In the realm of aerial imaging, the ability to detect small objects is pivotal for a myriad of applications, encompassing environmental surveillance, urban design, and crisis management. Leveraging RetinaNet, this work unveils DDR-Net: a data-driven, deep-learning model devised to enhance the detection of diminutive objects. DDR-Net introduces novel, data-driven techniques to autonomously ascertain optimal feature maps and anchor estimations, cultivating a tailored and proficient training process while maintaining precision. Additionally, this paper presents an innovative sampling technique to bolster model efficacy under limited data training constraints. The model's enhanced detection capabilities support critical applications including wildlife and habitat monitoring, traffic flow optimization, and public safety improvements through accurate identification of small objects like vehicles and pedestrians. DDR-Net significantly reduces the cost and time required for data collection and training, offering efficient performance even with limited data. Empirical assessments over assorted aerial avian imagery datasets demonstrate that DDR-Net markedly surpasses RetinaNet and alternative contemporary models. These innovations advance current aerial image analysis technologies and promise wide-ranging impacts across multiple sectors including agriculture, security, and archaeology.

The problem of path planning for autonomously searching and tracking multiple objects is important to reconnaissance, surveillance, and many other data-gathering applications. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce $\textit{UniSaT}$ ($\textit{Unified Search and Track}$), a unified-objective formulation for the search and track problem based on Random Finite Sets (RFS). This is done by modeling both the unknown and known objects through a combined generalized labeled multi-Bernoulli (GLMB) filter. For the unseen objects, we can leverage both cardinality and spatial prior distributions, which means $\textit{UniSaT}$ does not rely on knowing the exact count of the expected number of objects in the space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment and show both qualitative results as well as quantitative improvements over a multi-objective method.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

Logical reasoning, i.e., deductively inferring the truth value of a conclusion from a set of premises, is an important task for artificial intelligence with wide potential impacts on science, mathematics, and society. While many prompting-based strategies have been proposed to enable Large Language Models (LLMs) to do such reasoning more effectively, they still appear unsatisfactory, often failing in subtle and unpredictable ways. In this work, we investigate the validity of instead reformulating such tasks as modular neurosymbolic programming, which we call LINC: Logical Inference via Neurosymbolic Computation. In LINC, the LLM acts as a semantic parser, translating premises and conclusions from natural language to expressions in first-order logic. These expressions are then offloaded to an external theorem prover, which symbolically performs deductive inference. Leveraging this approach, we observe significant performance gains on FOLIO and a balanced subset of ProofWriter for three different models in nearly all experimental conditions we evaluate. On ProofWriter, augmenting the comparatively small open-source StarCoder+ (15.5B parameters) with LINC even outperforms GPT-3.5 and GPT-4 with Chain-of-Thought (CoT) prompting by an absolute 38% and 10%, respectively. When used with GPT-4, LINC scores 26% higher than CoT on ProofWriter while performing comparatively on FOLIO. Further analysis reveals that although both methods on average succeed roughly equally often on this dataset, they exhibit distinct and complementary failure modes. We thus provide promising evidence for how logical reasoning over natural language can be tackled through jointly leveraging LLMs alongside symbolic provers. All corresponding code is publicly available at https://github.com/benlipkin/linc

For many islanders, delays to the postal service are an inescapable part of life. But that should no longer be the case for those living in Orkney, after it became the first place in Britain to have mail delivered by a drone. The new Royal Mail service will see post transported from the Kirkwall delivery office to the village of Stromness, where drones will then transfer items to posties on the islands of Hoy and Graemsay for their regular routes. Currently, mail arrives at Kirkwall Airport before being sent by plane or ferry to Orkney's 19 inhabited islands. But the challenging geography and weather conditions often result in delivery disruptions.

Wind energy resource assessment typically requires numerical models, but such models are too computationally intensive to consider multi-year timescales. Increasingly, unsupervised machine learning techniques are used to identify a small number of representative weather patterns to simulate long-term behaviour. Here we develop a novel wind energy workflow that for the first time combines weather patterns derived from unsupervised clustering techniques with numerical weather prediction models (here WRF) to obtain efficient and accurate long-term predictions of power and downstream wakes from an entire wind farm. We use ERA5 reanalysis data clustering not only on low altitude pressure but also, for the first time, on the more relevant variable of wind velocity. We also compare the use of large-scale and local-scale domains for clustering. A WRF simulation is run at each of the cluster centres and the results are aggregated using a novel post-processing technique. By applying our workflow to two different regions, we show that our long-term predictions agree with those from a year of WRF simulations but require less than 2% of the computational time. The most accurate results are obtained when clustering on wind velocity. Moreover, clustering over the Europe-wide domain is sufficient for predicting wind farm power output, but downstream wake predictions benefit from the use of smaller domains. Finally, we show that these downstream wakes can affect the local weather patterns. Our approach facilitates multi-year predictions of power output and downstream farm wakes, by providing a fast, accurate and flexible methodology that is applicable to any global region. Moreover, these accurate long-term predictions of downstream wakes provide the first tool to help mitigate the effects of wind energy loss downstream of wind farms, since they can be used to determine optimum wind farm locations.

Royal Mail is building a fleet of 500 drones to carry mail to remote communities all over the UK, including the Isles of Scilly and the Hebrides. The postal service, which has already conducted successful trials over Scotland and Cornwall, will create more than 50 new postal drone routes over the next three years as part of a new partnership with London company Windracers. Drones, or UAVs (uncrewed aerial vehicles), can help reduce carbon emissions and improve the reliability of island mail services, Royal Mail claims. They offer an alternative to currently-used delivery methods that can be affected by bad weather – ferries, conventional aircraft and land-based deliveries. They can also take off from any flat surface (sand, grass or tarmac) providing it is long enough.

Java-wise, I sometimes pick up a medium decaf at the Pastry Chef, where I might also grab a plain croissant, though most days I have coffee and a toasted poppy bagel at Bella's Restaurant. I do not get the bagel hollowed out, viewing this as an affectation. I freely grant this information; all you data miners fooling around inside my car can do with your findings what you please. The deeply paranoid national obsession with hiding personal info from online snoops is misplaced and counterproductive. These guys are going to get it anyway, so why obsess about it?

The 2016 Summer Movie Preview is a snapshot of the films opening through early September. Release dates and other details, as compiled by Kevin Crust, are subject to change. The view of Earth from space and the information it reveals about humanity's effect on the planet are examined in this large-format science documentary. Business suddenly picks up for a London kosher baker when his young Muslim apprentice accidentally drops a stash of pot into the mixer. Written by Yehudah Jez Freedman and Jonathan Benson. The kidnapping of a beloved kitty forces two naive cousins to infiltrate a street gang. Written by Peele & Alex Rubens. In 1913 Cambridge, England, a young Indian math genius joins forces with an eccentric professor. Written and directed by Matthew Brown. Written by Lily Hollander, Anya Kochoff Romano. After an auto accident, a young woman finds herself in a life at odds with the one she remembers. Written by Doc Pedrolie and Victoria Arch. The famous writer's downward spiral is witnessed by a young reporter during the revolution. With Minka Kelly, Giovanni Ribisi, Joely Richardson, Adrian Sparks. A lonely lombax and a tiny robot team with the Galactic Rangers to save their world in this animated adventure.