Human activity recognition using a single-photon direct time-of-flight sensor.

Single-Photon Avalanche Diode (SPAD) direct Time-of-Flight (dToF) sensors provide depth imaging over long distances, enabling the detection of objects even in the absence of contrast in colour or texture. However, distant objects are represented by just a few pixels and are subject to noise from solar interference, limiting the applicability of existing computer vision techniques for high-level scene interpretation. We present a new SPAD-based vision system for human activity recognition, based on convolutional and recurrent neural networks, which is trained entirely on synthetic data. In tests using real data from a 64×32 pixel SPAD, captured over a distance of 40 m, the scheme successfully overcomes the limited transverse resolution (in which human limbs are approximately one pixel across), achieving an average accuracy of 89% in distinguishing between seven different activities. The approach analyses continuous streams of video-rate depth data at a maximal rate of 66 FPS when executed on a GPU, making it well-suited for real-time applications such as surveillance or situational awareness in autonomous systems.

Instant Detection of Synthetic Cannabinoids on Physical Matrices, Implemented on a Low-Cost, Ultraportable Device.

Synthetic cannabinoids (SCs) make up a class of novel psychoactive substances (NPS), used predominantly in prisons and homeless communities in the U.K. SCs can have severe side effects, including psychosis, stroke, and seizures, with numerous reported deaths associated with their use. The chemical diversity of SCs presents the major challenge to their detection since approaches relying on specific molecular recognition become outdated almost immediately. Ideally one would have a generic approach to detecting SCs in portable settings. The problem of SC detection is more challenging still because the majority of SCs enter the prison estate adsorbed onto physical matrices such as paper, fabric, or herb materials. That is, regardless of the detection modality used, the necessary extraction step reduces the effectiveness and ability to rapidly screen materials on-site. Herein, we demonstrate a truly instant generic test for SCs, tested against real-world drug seizures. The test is based on two advances. First, we identify a spectrally silent region in the emission spectrum of most physical matrices. Second, the finding that background signals (including from autofluorescence) can be accurately predicted is based on tracking the fraction of absorbed light from the irradiation source. Finally, we demonstrate that the intrinsic fluorescence of a large range of physical substrates can be leveraged to track the presence of other drugs of interest, including the most recent iterations of benzodiazepines and opioids. We demonstrate the implementation of our presumptive test in a portable, pocket-sized device that will find immediate utility in prisons and law enforcement agencies around the world.

Video super-resolution for single-photon LIDAR.

3D time-of-flight (ToF) image sensors are used widely in applications such as self-driving cars, augmented reality (AR), and robotics. When implemented with single-photon avalanche diodes (SPADs), compact, array format sensors can be made that offer accurate depth maps over long distances, without the need for mechanical scanning. However, array sizes tend to be small, leading to low lateral resolution, which combined with low signal-to-background ratio (SBR) levels under high ambient illumination, may lead to difficulties in scene interpretation. In this paper, we use synthetic depth sequences to train a 3D convolutional neural network (CNN) for denoising and upscaling (×4) depth data. Experimental results, based on synthetic as well as real ToF data, are used to demonstrate the effectiveness of the scheme. With GPU acceleration, frames are processed at >30 frames per second, making the approach suitable for low-latency imaging, as required for obstacle avoidance.

3D target detection and spectral classification for single-photon LiDAR data.

3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.

Simultaneously Sorting Overlapping Quantum States of Light.

The efficient manipulation, sorting, and measurement of optical modes and single-photon states is fundamental to classical and quantum science. Here, we realize simultaneous and efficient sorting of nonorthogonal, overlapping states of light, encoded in the transverse spatial degree of freedom. We use a specifically designed multiplane light converter to sort states encoded in dimensions ranging from d=3 to d=7. Through the use of an auxiliary output mode, the multiplane light converter simultaneously performs the unitary operation required for unambiguous discrimination and the basis change for the outcomes to be spatially separated. Our results lay the groundwork for optimal image identification and classification via optical networks, with potential applications ranging from self-driving cars to quantum communication systems.

The importance of molecular axis alignment and symmetry-breaking in photoelectron elliptical dichroism.

Photoelectron angular distributions (PADs) produced from the photoionization of chiral molecules using elliptically polarized light exhibit a forward/backward asymmetry with respect to the optical propagation direction. By recording these distributions using the velocity-map imaging (VMI) technique, the resulting photoelectron elliptical dichroism (PEELD) has previously been demonstrated as a promising spectroscopic tool for studying chiral molecules in the gas phase. The use of elliptically polarized laser pulses, however, produces PADs (and consequently, PEELD distributions) that do not exhibit cylindrical symmetry about the propagation axis. This leads to significant limitations and challenges when employing conventional VMI acquisition and data processing strategies. Using novel photoelectron image analysis methods based around Hankel transform reconstruction tomography and machine learning, however, we have quantified-for the first time-significant symmetry-breaking contributions to PEELD signals that are of a comparable magnitude to the symmetric terms in the multiphoton ionization of (1R,4R)-(+)- and (1S,4S)-(-)-camphor. This contradicts any assumptions that symmetry-breaking can be ignored when reconstructing VMI data. Furthermore, these same symmetry-breaking terms are expected to appear in any experiment where circular and linear laser fields are used together. This ionization scheme is particularly relevant for investigating dynamics in chiral molecules, but it is not limited to them. Developing a full understanding of these terms and the role they play in the photoionization of chiral molecules is of clear importance if the potential of PEELD and related effects for future practical applications is to be fully realized.

The development of a creative work rehabilitation organisation.

Work as therapy has a place in mental healthcare, but there is disagreement about how and why it might be helpful, and how best to conceptualise or represent those benefits. Over the last 50 years, occupational and industrial therapy sheltered workshops have been key elements in the provision of work activities in psychiatric settings, and community-based horticultural activities and creative craft work have offered additional approaches. Using archival material, interviews, witness seminars and personal reflections, this article charts the birth and initial growth of Restore, a charity providing creative work-based services in Oxfordshire between 1977 and 1988. Although Restore might be understood as a response to national trends in mental healthcare policy and research, its trajectory reflects local contingencies.

Innovation in mental health care: Bertram Mandelbrote, the Phoenix Unit and the therapeutic community approach.

Bertram Mandelbrote was Physician Superintendent and Consultant Psychiatrist at Littlemore Hospital in Oxford from 1959 to 1988. A humane pragmatist rather than theoretician, Mandelbrote was known for his facilitating style of leadership and working across organisational boundaries. He created the Phoenix Unit, an innovative admission unit run on therapeutic community lines which became a hub for community outreach. Material drawn from oral histories and witness seminars reflects the remarkably unstructured style of working on the Phoenix Unit and the enduring influence of Mandelbrote and fellow consultant Benn Pomryn's styles of leadership. Practices initiated at Littlemore led to a number of innovative services in Oxfordshire. These innovations place Mandelbrote as a pioneer in social psychiatry and the therapeutic community approach.

The processes and context of innovation in mental healthcare: Oxfordshire as a case study.

This article introduces the four following articles and the Classic Text. They describe the development of a sequence of innovative local mental health services in Oxfordshire, and explore the processes of innovation, led by the humane pragmatism practised by Dr Bertram Mandelbrote, who was Physician Superintendent at Littlemore Hospital in Oxford from 1959 to 1988. The articles describe emerging patterns of therapeutic community practice, and trace the events leading to a set of discrete service developments outside the hospital. Together, they suggest a positive role for chance in these developments, and a focus on the then prevailing national and local regulatory culture. The Classic Text by David Millard provides an overview of the origins of the therapeutic community movement.

Rapid nanometer-precision autocorrelator.

The precise measurement of a target depth has applications in biophysics and nanophysics, and non-linear optical methods are sensitive to intensity changes on very small length scales. By exploiting the high sensitivity of an autocorrelator's dependency on path length, we propose a technique that achieves ≈30 nm depth precision for each pixel in 30 seconds. Our method images up-converted pulses from a non-linear crystal using a sCMOS (scientific Complementary Metal-Oxide-Semiconductor) camera and converts the intensity recorded by each pixel to a delay. By utilising statistical estimation theory and using the data from a set of 32×32 pixels, the standard error (SE) of the detected delay falls below 1 nm after 30 seconds of measurement. Numerical simulations show that this result is extremely close to what can be achieved with a shot-noise-limited source and is consistent with the precision that can be achieved with a sCMOS camera.

Intensity-corrected 4D light-in-flight imaging.

Light-in-flight (LIF) imaging is the measurement and reconstruction of light's path as it moves and interacts with objects. It is well known that relativistic effects can result in apparent velocities that differ significantly from the speed of light. However, less well known is that Rayleigh scattering and the effects of imaging optics can lead to observed intensities changing by several orders of magnitude along light's path. We develop a model that enables us to correct for all of these effects, thus we can accurately invert the observed data and reconstruct the true intensity-corrected optical path of a laser pulse as it travels in air. We demonstrate the validity of our model by observing the photon arrival time and intensity distribution obtained from single-photon avalanche detector (SPAD) array data for a laser pulse propagating towards and away from the camera. We can then reconstruct the true intensity-corrected path of the light in four dimensions (three spatial dimensions and time).

High-speed object detection with a single-photon time-of-flight image sensor.

3D time-of-flight (ToF) imaging is used in a variety of applications such as augmented reality (AR), computer interfaces, robotics and autonomous systems. Single-photon avalanche diodes (SPADs) are one of the enabling technologies providing accurate depth data even over long ranges. By developing SPADs in array format with integrated processing combined with pulsed, flood-type illumination, high-speed 3D capture is possible. However, array sizes tend to be relatively small, limiting the lateral resolution of the resulting depth maps and, consequently, the information that can be extracted from the image for applications such as object detection. In this paper, we demonstrate that these limitations can be overcome through the use of convolutional neural networks (CNNs) for high-performance object detection. We present outdoor results from a portable SPAD camera system that outputs 16-bin photon timing histograms with 64×32 spatial resolution, with each histogram containing thousands of photons. The results, obtained with exposure times down to 2 ms (equivalent to 500 FPS) and in signal-to-background (SBR) ratios as low as 0.05, point to the advantages of providing the CNN with full histogram data rather than point clouds alone. Alternatively, a combination of point cloud and active intensity data may be used as input, for a similar level of performance. In either case, the GPU-accelerated processing time is less than 1 ms per frame, leading to an overall latency (image acquisition plus processing) in the millisecond range, making the results relevant for safety-critical computer vision applications which would benefit from faster than human reaction times.

Robust super-resolution depth imaging via a multi-feature fusion deep network.

The number of applications that use depth imaging is increasing rapidly, e.g. self-driving autonomous vehicles and auto-focus assist on smartphone cameras. Light detection and ranging (LIDAR) via single-photon sensitive detector (SPAD) arrays is an emerging technology that enables the acquisition of depth images at high frame rates. However, the spatial resolution of this technology is typically low in comparison to the intensity images recorded by conventional cameras. To increase the native resolution of depth images from a SPAD camera, we develop a deep network built to take advantage of the multiple features that can be extracted from a camera's histogram data. The network is designed for a SPAD camera operating in a dual-mode such that it captures alternate low resolution depth and high resolution intensity images at high frame rates, thus the system does not require any additional sensor to provide intensity images. The network then uses the intensity images and multiple features extracted from down-sampled histograms to guide the up-sampling of the depth. Our network provides significant image resolution enhancement and image denoising across a wide range of signal-to-noise ratios and photon levels. Additionally, we show that the network can be applied to other data types of SPAD data, demonstrating the generality of the algorithm.

Artificial Neural Networks for Noise Removal in Data-Sparse Charged Particle Imaging Experiments.

We present the first demonstration of artificial neural networks (ANNs) for the removal of Poissonian noise in charged particle imaging measurements with very low overall counts. The approach is successfully applied to both simulated and real experimental image data relating to the detection of photoions/photoelectrons in unimolecular photochemical dynamics studies. Specific examples consider the multiphoton ionization of pyrrole and (S)-camphor. Our results reveal an extremely high level of performance, with the ANNs transforming images that are unusable for any form of quantitative analysis into statistically reliable data with an impressive similarity to benchmark references. Given the widespread use of charged particle imaging methods within the chemical dynamics community, we anticipate that the use of ANNs has significant potential impact - particularly, for example, when working in the limit of very low absorption/photoionization cross-sections, or when attempting to reliably extract subtle image features originating from phenomena such as photofragment vector correlations or photoelectron circular dichroism.

Tracking the polarisation state of light via Hong-Ou-Mandel interferometry.

We provide a statistically robust and accurate framework to measure and track the polarisation state of light employing Hong-Ou-Mandel interference. This is achieved by combining the concepts of maximum likelihood estimation and Fisher information applied to photon detection events. Such an approach ensures that the Cramér-Rao bound is saturated and changes to the polarisation state are established in an optimal manner. Using this method, we show that changes in the linear polarisation state can be measured with 0.6 arcminute precision (0.01 degrees).

Psychological treatments for post-traumatic stress disorder in adults: a network meta-analysis.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a potentially chronic and disabling disorder affecting a significant minority of people exposed to trauma. Various psychological treatments have been shown to be effective, but their relative effects are not well established. METHODS: We undertook a systematic review and network meta-analyses of psychological interventions for adults with PTSD. Outcomes included PTSD symptom change scores post-treatment and at 1-4-month follow-up, and remission post-treatment. RESULTS: We included 90 trials, 6560 individuals and 22 interventions. Evidence was of moderate-to-low quality. Eye movement desensitisation and reprocessing (EMDR) [standardised mean difference (SMD) -2.07; 95% credible interval (CrI) -2.70 to -1.44], combined somatic/cognitive therapies (SMD -1.69; 95% CrI -2.66 to -0.73), trauma-focused cognitive behavioural therapy (TF-CBT) (SMD -1.46; 95% CrI -1.87 to -1.05) and self-help with support (SMD -1.46; 95% CrI -2.33 to -0.59) appeared to be most effective at reducing PTSD symptoms post-treatment v. waitlist, followed by non-TF-CBT, TF-CBT combined with a selective serotonin reuptake inhibitor (SSRI), SSRIs, self-help without support and counselling. EMDR and TF-CBT showed sustained effects at 1-4-month follow-up. EMDR, TF-CBT, self-help with support and counselling improved remission rates post-treatment. Results for other interventions were either inconclusive or based on limited evidence. CONCLUSIONS: EMDR and TF-CBT appear to be most effective at reducing symptoms and improving remission rates in adults with PTSD. They are also effective at sustaining symptom improvements beyond treatment endpoint. Further research needs to explore the long-term comparative effectiveness of psychological therapies for adults with PTSD and also the impact of severity and complexity of PTSD on treatment outcomes.

Non-line-of-sight tracking of people at long range.

A remote-sensing system that can determine the position of hidden objects has applications in many critical real-life scenarios, such as search and rescue missions and safe autonomous driving. Previous work has shown the ability to range and image objects hidden from the direct line of sight, employing advanced optical imaging technologies aimed at small objects at short range. In this work we demonstrate a long-range tracking system based on single laser illumination and single-pixel single-photon detection. This enables us to track one or more people hidden from view at a stand-off distance of over 50 m. These results pave the way towards next generation LiDAR systems that will reconstruct not only the direct-view scene but also the main elements hidden behind walls or corners.

Fair sampling perspective on an apparent violation of duality.

In the event in which a quantum mechanical particle can pass from an initial state to a final state along two possible paths, the duality principle states that "the simultaneous observation of wave and particle behavior is prohibited" [Scully MO, Englert B-G, Walther H (1991) Nature 351:111-116]. Whereas wave behavior is associated with the observation of interference fringes, particle behavior generally corresponds to the acquisition of which-path information by means of coupling the paths to a measuring device or part of their environment. In this paper, we show how the consequences of duality change when allowing for biased sampling, that is, postselected measurements on specific degrees of freedom of the environment of the two-path state. Our work gives insight into a possible mechanism for obtaining simultaneous high which-path information and high-visibility fringes in a single experiment. Further, our results introduce previously unidentified avenues for experimental tests of duality.

Can a partnership between general practitioners and ambulance services reduce conveyance to emergency care?

BACKGROUND: Emergency services are facing increasing workload pressures, and new models of care are needed. We evaluate the impact of a service development involving a partnership between emergency ambulance crews and general practitioners (GPs) on reducing conveyance rates to the Hospital Emergency Department(ED) . METHODS: The service model was implemented in the West Midlands of England. Call handlers identified patients with needs that could be addressed by a GP using locally agreed criteria. GPs supported the assessment of such patients either at scene or by telephone. Routine data were collected from October 2012 to November 2013, from the ambulance service computer-aided dispatch system. Logistic regression models were used to determine the likelihood for patients being transported to ED. RESULTS: Of 23 395 emergency contacts during the evaluation period, 1903 (8.1 %) patients were triaged to GP supported assessment. Mean age (SD) was 61.8 (27.9) years; 42.9 % were aged 75 years and over. 1221 (64.2%) had face-to-face GP assessment and 682 (35.8%) via telephone. 1500 (78%) of those who received GP support were not transported to hospital. After controlling for confounders, those aged greater than 75 years (OR 0.67; 95% CI 0.52 to 0.86), and females (OR 0.64; 95% CI 0.51 to 0.82) were less likely to be transported, while those who received GP telephone input rather than face-to-face assessment were more likely to be transferred to an ED (OR 2.14; 95% CI 1.69 to 2.72). CONCLUSION: Support of the paramedic service by GPs enabled patients to avoid transfer to an ED, potentially avoiding subsequent hospital admission, reducing costs and improving quality of care for patients that are not in need of hospital services. However, use of services in the days following the call was not assessed, and hence the overall impact and safety requires further evaluation.

The Effects of Adipose-Derived Stem Cells Differentiated Into Endothelial Cells and Osteoblasts on Healing of Critical Size Calvarial Defects.

Delayed vascularization and resultant resorption limits the clinical use of tissue engineered bony constructs. The objective of this study is to develop a strategy to accelerate the neovascularization of tissue-engineered bony constructs using endothelial differentiated adipose-derived stem cells (ASC). The authors harvested ASC from inguinal fat pads of male Lewis rats (n = 5) and induced toward endothelial and osteoblastic lineages. The authors created critical size calvarial defects on male Lewis rats (n = 30) and randomized the animals into 4 groups. For the repair of the defects the authors used hydroxyapatite/poly(lactide-co-glycolide) [HA-PLG] scaffolds in group I, HA-PLG scaffolds seeded with ASC in group II, HA-PLG scaffolds seeded with ASC-derived endothelial cells in group III, and HA-PLG scaffolds seeded with ASC-derived osteoblasts in group IV. The authors evaluated the bone healing histologically and with micro-computed tomography (CT) scans 8 weeks later. Adipose-derived stem cells exhibited the characteristics of endothelial and osteogenic lineages, and attached on HA-PLG scaffolds after differentiation. Micro-CT analysis revealed that highest bone mineral density was in group IV (1.46 ± 0.01 g/cm) followed by groups III (1.43 ± 0.05 g/cm), I (1.42 ± 0.05 g/cm), and II (1.3 ± 0.1 g/cm). Hematoxylin-Eosin and Masson Trichrome staining revealed similar results with the highest bone regeneration in group IV followed by groups II, III, and I. Regenerated bone in group IV also had the highest vascular density, but none of these differences achieved statistical significance (P > 0.05). The ASC-derived endothelial cells and osteoblasts provide a limited increase in calvarial bone healing when combined with HA-PLG scaffolds.