Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodesics in First-Passage Percolation and Random Walks on Critical Percolation Clusters

It has been observed that statistical mechanical systems with quenched disorder can exhibit dramatically different properties than their disorder-free counterparts. The introduction of disorder can change the spatial structure of a system’s ground state or the typical behavior of the system’s free energy (or other quantities of interest). In many systems, the changes to spatial and quantitative behavior are closely linked. This dissertation explores these themes in two model disordered systems: firstpassage percolation and critical percolation. In first-passage percolation, a model for paths in a random potential, the energy of the ground state path of length n is expected to fluctuate like n, and the ground state path is expected to deviate from a straight line by a distance of n. There are longstanding conjectures about the values of χ and ξ, and the relation χ = 2ξ − 1 has recently been rigorously established. The prediction that χ < 1/2 (in contrast to a situation where each step of unit length contributed independently to the ground state path’s energy) is related to the existence of many “approximate ground states,” and such techniques have proved fruitful for rigorously bounding χ. In the work presented here, we consider the related issue of wandering of infinite energy-minimizing paths–that is, infinite paths whose energy cannot be reduced by altering any finite segment. One could ask whether such paths tend to have asymptotic direction or remain in sectors. This and related questions have been studied in first-passage percolation and related models; in the setting of first-passage percolation, there are few rigorous results except under strong assumptions. The work presented here develops a framework for studying these questions, and provides the first minimal-assumption results on directional concentration of infinite ground state paths. The critical percolation systems studied in this dissertation are two related models for the two-dimensional infinite cluster “at the critical point”: the incipient infinite iii cluster and the invasion percolation cluster. The disorder in these systems gives them a fundamentally different geometry than an ordinary lattice, affecting their transport properties. It was previously shown rigorously that a diffusing particle on the incipient infinite cluster moves strictly slower than a diffusion on the square lattice, on average over the disorder. The work presented here removes the average over disorder to show a quenched result: for a particular typical realization of the incipient infinite cluster, the diffusion is slow. The result is extended to diffusions on the invasion cluster. The work shows the relationship between the geometrical and transport properties of these models by deriving an upper bound for the speed in terms of critical exponents of the percolation models.
A IMP-LEACH-Based Routing Protocol For Energy Optimization In Wireless Sensor Network

obtained from all the cluster members before forwarding it to the base station. Data aggregation helps to minimize data redundancy and reduce the communication load because much less actual data needs to be transmitted from the cluster to the base station. Clustering techniques have emerged as a popular choice for achieving energy efficiency and scalable performance in large scale sensor networks. In this paper we have proposed an algorithm based on IMP-LEACH (Low-Energy Adaptive Clustering Hierarchy) which increases the overall energy utilization of the WSN. Abstract: Due to lack of power source, energy is the key concern area in Wireless Sensor Networks. Maximum energy is used in transmission of data. Many research works have been done to develop routing algorithms to increase the lifetime of a sensor network. Among them clustering algorithms have been widely used to reduce energy consumption. In this context, the key point in such topology is to select a cluster. One of solutions is to select a cluster alternately. However, this choice does not consider the energy as important criteria in actual papers. In order to limit energy consumption, our new method is proposed in this paper to improve Low Energy Adaptive Clustering Hierarchy (IMP-LEACH) to improve existing LEACH by selecting cluster according to the residual energy of nodes dynamically. The simulation results show that proposed algorithm achieves longer stability by comparison to original LEACH.
Multimodal Interaction Aware Platform for Collaborative Learning

: A number of studies in the research field of Computer Supported Collaborative Learning (CSCL) have proposed various systems in order to facilitate learning in the context of social interactions. In a collaborative learning, participants exchange not only verbal but also nonverbal cues such as utterance, gaze and gesture for maintaining the relationships among one another. Nevertheless, very few attempts have been made to construct a CSCL system that can utilize such multimodal (verbal and non-verbal) information to support communication in a collaborative learning. In this paper, we propose a novel platform that enables CSCL system developers to construct their learning support tools that have original functions to process such multimodal information. By building learning support tools on our multimodal aware platform, we confirmed its usefulness and also potential to pioneer unexploited filed of research in learning analytics for CSCL and methods to intervene in collaborative learning processes using verbal and non-verbal information unutilized so far.
Speech Emotion Recognition in Portuguese for SofiaFala: SER SofiaFala

Emotion recognition through speech processing has been increasingly demanded as a response to scientific advances and improvement in information technologies. However, a gap exists when the demand concerns projects in the Portuguese language. Here, we propose a method for extracting and recognizing emotion in the Portuguese language. We have evaluated response time, length, silence ratio, long silence ratio, and silence rate. According to the SER 2022 evaluation, our strategy can reach a macro-averaged F1 score of 55% on a very imbalanced dataset. We have aligned our results with the SofiaFala project, which supports speech training in children with Down syndrome.
Fiscal competition and public expenditure composition in the era of globalization: Panel data analysis

Amidst the ongoing liberalization of international trade and transnational movement of capital, each nation-state’s fiscal policies have gradually become more dependent on other countries. Developing countries, especially, are forced to make changes to taxation and public expenditure in this era of globalization. It is thus important to understand what kind of relationship exists between globalization and public spending. This study aims to clarify the effects of fiscal competition on public expenditure, from the perspective of the compensation and efficiency theses. These effects currently remain uncertain, despite the growing wave of academic interest in the field. To see how fiscal competition affects the structure of public expenditure, the determinants of public expenditures have been examined for 10 Organization for Economic Co-operation and Development (OECD) countries from 2006-2014. Eleven panel data models have been set up. Public expenditures, as the dependent variable, have been addressed in 10 sub-groups – based on their distribution both as a whole and per their socio-economic functions. Thus, we analyzed the relationship between fiscal competition and both total of public expenditures and the composition of public expenditures. The study thus contributes to the existing literature on fiscal competition. It is observed that corporate tax competition has decreasing effects on all public expenditures. Falling tax revenues due to fiscal competition have the effect of reducing overall public expenditures by decreasing the resources available for use by public policymakers. We can thus evaluate corporate tax competition as having an efficiency increasing effect, by decreasing the government’s share of within the economy.
Phytochemicals and biopesticides: Development, current challenges and effects on human health and diseases

For the last 50 years, the protection of crops is depended on synthetic pesticides. However, these synthetic pesticides are highly toxic, persistent, have adverse residual effects on crops, groundwater, soil, and their excessive application, leading to increased resistance of pests. On the other hand, biopesticides are less persistent, less toxic to non-target organisms, and eco-friendly. In this review, the complete process of developing biopesticides from plant-based extracts is described along with the marketing aspects that may help the entrepreneur open his venture on botanical pesticides. The complete process of developing biopesticides includes extraction, separation, identification of phytochemicals, the bio-efficacy study of crude extracts, and the formulation of biopesticides. Though India has a good market, the commercialization of biopesticides is not happening on that large scale. Because the system that is designed for synthetic pesticides generally regulates biopesticides also. It imposes high costs on biopesticides; thus, it makes a barrier to prevent the market entry of biopesticides. Some policy gaps and technological faults have been identified to reduce the utilization of biopesticides. One of the main problems in commercializing biopesticides is the lack of awareness about the effective use of biopesticides and their advantages. Some other problems in promoting biopesticides are limited resources, lack of profile, unhealthy relationships between producers and regulators, and limited capabilities. To increase the use of biopesticides at the country level, it is necessary to understand their mode of action, contribution to sustainable agriculture, and their effects on human health and diseases. Effects of total dietary polyphenols on plasma nitric oxide and blood pressure in a high cardiovascular risk cohort. The
Collisionless shock formation and the prompt acceleration of solar flare ions

The formation mechanisms of collisionless shocks in solar flare plasmas are investigated. The priamry flare energy release is assumed to arise in the coronal portion of a flare loop as many small regions or 'hot spots' where the plasma beta locally exceeds unity. One dimensional hybrid numerical simulations show that the expansion of these 'hot spots' in a direction either perpendicular or oblique to the ambient magnetic field gives rise to collisionless shocks in a few Omega(i), where Omega(i) is the local ion cyclotron frequency. For solar parameters, this is less than 1 second. The local shocks are then subsequently able to accelerate particles to 10 MeV in less than 1 second by a combined drift-diffusive process. The formation mechanism may also give rise to energetic ions of 100 keV in the shock vicinity. The presence of these energetic ions is due either to ion heating or ion beam instabilities and they may act as a seed population for further acceleration. The prompt acceleration of ions inferred from the Gamma Ray Spectrometer on the Solar Maximum Mission can thus be explained by this mechanism.
Multi-Label Video Classiﬁcation with QRNN and DenseNet

This paper is an extension of the paper ”Large-Scale MultiLabel Video Classification Using QRNNs” (To, 2018 [1]). The paper, part of an Undergraduate Independent Studies Project explored using Quasi-Recurrent Neural Networks as a method to predict relevant labels to any given video. A Quasi-Recurrent Neural Network (Bradbury & Merity et al., 2016 [2]) is a type of Neural Network that alternate between Convolutional layers and Pooling layers. This type of Neural Network was chosen due to the architecture being inherently smaller in file size, as well as requiring less computational resources for training.
Too Chicken to Cross the Road: Reinforcement Learning Models of Anxiety

Anxiety disorders are characterized by persistent maladaptive beliefs and actions, despite contrary evidence. This disconnect between the environment and the emotional response can cause intense distress (APA, 2013). For example, imagine an anxious agent attempting to cross the street, but is afraid of getting hit by a car. This is a simplistic but instructive example. Naturally, pedestrians should have a healthy fear of cars to motivate them to look both ways before crossing the street. At a reportedly perilous intersection, even a healthy agent would feel some anxiety, and perhaps might even cross the street elsewhere. However, a pathologically anxious agent would, in contrast, not even dare to set foot in the street. They might only cross at a select few intersections that are known to be 100% safe. They might generalize this fear to a fear of crossing all streets, including ones that are not frequented by cars. Clearly, this avoidance would constitute a high degree of dysfunction and distress for the agent, distinguishing them from non-pathologically anxious pedestrians. Our current understanding of anxiety does not fully explain how such maladaptive fears and behaviors, such as avoidance, are maintained. Furthermore, current psychotherapeutic approaches leave much to be desired in that, even after treatment, avoidant behavior often resurfaces (Moutoussis et al., 2017). To better help people with anxiety disorders, we must incorporate knowledge from computational models of anxiety. To that end, this paper asks how reinforcement learning models of anxiety can explain and inform the behavior and brain activity characteristic of anxiety. We begin by presenting two complementary models: a Q-learning model built on sequential evaluation, as well as the Latent Cause model. These models present two different but connected accounts of the maladaptive beliefs underlying anxiety. To supplement this knowledge, we then explore a model of the modulatory role of dopamine in anxiety.
Quantitative Content Analysis Methods in Instructional Technology Research: Defining, Coding, Analyzing and Modeling (DCAM)

Content analysis has been applied in the research of instructional design and technology to analyze (a) text-based contents, such as online discussions, social media communications, or published articles, and (b) other formats of contents such as videos, audios, or pictures. The purpose of this article is to introduce a method of DCAM (Defining, Coding, Analyzing and Modeling) for content analysis with practice examples. DCAM is a quantitative method generated from a series of studies in instructional design conducted by the author, and supported by the literature in the field. The variables defined from the text-content or other formats of contents can be design related variables, learning related variables, micro-activities in learning, or behavior-performance related learning outcome. In this article, first, nominal, ordinal and scaled coding methods on those variables are demonstrated. Second, reliability measures in content-variable coding are reviewed and explored. Third, parametric and nonparametric statistics methods to examine those variables for content analysis are presented. Finally, some cautions and suggestions to conduct content analysis is discussed.
I NCENTIVE M ECHANISMS IN S TRATEGIC L EARNING

We study the design of a class of incentive mechanisms that can effectively improve algorithm robustness in strategic learning. A conventional strategic learning problem is modeled as a Stackelberg game between an algorithm designer (a principal, or decision maker) and individual agents subject to the algorithm’s decisions, potentially from different demographic groups. While the former benefits from the decision accuracy, the latter may have an incentive to game the algorithm into making favorable but erroneous decisions by merely changing their observable features without affecting their true labels. While prior works tend to focus on how to design decision rules robust to such strategic maneuvering, this study focuses on an alternative, which is to design incentive mechanisms to shape the utilities of the agents and induce improvement actions that genuinely improve their skills and true labels and thus in turn benefit both parties in the Stackelberg game. Specifically, the principal and the mechanism provider (could be the principal itself) move together in the first stage, publishing and committing to a classifier and an incentive mechanism. The agents are second movers and best respond to the published classifier and incentive mechanism. We study how the mechanism can induce improvement actions, positively impact a number of social well-being metrics, such as the overall skill levels of the agents (efficiency) and positive or true positive rate differences between different demographic groups (fairness).
UNCERTAINTY ANALYSIS METHODS

This report surveys available analysis techniques to quantify the uncertainty in performance assessment (PA) arising from various sources. Three sources of uncertainty – physical variability, data uncertainty, and model error – are considered. The uncertainty quanti ﬁ cation methods are described in the context of four types of analyses needed, namely, (1) quanti ﬁ cation of uncertainty in the inputs to the PA models, (2) propagation of input uncertainty through the PA models, (3) model error quanti ﬁ ed through veri ﬁ cation and validation activities, and (4) probabilistic PA. Random variable and random process descriptions of physical variability are outlined. Methods for handling data uncertainty through ﬂ exible families of probability distributions, con ﬁ dence bounds, interval analysis and Bayesian analysis are described. Useful surrogate modeling and sensitivity analysis techniques for ef ﬁ cient uncertainty propagation analysis are discussed, as well as methods to quantify the various sources of model error. Statistical hypothesis testing techniques (both classical and Bayesian) are discussed for the validation of PA models, and a Bayesian approach to quantify the con ﬁ dence in model prediction with respect to ﬁ eld conditions is developed. First-order approximations as well as ef ﬁ cient Monte Carlo sampling techniques for probabilistic PA are described.
My 50 years of cochlear modeling

The goal of this presentation is two-fold: The primary goal is to discuss my present understanding of cochlear function. A secondary goal is to review my earlier (1970-2021) cochlear modeling work, along with the roles of four close friends: Egbert De Boer, Steve Neely, Paul Fahey and George Zweig. To understanding of how the cochlea works, one needs an understanding of the experimental data on: 1) cochlear function (both basilar (BM) and tectorial membranes (TM), 2) tympanic membrane, 3) middle ear (ME), 4) inner and outer hair cells (IHC, OHC), 5) auditory nerve (AN), and 6) cochlear ampliﬁer (CA). My views on these topics have been greatly sharpened by looking back and unifying this complex puzzle. A great deal of progress has been made in the last 50 years. Conclusions: My recent review of neural tuning curve data from 1985, using nonlinear (NL) distortion product generation, has revealed a deeper understanding of cochlear function. The most important, and surprising result, is that the cochlea is much more linear in its ﬁltering properties than I previously assumed. When the suppressor frequency f s is at least 1/2 octave lower than the characteristic (“best”) frequency ( f cf ), it is best known as “low-side” suppression. There is no “low-side” suppression for suppressors below 65 [dB-SPL]Fahey and Allen 40 . For suppressors above 65 [dB-SPL], suppression is engaged, with a slope between 1-2 [dB/dB]. Since the excitation threshold is also 65 [dB-SPL], we conclude that the neural threshold of excitation to both the inner and outer hair cells have nearly the same threshold. That is the suppression threshold of the OHC are nearly equal to, the IHC threshold. raises If the and OHC 65 [dB] thresholds are the same in the tails of the tuning curves, how can the CA function at threshold levels? Furthermore this is a highly unexpected result because low-side suppression, as measured on the basilar membrane, has a 20-30 [dB] higher threshold 31,43 . Is the OHC action restricted to the neighborhood of the neuron’s best frequency? This would require that the neural low-side suppression and loudness recruitment (the reduced loudness of low-intensity sounds in the hearing-impaired ear) are closely related (i.e., are the same phenomena). The ramiﬁcations of this observation seem signiﬁcant as they must impact our fundamental understanding of hearing and thus hearing loss 30 , (p. 332, Allen90) 9 . In summary: Two-tone suppression acts like an automatic gain control, elevating the loudness threshold, with little audible distortion. We then discuss the properties of the CA, functionally measuring the CA gain. The URL for cited manuscripts: https://auditorymodels.org/index.php? n=Main.Publications ; https://www.mechanicsofhearing. org/
Incremental Influential Community Detection in Large Networks

The concept of network communities has been studied thoroughly in the network science literature since it has many important applications in diverse fields. Recently, the community concept has been combined with the concept of influence. The aim of this combination is to allow for the detection of communities that have also a high degree of influence. To achieve this, there is a need to guarantee that communities are good with respect to their structure and also influential with respect to attribute values of the nodes participating in the community. In the literature, there are two main directions to attack the problem: i) the online approach, which computes influential communities in increasing influence value order, and ii) the index-based approach, which pre-computes influential communities and stores appropriate information in a tree-based index structure. Based on these two directions, we propose a new technique with the following properties: i) there is no need to process the graph each time a new query arrives, and ii) there is no need to waste computational resources to maintain parts of the index that users are not interested in. This is achieved by starting without any index in memory. Then, using online algorithms, as new queries arrive, we incrementally build parts of the index that help answering similar future queries. Extensive experimental results, on real world graphs, demonstrate the efficiency of our method against existing approaches in most realistic cases.
Stability Analysis of Grid Forming Converter Using 3-by-3 Admittance Measurements

The objective of this paper is to conduct stability analysis on a grid forming voltage source converter (VSC). The effects of the dc-link control and the power droop parameters are investigated. Stability analysis is based on a $3\times 3$ admittance model defining the current and voltage relationship viewed from dc and ac ports. The model is identified from experiment measurement data. The study shows that increasing the dc-link controller gain or the real power droop coefficient within suitable limits can enhance the dc-side stability of the VSC system. The analysis results have been verified by the electromagnetic transient (EMT) simulation results.