Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss

Although deep learning (DL) has led to several breakthroughs in many disciplines as diverse as chemistry, computer science, electrical engineering, mathematics, medicine, neuroscience, and physics, a comprehensive understanding of why and how DL is empirically successful remains fundamentally elusive. To attack this fundamental problem and unravel the mysteries behind DL's empirical successes, significant innovations toward a unified theory of DL have been made. These innovations encompass nearly fundamental advances in optimization, generalization, and approximation. Despite these advances, however, no work to date has offered a way to quantify the testing performance of a DL-based algorithm employed to solve a pattern classification problem. To overcome this fundamental challenge in part, this paper exposes the fundamental testing performance limits of DL-based binary classifiers trained with hinge loss. For binary classifiers that are based on deep rectified linear unit (ReLU) feedforward neural networks (FNNs) and ones that are based on deep FNNs with ReLU and Tanh activation, we derive their respective novel asymptotic testing performance limits. The derived testing performance limits are validated by extensive computer experiments

Change in differences between the sexes in mathematics achievement at the lower secondary school level in Australia : over time.

In this paper an investigation is reported on whether changes have occurred in the differences between the sexes in mathematics achievement at the lower secondary school level over the 30 year period from 1964 to 1994. In order to make meaningful comparisons the mathematics test scores from the three studies conducted in Australia under the auspices of the International Association for Evaluation of Educational Achievement were brought to a common interval scale using Rasch measurement procedures. The scale scores are used to examine differences between boys and girls in mathematics achievement on the three occasions as well as the changes that have occurred between occasions. No significant sex differences in mathematics achievement are found on each of the occasions. However, a significant decline in mathematics achievement is recorded for boys between 1964 and 1994, but not for girls. The decline in mathematics achievement over this 30 year period for boys is equivalent to nearly one year of mathematics learning, while the drop for girls is only approximately equivalent to half a year of mathematics learning. [Author abstract

Deep Learning-Enabled Text Semantic Communication under Interference: An Empirical Study

At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler by promising to minimize bandwidth consumption, transmission delay, and power usage. Among text SemCom techniques, \textit{DeepSC} is a popular scheme that leverages advancements in DL and NLP to reliably transmit semantic information in low signal-to-noise ratio (SNR) regimes. To understand the fundamental limits of such a transmission paradigm, our recently developed theory \cite{Getu'23_Performance_Limits} predicted the performance limits of DeepSC under radio frequency interference (RFI). Although these limits were corroborated by simulations, trained deep networks can defy classical statistical wisdom, and hence extensive computer experiments are needed to validate our theory. Accordingly, this empirical work follows concerning the training and testing of DeepSC using the proceedings of the European Parliament (Europarl) dataset. Employing training, validation, and testing sets \textit{tokenized and vectorized} from Europarl, we train the DeepSC architecture in Keras 2.9 with TensorFlow 2.9 as a backend and test it under Gaussian multi-interferer RFI received over Rayleigh fading channels. Validating our theory, the testing results corroborate that DeepSC produces semantically irrelevant sentences as the number of Gaussian RFI emitters gets very large. Therefore, a fundamental 6G design paradigm for \textit{interference-resistant and robust SemCom} (IR$^2$ SemCom) is needed

Beekeepers' honeybee colony selection practice in Tigray, Northern Ethiopia.

Selection of colonies plays an important role for successful harvesting of desired products from honeybees. The purpose of this study was therefore to assess local knowledge and experience of beekeepers in Tigray regional state of Ethiopia with regard to colony selection and management practices during purchase and multiplication. Respondent selection was carried out based on the existing conventional agroecological zones namely Dega (highland), Kolla (lowland) and Weinadega (midland). Four woredas (districts) from Dega zone, and three from each of Kolla and Weinadega zones were sampled. A total of 185 beekeepers were interviewed to understand the criteria they were using to select colonies. Preference ranking data were indexed using linear programming. The result indicated that beekeepers were using six local selection criteria namely worker bee population, body color, comb building direction, aggressiveness, honey yield history and age of the colony ordered according to their preference rank from 1 to 6. Beekeepers understood that selection of honeybee colonies was important because productivity, management easiness and agroclimatic adaptation of colonies are different for different colonies. As a result colonies with dominant black colored bees were chosen as first priority for their merits of better honey productivity, tolerance to absconding and multiplication easiness in Weinadega and Kolla agroecologies. However, red/yellowish colored bees were preferred in Dega agroecology

A bilobed Gallbladder (Vesica Fellea Divisa) in Cattle Slaughtered at Jimma Municipal Abattoir, West Oromiya, Ethiopia

Gallbladder abnormalities occur rarely. The recognized abnormalities recorded so far comprised duplication, septation, abnormal position and total absence of the gallbladder. The bilobed gallbladder of the cross bred oxen slaughtered at Jimma municipality abattoir constituted two lobes separated by a deep cleft. However, the two lobes were joined at the neck and drained by one duct. Both the lobes were of equal size and filled with bile.Key words: Bilobed, Cattle, gallbladder, Jimma, Muncipal abattoi

Mineral contents of barley grains and its processed foods (kolo, porridge, bread and injera) consumed in Ethiopia

ABSTRACT. Barley (Hordeum vulgare L.) is one of the most widely cultivated stable food crops in the world. Barley grain samples were collected from four selected areas (Bahir Dar, Bure, Finote Selam and Debre Markos) of Ethiopia and four types of processed food (kolo, porridge, bread and injera) were prepared from it. The levels of essential and non-essential metals in barley grains and its processed food were determined by microwave plasma-atomic emission spectrometry after wet digestion with a mixture of HNO3 and HClO4 (5:1, v/v). The concentration (mg/kg dry weight) in the barley grains were in the ranges K (5482-6516), Mg (546-643), Ca (445-684), Mn (7.31-9.80), Fe (127-439), Cu (0.88-1.86), Zn (42.8-56.8), Pb (0.39-2.73), Cd (3.01-4.66). The concentrations of all the metals in the four types of processed barley foods showed variation among each other. The results indicate that Ethiopian barley grains and its processed foods are good source of essential metals.                     KEY WORDS: Barley, Hordeum vulgare L., Processed foods, Macro-minerals, Micro-minerals, Toxic metals   Bull. Chem. Soc. Ethiop. 2021, 35(3), 471-484. DOI: https://dx.doi.org/10.4314/bcse.v35i3.

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

A deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler while promising to minimize power usage, bandwidth consumption, and transmission delay by minimizing irrelevant information transmission. However, the benefits of such a semantic-centric design can be limited by radio frequency interference (RFI) that causes substantial semantic noise. The impact of semantic noise due to interference can be alleviated using an interference-resistant and robust (IR$^2$) SemCom design. Nevertheless, no such design exists yet. To shed light on this knowledge gap and stimulate fundamental research on IR$^2$ SemCom, the performance limits of a text SemCom system named DeepSC are studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC's practical limits and a lower bound on its outage probability under multi-interferer RFI. Toward a fundamental 6G design for an IR$^2$ SemCom, moreover, we propose a generic lifelong DL-based IR$^2$ SemCom system. Eventually, we corroborate the derived performance limits with Monte Carlo simulations and computer experiments, which also affirm the vulnerability of DeepSC and DL-enabled text SemCom to a wireless attack using RFI

Modeling the probability of giving birth at health institutions among pregnant women attending antenatal care in West Shewa Zone, Oromia, Ethiopia: A cross sectional study

Background: Although ante natal care and institutional delivery is effective means for reducing maternal morbidity and mortality, the probability of giving birth at health institutions among ante natal care attendants has not been modeled in Ethiopia. Therefore, the objective of this study was to model predictors of giving birth at health institutions among expectant mothers following antenatal care.Methods: Facility based cross sectional study design was conducted among 322 consecutively selected mothers who were following ante natal care in two districts of West Shewa Zone, Oromia Regional State, Ethiopia. Participants were proportionally recruited from six health institutions. The data were analyzed using SPSS version 17.0. Multivariable logistic  regression was employed to develop the prediction model.Results: The final regression model had good discrimination power (89.2%), optimum sensitivity (89.0%) and specificity (80.0%) to predict the probability of giving birth at health institutions. Accordingly, self efficacy (beta=0.41), perceived barrier (beta=-0.31) and perceived susceptibility (beta=0.29) were significantly predicted the probability of giving birth at health institutions.Conclusion: The present study showed that logistic regression model has predicted the probability of giving birth at health institutions and identified significant predictors which health care providers should take into account in promotion of institutional delivery.Key word: Institutional delivery, intention, ANC, probabilit

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulation-based comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm