mengikuti praktikum analisis rngkaian listrik tahun akademik 2018/2019 di program studi pendidikan Fisika IKIP PGRI Pontianak. Teknik pengumpulan data yang digunakan adalah teknik komunikasi tidak langsung. Hasil validasi secara keseluruhan menunjukkan bahwa modul penuntun praktikum memiliki skor validasi 4,2 dengan kriteria baik
UTILIZING LANDSAT AND SENTINEL-2 TO REMOTELY MONITOR AND EVALUATE THE PERFORMANCE OF WINTER COVER CROPS THROUGHOUT MARYLAND

Abstract. Winter cover crops have been shown to limit erosion and nutrient runoff from agricultural land. To promote their usage, the Maryland Department of Agriculture (MDA) subsidizes farmers who plant cover crops. Conventional verification of cover crop planting and analysis of subsequent crop performance requires on-the-ground fieldwork, which is costly and labor intensive. In partnership with the MDA, NASA's DEVELOP program utilized imagery from Landsat 5, Landsat 8, and the European Space Agency’s Sentinel-2 to create a decision support tool for satellite-based monitoring of cover crop performance throughout Maryland. Our teams created CCROP, an interactive graphical user interface, in Google Earth Engine which analyzes satellite imagery to calculate the normalized difference vegetation index (NDVI) of fields across the state. Linear regression models were applied to convert NDVI to estimates of crop biomass and percent green ground cover, with measure of fit (R2) values ranging from 0.4 to 0.7. These crop metrics were implemented into an interactive filtering tool within CCROP which allows users to examine cover crop performance based on a variety of growing parameters. CCROP also includes a time series analysis routine for examining the progression of NDVI throughout the spring to help determine farmer-induced termination dates of cover crops. With this decision support tool, the MDA can analyze the effectiveness of cover crops throughout the state with reduced need to manually spot-check enrolled production fields, and can identify variables influencing overall cover crop performance to optimize implementation of their winter cover crop program via adaptive management approaches.
LoRaWAN Range Extender for Industrial IoT

The LoRaWAN technology, an example of low power wide area network, is considered a possible IoT-derived solution for realizing private cellular (single-hop) communications in the industrial domain. However, despite LoRaWAN promises long range and dense environments with many obstacles (as the industrial ones), it may suffer from coverage issue. Additionally, the inverse relationship between data rates and range may be unacceptable for many industrial applications. In this article, an innovative LoRaWAN range extender (based on an enhanced LoRaWAN node) is described, and its integration in the infrastructure of industrial Internet of Things (IIoT)-enabled industrial wireless networks is presented. A frame relay strategy is suggested, thus avoiding to tradeoff the highest data rates against an increased sensitivity. The feasibility of the solution is formally verified and the features of the realized proof-of-concept prototype, based on commercially available hardware, are discussed. The experimental results, obtained with a purposely designed test bench, show the effectiveness of the proposed approach. In particular, the range extender is transparent for legacy LoRaWAN networks as confirmed by correct relaying of uplink and downlink for different types of LoRaWAN message.
The Italian version of the Thinking About Life Experiences Questionnaire and its relationship with gender, age, and life events on Facebook

The present study provided a cross-cultural validation of the Thinking About Life Experiences scalerevised (TALE-R) in an Italian sample of Facebook users (N=492; Female=378; Male=114; mean age 26.1) to test for replication and universality of the TALE-R three-factor model. Furthermore, it explored the interrelations among gender, age, the scores at the TALE-R, and the frequency of posting textual/visual information about individuals’ life events on Facebook. Results at exploratory and confirmatory factor analysis gave empirical support both of a tripartite model for the functions of autobiographical memory (i.e., directive-behavior, socialbonding, and self-continuity) and measurement invariance of this three-factor model across gender and age. Further results at linear correlation and regression analyses showed that directive-behavior and self-continuity functions of autobiographical memory are significantly related to the ways people use Facebook for personal documentation. Age differences more than gender influence this association. Discussion and conclusion reported both theoretical and empirical implications of the findings of the study.
Individual cognition and collective behaviour in ants

Eusocial insects are impressive on individual and collective level. Collectively, they build nests and efficiently exploit and monopolise nearby resources by sharing information amongst their members. Such collective behaviours are shaped by remarkable and multi-facetted individual abilities and decision-making processes. By revealing and investigating factors which influence individual decision making, this thesis provides a deeper insight into the cognitive lives of ants while contributing to a more cohesive understanding of colony behaviour as a whole. In chapter 2, we demonstrated that foraging ants form expectations about value-neutral qualities of a food source – such as its taste – and dislike food which deviates from those expectations. As such dislike is translated into reduced recruitment, it will have a direct impact on colony-level behaviour. Expectations are thus a potential driver of foraging decisions. Ants excel in navigation, and we showed in chapter 3 that ants can rapidly learn olfactory or spatial cues to localise food sources. Moreover, when those two types of private information where put into conflict, ants exclusively relied on olfaction. This demonstrates that one type of information can entirely dominate decisions in certain situations. While we demonstrated that ants learn simple associations rapidly, nothing was known about their ability to learn abstract relations between stimuli, although concept learning has been reported in honeybees. Thus, in chapter 4, we trained ants to learn a relational rule of same/different, but found that the ants, instead of solving such a complex task via learning, resorted to heuristics such as ‘go left’ or ‘go to the more salient cue’. Intriguingly, the heuristics used varied between individual ants. Learning is dependent on reward and motivation and in chapter 5 we investigated whether those factors alone could explain individual and collective foraging behaviour. While we only found small effects of reward magnitude and motivation on learning, persistence rates varied dramatically in lower motivated colonies. An agent-based model using the empirical data further demonstrated that individual decisions alone can cause ecologically sensible colony-level foraging behaviour. Finally, in chapter 6 we explored whether colonies could make sensible collective decisions in the context of trail-clearing by Australian meat ants. We found that ant colonies preferentially cleared trails toward a food source if the alternative detours were long. The underlying mechanisms seem to be dependent on individual propensities to remove obstacles which leads to the emergence of paths. Taken together, the results of this thesis broaden our understanding of individual cognition in ants and demonstrate that properties inherent to individuals, such as experience or task propensities, systematically influence decisions and thus impact the collective.
Exploring the Effects of Effectors

We explore the effects of trained musical movements on sensorimotor interactions in order to clarify the interpretation of previously observed expertise differences. Pianists and non-pianists listened to an auditory sequence and identified whether the final event occurred in time with the sequence. In half the trials participants listened without moving, and in half they synchronized keystrokes while listening. Pianists and non-pianists were better able to identify the timing of the final tone after synchronizing keystrokes compared to listening only. Curiously, this effect of movement did not differ between pianists and non-pianists despite substantial training differences with respect to finger movements. We also found few group differences in the ability to align keystrokes with events in the auditory sequence; however, movements were less variable (lower coefficient of variation) in pianists compared to non-pianists. Consistent with the idea that the benefits of synchronization on rhythm perception are constrained by motor effector kinematics, this work helps clarify previous findings in this paradigm. We discuss these outcomes in light of training and the kinematics involved in pianist keystrokes compared to musicians synchronizing movements in other studies. We also overview how these differences across motor effector synchronization and training must be accounted for in models of perception and action.
2D Features-based Detector and Descriptor Selection System for Hierarchical Recognition of Industrial Parts

Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower.
Communication Learning via Backpropagation in Discrete Channels with Unknown Noise

This work focuses on multi-agent reinforcement learning (RL) with inter-agent communication, in which communication is differentiable and optimized through backpropagation. Such differentiable approaches tend to converge more quickly to higher-quality policies compared to techniques that treat communication as actions in a traditional RL framework. However, modern communication networks (e.g., Wi-Fi or Bluetooth) rely on discrete communication channels, for which existing differentiable approaches that consider real-valued messages cannot be directly applied, or require biased gradient estimators. Some works have overcome this problem by treating the message space as an extension of the action space, and use standard RL to optimize message selection, but these methods tend to converge slower and to inferior policies. In this paper, we propose a stochastic message encoding/decoding procedure that makes a discrete communication channel mathematically equivalent to an analog channel with additive noise, through which gradients can be backpropagated. Additionally, we introduce an encryption step for use in noisy channels that forces channel noise to be message-independent, allowing us to compute unbiased derivative estimates even in the presence of unknown channel noise. To the best of our knowledge, this work presents the first differentiable communication learning approach that can compute unbiased derivatives through channels with unknown noise. We demonstrate the effectiveness of our approach in two example multi-robot tasks: a path finding and a collaborative search problem. There, we show that our approach achieves learning speed and performance similar to differentiable communication learning with real-valued messages (i.e., unlimited communication bandwidth), while naturally handling more realistic real-world communication constraints. Content Areas: Multi-Agent Communication, Reinforcement Learning.
Optimization by Genetic Algorithm in Wireless Sensor Networks Utilizing Multiple Sinks

WSN (Wireless Sensor Network) comprises of small-sized and constraint-capability SN (Sensor Nodes) which record, send and receive data, sensed to a sink. The network lifetime and energy usability are important challenges to be dealt with. During the working of the SN, the maximum amount of energy is consumed than sensing and processing of data. Therefore, an efficient transmission of the data is required so that the energy can be saved. In this paper, a novel routing and scheduling method for WSNs using GA (Genetic Algorithm) is presented, where the sinks employed on four sides of the sensor field. These sinks collect the data from the SNs having the optimal distance towards the respective sink. The proposed scheme finds the optimized path using GA, during transmission of data from SN to the nearest sink. First, we run the GA for determination of routing paths, where a source SN finds the possible number of optimal hops. Second, the hops or intermediate relay SNs are assumed to relay the data towards the sink, efficiently. The performance is experimented and evaluated using MATLAB R2016b. The simulations have carried out through comparing the proposed scheme with TEEN (Threshold Sensitive Energy Efficient Sensor Network Protocol). The results of simulation comprise of 10 and 20 number of SNs, discretely. Additionally, the direct distance of each node is calculated and the distance through multiple hops from/to the nearest sink is also evaluated. The achievements of the proposed technique are to save both energy and distance for the sake of network longevity and optimal and precise data delivery by multiple hops.
Environmental oxygen regulates astrocyte proliferation to guide angiogenesis during retinal development

Transient neonatal exposure to high oxygen levels is a major risk factor for retinopathy of prematurity (ROP), a disease of perturbed retinal vascular development. The factors that interrupt normal angiogenesis following oxygen exposure remain unclear. Formation of retinal vasculature requires guidance cues from astrocytes, suggesting that astrocyte disturbances could contribute to oxygen-induced retinopathy. However, it is presently unknown whether environmental oxygen influences astrocyte development. To investigate this question we raised neonatal mice in hyperoxic conditions (75% O2) from postnatal day 0-4, during astrocyte network formation. We found that hyperoxia suppressed proliferation of retinal astrocytes, whereas return to room air at P4 induced a major astrocyte proliferative response, massively increasing astrocyte numbers and disturbing their spatial patterning prior to arrival of developing vasculature. This treatment paradigm also disrupted vascular development in a manner reminiscent of ROP, leading to vitreous hemorrhage and neuroretina defects. Moreover, within individual animals, the severity of vascular phenotypes was strongly correlated with the number of excess astrocytes. Direct exposure to hypoxia (10% O2) produced similar effects, indicating that developing astrocytes are sensitive to relative decrements in oxygen availability. Molecular analysis revealed that the proliferative response was blunted in mice bearing an astrocyte-specific ablation of the HIF2α transcription factor. Together, our results support a model in which neonatal astrocytes use a HIF2α-dependent pathway to sense oxygen levels, thereby regulating the number of astrocytes within the angiogenic template. In this model, fluctuations in environmental oxygen can become pathogenic when they over-stimulate this proliferation control mechanism, leading to excess astrocytes that interfere with retinal angiogenesis.
Citrullus lanatus morphophysiological responses to the combination of salicylic acid and salinity stress

The salinity of irrigation water is one of modern agriculture’s major obstacles, as it can damage the plants metabolism and, consequently, its development. Currently there is a great demand for substances that may mitigate such effects, such as salicylic acid. Thus, this study aimed to evaluate the effects of salicylic acid application on watermelon plants (Citrullus lanatus L.) under salinity stress. The experimental design was a randomized block design, in an incomplete factorial scheme, with four replications and two plants per replicate. The treatments resulted from the combination of five concentrations of salicylic acid (0.00, 0.15, 0.50, 0.85 and 1.00 mM) and five electrical conductivities of the irrigation water (0.50, 1.01, 2.25, 3.49 and 4.00 dS m-1). The development, gas exchange, chlorophyll index and fluorescence were evaluated 30 days after transplanting. This data was submitted to analysis of variance and then confidence bands (confbands) and Pearson correlation were produced. The application of salicylic acid (up to 0.85 mM) promotes beneficial effects for the watermelon plants morphophysiology, while the increase in electrical conductivity of the irrigation water is harmful.
Laser-Assisted Tailoring of Surface Wettability - Fundamentals and Applications: A Critical Review

The control of ubiquitous wetting behavior that is determined by the surface free energy of the solid and the surface tension of the interacting liquid is an area of intense research, largely due to its applications in various sectors, ranging from healthcare to automotive industry. The failure and/or toxic nature of the conventionally employed chemical methods to engineer the surface free energy of solids paved the way for utilizing alternative techniques such as laser-based physical texturing of the surface to achieve the desired wettability behavior. This review provides insight into the implementation of lasers to engineer the surfaces of various kinds of materials and how the surface engineering manipulates their wetting characteristics based on the state of the art knowledge of the field. Various theories that explain the wetting behavior of droplets on the surfaces of homogeneous, heterogeneous, and complex structures are discussed with a special emphasis on the role of surface roughness. Further, the article focuses on the lasermatter interaction at different time scales and modification of the surfaces with different kinds of lasers (nano, pico, and femtosecond laser) and their applications. A critical analysis of the effects of experimental parameters such as laser fluence, repetition rate, laser wavelength, inter-pattern spacing on the experimentally observed wetting behavior in the recent literature is also presented. Emerging applications of laser structured surfaces in various fields, including biomedical, water harvesting, anti-bacterial, spectroscopic analysis, oil/water separation, etc. are discussed in detail.
Re-Establishing Stage 0 of COPD

During this prolong pre-clinical period of the natural history of the disease significant changes occurs in the lungs on molecular and cellular level [8-12]. The small airways, in the periphery of the lungs, are usually the site where the initiation of those alterations could be detected [13]. Thereafter, those pathological changes may proceed towards the larger airways developing the phenotype of chronic bronchitis or towards the alveoli, causing emphysema or more commonly in both directions, developing the frequent phenotype of both entities (chronic bronchitis and emphysema) in the same patient, the so called COPD patient [14,15].
METTL4 catalyzes m6Am methylation in U2 snRNA to regulate pre-mRNA splicing

N6-methylation of 2’-O-methyladenosine (Am) in RNA occurs in eukaryotic cells to generate N6,2’-O-dimethyladenosine (m6Am). Identification of the methyltransferase responsible for m6Am catalysis has accelerated studies on the function of m6Am in RNA processing. While m6Am is generally found in the first transcribed nucleotide of mRNAs, the modification is also found internally within U2 snRNA. However, the writer required for catalyzing internal m6Am formation had remained elusive. By sequencing transcriptome-wide RNA methylation at single-base-resolution, we identified human METTL4 as the writer that directly methylates Am at U2 snRNA position 30 into m6Am. We found that METTL4 localizes to the nucleus and its conserved methyltransferase catalytic site is required for U2 snRNA methylation. By sequencing human cells with overexpressed Mettl4, we determined METTL4’s in vivo target RNA motif specificity. In the absence of Mettl4 in human cells, U2 snRNA lacks m6Am thereby affecting a subset of splicing events that exhibit specific features such as overall 3’ splice-site weakness with certain motif positions more affected than others. This study establishes that METTL4 methylation of U2 snRNA regulates splicing of specific pre-mRNA transcripts.