Ethiopian Floriculture and Its Impact on the Environment

No Abstrac

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

Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions

Abstract Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national laboratories have been developing proposals for the sixth-generation (6G), whose materialization is fraught with many fundamental challenges. To alleviate these challenges, a deep learning (DL)-enabled semantic communication (SemCom) has emerged as a promising 6G technology enabler, which embodies a paradigm shift that can change the status quo viewpoint that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meanings have been ignored. Since 6G is also critical for the materialization of major SemCom use cases, the paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration of 6G and SemCom. For this purpose, this comprehensive article provides the fundamentals of semantic information, semantic representation, and semantic entropy; details the state-of-the-art SemCom research landscape; presents the major SemCom trends and use cases; discusses current SemCom theories; exposes the fundamental and major challenges of SemCom; and offers future research directions for SemCom. We hope this article stimulates many lines of research on SemCom theories, algorithms, and implementation.Abstract Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national laboratories have been developing proposals for the sixth-generation (6G), whose materialization is fraught with many fundamental challenges. To alleviate these challenges, a deep learning (DL)-enabled semantic communication (SemCom) has emerged as a promising 6G technology enabler, which embodies a paradigm shift that can change the status quo viewpoint that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meanings have been ignored. Since 6G is also critical for the materialization of major SemCom use cases, the paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration of 6G and SemCom. For this purpose, this comprehensive article provides the fundamentals of semantic information, semantic representation, and semantic entropy; details the state-of-the-art SemCom research landscape; presents the major SemCom trends and use cases; discusses current SemCom theories; exposes the fundamental and major challenges of SemCom; and offers future research directions for SemCom. We hope this article stimulates many lines of research on SemCom theories, algorithms, and implementation

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

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

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

Abstract Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission – minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise’s impact can be alleviated using an interference-resistant and robust (IR 2 ) SemCom design, though no such design exists yet. To stimulate fundamental research on IR 2 SemCom, the performance limits of a popular 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, and propose a (generic) lifelong DL-based IR 2 SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI.Abstract Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission – minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise’s impact can be alleviated using an interference-resistant and robust (IR 2 ) SemCom design, though no such design exists yet. To stimulate fundamental research on IR 2 SemCom, the performance limits of a popular 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, and propose a (generic) lifelong DL-based IR 2 SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI

Novel B19-like parvovirus in the brain of a harbor seal

Using random PCR in combination with next-generation sequencing, a novel parvovirus was detected in the brain of a young harbor seal (Phoca vitulina) with chronic non-suppurative meningo-encephalitis that was rehabilitated at the Seal Rehabilitation and Research Centre (SRRC) in the Netherlands. In addition, two novel viruses belonging to the family Anelloviridae were detected in the lungs of this animal. Phylogenetic analysis of the coding sequence of the novel parvovirus, tentatively called Seal parvovirus, indicated that this virus belonged to the genus Erythrovirus , to which human parvovirus B19 also belongs. Although no other seals with similar signs were rehabilitated in SRRC in recent years, a prevalence study of tissues of seals from the same area collected in the period 2008-2012 indicated that the Seal parvovirus has circulated in the

When workplace unionism in global value chains does not function well : exploring the impediments

Improving working conditions at the bottom of global value chains has become a central issue in our global economy. In this battle, trade unionism has been presented as a way for workers to make their voices heard. Therefore, it is strongly promoted by most social standards. However, establishing a well-functioning trade union is not as obvious as it may seem. Using a comparative case study approach, we examine impediments to farm-level unionism in the cut flower industry in Ethiopia. For this purpose, we propose an integrated framework combining two lenses, namely a vertical one (governance and structure of global value chains) and a horizontal one (socio-economic context). We identify 10 impediments that point to three major dimensions contributing to unionisation. These three dimensions include awareness of and interest from workers, legitimacy of trade unions, and capacity of trade unions to act. Furthermore, our results suggest that private social standards may, in certain cases, be counterproductive for the efficient functioning of trade unions. Although we argue that there is no ‘quick fix’ solution to weak workplace unionism at the bottom of global value chains, we stress the importance of considering the dynamics of, and interactions between, the impediments when designing potential support measures that mitigate negative impacts

High seroprevalence of human herpesviruses in HIV-infected individuals attending primary healthcare facilities in rural South Africa

Seroprevalence data of human herpesviruses (HHVs) are limited for sub-Saharan Africa. These are important to provide an indication of potential burden of HHV-related disease, in particular in human immunodeficiency virus (HIV)-infected individuals who are known to be at increased risk of these conditions in the Western world. In this cross-sectional study among 405 HIV-infected and antiretroviral therapy naïve individuals in rural South Africa the seroprevalence of HHVs was: herpes simplex virus type 1 (HSV-1) (98%), herpes simplex virus type 2 (HSV-2) (87%), varicella zoster virus (VZV) (89%), and 100% for both Epstein-Barr virus (EBV) and cytomegalovirus (CMV). Independent factors associated with VZV seropositivity were low educational status and having children. Lack of in-house access to drinking water was independently associated with positive HSV-1 serostatus, whereas Shangaan ethnicity was associated with HSV-2 seropositivity. Increasing age was associated with higher IgG titres to both EBV and CMV, whereas CD4 cell count was negatively associated with EBV and CMV IgG titres. Moreover, IgG titres of HSV-1 and 2, VZV and CMV, and CMV and EBV were positively correlated. The high HHV seroprevalence emphasises the importance of awareness of these viral infections in HIV-infected individuals in South Africa

Drug sensitivity of clinical isolates of Mycobacterium tuberculosis and its association with bacterial genotype in the Somali region, Eastern Ethiopia

Background: Drug resistance is becoming a major bottleneck for tuberculosis (TB) control programs in countries with high TB burdens. Although several studies were conducted on the drug sensitivity of Mycobacterium tuberculosis (M. tuberculosis) in central Ethiopia, there is a lack of data on the drug sensitivity of M. tuberculosis in the peripheral regions of the country including in the Somali region. Therefore, the objective of this study was to evaluate the drug sensitivity of M. tuberculosis and its association with bacterial genotype and evaluate the performance of Xpert MTB/RIF (Xpert) in detecting resistance to rifampicin (RIF). Methods: A total of 302 M. tuberculosis were tested using the BD BACTEC-Mycobacteria Growth Indicator Tube 960 (MGIT 960) system for their drug sensitivity to the first-line anti-TB drugs. Besides, the drug sensitivity of 10 multidrug-resistant (MDR) M. tuberculosis isolates was evaluated for the second-line anti-TB drugs. Additionally, 177 of the 302 isolates were tested for genotypic drug resistance using Xpert. Chi-square and Fisher's exact tests were used for the evaluation of the association between variables and drug sensitivity. Results: The overall prevalence of resistance to at least one drug was 11.6% (95% CI: 7.9-15.2%), while the prevalence of MDR was 3.3% (95% CI: 1.3-5.3%). Two of the 10 MDR isolates were resistant to capreomycin. The spoligotype Shared International Type (SIT) 149 was significantly associated with either monoresistance or MDR (p < 0.05). Of the 177 isolates tested by Xpert, 6.2% (11/177) were RIF-resistant. Discordant between Xpert and MGIT 960 was observed in one isolate and linked with probe-binding delay (DeltaCT max = 5.8). The sensitivity and specificity of the Xpert assay were 100 and 99.4%, respectively, while its positive and negative predictive values were 90.9 and 100%, respectively. Conclusion: The magnitude of MDR M. tuberculosis in the Somali region of Ethiopia was higher than the national prevalence of MDR-TB warranting the strengthening of the TB control program in the Somali region. Besides, drug resistance was associated with SIT 149 spoligotype (genotype). The Xpert assay was observed to have high sensitivity and specificity in detecting RIF-resistant M. tuberculosis, which is encouraging for its application widely