Using baseline-dependent window functions for data compression and field-of-interest shaping in radio interferometry

In radio interferometry, observed visibilities are intrinsically sampled at some interval in time and frequency. Modern interferometers are capable of producing data at very high time and frequency resolution; practical limits on storage and computation costs require that some form of data compression be imposed. The traditional form of compression is a simple averaging of the visibilities over coarser time and frequency bins. This has an undesired side effect: the resulting averaged visibilities "decorrelate", and do so differently depending on the baseline length and averaging interval. This translates into a non-trivial signature in the image domain known as "smearing", which manifests itself as an attenuation in amplitude towards off-centre sources. With the increasing fields of view and/or longer baselines employed in modern and future instruments, the trade-off between data rate and smearing becomes increasingly unfavourable. In this work we investigate alternative approaches to low-loss data compression. We show that averaging of the visibility data can be treated as a form of convolution by a boxcar-like window function, and that by employing alternative baseline-dependent window functions a more optimal interferometer smearing response may be induced. In particular, we show improved amplitude response over a chosen field of interest, and better attenuation of sources outside the field of interest. The main cost of this technique is a reduction in nominal sensitivity; we investigate the smearing vs. sensitivity trade-off, and show that in certain regimes a favourable compromise can be achieved. We show the application of this technique to simulated data from the Karl G. Jansky Very Large Array (VLA) and the European Very-long-baseline interferometry Network (EVN)

Data compression, field of interest shaping and fast algorithms for direction-dependent deconvolution in radio interferometry

In radio interferometry, observed visibilities are intrinsically sampled at some interval in time and frequency. Modern interferometers are capable of producing data at very high time and frequency resolution; practical limits on storage and computation costs require that some form of data compression be imposed. The traditional form of compression is simple averaging of the visibilities over coarser time and frequency bins. This has an undesired side effect: the resulting averaged visibilities “decorrelate”, and do so differently depending on the baseline length and averaging interval. This translates into a non-trivial signature in the image domain known as “smearing”, which manifests itself as an attenuation in amplitude towards off-centre sources. With the increasing fields of view and/or longer baselines employed in modern and future instruments, the trade-off between data rate and smearing becomes increasingly unfavourable. Averaging also results in baseline length and a position-dependent point spread function (PSF). In this work, we investigate alternative approaches to low-loss data compression. We show that averaging of the visibility data can be understood as a form of convolution by a boxcar-like window function, and that by employing alternative baseline-dependent window functions a more optimal interferometer smearing response may be induced. Specifically, we can improve amplitude response over a chosen field of interest and attenuate sources outside the field of interest. The main cost of this technique is a reduction in nominal sensitivity; we investigate the smearing vs. sensitivity trade-off and show that in certain regimes a favourable compromise can be achieved. We show the application of this technique to simulated data from the Jansky Very Large Array and the European Very Long Baseline Interferometry Network. Furthermore, we show that the position-dependent PSF shape induced by averaging can be approximated using linear algebraic properties to effectively reduce the computational complexity for evaluating the PSF at each sky position. We conclude by implementing a position-dependent PSF deconvolution in an imaging and deconvolution framework. Using the Low-Frequency Array radio interferometer, we show that deconvolution with position-dependent PSFs results in higher image fidelity compared to a simple CLEAN algorithm and its derivatives

Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis

Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain model reasoning can potentially increase trust in deep learning among clinical stakeholders. In the literature, much of the research on attribution in medical imaging focuses on visual inspection rather than statistical quantitative analysis. In this paper, we proposed an image-based saliency framework to enhance the explainability of deep learning models in medical image analysis. We use adaptive path-based gradient integration, gradient-free techniques, and class activation mapping along with its derivatives to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models. The proposed framework integrates qualitative and statistical quantitative assessments, employing Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) to measure the effectiveness of saliency methods in retaining critical image information and their correlation with model predictions. Visual inspections indicate that methods such as ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produce focused and clinically interpretable attribution maps. These methods highlighted possible biomarkers, exposed model biases, and offered insights into the links between input features and predictions, demonstrating their ability to elucidate model reasoning on these datasets. Empirical evaluations reveal that ScoreCAM and XRAI are particularly effective in retaining relevant image regions, as reflected in their higher AUC values. However, SICs highlight variability, with instances of random saliency masks outperforming established methods, emphasizing the need for combining visual and empirical metrics for a comprehensive evaluation. The results underscore the importance of selecting appropriate saliency methods for specific medical imaging tasks and suggest that combining qualitative and quantitative approaches can enhance the transparency, trustworthiness, and clinical adoption of deep learning models in healthcare. This study advances model explainability to increase trust in deep learning among healthcare stakeholders by revealing the rationale behind predictions. Future research should refine empirical metrics for stability and reliability, include more diverse imaging modalities, and focus on improving model explainability to support clinical decision-making

What do Deep Neural Networks Learn in Medical Images?

Deep learning is increasingly gaining rapid adoption in healthcare to help improve patient outcomes. This is more so in medical image analysis which requires extensive training to gain the requisite expertise to become a trusted practitioner. However, while deep learning techniques have continued to provide state-of-the-art predictive performance, one of the primary challenges that stands to hinder this progress in healthcare is the opaque nature of the inference mechanism of these models. So, attribution has a vital role in building confidence in stakeholders for the predictions made by deep learning models to inform clinical decisions. This work seeks to answer the question: what do deep neural network models learn in medical images? In that light, we present a novel attribution framework using adaptive path-based gradient integration techniques. Results show a promising direction of building trust in domain experts to improve healthcare outcomes by allowing them to understand the input-prediction correlative structures, discover new bio-markers, and reveal potential model biases

Maize grain yield response to changes in acid soil characteristics with yearly leguminous crop rotation, fallow, slash, burn and liming practices

Open Access JournalAn experiment was conducted for 4 years to assess the effectiveness of fallow, slash and burn farming systems on maize grain yield and soil chemical characteristics. It was also meant to measure the response to yearly rotation of maize and leguminous crops (cowpea and mucuna), as options for managing the acidity of the soil of the study site. The maize tolerant cultivar (cvr) out yielded the sensitive cvr and the farmers’ variety by 43% and 16% respectively. On the maize/grain legume rotation plots, the tolerant and sensitive cvr yielded 5% and 7% respectively more than their corresponding yields on plots with fallow, slash and burn rotation. Maize/grain legume rotation demonstrated one of the least soil acidifications, exhibiting the least increase in exchangeable Al (23%), H (24%), and Al saturation (5%) resulting in improved soil fertility through increase in available Ca (2%), Mg (85%), P (75%), and CEC (14%). The fallow, slash and burn rotation, associated with the tolerant cvr showed similar grain yield with grain legume rotation, but contributed more to soil acidification. Maize/leafy legume rotation gave a similar yield to the above mentioned practices. The yearly application of 250 kg ha-1 of dolomitic lime for four consecutive years did not result in significant changes in soil characteristics and grain yield especially for the Al tolerant cvr. However, application of 2250 kg ha-1 of lime neutralized the Al toxicity, regardless of the rotation scheme. The study concluded that the four years maize cultivation through fallow/ slash and burn rotation extensively used in the humid forest zone is not the best option on acid soil

Assessing Farmers’ Knowledge on the Role of Cowpea in Improving Soil Fertility in Cropping Systems in Southern Cameroon

A study was carried out to identify farmers’ preferred cowpea traits and assess their knowledge on the role of grain legume in improving soil fertility in cropping systems  in the humid forest zone (HFZ) of Cameroon. This study was conducted at five sites (Asso'oseng, Nkoemnvone, Nkolfoulou, Nkoemetou II and Nkometou III) in the HFZ of Cameroon, between December 2012 and March 2013.  A two stage stratified sampling procedure was applied. In the first stage, each study site formed a sampling stratum.  In each site, two focus groups were constructed. The groups included both women and men of various ages. Focus group discussions with 6 -10 farmers per group were carried out during periods when the farmers are less busy in their farms (December and January). In the second stage semi-structured questionnaires were administered (January – March 2013) after the focus group discussion (FGD) to a total of 165 farmers. A total of 44 respondents were interviewed in Asso'oseng, 35 in Nkoemvone, 17 in Nkolfoulou II, 38 in Nkometou II, and 31 in Nkometou III making a total of 165 respondents. Demographic questions included personal details such as gender, age, level of education, position in the house hold, and household size. The results indicated that the age of the respondents ranged from 18 to 70 years with the majority falling between 36-45 years, representing 58% of the respondents. Seventy six percent of the respondents were females. Generally the farmers grew four varieties of cowpea: brown, black, speckled and white. White was the dominant (75%) and preferred variety. They also grew and preferred mostly the erect and early maturing cowpea type (94%). Cowpea was mostly intercropped (69%) with cereals and other crops while a minor proportion of the farmers practised sole cropping (29%) and rotation or shifting cultivation (2%). The farmers identified poor soils as the main cowpea production constraints. On average, less that 30% of the respondents were aware of the role legumes play in soil fertility restoration except in Nkometou III where 50% of the farmers surveyed did have some knowledge. To 90% of the farmers, root nodules represented organs that harbor disease agents, which they referred to as soil “cystsâ€. From the study, it can be concluded that farmers in the humid forest zone of Cameroon are aware of the soil fertility decline on their farms. Respondents lack knowledge on importance of legumes in cropping system and grow and prefer white coated cowpea with erect growth habit. The farmers are however, ready to cultivate grain legumes for soil fertility restoration purpose if this could be demonstrated on-farm

Sustainable Waste-to-Energy Production: Performance Evaluation of Distributed Generation fuelled by Landfill Gas

The environmental impact of landfill is a growing concern in waste management practices. Thus, assessing the effectiveness of the solutions implemented to alter the issue is of importance. The objectives of the study were to provide an insight of landfill advantages, and to consolidate landfill gas importance among others alternative fuels. Finally, a case study examining the performances of energy production from a land disposal at Ylivieska was carried out to ascertain the viability of waste to energy project. Both qualitative and quantitative methods were applied. The study was conducted in two parts; the first was the review of literatures focused on landfill gas developments. Specific considerations were the conception of mechanism governing the variability of gas production and the investigation of mathematical models often used in landfill gas modeling. Furthermore, the analysis of two main distributed generation technologies used to generate energy from landfill was carried out. The review of literature revealed a high influence of waste segregation and high level of moisture content for waste stabilization process. It was found that the enhancement in accuracy for forecasting gas rate generation can be done with both mathematical modeling and field test measurements. The result of the case study mainly indicated the close dependence of the power output with the landfill gas quality and the fuel inlet pressure