Billions in development aid is provided annually by international donors in the Majority World, much of which funds health equity. Yet, common neocolonial practices persist in development that compromise what is done in the name of well-intentioned policymaking and programming. Based on a qualitative analysis of fifteen case studies presented at a 2022 conference, this research examines trends involving unethical partnerships, policies, and practices in contemporary global health. The analysis identifies major modern-day issues of harmful policy and programming in international aid. Core issues include inequitable partnerships between and representation of international stakeholders and national actors, abuse of staff and unequal treatment, and new forms of microaggressive practices by Minority World entities on low-/middle-income nations (LMICs), made vulnerable by severe poverty and instability. When present, these issues often exacerbate institutionalized discrimination, hostile work environments, ethnocentrism, and poor sustainability in development. These unbalanced systems perpetuate a negative development culture and can place those willing to speak out at risk. At a time when the world faces increased threats including global warming and new health crises, development and global health policy and practice must evolve through inclusive dialogue and collaborative effort.

We have created a database for the storage of DICOM data using MongoDB. Data is stored in chunks using gridfs and accessible via queries on any field of the DICOM header, as well as other pre-defined metadata fields. When data is imported into the database, a custom Python plugin extracts metadata from the DICOM header for each file, which is used to populate the database. In addition to the UID values stored in the DICOM header, an MD5 checksum is computed for each file and referenced in the database to ensure that data is not duplicated. The metadata is tracked using a local instance of MongoDB Charts. Data is replicated across two NVMe servers connected with 100 Gbps ethernet for fast replication. The two servers are configured as a MongoDB replica set with an arbiter running on a third server that is responsible for promoting either of the data-bearing nodes to be the primary access point based on usage and availability. The database is accessible via a custom PyTorch connector that connects the database to the 100 Gbps network for access by a HPC cluster for deep learning applications and via a web interface on the local network used to pull or upload data. The database is being utilized for the storage of SPECT Medical Imaging data. The database improves our ability to catalog collaboration data, identify data that is relevant for deep learning studies, and to perform data mining on the existing dataset.

Model observers are mathematical models used to perform a specific task, such as lesion detection. In this document, we will restrict ourselves to ideal model observers, which do not try to mimic human performance but try to perform perfect classification. However, we will not be following the usual definition of ideal model observer, which describes the model observer as a statistical classifier between two classes. Instead we will define a GAN network and train it to generate images from class H0, without lesions, and then use the discriminator of the GAN network as a model observer. Our method relies on pix2pix, which is a type of conditional GAN, the network is first trained to generate SPECT reconstructions-like data from the corresponding CT images. Later, the discriminator is used on simulated lesions to validate is usage as a classifier

Emerging memory devices are an attractive choice for implementing very energy-efficient in-situ matrix-vector multiplication (MVM) for use in intelligent edge platforms. Despite their great potential, device-level non-idealities have a large impact on the application-level accuracy of deep neural network (DNN) inference. We introduce a low-density parity-check code (LDPC) based approach to correct non-ideality induced errors encountered during in-situ MVM. We first encode the weights using error correcting codes (ECC), perform MVM on the encoded weights, and then decode the result after in-situ MVM. We show that partial encoding of weights can maintain DNN inference accuracy while minimizing the overhead of LDPC decoding. Within two iterations, our ECC method recovers 60% of the accuracy in MVM computations when 5% of underlying computations are error-prone. Compared to an alternative ECC method which uses arithmetic codes, using LDPC improves AlexNet classification accuracy by 0.8% at iso-energy. Similarly, at iso-energy, we demonstrate an improvement in CIFAR-10 classification accuracy of 54% with VGG-11 when compared to a strategy that uses 2× redundancy in weights. Further design space explorations demonstrate that we can leverage the resilience endowed by ECC to improve energy efficiency (by reducing operating voltage). A 3.3× energy efficiency improvement in DNN inference on CIFAR-10 dataset with VGG-11 is achieved at iso-accuracy.

The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation targeting energy-efficient digital accelerators have been investigated in the past, however, they do not completely encapsulate the energy costs at a system level. To address this shortcoming and to account for various overheads, we synthesize the controller and memory for different encoding schemes and extract the energy costs from these synthesized blocks. Additionally, we introduce functional encoding for structured connectivity such as the connectivity in convolutional layers. Functional encoding offers a 58% reduction in the energy to implement a backward pass and weight update in such layers compared to existing index-based solutions. We show that for a 2 layer spiking neural network trained to retain a spatio-temporal pattern, bitmap (PB-BMP) based organization can encode the sparser networks more efficiently. This form of encoding delivers a 1.37x improvement in energy efficiency coming at the cost of a 4% degradation in network retention accuracy as measured by the van Rossum distance.