Accelerating science with human-aware artificial intelligence

Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences cognitively accessible to experts dramatically improves (up to 400%) AI prediction of future discoveries beyond those focused on research content alone, especially when relevant literature is sparse. These models succeed by predicting human predictions and the scientists who will make them. By tuning human-aware AI to avoid the crowd, we can generate scientifically promising "alien" hypotheses unlikely to be imagined or pursued without intervention until the distant future, which hold promise to punctuate scientific advance beyond questions currently pursued. Accelerating human discovery or probing its blind spots, human-aware AI enables us to move toward and beyond the contemporary scientific frontier

The economic geology of the Okiep copper deposits, Namaqualand, South Africa

The Okiep Copper District situated in the north-western Cape Province, covers some 3 000 km and is the oldest mining area in the Republic of South Africa. The O'okiep Copper Company Limited commenced production in 1940 with a proven ore reserve of 9 million tons at 2,45 % cu. Production since 1940 and present ore reserves total some 93 million tonnes at 1,08 % Cu. The rocks comprising the Okiep Copper District are of Proterozoic age and have been subdivided into a meta-volcanosedimentary succession, intruded by various sub-horizontally emplaced granitoid intrusions. The various intrusions occurred at different stages relative to the main structural and metamorphic events. The copper deposits are confined to basic rocks which are the youngest major group of intrusives in the District. They occur as swarms of generally easterly-trending, steep northdipping, irregular dyke-like bodies consisting of diorite, anorthosite and norite. The dominant silicate constituents are andesite ranging to labradorite, hypersthene, biotite and phlogopite. Copper sulphides are preferentially associated with the more basic varieties. The copper sulphides are mainly chalcopyrite, bornite and subsidiary chalcocite. The copper content of the basic rocks is erratic ranging over small distances from a mere trace to several percent. Emplacement of the cupriferous basic rocks is predisposed to a large extent by enigmatic structural features locally referred to as steep structures. The most common manifestation of steep structure deformation is typically a narrow antiformal linear feature along which continuity of the country rocks has been interrupted by piercement folding and shearing. In places, pipelike bodies of megabreccia occur along steep structures, and also act as hosts to the basic rock. Areas of steep structure are thus prime exploration targets, due to their close spatial association with the cupriferous basic rocks. Exploration techiques employed in the Okiep Copper District in~ elude regional and detailed geological mapping, geophysical surveys utilizing magnetic, gravimetric and electrical methods, as well as limited application of soil and stream-sediment geochemistry. Final evaluation is by surface and underground diamond drilling. Exploration has to date discovered 18 new mines with individual ore reserves ranging from 200 000 to 37 000 000 tonnes. All are underground operations, and the sub-level open stoping method of mining is standard

Target recognition techniques for multifunction phased array radar

This thesis, submitted for the degree of Doctor of Philosophy at University College London, is a discussion and analysis of combined stepped-frequency and pulse-Doppler target recognition methods which enable a multifunction phased array radar designed for automatic surveillance and multi-target tracking to offer a Non Cooperative Target Recognition (NCTR) capability. The primary challenge is to investigate the feasibility of NCTR via the use of high range resolution profiles. Given stepped frequency waveforms effectively trade time for enhanced bandwidth, and thus resolution, attention is paid to the design of a compromise between resolution and dwell time. A secondary challenge is to investigate the additional benefits to overall target classification when the number of coherent pulses within an NCTR wavefrom is expanded to enable the extraction of spectral features which can help to differentiate particular classes of target. As with increased range resolution, the price for this extra information is a further increase in dwell time. The response to the primary and secondary challenges described above has involved the development of a number of novel techniques, which are summarized below: • Design and execution of a series of experiments to further the understanding of multifunction phased array Radar NCTR techniques • Development of a ‘Hybrid’ stepped frequency technique which enables a significant extension of range profiles without the proportional trade in resolution as experienced with ‘Classical’ techniques • Development of an ‘end to end’ NCTR processing and visualization pipeline • Use of ‘Doppler fraction’ spectral features to enable aircraft target classification via propulsion mechanism. Combination of Doppler fraction and physical length features to enable broad aircraft type classification. • Optimization of NCTR method classification performance as a function of feature and waveform parameters. • Generic waveform design tools to enable delivery of time costly NCTR waveforms within operational constraints. The thesis is largely based upon an analysis of experimental results obtained using the multifunction phased array radar MESAR2, based at BAE Systems on the Isle of Wight. The NCTR mode of MESAR2 consists of the transmission and reception of successive multi-pulse coherent bursts upon each target being tracked. Each burst is stepped in frequency resulting in an overall bandwidth sufficient to provide sub-metre range resolution. A sequence of experiments, (static trials, moving point target trials and full aircraft trials) are described and an analysis of the robustness of target length and Doppler spectra feature measurements from NCTR mode data recordings is presented. A recorded data archive of 1498 NCTR looks upon 17 different trials aircraft using five different varieties of stepped frequency waveform is used to determine classification performance as a function of various signal processing parameters and extent (numbers of pulses) of the data used. From analysis of the trials data, recommendations are made with regards to the design of an NCTR mode for an operational system that uses stepped frequency techniques by design choice

Quantum communication in noisy environments

In this thesis, we investigate how protocols in quantum communication theory are influenced by noise. Specifically, we take into account noise during the transmission of quantum information and noise during the processing of quantum information. We describe three novel quantum communication protocols which can be accomplished efficiently in a noisy environment: (1) Factorization of Eve: We show that it is possible to disentangle transmitted qubits a posteriori from the quantum channel's degrees of freedom. (2) Cluster state purification: We give multi-partite entanglement purification protocols for a large class of entangled quantum states. (3) Entanglement purification protocols from quantum codes: We describe a constructive method to create bipartite entanglement purification protocols form quantum error correcting codes, and investigate the properties of these protocols, which can be operated in two different modes, which are related to quantum communication and quantum computation protocols, respectively

Development of a Novel Method to Assess the Effects of Predictability and Chronic Stress on Neuronal Morphology and Decision-Making in Rats

The Golgi-Cox stain remains the gold standard for studying changes in neuronal morphology offering the greatest detail and clearest spine visualisation. Nevertheless, the method has limitations particularly in thick sections where laser penetration, inadequate 3D cell reconstructions, background staining and inadequate cell visualisation within fixed or otherwise non-fresh tissue offer challenges to the microscopist. Here I describe the development of a more efficient, cost effective and more broadly applicable stain together with my attempt to apply this new stain to modern tissue clearing techniques. Not only did the new methodology improve the staining of cells, it enhanced CLARITY and CUBIC clearing techniques allowing the clearing of brain tissue within a fraction of the usual time. Having developed this new methodology, I then applied this new approach to study the changes in neuronal morphology induced by chronic stress in rats. Changes in morphology in a large number of brain regions were analysed and relate to the functional effects of chronic stress. Chronic stress has been repeatedly shown to change morphology in profound ways, and I observed both increases and decreases in dendritic lengths and spine densities in different brain regions. In addition to the morphology analysis, I investigated the concomitant effects of chronic stress on choice and decision-making in the instrumental conditioning situation. Chronically stressed rats presented with decreased sensitivity to changes in the value of the instrumental outcome and in the action-outcome contingency. Furthermore, chronically stressed rats expressed a facilitation of outcome specific Pavlovian-instrumental-transfer (sPIT). The ability to control or to predict the application of stress has been reported to ameliorate its effects and, in a subsequent experiment, I compared the effects of random chronic stress and predicted chronic stress. Consistent with the previous literature, when the rats could predict the application of stress, its detrimental effects were reduced. Although the effects of random chronic stress were similar to those observed in the previous experiment, rats exposed to predictable stress showed sensitivity to changes in outcome value and in the action-outcome contingency comparable to unstressed controls. Nevertheless, in other tests of decision-making predictably stressed rats showed a further facilitation in the sPIT effect together with a deficit in delayed discounting. These changes in decision-making were correlated to changes in neuronal morphology caused by chronic stress. Again, randomly stressed rats presented with degenerated dendritic length and synaptic spine density across many subregions of the prefrontal cortex, and proliferated morphology within the nucleus accumbens core and basolateral amygdala (BLA). Consistent with the idea that predicted stress would protect the animal from the detrimental effects of chronic stress, predicted stress rats expressed as controls within the dorsomedial striatum (DMS), hippocampus and medio-orbitofrontal cortex (MO), along with other morphology changes not consistent with random stress rats, such as a proliferation of the BLA more so than controls but not as much as random stress rats. Of the many arguments made, principally we propose that the protection of the DMS and MO and their crucial involvement in goal direct action, explains why, unlike random stress rats, predicted stress rats maintain sensitivity to outcome devaluation and contingency degradation. These morphological changes were analysed with the newly developed ultra-rapid Golgi (URG) stain. Which with further development and use of two-photon microscopy has been designed to excite stained neurons to auto-fluoresce. The two-photon laser was manipulated to excite the mercury compounds impregnated into the cells, energising the mercury to the point of electron displacement, this is a completely novel visualisation of Golgi stained neurons, offering even greater visual clarity and analysis

On the Key Processes that Drive Galaxy Evolution: the Role of Galaxy Mergers, Accretion, Local Environment and Feedback in Shaping the Present-Day Universe

The study of galaxy evolution is a fundamental discipline in modern astrophysics, dealing with how and why galaxies of all types evolve over time. The diversity of present-day galaxies is a reflection of the processes through which these populations were assembled and offers insights into how these processes influence and regulate their mass assembly over the lifetime of the Universe. The currently favoured hierarchical paradigm of structure formation hypothesises that much of a galaxy’s evolution must be driven by mergers. It is therefore important to understand the role of the merger process in shaping the galaxy populations in today’s Universe. Together with data from large observational surveys, statistical studies of galaxy evolution rely on comparison to simulations, which can be used to make realistic survey-scale predictions. Together these two approaches can offer powerful insights into the processes that drive galaxy evolution over cosmic time. I have used the Horizon-AGN simulation to study the effect of galaxy mergers on the stellar populations and central super-massive black holes of galaxies over cosmic time. I have shown that, while mergers can enhance star formation and black-hole growth significantly in the low redshift Universe, these enhancements are small at high redshift when the cosmic SFH peaks. This is because galaxies are already gas-rich at early epochs and mergers are not able to increase gas densities in the central regions of the galaxy. As a result, mergers are directly responsible for creating only around 30 per cent of the stellar mass and black-hole mass found and in today’s galaxies and that mergers never dominate the budget (e.g. ~35 and ~20 per cent of star formation at z~3 and z~1 respectively are a result of mergers). Notwithstanding their relatively minor role in driving stellar and BH mass growth, mergers are important drivers of morphological change, with major and minor mergers accounting for essentially all (95 per cent) of the morphological change experienced by massive present-day spheroids over their lifetime. However, at a given stellar mass, the average merger histories of discs and spheroids do not differ strongly enough to explain the survival of discs to the present day. Instead, their survival is largely due to a preponderance of prograde and gas rich mergers. Prograde mergers trigger milder morphological transformation than retrograde mergers - the average change due to retrograde mergers is around twice that due to their prograde counterparts at ɀ ~ 0 and remnant morphology also depends strongly on the gas fraction of a merger, with gas-rich mergers routinely re-growing discs. My results also emphasise the important role of minor mergers, which dominate the stellar mass and black-hole growth budget after ɀ = 1 and are a potentially important reservoir of cold gas which plays a role in the rejuvenation and survival of discs. I have also investigated the biases that this morphological evolution produces in observational studies of galaxy populations. In particular, I have shown that ‘progenitor bias’ i.e. the bias produced by using only early-type galaxies to define the progenitor population of today’s early-types, is a significant problem at all but the lowest redshifts and an important considerations for large, deep observational surveys (JWST, LSST etc.). For example while early-types attain their final morphology at relatively early epochs – by ɀ ~ 1, around 60 per cent of today’s early-types have had their last significant merger, progenitor bias is severe at all but the lowest redshifts. At ɀ ~ 0.6, less than 50 per cent of the stellar mass in today’s early-types is actually in progenitors with early-type morphology, while, at the peak epoch of cosmic of star-formation (ɀ ~ 2), studying only early-types misses almost all (80 per cent) of the stellar mass that eventually ends up in local early-type systems. I have explored the significance and formation mechanisms of low-surface-brightness galaxies (LSBGs). For M ͙ > 108Mʘ, LSBGs contribute 50 per cent of the local number density and exist in significant numbers across all environments. Their progenitors have stronger, burstier star formation at high redshift which causes stronger supernova feedback. This feedback flattens the gas-density profiles (but does not remove the gas reservoirs). This, in turn, gives rise to flatter stellar profiles, which are more susceptible to environmental processes and galaxy interactions, which produce today’s LSBG populations by driving the steady removal of cold gas and gradually increasing galaxy effective radii over time. The ability of these populations to elucidate key questions in the field of galaxy evolution and significantly alter our current paradigm is becoming increasingly clear, especially with the advent of new deep surveys. Finally, I have implemented a new unsupervised machine learning technique (UML) on images from the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey. The algorithm autonomously reduces galaxy populations down to a small number of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications reproduce known trends in key galaxy properties as a function of morphological type (e.g. stellar mass functions and colours). This study demonstrates the power of UML in performing accurate morphological analysis, which will become indispensable in the forthcoming era of deep-wide surveys