The proliferation of mobile money in Uganda has been a cornerstone of financial inclusion, yet its security mechanisms remain a critical concern. This study investigates a significant public response to perceived security failures: the #StopAirtelThefty Twitter campaign of August 2025 Sparked by an incident publicized by Dr. Jim Spire Ssentongo where a phone thief accessed a victim's account, withdrew funds, and procured a loan, the campaign revealed deep seated public anxiety over the safety of mobile money. This research employs qualitative analysis to systematically examine the complaints raised during this campaign, extracting key themes related to security vulnerabilities and user dissatisfaction. By synthesizing these public sentiments, the paper provides crucial insights into the specific security gaps experienced by users and situates these findings within the larger framework of Uganda's mobile money regulatory and operational environment. The study concludes with implications for providers, policymakers, and the future of secure digital finance in Uganda.

Model-based reinforcement learning (MBRL) algorithms learn a dynamics model from collected data and apply it to generate synthetic trajectories to enable faster learning. This is an especially promising paradigm in offline reinforcement learning (RL) where data may be limited in quantity, in addition to being deficient in coverage and quality. Practical approaches to offline MBRL usually rely on ensembles of dynamics models to prevent exploitation of any individual model and to extract uncertainty estimates that penalize values in states far from the dataset support. Uncertainty estimates from ensembles can vary greatly in scale, making it challenging to generalize hyperparameters well across even similar tasks. In this paper, we present Morse Model-based offline RL (MoMo), which extends the anti-exploration paradigm found in offline model-free RL to the model-based space. We develop model-free and model-based variants of MoMo and show how the model-free version can be extended to detect and deal with out-of-distribution (OOD) states using explicit uncertainty estimation without the need for large ensembles. MoMo performs offline MBRL using an anti-exploration bonus to counteract value overestimation in combination with a policy constraint, as well as a truncation function to terminate synthetic rollouts that are excessively OOD. Experimentally, we find that both model-free and model-based MoMo perform well, and the latter outperforms prior model-based and model-free baselines on the majority of D4RL datasets tested.

Given the remarkable results of motion synthesis with diffusion models, a natural question arises: how can we effectively leverage these models for motion editing? Existing diffusion-based motion editing methods overlook the profound potential of the prior embedded within the weights of pre-trained models, which enables manipulating the latent feature space; hence, they primarily center on handling the motion space. In this work, we explore the attention mechanism of pre-trained motion diffusion models. We uncover the roles and interactions of attention elements in capturing and representing intricate human motion patterns, and carefully integrate these elements to transfer a leader motion to a follower one while maintaining the nuanced characteristics of the follower, resulting in zero-shot motion transfer. Editing features associated with selected motions allows us to confront a challenge observed in prior motion diffusion approaches, which use general directives (e.g., text, music) for editing, ultimately failing to convey subtle nuances effectively. Our work is inspired by how a monkey closely imitates what it sees while maintaining its unique motion patterns; hence we call it Monkey See, Monkey Do, and dub it MoMo. Employing our technique enables accomplishing tasks such as synthesizing out-of-distribution motions, style transfer, and spatial editing. Furthermore, diffusion inversion is seldom employed for motions; as a result, editing efforts focus on generated motions, limiting the editability of real ones. MoMo harnesses motion inversion, extending its application to both real and generated motions. Experimental results show the advantage of our approach over the current art. In particular, unlike methods tailored for specific applications through training, our approach is applied at inference time, requiring no training. Our webpage is at https://monkeyseedocg.github.io.

Training a modern machine learning architecture on a new task requires extensive learning-rate tuning, which comes at a high computational cost. Here we develop new adaptive learning rates that can be used with any momentum method, and require less tuning to perform well. We first develop MoMo, a Momentum Model based adaptive learning rate for SGD-M (Stochastic gradient descent with momentum). MoMo uses momentum estimates of the batch losses and gradients sampled at each iteration to build a model of the loss function. Our model also makes use of any known lower bound of the loss function by using truncation, e.g. most losses are lower-bounded by zero. We then approximately minimize this model at each iteration to compute the next step. We show how MoMo can be used in combination with any momentum-based method, and showcase this by developing MoMo-Adam - which is Adam with our new model-based adaptive learning rate. Additionally, for losses with unknown lower bounds, we develop on-the-fly estimates of a lower bound, that are incorporated in our model. Through extensive numerical experiments, we demonstrate that MoMo and MoMo-Adam improve over SGD-M and Adam in terms of accuracy and robustness to hyperparameter tuning for training image classifiers on MNIST, CIFAR10, CIFAR100, Imagenet, recommender systems on the Criteo dataset, and a transformer model on the translation task IWSLT14.

We propose a self-supervised shared encoder model that achieves strong results on several visual, language and multimodal benchmarks while being data, memory and run-time efficient. We make three key contributions. First, in contrast to most existing works, we use a single transformer with all the encoder layers processing both the text and the image modalities. Second, we propose a stage-wise training strategy where the model is first trained on images, then jointly with unimodal text and image datasets and finally jointly with text and text-image datasets. Third, to preserve information across both the modalities, we propose a training pipeline that learns simultaneously from gradient updates of different modalities at each training update step. The results on downstream text-only, image-only and multimodal tasks show that our model is competitive with several strong models while using fewer parameters and lesser pre-training data. For example, MoMo performs competitively with FLAVA on multimodal (+3.1), image-only (+1.1) and text-only (-0.1) tasks despite having 2/5th the number of parameters and using 1/3rd the image-text training pairs. Finally, we ablate various design choices and further show that increasing model size produces significant performance gains indicating potential for substantial improvements with larger models using our approach.

Machine learning methods have been used to accelerate the molecule optimization process. However, efficient search for optimized molecules satisfying several properties with scarce labeled data remains a challenge for machine learning molecule optimization. In this study, we propose MOMO, a multi-objective molecule optimization framework to address the challenge by combining learning of chemical knowledge with Pareto-based multi-objective evolutionary search. To learn chemistry, it employs a self-supervised codec to construct an implicit chemical space and acquire the continues representation of molecules. To explore the established chemical space, MOMO uses multi-objective evolution to comprehensively and efficiently search for similar molecules with multiple desirable properties. We demonstrate the high performance of MOMO on four multi-objective property and similarity optimization tasks, and illustrate the search capability of MOMO through case studies. Remarkably, our approach significantly outperforms previous approaches in optimizing three objectives simultaneously. The results show the optimization capability of MOMO, suggesting to improve the success rate of lead molecule optimization.

Many firms have begun to embrace artificial intelligence (AI) and the technology has enabled them to outperform their competitors, according to insiders. Pham Quang Vinh, Head of the Enterprise Solutions Division, Viettel Cyberspace Centre, made reference to an Accenture report in June to show that 60 per cent of finance and banking firms were engaging in AI and the technology earned them 50 per cent higher revenues than their rivals. "AI will become an official means of customer-firm interaction, especially for banks," he said. The head of the division also said 42 per cent of specialists working at firms are unaware of the risk that their firms would fall behind the curve shoud they fail to embark on AI. In fact, AI-enabled finance and banking firms have seen their revenues rise steadily since 2019.

Patients regularly continue assessment or treatment in other facilities than they began them in, receiving their previous imaging studies as a CD-ROM and requiring clinical staff at the new hospital to import these studies into their local database. However, between different facilities, standards for nomenclature, contents, or even medical procedures may vary, often requiring human intervention to accurately classify the received studies in the context of the recipient hospital's standards. In this study, the authors present MOMO (MOdality Mapping and Orchestration), a deep learning-based approach to automate this mapping process utilizing metadata substring matching and a neural network ensemble, which is trained to recognize the 76 most common imaging studies across seven different modalities. A retrospective study is performed to measure the accuracy that this algorithm can provide. To this end, a set of 11,934 imaging series with existing labels was retrieved from the local hospital's PACS database to train the neural networks. A set of 843 completely anonymized external studies was hand-labeled to assess the performance of our algorithm. Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted. In comparison to a commercial product (96.20% predictive power, 82.86% accuracy, 1.36% minor errors), a neural network ensemble alone performs the classification task with less accuracy (99.05% predictive power, 72.69% accuracy, 10.3% minor errors). However, MOMO outperforms either by a large margin in accuracy and with increased predictive power (99.29% predictive power, 92.71% accuracy, 2.63% minor errors).

Humanity has migrated to subaquatic domes to escape the lethal consequences of a vastly deteriorated ozone layer. Tremendous advances in solar power have made this shift possible, and an android underclass provides maintenance labor. Sentient but without rights, they are manufactured with organs that can be harvested by humans. Gradually, Momo grows enlightened to the oppression of androids, connecting the dots between a surgery she had as a child and the disappearance of her childhood best friend. There's an awful lot going on in this short work: new religions form in this future world, the Pacific Ocean territories are divided between countries like the United States and corporations like Toyota, and then there are the peculiar skin treatments at Momo's salon.

When a survey in 2015 revealed that more than half of Guangzhou's female commuters had experienced some form of sexual harassment ("inappropriate touching") on public transport, a handful of Chinese cities began reserving subway cars for female commuters. But the designated carriages, which were sometimes labelled in pink Chinese characters with floral adornments, did little to deter men from squeezing aboard. "When everyone is rushing to work, no one cares whether it is a female-only car or not," one commuter complained on weibo. Indeed, many men have either blatantly ignored the restrictions or were oblivious to women-only subway carriages. Enforcement has lacked teeth – in part because the metro system is so overcrowded.