An algorithm designed by a research group from the Universities of Bath, Oxford and Twente may be able to help save African elephants from extinction. Coupled with high-resolution imagery, the algorithm enables a satellite to scan large areas of land in short periods of time and collect 5,000 km2 worth of photos, a good fit for the animals' grassland and forest habitats. The tech development is desperately needed as elephant numbers in Africa are estimated to be at just 415,000. The savanna elephant population has reduced by 60 per cent in the last 50 years and the number of forest elephants have fallen by 86 per cent in the previous three decades. The AI technology carries less risk of double counting, does not endanger humans in the data collection process and is less disturbing for the animals - an improvement on techniques used in the past. Earlier this year Dr Ben Okita, co-chair of the IUCN elephant specialist group, named poaching as one of the biggest threats to African elephants who are targeted by ivory traders.

AMP algorithms have two features that make them particularly attractive. First, they can easily be tailored to take advantage of prior information on the structure of the signal, such as sparsity or other constraints. Second, under suitable assumptions on a design or data matrix, AMP theory provides precise asymptotic guarantees for statistical procedures in the high-dimensional regime where the ratio of the number of observations n to dimensions p converges to a constant (Bayati and Montanari, 2012; Donoho et al., 2013; Sur et al., 2017). More generally, AMP has been also used to obtain lower bounds on the estimation error of first-order methods (Celentano et al., 2020), and in linear regression and low rank matrix estimation, it plays a fundamental role in understanding the performance gap between information-theoretically optimal and computationally feasible estimators (Reeves and Pfister, 2019; Barbier et al., 2019; Lelarge and Miolane, 2019). In these settings, it is conjectured that AMP achieves the optimal asymptotic estimation error among all polynomial-time algorithms (cf.

Image registration as an important basis in signal processing task often encounter the problem of stability and efficiency. Non-learning registration approaches rely on the optimization of the similarity metrics between the fix and moving images. Yet, those approaches are usually costly in both time and space complexity. The problem can be worse when the size of the image is large or the deformations between the images are severe. Recently, deep learning, or precisely saying, the convolutional neural network (CNN) based image registration methods have been widely investigated in the research community and show promising effectiveness to overcome the weakness of non-learning based methods. To explore the advanced learning approaches in image registration problem for solving practical issues, we present in this paper a method of introducing attention mechanism in deformable image registration problem. The proposed approach is based on learning the deformation field with a Transformer framework (AiR) that does not rely on the CNN but can be efficiently trained on GPGPU devices also. In a more vivid interpretation: we treat the image registration problem as the same as a language translation task and introducing a Transformer to tackle the problem. Our method learns an unsupervised generated deformation map and is tested on two benchmark datasets. The source code of the AiR will be released at Gitlab.

Sequential decision-making under cost-sensitive tasks is prohibitively daunting, especially for the problem that has a significant impact on people's daily lives, such as malaria control, treatment recommendation. The main challenge faced by policymakers is to learn a policy from scratch by interacting with a complex environment in a few trials. This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials. Specifically, the solution is a model-based reinforcement learning method. To avoid model bias, we apply Gaussian Process~(GP) regression to estimate the transitions explicitly. With the GP world model, we propose a variance-bonus reward to measure the uncertainty about the world. Adding the reward to the planning with MCTS can result in more efficient and effective exploration. Furthermore, the derived polynomial sample complexity indicates that VB-MCTS is sample efficient. Finally, outstanding performance on a competitive world-level RL competition and extensive experimental results verify its advantage over the state-of-the-art on the challenging malaria control task.

AI offers great potential to increase the adoption of renewable energy. Within two months, Omdena's AI community built an interactive map showing the top Nigerian regions for solar power installments. The solutions will provide helpful insights for the government and policy makers to take make decisions on where to allocate resources in the most effective way. Many communities are not connected to the national electricity grid altogether. Most of them work with environmentally devastating fossil fuel, which is expensive, unsustainable, noisy, and health-threatening.

COVID-19 has impacted the world in unprecedented ways, fast-tracking the use of digital tools and innovation to adapt. In a previous blog, we outlined six key technology trends driving social and behavioural changes in West Africa as a result of COVID-19. As we look towards life after the pandemic, we revisit some of these trends and detail key frontier technologies gaining traction in developing economies. The pandemic has driven the uptake of big data public-private partnerships for crisis response. Layering multiple types of data points (including mobile phone data, satellite imagery, ground weather measurements and open street maps) can be extremely effective when combined.

Artificial intelligence (AI) is supposed to help us make better choices. Some of these choices are small, like what route to take to work, or what music to listen to. Others are big, like what treatment to administer for a disease or how long to sentence someone for a crime. If AI can assist with these big decisions, we might think it can also help with hard choices, cases where alternatives are neither better, worse nor equal but on a par. The aim of this paper, however, is to show that this view is mistaken: the fact of parity shows that there are hard limits on AI in decision making and choices that AI cannot, and should not, resolve.

Influenced by the field of Computer Vision, Generative Adversarial Networks (GANs) are often adopted for the audio domain using fixed-size two-dimensional spectrogram representations as the "image data". However, in the (musical) audio domain, it is often desired to generate output of variable duration. This paper presents VQCPC-GAN, an adversarial framework for synthesizing variable-length audio by exploiting Vector-Quantized Contrastive Predictive Coding (VQCPC). A sequence of VQCPC tokens extracted from real audio data serves as conditional input to a GAN architecture, providing step-wise time-dependent features of the generated content. The input noise z (characteristic in adversarial architectures) remains fixed over time, ensuring temporal consistency of global features. We evaluate the proposed model by comparing a diverse set of metrics against various strong baselines. Results show that, even though the baselines score best, VQCPC-GAN achieves comparable performance even when generating variable-length audio. Numerous sound examples are provided in the accompanying website, and we release the code for reproducibility.

We consider a rank regression setting, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons. Specifically, there exists a latent total ordering of the samples; when presented with a pair of samples, a noisy oracle identifies the one ranked higher with respect to the underlying total ordering. A learner observes a dataset of such comparisons and wishes to regress sample ranks from their features. We show that to learn the model parameters with $\epsilon > 0$ accuracy, it suffices to conduct $M \in \Omega(dN\log^3 N/\epsilon^2)$ comparisons uniformly at random when $N$ is $\Omega(d/\epsilon^2)$.

Tensor computations overwhelm traditional general-purpose computing devices due to the large amounts of data and operations of the computations. They call for a holistic solution composed of both hardware acceleration and software mapping. Hardware/software (HW/SW) co-design optimizes the hardware and software in concert and produces high-quality solutions. There are two main challenges in the co-design flow. First, multiple methods exist to partition tensor computation and have different impacts on performance and energy efficiency. Besides, the hardware part must be implemented by the intrinsic functions of spatial accelerators. It is hard for programmers to identify and analyze the partitioning methods manually. Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge. The design space needs to be efficiently explored. To this end, we propose an agile co-design approach HASCO that provides an efficient HW/SW solution to dense tensor computation. We use tensor syntax trees as the unified IR, based on which we develop a two-step approach to identify partitioning methods. For each method, HASCO explores the hardware and software design spaces. We propose different algorithms for the explorations, as they have distinct objectives and evaluation costs. Concretely, we develop a multi-objective Bayesian optimization algorithm to explore hardware optimization. For software optimization, we use heuristic and Q-learning algorithms. Experiments demonstrate that HASCO achieves a 1.25X to 1.44X latency reduction through HW/SW co-design compared with developing the hardware and software separately.