Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Filtering and Neural Networks with Non Commutative Algebras

—In this paper we provide stability results for algebraic neural networks (AlgNNs) based on non commutative algebras. AlgNNs are stacked layered structures with each layer associated to an algebraic signal model (ASM) determined by an algebra, a vector space, and a homomorphism. Signals are modeled as elements of the vector space, ﬁlters are elements in the algebra, while the homomorphism provides a realization of the ﬁlters as concrete operators. We study the stability of the algebraic ﬁlters in non commutative algebras to perturbations on the homomorphisms, and we provide conditions under which stability is guaranteed. We show that the commutativity between shift operators and between shifts and perturbations does not affect the property of an architecture of being stable. This provides an answer to the question of whether shift invariance was a necessary attribute of convolutional architectures to guarantee stability. Additionally, we show that although the frequency responses of ﬁlters in non commutative algebras exhibit substantial differences with respect to ﬁlters in commutative algebras, their derivatives for stable ﬁlters have a similar behavior.
Multiclass Classification of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks

Ocular toxoplasmosis (OT) is usually diagnosed by a specialist through the analysis of fundus images of the eye. Deep learning has been used to perform binary classification of the disease; however, to the best of our knowledge this is the first work that performs a multi-class classification to differentiate between active or inactive OT lesions which gives more information to the clinician when making the diagnosis. Using a predictive model to differentiate healthy eyes from others with different type of lesions can be useful to save time, help diagnose atypical cases and assist specialists with less experience. In this work, we investigate the application of a deep learning model to perform multi-class image classification of eye fundus images. A pretrained residual neural network is fine tuned on a dataset of samples collected at the Hospital de Cl\’inicas and Hospital General Pediátrico Acosta Nu medical centers from Asunci\’on, Paraguay. The proposed model results are highly promising, scoring sensitivity and specificity rates equal to 91% and 98% respectively. In order to replicate the results and continue researching this area an open data set of images of the eye fundus labeled by ophthalmologists is also published as part of this work.
Clutter-Invariant Regularization for DNN-based SAR Target Recognition

Latest developments in Deep Neural Networks (DNNs) emerge the ranking efficacy and efficiency of Synthetic Aperture Radar Automated Target Recognition (SAR ATR). However, it is difficult to collect SAR datasets in various scenes due to the high cost and the non-cooperative property. The current DNN-based SAR ATR algorithms are thus trapped into overfitting the target acquisition environment and are difficult to robustly identify the target in unknown background clutters. Based on the Shapley value, which is designed to attribute the contribution of each input component to the inference, we quantitatively analyze how much the target-clutter correlation affects the DNN model and reveal the decline of its performance under an unknown environment. Further, a Clutter-Invariant Regularization (CIR) training scheme is proposed to instruct the model in learning the invariant target features. Particularly, we generate blank or random clutter variants as a reference and regularize the model to extract the invariant target features from diverse types of clutters. Comprehensive experiments on the MSTAR dataset and three cutting-edge DNN models demonstrated the efficacy of the proposed method. The results showed that models trained by our CIR scheme can alleviate the distraction of data bias, and make robust as well as consistent decisions for the target with measured unknown background clutters.
Teaching Assignment Based on NASH Equilibrium and Genetic Algorithm

Teaching assignment refers to the process of allocating classes to instructors for a given academic semester. Its outcome is a schedule that must adhere to various constraints, such as ensuring that all classes have assigned instructors, avoiding schedule conflicts among instructors, and adhering to predetermined limits on the number of classes assigned to each instructor. It is a complex task due to the involvement of multiple stakeholders such as instructors, and academic departments, each with their own interests. In many cases, these interests may conflict with each other. In this study, we propose using the NASH equilibrium, a branch of game theory, to analyze conflicts of interest among stakeholders and modeling the teaching assignment as a multi-objective optimization problem. Furthermore, we develop a genetic algorithm to solve this problem. Our algorithm is experimented with a real-world dataset consisting of 153 classes and 25 instructors. The experimental results demonstrate the feasibility and effectiveness of our proposition.
Physical Layer Latency Analysis for 5G NR

Latency is one of the key performance of the fifth-generation mobile communication technology (5G) new radio (NR) network. 5G NR network can be deployed in FDD (Frequency Division Duplex) mode and TDD (Time Division Duplex) mode. Due to the discontinuous arrangement of slots in TDD mode, the 5G NR data latency in TDD mode is different from that in FDD mode. In order to further reduce the latency to meet the new service requirements of 5G NR, it is necessary to analyze the data latency in FDD mode and TDD mode, and propose the ideas of reducing 5G NR latency. Firstly, this paper establishes the physical layer latency model of mobile communication network, then analyzes the 5G NR data scheduling latency and data transmission latency in FDD mode and TDD mode, as well as the overall data transmission latency. Then the characteristics of 5G NR data latency in FDD mode and TDD mode are given. Finally, some research suggestions are provided on further reducing 5G NR data latency.
Tesla’s Legacy for New Millennium: Path to Quantum-Holographic Primal SourceTesla’s Legacy for New Millennium: Path to Quantum-Holographic Primal Source

Nikola Tesla is undoubtedly the greatest inventor in the history of electrical engineering, and what makes him especially fascinating was his unusual mental control of creative visions. Regarding presented Tesla’s extraordinary creative experiences in his altered states of consciousness (categorized in his time as nonphysical phenomena) – Tesla’s conviction "when we start to study nonphysical phenomena, we will progress more in ten years than we have for centuries" then becomes clearer. In this context, it seems that Tesla's legacy for the new millennium is introspective evidence of his transpersonal creative communications, with fascinating possibility of his meditative eidetic control & increase of macroscopic quantum correlations with the quantum-holographic primal source of collective consciousness. In this lecture it will be pointed out that they could serve as an extraordinary case study for understanding biophysical basis of both transpersonal psychology & control of creative processes, based on subtle interplay of normal & transitional & altered states of consciousness in meditation & sleep, which may help in developing future strategies for education. These insights could also provide deeper understanding of Hopfield-like neural-networks quantum-holographic framework for quantuminformational transpersonal psychosomatics, based on decrease of quantum entropy & macroscopic quantum correlations of acupuncture system / individual consciousness & collective consciousness, which may help in developing future strategies for integrative medicine & transpersonal psychology. In wider context, Tesla’s whole research & life could also provide an inspiration for reconsideration of global educational / informational / political goals – with reorientation towards holistic gentle actions for solutions of the global risk society.
Age of Information in Reservation Multi-Access Networks with Stochastic Arrivals

This paper investigates the Age of Information (AoI) performance of Frame Slotted ALOHA with Reservation and Data slots (FSA-RD). We consider a symmetric multi-access network where each user transmits its randomly generated status updates to an access point in a framed manner. Each frame consists of one reservation slot and several data slots. The reservation slot is made up of some mini-slots. In each reservation slot, users, with a status update packet to transmit, randomly send short reservation packets in one of the mini-slots to contend for data slots of the frame. The data slots are assigned to those users that succeed in reservation slot. To provide insights in optimizing the information freshness of FSA-RD, we manage to derive a closed-form expression of the average AoI under FSA-RD by applying a recursive method. Numerical results validate the analytical expression and demonstrate the influence of the frame size and reservation probability on the average AoI. We finally perform a comparison between the AoI performance of FSA-RD with optimized frame size and reservation probability, and that of slotted ALOHA with optimized transmission probability. The comparison results show that FSA-RD can effectively reduce the AoI performance of multi-access networks, especially when the status arrival rate of the network becomes large.
Understanding Hydrological Processes and Estimating Model Parameter Values in Large Basins : The Case of the Congo River Basin

Many large basins of the world are located in developing countries where the hydrometric networks are limited and where hydrological models have the potential to contribute to water resources management. However, it is difficult to ensure that models adequately represent the dominant hydrological processes, a problem further exacerbated by spatial scale issues and the typically large size of the modelling units. If models do not satisfactorily represent the hydrological processes, they may not be representing the runoff responses from ungauged areas and may not be useful for investigating the impacts of future water or land use developments. This paper reports on a study of the Congo River basin where the available stream flow data have been identified for 16 gauging stations within the total basin area of 3 680 000 km2. The initial application of the model (Pitman monthly time-step model) involved manual calibration, which was followed by an exploration of the behavioural parameter sets in the context of the available basin physical property data (topography, drainage patterns, geology, soils, vegetation, etc.) in an attempt to constrain the plausible parameter sets to those that are conceptually realistic and consistent with real hydrological processes.
Neural and genetic underpinnings of response inhibition in adolescents with attention-deficit/hyperactivity disorder

Background : Deficits in response inhibition have been associated with attention-deficit/ hyperactivity disorder (ADHD). Given the role of serotonin in ADHD and impulsivity, we postulated that genetic variants within the serotonin pathway might influence response inhibition. Methods : We measured neural activation during stop-signal task performance in adolescents and young adults with ADHD (N=185), their unaffected siblings (N=111), and healthy controls (N=124) (age=16.9), and investigated the relationship of two serotonin polymorphisms (the rs6296 sNP of the HTR1B gene and HTTLPR variants of the 5-HTT gene) with the neural correlates of response inhibition. Additionally, we tested if these variants were associated with the ADHD phenotype and response inhibition performance, and whether they could explain our previously found association of ADHD with neural activation during response inhibition. Results : The whole-brain analyses demonstrated large scale neural activation differences in the inferior and medial frontal and temporal/parietal regions of the response inhibition network between the different variants of both the HTR1B and 5HTT genes. Activation in these regions was significantly associated with stop-task performance, but not with ADHD diagnosis or severity. No associations were found between the HTR1B and 5HTT variants and ADHD or ADHD-related neural activation during response inhibition. Conclusions: These results provide novel evidence that serotonin may play an important role in the neurobiology of response inhibition. Although response inhibition is strongly linked to ADHD, genetic variants associated with response inhibition and its neural correlates do not explain variance of the ADHD phenotype.
Design and Testing of Spraying Drones on Durian Farms

In modern times, the development of smart agriculture and digital countryside has become an important aspect of agricultural growth, as Thailand increasingly focuses on economics to improve farming practices. In Thailand, unmanned aerial vehicles (UAVs), also known as drones, are being utilized in durian farms with dense canopies of leaves. This technology enables more sustainable chemical treatments, by reducing the amount of chemicals sprayed, saving time, and protecting people's health. This study aimed to identify the best drone spraying solution for durian trees by analyzing sprayer droplet images. A smartphone app called Snap Card was used to evaluate the coverage and effectiveness of spray treatments on water-sensitive paper(WSP). The results reveal that the DJI T30 sprayer performs better than the DJI T20P due to the UAVs' droplet distribution and coverage. Additionally, the flight parameters, the environment's conditions, and various nozzle types are addressed for effective distribution in Durian trees. According to the findings of this study, unmanned aerial vehicles (UAVs) provide fascinating prospects for increasing modern technology efficiency in agriculture.
Speech Enhancement: A Signal Subspace Perspective

Speech enhancement is a classical problem in signal processing, yet still largely unsolved. Two of the conventional approaches for solving this problem are linear filtering, like the classical Wiener filter, and subspace methods. These approaches have traditionally been treated as different classes of methods and have been introduced in somewhat different contexts. Linear filtering methods originate in stochastic processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory. This book bridges the gap between these two classes of methods by showing how the ideas behind subspace methods can be incorporated into traditional linear filtering. In the context of subspace methods, the enhancement problem can then be seen as a classical linear filter design problem. This means that various solutions can more easily be compared and their performance bounded and assessed in terms of noise reduction and speech distortion. The book shows how various filter designs can be obtained in this framework, including the maximum SNR, Wiener, LCMV, and MVDR filters, and how these can be applied in various contexts, like in single-channel and multichannel speech enhancement, and in both the time and frequency domains. First short book treating subspace approaches in a unified way for time and frequency domains, single-channel, multichannel, as well as binaural, speech enhancement. Bridges the gap between optimal filtering methods and subspace approaches. Includes original presentation of subspace methods from different perspectives.
Belief in Redeemability and Punitive Public Opinion: “Once a Criminal, Always a Criminal” Revisited

In 2009, Maruna and King presented results from a British survey showing that the public’s belief in the redeemability of people who committed offenses curbed their level of punitiveness. Based on a 2017 national survey in the United States (n = 1,000), the current study confirms that redeemability is negatively related to punitive attitudes. In addition, the analyses reveal that this belief predicts support for rehabilitation and specific inclusionary policies (i.e., ban-the-box in employment, expungement of criminal records, and voting rights for people with a felony conviction). Findings regarding measures for punishment and rehabilitation were confirmed by a 2019 Mechanical Turk (MTurk) survey. These results suggest that beliefs about capacity for change among people who committed offenses are key to understanding crime-control public policy.
Factors influencing mobilization of Kenyan resources for health and development

Community Based Organizations (CBOs) have performed a key role in complementing initiatives of governments towards meeting the basic needs of its citizens not only in Kenya. This was a descriptive study that employed both qualitative and quantitative methodologies. Three sampling methods were used namely purposive, stratified and random. Purposive sampling was used to identify all the relevant CBOs while stratified sampling was applied on gender composition, nature of activities, level of performance, size of membership, geographical location among others while random sampling was utilized to identify CBOs within the various stratums. 14 CBOs were selected for the study from a population of 41 CBOs. Findings indicate that inaccessibility to resources from the corporate sector, presence of management structures and governance in CBOs among others has greatly influenced the ability of the CBOs to mobilize internal resources.
Effect of different micronutrients and their methods of application on growth, yield and quality of broccoli ( Brassica oleracea var

A field experiment was conducted at College Farm, College of Horticulture, Sardarkrushinagar Dantiwada Agricultural University, Jagudan, Gujarat during the Rabi season of the year 2019-20 and 2020-21 to study the effect of different micronutrients and their methods of application on growth, yield and quality of broccoli ( Brassica oleracea var. italica ) cv. Palam Samridhi. The experiment was laid out in Randomized Block Design with Factorial concept comprising of two factors. The first factor was micronutrients (C) with seven levels viz. , C1-B, C2 – Zn, C3 – Fe, C4 – B + Zn, C5 – B + Fe, C6 – Zn + Fe and C7 – B + Zn + Fe and the second factor was methods of application (M) i.e . , M1-soil application, M2-single spray of micronutrients at 25 DATP and M3-two sprays of micronutrients at 25 and 50 DATP. The treatments were replicated thrice. The individual effect of micronutrients and their method of application as well as their interaction effect on growth and yield of broccoli cv. Palam Samridhi was recorded. The results indicate that effect of micronutrient using B + Zn + Fe (C7) treatment was the most effective treatment in all studied traits. Method of micronutrients application (M3) two spray of micronutrients at 25 and 50 DATP was found superior for growth and yield parameters except plant height (cm) at harvest, number of leaves per plant at harvest and number of sprouts per plant at harvest. The interaction effect of micronutrients and their methods of application C7M3 i.e . , two spray of B @ 100 ppm + Zn @ 100 ppm + Fe @ 100 ppm at 25 and 50 DATP were found significant for all parameters of growth and yield, except sprout weight (g) per plant. Thus, from the present investigation it could be concluded that for successful cultivation of broccoli in North Gujarat two foliar applications of micronutrients i.e. , B @ 100 ppm + Zn @ 100 ppm + Fe @ 100 ppm at 25 and 50 DATP along with RDF (120:80:80 NPK) is beneficial for optimum growth and higher yield.
Evaluation of obsessive-compulsive disorder prevalence in Iranian patients with multiple sclerosis and its relationship with demographic characteristics

Background: The present study examined the prevalence of obsessive-compulsive disorder (OCD) among Iranian patients with multiple sclerosis (MS) and the relationship between OCD and the patient’s demographic characteristics. Methods: The present study used a cluster sampling method to randomly select 297 patients with MS from the patients referred to the MS Clinic and Research Center of Sina University Hospital, Tehran, Iran, during 2018-2019. To gather the required data, a questionnaire consisting of demographic characteristics and disease information sections was used. Moreover, Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Obsessive-Compulsive Inventory-Revised (OCI-R), and Hospital Anxiety and Depression Scale (HADS) were employed in the present study. The comparison of qualitative and quantitative values between OCD-positive and negative groups was examined with chi-square test and independent samples t-test, respectively. Binary logistic regression was used as multivariate modeling to adjust the effects of potential confounders that could distort the relation of OCD with intended variables. Data were analyzed using SPSS software. The significance level was considered lower than 0.05. Results: Of the total of 297 patients, 77.8% were women. Moreover, analysis of the data obtained from the HADS questionnaire revealed that 11.44% (n = 34) and 15.15% (n = 45) of patients had severe depression and severe anxiety, respectively. The results of Y-BOCS indicated that the prevalence of severe OCD among patients was 19.9%. The OCI-R questionnaire revealed that the prevalence of OCD was 47.8%, which was consistent with the total of moderate, severe, and extreme values of Y-BOCS (47.9%). Furthermore, the subscales of ordering-arranging with 69 patients (22.9%) and obsessing with 46 patients (15.5%) indicated the highest frequencies as compared to other subscales. Moreover, OCD had a significant relationship with depression, anxiety, and type of MS in this regard. Conclusion: In this study, the prevalence rate of OCD among patients with MS was higher than its rate among the general population.