Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Disaster Effects on Popular Sentiment Toward Finance

Abstract We use a text-based measure of popular sentiment toward finance to study how finance sentiment responds to rare historical disasters and to the ongoing COVID-19 pandemic. Finance sentiment declines after epidemics and earthquakes but rises following severe droughts, floods, and landslides. These heterogeneous effects suggest finance sentiment responds differently to the realization of insured versus uninsured risks. Finance sentiment declines at the start of the COVID-19 pandemic, but recovers in countries that experienced high stock markets returns and that responded with large fiscal spending. Finance sentiment seems to depend on the insurance provided by private markets and by public finance.
Anticipated effect of education on economic growth in Ethiopia: time series analysis

Financing in education has newlyinstitutedvital social objectives for a rationallyvirtuous level of human capital raises a laborer’s proficiency’s, productivity and value of life.This research basically focused on the anticipated effect ofeducation Ethiopian economic growth is verified empirically. The researchplanned at decomposing the relationship between education and economic growth using time series data from 1975/76 to 2015/16. All those independent variables were found to be stationary in first difference. Long-run associationamong variables, short run andlong run dynamics were inveterate through Johnson co-integration analysis and VECM specification. The finding showsin the long run on government education expenditure,labour force, quality education, secondary enrolment rate and tertiary enrolment ratewould affect the economic growth. Lastly, the Granger causality test was conducted and the results show that tertiary education and labour force can have a power on economic growth.
Periodic neural codes and sound localization in barn owls

Inspired by the sound localization system of the barn owl, we define a new class of neural codes, called periodic codes, and study their basic properties. Periodic codes are binary codes with a special patterned form that reflects the periodicity of the stimulus. Because these codes can be used by the owl to localize sounds within a convex set of angles, we investigate whether they are examples of convex codes, which have previously been studied for hippocampal place cells. We find that periodic codes are typically not convex, but can be completed to convex codes in the presence of noise. We introduce the convex closure and Hamming distance completion as ways of adding codewords to make a code convex, and describe the convex closure of a periodic code. We also find that the probability of the convex closure arising stochastically is greater for sparser codes. Finally, we provide an algebraic method using the neural ideal to detect if a code is periodic. We find that properties of periodic codes help to explain several aspects of the behavior observed in the sound localization system of the barn owl, including common errors in localizing pure tones.
Shining a Light on s-Triazine-Based Polymers

The strong interplay between the structure and optical properties of conjugated s-triazine-based framework (CTF) materials is explored in a combined experimental and computational study. The experimental absorption and fluorescence spectra of the CTF-1 material, a polymer obtained through the trimerization of 1,4-dicyanobenzene, are compared with the results of time-dependent density functional theory and approximate coupled cluster theory (CC2) calculations on cluster models of the polymer. To help explain the polymer data, we compare its optical properties with those measured and predicted for the 2,4,6-triphenyl-1,3,5-triazine model compound. Our analysis shows that CTFs, in line with experimental diffraction data, are likely to be layered materials based around flat hexagonal-like sheets and suggests that the long-wavelength part of the CTF-1 absorption spectrum displays a pronounced effect of stacking. Red-shifted peaks in the absorption spectrum appear that are absent for an isolated sheet. We also .
Oxidative stress markers in neurological diseases and disorders: electrochemical detection of hydrogen peroxide and nitric oxide

The aim of this thesis is to further demonstrate the electrochemical detection of nitric oxide (NO) and hydrogen peroxide (H2O2) in-vitro, to advance the previously demonstrated detection of brain NO and to demonstrate the novel in-vivo detection of H2O2 using a paired catalase-based biosensor. We have recently successfully demonstrated the real-time detection of brain NO using a previously characterised Nafion®-modified platinum (Pt) electrochemical sensor. Additionally, the preliminary in-vitro development of a paired catalase based Pt sensor for the purpose of H2O2 detection in the physiological environment has previously been reported by our research group. A validation of the previously reported in-vitro characteristics of the NO sensor with the inclusion of novel post in-vivo stability attributes is presented in this thesis (see Chapter 6). Also, the suitable functionality of the NO sensor in the neuronal environment as has been published recently is supported in Chapter 6. A verification of the previously reported in-vitro attributes of the paired catalase based H2O2 sensor is shown in Chapter 4. Additionally, advancements to the initial in-vitro characterisation of the paired H2O2 sensor are described in Chapter 4. The previously unreported real-time detection of brain H2O2 in freely-moving animals is presented in Chapter 5. The main focus of this thesis is the detection of NO and H2O2 as oxidative/nitrosative stress markers in neurological disease and dysfunction. As such the role of oxidative/nitrosative stress in Parkinson’s disease (PD) and autism spectrum disorders (ASDs) is investigated in this thesis. The novel detection of NO in a propionic acid (PPA) induced animal model of ASDs is presented in Chapter 7. The electrochemical detection of NO and the preliminary dual detection of NO and H2O2 in a paraquat (PQ) mediated model of PD is presented in Chapter 8. The specific focus of these investigations is to elucidate the role of oxidative/nitrosative stress in the etiology and pathophysiology of PD and ASDs.
Development of Decentralized Electronic Payment System Using Blockchain

—In Indonesia there are countless of electronic payment provider, but almost all of them use a centralized server architecture. This caused quite a few problems such as single point of failure, fragmentation of services and concern of privacy. To solve this problem a decentralized system will be made to utilize blockchain as a mean to do transaction. The chosen blockchain technologies are Stellar and Hyperledger. Stellar is used for transaction while Hyperledger is used for managing identity. The decentralized system made in this paper able to do basic functionality of electronic payment system while also accomplish the requirement for stability and response time.
Research Division Federal Reserve Bank of St . Louis Working Paper Series Unemployment Insurance Fraud and Optimal Monitoring

The most prevalent incentive problem in the U.S. unemployment insurance system is that individuals collect unemployment benefits while being gainfully employed. We show how to efficiently use a combination of tax/subsidy and monitoring to prevent such fraud. The optimal policy monitors the unemployed at fixed intervals. Employment tax is nonmonotonic: it increases between verifications but decreases after a verification. Unemployment benefits are relatively flat between verifications but decrease sharply after a verification. JEL Classification Numbers: D82, D86, J65.
Two-Stage Tandem Network with Independent Service Times

Networks of Queues are used to model potential contention and queuing when a set of resources is shared. Such a network can be modeled by a set of service centers. Each service center may contain one or more servers. After a job is served by a service center, it may reenter the same service center, move to another one, or leave the system. In an open queuing network, jobs enter and depart from the network. In a closed queuing network, jobs neither enter nor depart from the network. Open queuing networks can be further divided into two categories: open feed forward queuing networks and open feedback queuing networks. In an open feed forward queuing network, a job cannot appear in the same queue for more than one time. In an open feedback queuing network, after a job is served by a queue, it may reenter the same queue.
FORMULATION AND IN VITRO-IN VIVO EVALUATIONS OF TIMOLOL MALEATE VISCOUS EYE DROPS FOR THE TREATMENT OF GLAUCOMA

The purpose of this research was to provide comfort and compliance to the patient and yet improve the therapeutic performance of the drug over conventional systems by preparing of functional and patient friendly viscous eye drops of Timolol Malate (TM) for the treatment of glaucoma. Hydroxy propyl methyl cellulose (HPMC K4M) and Carbopol 940 (CP 940) were explored as potential viscosity enhancing vehicles for prolonging the therapeutic effectiveness. The developed viscous eye drops were evaluated for clarity, pH, osmolarity, viscosity, mucoadhasion test and in vitro drug release. The optimized formula was subjected for in vivo testing by measurement of IOP (intraocular pressure), ocular irritation test and stability studies. TM viscous eye drops containing 1 % HPMC K4M and 0.3 % CP 940 exhibited non-newtonian flow behavior. HPMC K4M and CP 940 were responsible for higher viscosity and mucoadhasion respectively. Developed viscous eye drops exhibited the diffusion mechanism of drug release. The values of slope (n) were in the range of 0.65 to 0.99. IOP measurement of rabbit eye treated with optimized batch showed significant reduction in ocular pressure for 8 hrs. Ocular irritation test in rabbits showed no redness, swelling or watering. The overall study reveals that ratio of HPMC K4M (0.75 mg) and CP 940(0.075 mg) was gave favorable clinical response and used to prolong the therapeutic effectiveness of TM.
Trado-Medical uses, Chemical Constituents and Biological Activities of Newbouldia laevis (Bignoniaceae): A Review

Newbouldia laevis (P. Beauv.) Seem. (Family, Bignoniaceae), commonly known as tree of life, is a purple-flowering plant that is widely distributed in many parts of Africa. Different parts of the plant, including the leaves, flower, stems and roots are prevalently used in African traditional medicine for the management of many diseases and conditions like diabetes, hypertension, skin diseases, ulcer, tumors, pains, infectious diseases, inflammation, dysentery, sickle cell disease and impotency. This review discusses the trado-medical uses, chemical constituents, and biological activities of N. laevis. Based on information generated from scientific investigations deposited in PubMed and SCOPUS, the chemical constituents of the plant include glycosides, anthraquinolones, volatile oils, tannins, steroids, alkaloids, flavonoids, terpenoids and sterols. Extracts different parts of the plant and compounds isolated from them have been reported to have several health-promoting potentials such as antioxidant, antimalarial, trypanocidal, antimicrobial, anthelmintic, analgesic, anti-inflammatory, antidiabetic, anti-arthritic, anti-thrombotic, cytoprotective, anti-hypertensive, central nervous system modulatory, male reproduction enhancing and oxytocic properties. These scientific investigations have led credence to the ethnobotanical uses of the plant in folkloric practice. In addition, the presence of phytochemical constituents in the plant might be responsible for the wide biological potentials.
Finding surfaces in simplicial complexes with bounded-treewidth 1-skeleton

We consider the problem 2-Dim-Bounding-Surface. 2-Dim-Bounded-Surface asks whether or not there is a subcomplex S of a simplicial complex K homeomorphic to a given compact, connected surface bounded by a given subcomplex B ⊂ K. 2-Dim-Bounding-Surface is NP-hard. We show it is fixed-parameter tractable with respect to the treewidth of the 1-skeleton of the simplicial complex K. Using some of the techniques we developed for the 2-Dim-Bounded-Surface problem, we obtain fixed parameter tractable algorithms for other topological problems such as computing an optimal chain with a given boundary and computing an optimal chain in a given homology class. ∗Preliminary results of this work were presented at the Young Researchers Forum at CG Week 2020. †School of Electrical Engineering and Computer Science, Oregon State University; blackmit@eecs.oregonstate. edu . ‡School of Electrical Engineering and Computer Science, Oregon State University; nayyeria@eecs.oregonstate. edu . ar X iv :2 10 7. 10 33 9v 2 [ cs . C G ] 1 9 D ec 2 02 1
A Two-Part Controller Synthesis Approach for Nonlinear Stochastic Systems Perturbed by Lévy Noise Using Renewal Theory and HJB-Based Impulse Control

We are motivated by the lack of discussion surrounding methodological control design procedures for nonlinear shot and L\'evy noise stochastic systems to propose a hierarchical controller synthesis method with two parts. The first part is a primitive pattern-learning component which recognizes specific state sequences and stores in memory the corresponding control action that needs to be taken when the sequence has occurred. The second part is a modulation control component which computes the optimal control action for a pattern when it has occurred for the first time. Throughout our presentation of both components, we provide a self-contained discussion of theoretical concepts from Poisson processes theory, renewal theory, and impulse control, all of which are necessary as background. We demonstrate application of this controller to the simplified, concrete case studies of fault-tolerance and vehicle congestion control.
A Computational Method for the Determination of the Elastic Displacement Field using Measured Elastic Deformation Field

A novel approach was derived to compute the elastic displacement field from a measured elastic deformation field (i.e., deformation gradient or strain). The method is based on integrating the deformation field using Finite Element discretisation. Space and displacement fields are approximated using piece-wise interpolation functions. Hence, the full elastic deformation field can be expressed as nodal displacements, the unknowns. The nodal displacements are then obtained using a least square method. The proposed method was applied to the symmetrical (residual) elastic deformation field measured using high (angular) resolution electron backscatter diffraction around a Vickers micro-indenting impression on a (001) mono-Si crystal sample with the integrated out-of-plane surface displacements matched with the impression topography measured using. The (residual) displacement field was used as the boundary conditions to calculate the three-dimensional stress intensity factors (K_(I,II,III)) at the cracks emanating from the indentation.
A Sparsity Algorithm with Applications to Corporate Credit Rating

In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular"black box"attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new"sparsity algorithm"which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. We validate the sparsity algorithm with a synthetically generated dataset and we further apply it to quarterly financial statements from companies in financial, healthcare and IT sectors of the US market. We provide evidence that the counterfactual explanation can capture the nature of the real statement features that changed between the current quarter and the following quarter when ratings improved. The empirical results show that the higher the rating of a company the greater the"effort"required to further improve credit rating.
HANT: Hardware-Aware Network Transformation

Given a trained network, how can we accelerate it to meet efﬁciency needs for deployment on particular hardware? The commonly used hardware-aware network compression techniques address this question with pruning, kernel fusion, quantization and lowering precision. However, these approaches do not change the underlying network operations. In this paper, we propose hardware-aware network transformation (HANT), which accelerates a network by replacing in-efﬁcient operations with more efﬁcient alternatives using a neural architecture search like approach. HANT tackles the problem in two phase: In the ﬁrst phase, a large number of alternative operations per every layer of the teacher model is trained using layer-wise feature map distillation. In the second phase, the combinatorial selection of efﬁcient operations is relaxed to an integer optimization problem that can be solved in a few seconds. We extend HANT with kernel fusion and quantization to improve throughput even further. Our experimental results on accelerating the EfﬁcientNet family show that HANT can accelerate them by up to 3.6 × with < 0.4% drop in the top-1 accuracy on the ImageNet dataset. When comparing the same latency level, HANT can accelerate EfﬁcientNet-B4 to the same latency as EfﬁcientNet-B1 while having 3 % higher accuracy. We examine a large pool of operations, up to 197 per layer, and we provide insights into the selected operations and ﬁnal architectures.
A Sensor Fusion-based Cutting Device Attitude Control to Improve the Accuracy of Korean Cabbage Harvesting

HighlightsDeveloped the attitude control system of the cutting device to improve the accuracy of Korean cabbage harvesting.The cutting device provides attitude control, allowing precise harvesting.The attitude control algorithm was designed based on sensor fusion.Field experiments were conducted to compare attitude maintenance and harvest success rates as the attitude control system of cutting device was with/without. Abstract. Harvesting Korean cabbage requires precise cutting since the cutting method determines its quality. However, Korean cabbage fields have uneven surfaces and severe slopes, making it challenging to automatically harvest the crops. This unstructured environment hindered precise cutting, delaying the commercialization of the Korean cabbage harvester. Therefore, we designed an attitude control system for the cutting machine to improve the accuracy of cabbage harvesting. The proposed system controls the cutting level, angle, and height according to the field to improve the harvest performance. This study applied the Kalman filter-based sensor fusion to measure the cutting angle and height for pose estimation and attitude control. The attitude of the cutting device is estimated even in soil and mud by fusing a gyroscope, accelerometer, and linear potentiometer. Subsequently, the attitude is controlled by two hydraulic cylinders so that the cutting machine has the desired cutting angle and height. For the validation of the attitude control, the root mean square error (RMSE) of the cutting angle and height was calculated through experiments in a sloped environment. As a result, the proposed system reduced the RMSE for the cutting angle and height by 92% and 72%, respectively. Furthermore, field tests were performed on farmland with a Korean cabbage harvester to evaluate the harvesting performance of the attitude control system. The harvesting performance was quantitatively computed by scoring the cutting surface of the cabbage, and the performance improved from 56 to 89 points when the attitude control was applied to the cutting machine. Keywords: Attitude control, Automatic cabbage harvesting, Kalman filter, Korean cabbage harvester, Sensor fusion.
Philosophical Specification of Empathetic Ethical Artificial Intelligence

In order to construct an ethical artificial intelligence (AI) two complex problems must be overcome. First, humans do not consistently agree on what is or is not ethical. Second, contemporary AI and machine learning methods tend to be blunt instruments which either search for solutions within the bounds of predefined rules or mimic behavior. An ethical AI must be capable of inferring unspoken rules, interpreting nuance and context, possess and be able to infer intent, and explain not just its actions but its intent. Using enactivism, semiotics, perceptual symbol systems, and symbol emergence, we specify an agent that learns not just arbitrary relations between signs but their meaning in terms of the perceptual states of its sensorimotor system. Subsequently it can learn what is meant by a sentence and infer the intent of others in terms of its own experiences. It has malleable intent because the meaning of symbols changes as it learns, and its intent is represented symbolically as a goal. As such it may learn a concept of what is most likely to be considered ethical by the majority within a population of humans, which may then be used as a goal. The meaning of abstract symbols is expressed using perceptual symbols of raw, multimodal sensorimotor stimuli as the weakest (consistent with Ockham’s Razor) necessary and sufficient concept, an intensional definition learned from an ostensive definition, from which the extensional definition or category of all ethical decisions may be obtained. Because these abstract symbols are the same for both situation and response, the same symbol is used when either performing or observing an action. This is akin to mirror neurons in the human brain. Mirror symbols may allow the agent to empathize, because its own experiences are associated with the symbol, which is also associated with the observation of another agent experiencing something that symbol represents.
Covering Symmetric Sets of the Boolean Cube by Affine Hyperplanes

Alon and F\"uredi (European J. Combin., 1993) proved that any family of hyperplanes that covers every point of the Boolean cube $\{0,1\}^n$ except one must contain at least $n$ hyperplanes. We obtain two extensions of this result, in characteristic zero, for hyperplane covers of symmetric sets of the Boolean cube (subsets that are closed under permutations of coordinates), as well as for `polynomial covers' of `weight-determined' sets of `strictly unimodal uniform' (SU$^2$) grids. As a main tool for solving our problems, we give a combinatorial characterization of (finite-degree) Zariski (Z-) closures of symmetric sets of the Boolean cube -- the Z-closure of a symmetric set is symmetric. In fact, we obtain a characterization that concerns, more generally, weight-determined sets of SU$^2$ grids. However, in this generality, our characterization is not of the Z-closures -- unlike over the Boolean cube, the Z-closure of a weight-determined set need not be weight-determined. We introduce a new closure operator exclusively for weight-determined sets -- the `(finite-degree) Z*-closure' -- defined to be the maximal weight-determined set in the Z-closure. (This coincides with the Z-closure over the Boolean cube, for symmetric sets.) We obtain a combinatorial characterization of the finite-degree Z*-closures of weight-determined sets of an SU$^2$ grid. This characterization may also be of independent interest. Indeed, as further applications, we (i) give an alternate proof of a lemma by Alon et al. (IEEE Trans. Inform. Theory, 1988), and (ii) characterize the `certifying degrees' of weight-determined sets. Over the Boolean cube, our above characterization can also be derived using a result of Bernasconi and Egidi (Inf. Comput., 1999). However, our proof is independent of this result, works for all SU$^2$ grids, and could be regarded as being more combinatorial.