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Background: Ultra-high dose rate proton therapy shows promise in tissue sparing by enhancing therapeutic ratio through the FLASH effect. In radiotherapy, accurate in vivo dosimetry is crucial for quality assurance, but remains challenging for UHDR as existing systems lack spatial and temporal resolution to verify dose and dose rate in complex anatomical regions, especially for PBS proton therapy. Purpose: To develop and evaluate a novel 3D surface dosimetry method for UHDR PBS proton therapy using high-speed imaging of a scintillator array for real-time, high-resolution surface dose monitoring. The spatial, temporal, and dosimetric components are validated via imaging of a QA phantom and comparison against TPS predictions. Methods: A deformable multi-element scintillator array was designed with 7.5mm element pitch and 0.5mm inter-element gap. Scintillation linearity with dose was evaluated with variation in response to increasing imaging and irradiation angles. WED testing evaluated beam attenuation at two energy levels. Scintillation emission was imaged at 1kHz and mesh position was monitored via 2-camera stereovision. System setup was validated using a 3D QA phantom to assess spatial accuracy and guide setup correction. Stereovision properties of array elements guided angular correction and geometric transformation. Kernel-based residual spot fitting derived cumulative dose maps compared to TPS dose profile of 5x5cm UHDR PBS delivery using 3%/2mm gamma analysis. PBS and maximum dose rate maps were calculated. Results: Setup achieved average localization error of 0.62 mm, surpassing typical 1+ mm clinical threshold. Intensity correction based on angular information yielded cumulative spot dose uncertainty of ~1% (5.428mGy). Processed dose map compared to TPS via gamma analysis showed 99.9% passing rate at 3%/2mm. WED of the array measured 1.1mm, minimizing impact on dose distribution., Comment: 17 pages, 9 figures, submitted to Medical Physics

Accurate patient positioning is critical for precise radiotherapy dose delivery, as positioning errors can significantly affect treatment outcomes. This study introduces a novel method for tracking loco-regional tissue deformation through Cherenkov image analysis during fractionated breast cancer radiotherapy. The primary goal was to develop and test an algorithm for Cherenkov-based regional position accuracy quantification, specifically for loco-regional deformations, which lack ideal quantification methods in radiotherapy. Blood vessel detection and segmentation were developed in Cherenkov images using a tissue phantom with incremental movements, and later applied to images from fractionated whole breast radiotherapy in human patients (n=10). A combined rigid and non-rigid registration technique was used to detect inter- and intra-fractional positioning variations. This approach quantified positioning variations in two parts: a global shift from rigid registration and a two-dimensional variation map of loco-regional deformation from non-rigid registration. The methodology was validated using an anthropomorphic chest phantom experiment, where known treatment couch translations and respiratory motion were simulated to assess inter- and intra-fractional uncertainties, yielding an average accuracy of 0.83 mm for couch translations up to 20 mm. Analysis of clinical Cherenkov data from ten breast cancer patients showed an inter-fraction setup variation of 3.7 plus minus 2.4 mm relative to the first fraction and loco-regional deformations (95th percentile) of up to 3.3 plus minus 1.9 mm. This study presents a Cherenkov-based approach to quantify global and local positioning variations, demonstrating feasibility in addressing loco-regional deformations that conventional imaging techniques fail to capture., Comment: 25 pages, 4 figures, 1 table, journal under review

Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during Radiation Therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However until now, no concerted analysis of this biological feature-based tracking was utilized because of the slow speed and accuracy of conventional image processing for feature segmentation. This study demonstrated the first deep learning framework for such an application, achieving video frame rate processing. To address the challenge of limited annotation of these features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet segmentation framework. Subsequently, a small Cherenkov dataset (1,483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on another 19 patients, including subcutaneous veins, scars, and pigmented skin. Average segmentation by the model achieved Dice score of 0.85 and required less than 0.7 milliseconds processing time per instance. The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting., Comment: 9 pages, 7 figures, 1 table, journal under review

Amidst the array of quantum machine learning algorithms, the quantum kernel method has emerged as a focal point, primarily owing to its compatibility with noisy intermediate-scale quantum devices and its promise to achieve quantum advantage. This method operates by nonlinearly transforming data into feature space constructed with quantum states, enabling classification and regression tasks. In this study, we present a novel feature space constructed using Kerr coherent states, which generalize su(2), su(1, 1) coherent states, and squeezed states. Notably, the feature space exhibits constant curvature, comprising both spherical and hyperbolic geometries, depending on the sign of the Kerr parameter. Remarkably, the physical parameters associated with the coherent states, enable control over the curvature of the feature space. Our study employs Kerr kernels derived from encoding data into the phase and amplitude of Kerr coherent states. We analyze various datasets ranging from Moon to breast cancer diagnostics. Our findings demonstrate the robustness of Kerr coherent states, attributed to their flexibility in accommodating different hyperparameters, thereby offering superior performance across noisy datasets and hardware setups.

Purpose: Ultra-fast scintillation imaging has been shown to provide a unique tool for spatio-temporal dosimetry of conventional cyclotron and synchrocyclotron pencil beam scanning (PBS) deliveries, indicating the potential use for characterization of ultra-UHDR PBS proton beams. The goal of this work is to introduce this novel concept and demonstrate its capabilities in recording complex dose rate maps at FLASH-capable proton beam currents, as compared to log-based dose rate calculation, internally developed UHDR beam simulation, and a fast point detector (EDGE diode). Methods: The light response of a scintillator sheet located at isocenter and irradiated by pencil beam scanning proton fields (40-210 nA, 250 MeV) was imaged by an ultra-fast iCMOS camera at 4.5-12 kHz sampling frequency. Image intensity was calibrated to dose with film and the recorded spatio-temporal data was compared to a PPC05 ion chamber, log-based reconstruction, and EDGE diode. Calculation of full-field mean and PBS dose rate maps were used to highlight the importance of high resolution, full-field information in UHDR studies. Results: Camera response was linear with dose (R2 = 0.997) and current (R2 = 0.98) in the range from 2-22 Gy and 40-210 nA, respectively, when compared to ion chamber readings. Planned and delivered spot positions agreed within 0.2+/-0.1 mm and total irradiation time agreed within 0.2+/-0.2 ms when compared with the log files, indicating the high concurrent spatial and temporal resolution. For all deliveries, the PBS dose rate measured at the diode location agreed between the imaging and the diode within 3+/-2% and with the simulation within 5+/-3%. Conclusion: The high linearity and various spatiotemporal metric reporting capabilities confirm the continued use of this camera system for UHDR beam characterization, especially for spatially resolved dose rate information.

The extra-large multiple-input multiple-output (XL-MIMO) architecture has been recognized as a technology for supporting the massive MTC (mMTC), providing very high-data rates in high-user density scenarios. However, the large dimension of the array increases the Rayleigh distance (dRayl), in addition to obstacles and scatters causing spatial non-stationarities and distinct visibility regions (VRs) across the XL array extension. We investigate the random access (RA) problem in crowded XL-MIMO scenarios; the proposed grant-based random access (GB-RA) protocol combining the advantage of non-orthogonal multiple access (NOMA) and strongest user collision resolutions in extra-large arrays (SUCRe-XL) named NOMA-XL can allow access of two or three colliding users in the same XL sub-array (SA) selecting the same pilot sequence. The received signal processing in a SA basis changes the dRayl, enabling the far-field planar wavefront propagation condition, while improving the system performance. The proposed NOMA-XL GB-RA protocol can reduce the number of attempts to access the mMTC network while improving the average sum rate, as the number of SA increases., Comment: 13 pages, 5 figures, 1 table, conference VTC 2023. arXiv admin note: substantial text overlap with arXiv:2303.00539

Massive MIMO is one of the key technologies to support the growth of massive access attempts by devices, such as in massive machine type communication (mMTC). The evolution of antenna array technology brought the recent extra-large scale massive multiple input multiple output (XL-MIMO) systems, seen as a promising technology for providing very high-data rates in high-user density scenarios. Spatial non stationarities and visibility regions (VRs) occur across all huge XL array extension, since its large dimension is of the same order of the distances to the user equipment (UE). We investigate the random access (RA) problem in crowded XL-MIMO scenarios. The proposed nonorthogonal multiple access (NOMA) visible region extra large array (NVR-XL) protocol takes advantage of the power domain NOMA to allow access of two or more users colliding in the same XL sub-array (SA) selecting the same pilot sequence. The NVRXL provides a reduction in the number of attempts to access the network, while improving the average sum-rate, as the number of SA increases., Comment: 4 pages, 5 figures, conference paper

A comparative resource allocation analysis in terms of received bits-per-antenna spectral efficiency (SE) and energy efficiency (EE) in downlink (DL) single-cell massive multiple-input multiple-output (mMIMO) and non-orthogonal multiple access (NOMA) systems considering a BS equipped with many ($M$) antennas, while $K$ devices {operate} with a single-antenna, and {the loading of devices} $\rho = \frac{K}{M}$ ranging in $0<\rho\leq 2$ is carried out under three different \ac{PA} strategies: the inverse of the channel power allocation (PICPA), a modified water-filling ($\Delta$-WF) allocation method, and the equal power allocation (EPA) reference method. Since the two devices per cluster are overlapped in the power domain in the NOMA system, the channel matrix requires transformation to perform the zero-forcing (ZF) precoding adopted in mMIMO. Hence, NOMA operating under many antennas can favor a group of devices with higher array gain, overcoming the mMIMO and operating conveniently in the higher loading range $0.6<\rho<2.0$. In such a scenario, a more realistic and helpful metric consists of {evaluating} the area under SE and EE curves, by measuring the bit-per-antenna and bit-per-antenna-per-watt efficiency, respectively. Our numerical results confirm a superiority of NOMA w.r.t. mMIMO of an order of 3x for the SE-area and 2x for the EE-area metric., Comment: 12 pages, 10 figures, 5, 4 tables

Objective: In this study, a fast imaging technique was developed for the first in vivo Cherenkov emission imaging from an ultra-high dose rate (UHDR) electron beam source at single pulse (360 Hz) submillimeter resolution. Approach: A CMOS camera, gated to the UHDR LINAC, imaged the Cherenkov emission profiles pulse by pulse passively during the irradiation of mice on their limbs and intestinal region. The utility of an intensifier was investigated for its effect on image quality including signal to noise and spatial resolution. Pulse by pulse variability in Cherenkov emission profile were quantified spatially and temporally. Main results: An intensifier improved the emission profile signal to noise ratio from 15 to 280, with reduced spatial resolution. The profile extended beyond of the treatment field due to the lateral scattering of the electrons in tissue and its optical properties. The CMOS camera with an intensifier detected the changes in Cherenkov emission profile during expiration and inspiration of the respiration cycle for the mice to be about 3 mm. Significance: This fast imaging technique can be utilized for in vivo intrafraction monitoring of FLASH patient treatments at single pulse resolution. It can display delivery differences during respiration, and variability in the delivered treatment's surface profile, which may perturb from the intended UHDR treatment more for pencil beam scanning systems. The technique may leverage Cherenkov emission surface profile to gate the treatment delivery via respiratory gating systems under FLASH conditions., Comment: 16 pages, 3 figures

This article extends the combinatorial approach to support the determination of contextuality amidst causal influences. Contextuality is an active field of study in Quantum Cognition, in systems relating to mental phenomena, such as concepts in human memory [Aerts et al., 2013]. In the cognitive field of study, a contemporary challenge facing the determination of whether a phenomenon is contextual has been the identification and management of disturbances [Dzhafarov et al., 2016]. Whether or not said disturbances are identified through the modelling approach, constitute causal influences, or are disregardableas as noise is important, as contextuality cannot be adequately determined in the presence of causal influences [Gleason, 1957]. To address this challenge, we first provide a formalisation of necessary elements of the combinatorial approach within the language of canonical9 causal models. Through this formalisation, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences. Thereafter, we develop a protocol through which these elements may be represented within a cognitive experiment. As human cognition seems rife with causal influences, cognitive modellers may apply the extended combinatorial approach to practically determine the contextuality of cognitive phenomena., Comment: 28 pages, 10 figures, 5 tables

Purpose: A diode EDGE Detector with a newly designed electrometer has been characterized for use in an UHDR electron beam and demonstrated appropriateness for UHDR FLASH radiotherapy dosimetry. Methods: Dose linearity, mean dose rate, and dose per pulse dependencies of the EDGE Detector were quantified and compared with dosimeters including a W1 scintillator detector, radiochromic film, and ionization chamber that were irradiated with a 10 MeV UHDR beam. The dose, dose rate and dose per pulse were controlled via an in-house developed scintillation-based feedback mechanism, repetition rate of the linear accelerator, and source-to-surface distance, respectively. Depth-dose profiles and temporal profiles at individual pulse resolution were compared to the film and scintillation measurements, respectively. The radiation-induced change in response sensitivity was quantified via irradiation of ~5kGy. Results: The EDGE Detector agreed with film measurements in the measured range with varying dose (up to 70 Gy), dose rate (nearly 200 Gy/s), and dose per pulse (up to 0.63 Gy/pulse) on average to within 2%, 5%, and 1%, respectively. The detector also agreed with W1 scintillation detector on average to within 2% for dose per pulse (up to 0.78 Gy/pulse). The EDGE Detector signal was proportional to ion chamber (IC) measured dose, and mean dose rate in the bremsstrahlung tail to within 0.4% and 0.2% respectively. The EDGE Detector measured percent depth dose agreed with film to within 3% and per pulse output agreed with W1 scintillator to within -6% to +5%. Conclusions: The EDGE Detector demonstrated dose linearity, mean dose rate independence, and dose per pulse independence for UHDR electron beams. It can quantify the beam spatially, and temporally at sub millisecond resolution., Comment: 22 Pages, 7 Figures

Background: Use of a linear accelerator in ultra-high dose rate (UHDR) mode can provide a conduit for wider access to UHDR FLASH effects, sparing normal tissue, but care needs to be taken in the use of such systems to ensure errors are minimized. Purpose: Failure Modes and Effects Analysis (FMEA) was carried out in a team that has been involved in converting a LINAC between clinical use and UHDR experimental mode for more than one year, following the proposed methods of TG100. Methods: A team of 9 professionals with extensive experience were polled to outline the process map and workflow for analysis, and developed fault trees for potential errors, as well as failure modes that would results. The team scored the categories of severity magnitude (S), occurrence likelihood (O), and detectability potential (D) in a scale of 1 to 10, so that a risk priority number (RPN=S*O*D) could be assessed for each. Results: A total of 46 potential failure modes were identified, including 5 with RPN>100. These failure modes involved 1) patient set up, 2) gating mechanisms in delivery, and 3) detector in the beam stop mechanism. Identified methods to mitigate errors included 1) use of a checklist post conversion, 2) use of robust radiation detectors, 3) automation of QA and beam consistency checks, and 4) implementation of surface guidance during beam delivery. Conclusions: The FMEA process was considered critically important in this setting of a new use of a LINAC, and the expert team developed a higher level of confidence in the ability to safely move UHDR LINAC use towards expanded research access., Comment: 26 Pages, 6 Figures, 4 Tables

Ultra-high dose rate electron sources require dose rate independent dosimeters and a calibrated dose control system for accurate delivery. In this study, we developed a single-pulse dose monitoring and a real-time dose-based control system for a converted clinical linear accelerator (LINAC). A point scintillator detector was coupled to a gated amplifier and a real-time controller for dose monitoring and feedback control loop. The controller was programmed to integrate dose and measure pulse width of each radiation pulse and gate the LINAC beam when the prescribed dose was delivered. The scintillator was mounted in solid water phantom and placed underneath mice skin for in vivo dose monitoring. Additionally, the scintillator was characterized in terms of its radiation stability, mean dose-rate, and dose per pulse dependence. Dose integration was performed for each radiation pulse and displayed in real-time. The scintillator was shown to be linear with mean dose-rate (40-380 Gy/s) and dose per pulse (0.3-1.3 Gy/Pulse) to within +/- 3%. However, the plastic scintillator was subject to significant radiation damage (16%/kGy) and would need to be calibrated frequently. Pulse-counting control was accurately implemented with direct correspondence between the intended and the actual delivered pulses. The dose-based control was sufficient to gate on any pulse of the LINAC. In-vivo dosimetry monitoring with a 1 cm circular cut-out revealed that a ramp-up of 4-5 pulses was present during which the average dose per pulse was ~0.045 +/- 0.004 Gy/Pulse, whereas after the ramp-up it stabilized at 0.65 +/- 0.01 Gy/Pulse. The tools presented in this study can be used to determine the beam parameter space pertinent to the FLASH effect. Additionally, this study is the first instance of real-time dose-based control for a modified LINAC at ultra-high dose rates., Comment: 30 Pages, 7 Figures

Purpose: Cherenkov imaging is now clinically available to track the course of radiation therapy as a treatment verification tool. The aim of this work was to discover the benefits of always-on Cherenkov images as a novel incident detection and quality improvement system through retrospective review of imaging in our center. Methods: Continuous imaging of all patients was attempted during a 12-month period by automating the acquisition of Cherenkov imaging using an always-on commercial system. Multi-camera systems were installed in two treatment bunkers in the radiation oncology clinic at our center and one bunker in an affiliated satellite clinic. Images were acquired as part of normal treatment procedure and reviewed retrospectively with potential incidents flagged for evaluation by the physician and medical physics teams. Results: In total, 622 patients were imaged as part of this study. In this summary, 9 patients were identified with incidents occurring during their course of treatment that were detected only with Cherenkov imaging. Incidents were found relating to issues during simulation, planning, pre-treatment review, and treatment delivery, however none of the incidents were detected prior to treatment delivery. Primary areas of improvement identified in this study are dose to unintended areas in planning, dose to unintended areas due to positioning, and non-ideal bolus placement during setup. Case studies are presented highlighting the detection of these issues using Cherenkov imaging. Conclusions: All detected events were deemed below the threshold for reporting, but their observation could lead to quality improvement in practice. Perhaps most importantly, the imaging was seamless with no effort required by the radiotherapy team and provided both real-time and permanent records of what was delivered in each fraction., Comment: 22 pages, 1 table, 12 figures

We are surrounded by an ever increasing amount of data that is stored in a variety of databases. In this article we will use a very liberal definition of \EM{database}. Basically any collection of data can be regarded as a database, ranging from the files in a directory on a disk, to ftp and web servers, through to relational or object-oriented databases. The sole reason for storing data in databases is that there is an anticipated need for the stored data at some time in the future. This means that providing smooth access paths by which stored information can be retrieved is at least as important as ensuring integrity of the stored information. In practice, however, providing users with adequate avenues by which to access stored information has received far less attention.

In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block, and only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale CNNs. This is to 1) eliminate the noisy from irrelevant part of a sentence; meanwhile 2) enhance the relevant block representation with both block-wise and inter-block-wise semantically enriched representation. Our method has the advantage of being independent of long sentence context since we only encode the sequential tokens within a block boundary. Experiments on two datasets i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our method. In particular, we achieve the new state-of-the-art performance on the KBP37 dataset; and comparable performance with the state-of-the-art on the SemEval2010 dataset.

Purpose: A Monte Carlo (MC) beam model and its implementation in a clinical treatment planning system (TPS, Varian Eclipse) are presented for a modified ultra-high dose-rate electron FLASH radiotherapy (eFLASH-RT) LINAC. Methods: The gantry head without scattering foils or targets, representative of the LINAC modifications, was modelled in Geant4. The energy spectrum ({\sigma}E) and beam source emittance cone angle ({\theta}cone) were varied to match the calculated and Gafchromic film measured central-axis percent depth dose (PDD) and lateral profiles. Its Eclipse configuration was validated with measured profiles of the open field and nominal fields for clinical applicators. eFLASH-RT plans were MC forward calculated in Geant4 for a mouse brain treatment and compared to a conventional (Conv-RT) plan in Eclipse for a human patient with metastatic renal cell carcinoma. Results: The beam model and its Eclipse configuration agreed best with measurements at {\sigma}E=0.5 MeV and {\theta}cone=3.9+/-0.2 degrees to clinically acceptable accuracy (the absolute average error was within 1.5% for in-water lateral, 3% for in-air lateral, and 2% for PDD). The forward dose calculation showed dose was delivered to the entire mouse brain with adequate conformality. The human patient case demonstrated the planning capability with routine accessories in relatively complex geometry to achieve an acceptable plan (90% of the tumor volume receiving 95% and 90% of the prescribed dose for eFLASH and Conv-RT, respectively). Conclusion: To the best of our knowledge, this is the first functional beam model commissioned in a clinical TPS for eFLASH-RT, enabling planning and evaluation with minimal deviation from Conv-RT workflow. It facilitates the clinical translation as eFLASH-RT and Conv-RT plan quality were comparable for a human patient. The methods can be expanded to model other eFLASH irradiators., Comment: 14 Pages, 6 Figures, 1 Supplementary Material

There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modelling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.

Purpose: In this study, procedures were developed to achieve efficient reversible conversion of a clinical linear accelerator (LINAC) and deliver electron FLASH (eFLASH) or conventional beams to the treatment room isocenter. Material & Methods: The LINAC was converted to deliver eFLASH beam within 20 minutes by retracting the x-ray target from the beam's path, positioning the carousel on an empty port, and selecting 10 MV photon beam energy in the treatment console. Dose per pulse and average dose rate were measured in a solid water phantom at different depths with Gafchromic film and OSLD. A pulse controller counted the pulses via scattered radiation signal and gated the delivery for preset pulse count. A fast photomultiplier tube-based Cherenkov detector measured per pulse beam output at 2 ns sampling rate. After conversion back to clinical mode, conventional beam output, flatness, symmetry, field size and energy were measured for all clinically commissioned energies. Results: Dose per pulse of 0.86 +/- 0.01 Gy (310 +/- 7 Gy/s average dose rate) were achieved at isocenter. The dose from simultaneous irradiation of film and OSLD were within 1%. The PMT showed the LINAC required about 5 pulses before the output stabilized and its long-term stability was within 3% for measurements performed at 3 minutes intervals. The dose, flatness, symmetry, and photon energy were unchanged from baseline and within tolerance (1%, 3%, 2%, and 0.1% respectively) after reverting to conventional beams. Conclusion: 10 MeV FLASH beams were achieved at the isocenter of the treatment room. The beam output was reproducible but requires further investigation of the ramp up time in the first 5 pulses, equivalent to <100 cGy. The eFLASH beam can irradiate both small and large subjects in minimally modified clinical settings and dose rates can be further increased by reducing the source to surface distance., Comment: Manuscript: 21 Pages, 1 Table, 8 Figures; Supplementary Material A: 2 Pages, 2 Figures; Appendix: Film Dosimetry: 2 Pages, 2 Tables

The use of sophisticated machine learning models for critical decision making is faced with a challenge that these models are often applied as a "black-box". This has led to an increased interest in interpretable machine learning, where post hoc interpretation presents a useful mechanism for generating interpretations of complex learning models. In this paper, we propose a novel approach underpinned by an extended framework of Bayesian networks for generating post hoc interpretations of a black-box predictive model. The framework supports extracting a Bayesian network as an approximation of the black-box model for a specific prediction. Compared to the existing post hoc interpretation methods, the contribution of our approach is three-fold. Firstly, the extracted Bayesian network, as a probabilistic graphical model, can provide interpretations about not only what input features but also why these features contributed to a prediction. Secondly, for complex decision problems with many features, a Markov blanket can be generated from the extracted Bayesian network to provide interpretations with a focused view on those input features that directly contributed to a prediction. Thirdly, the extracted Bayesian network enables the identification of four different rules which can inform the decision-maker about the confidence level in a prediction, thus helping the decision-maker assess the reliability of predictions learned by a black-box model. We implemented the proposed approach, applied it in the context of two well-known public datasets and analysed the results, which are made available in an open-source repository.

In this paper, by mapping datasets to a set of non-linear coherent states, the process of encoding inputs in quantum states as a non-linear feature map is re-interpreted. As a result of this fact that the Radial Basis Function is recovered when data is mapped to a complex Hilbert state represented by coherent states, non-linear coherent states can be considered as natural generalisation of associated kernels. By considering the non-linear coherent states of a quantum oscillator with variable mass, we propose a kernel function based on generalized hypergeometric functions, as orthogonal polynomial functions. The suggested kernel is implemented with support vector machine on two well known datasets (make circles, and make moons) and outperforms the baselines, even in the presence of high noise. In addition, we study impact of geometrical properties of feature space, obtaining by non-linear coherent states, on the SVM classification task, by using considering the Fubini-Study metric of associated coherent states.

While spatial dose conformity delivered to a target volume has been pushed to its practical limits with advanced treatment planning and delivery, investigations in novel temporal dose delivery are unfolding new mechanisms. Recent advances in ultra-high dose radiotherapy, abbreviated as FLASH, indicate the potential for reduction in healthy tissue damage while preserving tumor control. FLASH therapy relies on very high dose rate of > 40Gy/sec with sub-second temporal beam modulation, taking a seemingly opposite direction from the conventional paradigm of fractionated therapy. FLASH brings unique challenges to dosimetry, beam control, and verification, as well as complexity of radiobiological effective dose through altered tissue response. In this review, we compare the dosimetric methods capable of operating under high dose rate environments. Due to excellent dose-rate independence, superior spatial (~<1 mm) and temporal (~ns) resolution achievable with Cherenkov and scintillation-based detectors, we show that luminescent detectors have a key role to play in the development of FLASH-RT, as the field rapidly progresses towards clinical adaptation. Additionally, we show that the unique ability of certain luminescence-based methods to provide tumor oxygenation maps in real-time with submillimeter resolution can elucidate the radiobiological mechanisms behind the FLASH effect. In particular, such techniques will be crucial for understanding the role of oxygen in mediating the FLASH effect., Comment: Number of Pages: 26, Figures: 14, Table: 1 Updated Reference List and Table 1 was referred to as Table 2 in the original text. Submitted to Frontiers in Medical Physics and Imaging (Research Topic: Applications of Cherenkov and Radioluminescence Imaging to Medicine and Biology)

This paper uses deformed coherent states, based on a deformed Weyl-Heisenberg algebra that unifies the well-known SU(2), Weyl-Heisenberg, and SU(1,1) groups, through a common parameter. We show that deformed coherent states provide the theoretical foundation of a meta-kernel function, that is a kernel which in turn defines kernel functions. Kernel functions drive developments in the field of machine learning and the meta-kernel function presented in this paper opens new theoretical avenues for the definition and exploration of kernel functions. The meta-kernel function applies associated revolution surfaces as feature spaces identified with non-linear coherent states. An empirical investigation compares the deformed SU(2) and SU(1,1) kernels derived from the meta-kernel which shows performance similar to the Radial Basis kernel, and offers new insights (based on the deformed Weyl-Heisenberg algebra).

This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker's belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker's uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.

This article presents a unified probabilistic framework that allows both rational and irrational decision making to be theoretically investigated and simulated in classical and quantum games. Rational choice theory is a basic component of game theoretic models, which assumes that a decision maker chooses the best action according to their preferences. In this article, we define irrationality as a deviation from a rational choice. Bistable probabilities are proposed as a principled and straight forward means for modeling irrational decision making in games. Bistable variants of classical and quantum Prisoner's Dilemma, Stag Hunt and Chicken are analyzed in order to assess the effect of irrationality on agent utility and Nash equilibria. It was found that up to three Nash equilibria exist for all three classical bistable games and maximal utility was attained when agents were rational. Up to three Nash equilibria exist for all three quantum bistable games, however, utility was shown to increase according to higher levels of agent irrationality.

This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. We applied these algorithms in a real-world medical dataset containing information about patients with cancer, where we learn models that try to predict the type of cancer of the patient, given their set of medical activity records. We explored different algorithms based on neural network architectures using long short term deep neural networks, and random forests. Since there is a growing need to provide decision-makers understandings about the logic of predictions of black boxes, we also explored different techniques that provide interpretations for these classifiers. In one of the techniques, we intercepted some hidden layers of these neural networks and used autoencoders in order to learn what is the representation of the input in the hidden layers. In another, we investigated an interpretable model locally around the random forest's prediction. Results show learning an interpretable model locally around the model's prediction leads to a higher understanding of why the algorithm is making some decision. Use of local and linear model helps identify the features used in prediction of a specific instance or data point. We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well. In addition, the structured deep learning approach using autoencoders provided meaningful prediction insights, which resulted in the identification of nonlinear clusters correspondent to the patients' different types of cancer.

A large number of studies in cognitive science have revealed that probabilistic outcomes of certain human decisions do not agree with the axioms of classical probability theory. The field of Quantum Cognition provides an alternative probabilistic model to explain such paradoxical findings. It posits that cognitive systems have an underlying quantum-like structure, especially in decision-making under uncertainty. In this paper, we hypothesise that relevance judgement, being a multidimensional, cognitive concept, can be used to probe the quantum-like structure for modelling users' cognitive states in information seeking. Extending from an experiment protocol inspired by the Stern-Gerlach experiment in Quantum Physics, we design a crowd-sourced user study to show violation of the Kolmogorovian probability axioms as a proof of the quantum-like structure, and provide a comparison between a quantum probabilistic model and a Bayesian model for predictions of relevance., Comment: To be published at 42nd European Conference on Research in IR, ECIR 2020

This study utilises an experiment famous in quantum physics, the Stern-Gerlach experiment, to inform the structure of an experimental protocol from which a quantum cognitive decision model can be developed. The 'quantumness' of this model is tested by computing a discrete quasi-probabilistic Wigner function. Based on theory from quantum physics, our hypothesis is that the Stern-Gerlach protocol will admit negative values in the Wigner function, thus signalling that the cognitive decision model is quantum. A crowdsourced experiment of two images was used to collect decisions around three questions related to image trustworthiness. The resultant data was used to instantiate the quantum model and compute the Wigner function. Negative values in the Wigner functions of both images were encountered, thus substantiating our hypothesis. Findings also revealed that the quantum cognitive model was a more accurate predictor of decisions when compared to predictions computed using Bayes' rule.

Relevance is an underlying concept in the field of Information Science and Retrieval. It is a cognitive notion consisting of several different criteria or dimensions. Theoretical models of relevance allude to interdependence between these dimensions, where their interaction and fusion leads to the final inference of relevance. We study the interaction between the relevance dimensions using the mathematical framework of Quantum Theory. It is considered a generalised framework to model decision making under uncertainty, involving multiple perspectives and influenced by context. Specifically, we conduct a user study by constructing the cognitive analogue of a famous experiment in Quantum Physics. The data is used to construct a complex-valued vector space model of the user's cognitive state, which is used to explain incompatibility and interference between relevance dimensions. The implications of our findings to inform the design of Information Retrieval systems are also discussed., Comment: Accepted at ACM SIGIR International Conference on the Theory of Information Retrieval 2019

We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics nor on assumptions of the computational resources of the mind.

This article explores how probabilistic programming can be used to simulate quantum correlations in an EPR experimental setting. Probabilistic programs are based on standard probability which cannot produce quantum correlations. In order to address this limitation, a hypergraph formalism was programmed which both expresses the measurement contexts of the EPR experimental design as well as associated constraints. Four contemporary open source probabilistic programming frameworks were used to simulate an EPR experiment in order to shed light on their relative effectiveness from both qualitative and quantitative dimensions. We found that all four probabilistic languages successfully simulated quantum correlations. Detailed analysis revealed that no language was clearly superior across all dimensions, however, the comparison does highlight aspects that can be considered when using probabilistic programs to simulate experiments in quantum physics., Comment: 24 pages, 8 figures, code is available at https://github.com/askoj/bell-ppls

Models of a phenomenon are often developed by examining it under different experimental conditions, or measurement contexts. The resultant probabilistic models assume that the underlying random variables, which define a measurable set of outcomes, can be defined independent of the measurement context. The phenomenon is deemed contextual when this assumption fails. Contextuality is an important issue in quantum physics. However, there has been growing speculation that it manifests outside the quantum realm with human cognition being a particularly prominent area of investigation. This article contributes the foundations of a probabilistic programming language that allows convenient exploration of contextuality in wide range of applications relevant to cognitive science and artificial intelligence. Specific syntax is proposed to allow the specification of "measurement contexts". Each such context delivers a partial model of the phenomenon based on the associated experimental condition described by the measurement context. The probabilistic program is translated into a hypergraph in a modular way. Recent theoretical results from the field of quantum physics show that contextuality can be equated with the possibility of constructing a probabilistic model on the resulting hypergraph. The use of hypergraphs opens the door for a theoretically succinct and efficient computational semantics sensitive to modelling both contextual and non-contextual phenomena. Finally, this article raises awareness of contextuality beyond quantum physics and to contribute formal methods to detect its presence by means of hypergraph semantics., Comment: Accepted for "Theoretical Computer Science"

Ultrafast X-ray imaging provides high resolution information on individual fragile specimens such as aerosols, metastable particles, superfluid quantum systems and live biospecimen, which is inaccessible with conventional imaging techniques. Coherent X-ray diffractive imaging, however, suffers from intrinsic loss of phase, and therefore structure recovery is often complicated and not always uniquely-defined. Here, we introduce the method of in-flight holography, where we use nanoclusters as reference X-ray scatterers in order to encode relative phase information into diffraction patterns of a virus. The resulting hologram contains an unambiguous three-dimensional map of a virus and two nanoclusters with the highest lat- eral resolution so far achieved via single shot X-ray holography. Our approach unlocks the benefits of holography for ultrafast X-ray imaging of nanoscale, non-periodic systems and paves the way to direct observation of complex electron dynamics down to the attosecond time scale.

As computers approach the physical limits of information storable in memory, new methods will be needed to further improve information storage and retrieval. We propose a quantum inspired vector based approach, which offers a contextually dependent mapping from the subsymbolic to the symbolic representations of information. If implemented computationally, this approach would provide exceptionally high density of information storage, without the traditionally required physical increase in storage capacity. The approach is inspired by the structure of human memory and incorporates elements of Gardenfors' Conceptual Space approach and Humphreys et al.'s matrix model of memory., Comment: 8 pages; 2 figures