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The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT. This review comprehensively covers synthetic CT generation based on CBCT and proton-based studies. Some of the commonly employed architectures explored are convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models. Evaluation metrics including mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) consistently demonstrate the comparability of sCT images with gold-standard planning CTs (pCT), indicating their potential to improve treatment precision and patient outcomes. Challenges such as field-of-view (FOV) disparities and integration into clinical workflows are discussed, along with recommendations for future research and standardization efforts. In general, the findings underscore the promising role of sCT-based approaches in personalized treatment planning and adaptive radiation therapy, with potential implications for improved oncology treatment delivery and patient care., Comment: 21 pages, 14 Figures, Accepted in the IEEE Transactions on Radiation and Plasma Medical Sciences

The ability to physically move qubits within a register allows the design of hardware-specific error correction codes which can achieve fault-tolerance while respecting other constraints. In particular, recent advancements have demonstrated the shuttling of electron and hole spin qubits through a quantum dot array with high fidelity. Exploiting this, we design an error correction architecture, consisting merely of two parallel quantum dot arrays, an experimentally validated architecture compatible with classical wiring and control constraints. We develop a suite of heuristic methods for compiling any stabilizer error-correcting code's syndrome-extraction circuit to run with a minimal number of shuttling operations. In simulation, these heuristics show that fault tolerance can be achieved on several contemporary quantum error-correcting codes requiring only modestly-optimistic noise parameters. Furthermore, we demonstrate how constant column-weight qLDPC codes can be compiled in a provably minimal number of shuttles that scales constantly with code size using Shor-style syndrome extraction. In addition, we provide a proof of the NP hardness of minimizing the number of shuttle operations for codes not in that class., Comment: 21 pages, 19 figures

In this paper, we introduce a novel authentication scheme for satellite nodes based on quantum entanglement and measurement noise profiles. Our approach leverages the unique noise characteristics exhibited by each satellite's quantum optical communication system to create a distinctive "quantum noise fingerprint." This fingerprint is used for node authentication within a satellite constellation, offering a quantum-safe alternative to traditional cryptographic methods. The proposed scheme consists of a training phase, where each satellite engages in a training exercise with its neighbors to compile noise profiles, and an online authentication phase, where these profiles are used for real-time authentication. Our method addresses the inherent challenges of implementing cryptographic-based schemes in space, such as key management and distribution, by exploiting the fundamental properties of quantum mechanics and the unavoidable imperfections in quantum systems. This approach enhances the security and reliability of satellite communication networks, providing a robust solution to the authentication challenges in satellite constellations. We validated and tested several hypotheses for this approach using IBM System One quantum computers.

Given a stochastic process with inputs and outputs, how might its behaviour be related to pursuit of a goal? We model this using 'transducers', objects that capture only the external behaviour of a system and not its internal state. A companion paper summarises our results for cognitive scientists; the current paper gives formal definitions and proofs. To formalise the concept of a system that behaves as if it were pursuing a goal, we consider what happens when a transducer (a 'policy') is coupled to another transducer that comes equipped with a success condition (a 'teleo-environment'). An optimal policy is identified with a transducer that behaves as if it were perfectly rational in the pursuit of a goal; our framework also allows us to model constrained rationality. Optimal policies obey a version of Bellman's principle: a policy that's optimal in one time step will again be optimal in the next time step, but with respect to a different teleo-environment (obtained from the original one by a modified version of Bayesian filtering). This property sometimes also applies to the bounded-rational case; we give a sufficient condition. A policy is deterministic if and only if there exists a teleo-environment for which it is uniquely optimal among the set of all policies; we relate this to classical representation theorems from decision theory. This result need not hold in the bounded-rational case; we give an example related to the absent-minded driver problem. The formalism is defined using coinduction, following the style proposed by Czajka., Comment: This is the companion paper to "Formalising the intentional stance 1: attributing goals and beliefs to stochastic processes" (uploaded as version 2 of arXiv:2405.16490). The other paper is an overview aimed at cognitive scientists while this paper gives full mathematical details. 50 pages, no figures

Many studies have examined social determinants of health (SDoH) factors independently, overlooking their interconnected and intersectional nature. Our study takes a multifactorial approach to construct a neighborhood level measure of SDoH and explores how neighborhood residency impacts care received by endometrial cancer patients in Massachusetts. We used a Bayesian multivariate Bernoulli mixture model to create and characterize neighborhood SDoH (NSDoH) profiles using the 2015-2019 American Community Survey at the census tract level (n=1478), incorporating 18 variables across four domains: housing conditions and resources, economic security, educational attainment, and social and community context. We linked these profiles to Massachusetts Cancer Registry data to estimate the odds of receiving optimal care for endometrial cancer using Bayesian multivariate logistic regression. The model identified eight NSDoH profiles. Profiles 1 and 2 accounted for 27% and 25% of census tracts, respectively. Profile 1 featured neighborhoods with high homeownership, above median incomes, and high education, while Profile 2 showed higher probabilities of limited English proficiency, renters, lower education, and working class jobs. After adjusting for sociodemographic and clinical characteristics, we found no statistically significant association between NSDoH profiles and receipt of optimal care. However, compared to patients in NSDoH Profile 1, those in Profile 2 had lower odds of receiving optimal care, OR = 0.77, 95% CI (0.56, 1.07). Our results demonstrate the interconnected and multidimensional nature of NSDoH, underscoring the importance of modeling them accordingly. This study also highlights the need for targeted interventions at the neighborhood level to address underlying drivers of health disparities, ensure equitable healthcare delivery, and foster better outcomes for all patients., Comment: I am submitting this article for publication at BMC Public Health journal. The article has 31 pages including supplemental materials

In August of 2024, 495 hackers generated evaluations in an open-ended bug bounty targeting the Open Language Model (OLMo) from The Allen Institute for AI. A vendor panel staffed by representatives of OLMo's safety program adjudicated changes to OLMo's documentation and awarded cash bounties to participants who successfully demonstrated a need for public disclosure clarifying the intent, capacities, and hazards of model deployment. This paper presents a collection of lessons learned, illustrative of flaw reporting best practices intended to reduce the likelihood of incidents and produce safer large language models (LLMs). These include best practices for safety reporting processes, their artifacts, and safety program staffing., Comment: 8 pages, 5 figures

As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents - harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices., Comment: 8 pages, 0 figures

Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this paper, we analyze the value of a new tool to address this challenge: "personhood credentials" (PHCs), digital credentials that empower users to demonstrate that they are real people -- not AIs -- to online services, without disclosing any personal information. Such credentials can be issued by a range of trusted institutions -- governments or otherwise. A PHC system, according to our definition, could be local or global, and does not need to be biometrics-based. Two trends in AI contribute to the urgency of the challenge: AI's increasing indistinguishability from people online (i.e., lifelike content and avatars, agentic activity), and AI's increasing scalability (i.e., cost-effectiveness, accessibility). Drawing on a long history of research into anonymous credentials and "proof-of-personhood" systems, personhood credentials give people a way to signal their trustworthiness on online platforms, and offer service providers new tools for reducing misuse by bad actors. In contrast, existing countermeasures to automated deception -- such as CAPTCHAs -- are inadequate against sophisticated AI, while stringent identity verification solutions are insufficiently private for many use-cases. After surveying the benefits of personhood credentials, we also examine deployment risks and design challenges. We conclude with actionable next steps for policymakers, technologists, and standards bodies to consider in consultation with the public., Comment: 63 pages, 7 figures, 5 tables; minor additions to acknowledgments and wording changes for clarity; corrected typo; updated email address reference for author

BlueMUSE is a blue-optimised, medium spectral resolution, panoramic integral field spectrograph under development for the Very Large Telescope (VLT). With an optimised transmission down to 350 nm, spectral resolution of R$\sim$3500 on average across the wavelength range, and a large FoV (1 arcmin$^2$), BlueMUSE will open up a new range of galactic and extragalactic science cases facilitated by its specific capabilities. The BlueMUSE consortium includes 9 institutes located in 7 countries and is led by the Centre de Recherche Astrophysique de Lyon (CRAL). The BlueMUSE project development is currently in Phase A, with an expected first light at the VLT in 2031. We introduce here the Top Level Requirements (TLRs) derived from the main science cases, and then present an overview of the BlueMUSE system and its subsystems fulfilling these TLRs. We specifically emphasize the tradeoffs that are made and the key distinctions compared to the MUSE instrument, upon which the system architecture is built., Comment: 20 pages, 10 figures, proceedings of the SPIE astronomical telescopes and instrumentation conference, Yokohama, 16-21 June

Centaurs, distinguished by their volatile-rich compositions, play a pivotal role in understanding the formation and evolution of the early solar system, as they represent remnants of the primordial material that populated the outer regions. Stellar occultations offer a means to investigate their physical properties, including shape, rotational state, or the potential presence of satellites and rings. This work aims to conduct a detailed study of the centaur (54598) Bienor through stellar occultations and rotational light curves from photometric data collected during recent years. We successfully predicted three stellar occultations by Bienor, which were observed from Japan, Eastern Europe, and the USA. In addition, we organized observational campaigns from Spain to obtain rotational light curves. At the same time, we develop software to generate synthetic light curves from three-dimensional shape models, enabling us to validate the outcomes through computer simulations. We resolve Bienor's projected ellipse for December 26, 2022, determine a prograde sense of rotation, and confirm an asymmetric rotational light curve. We also retrieve the axes of its triaxial ellipsoid shape as a = (127 $\pm$ 5) km, b = (55 $\pm$ 4) km, and c = (45 $\pm$ 4) km. Moreover, we refine the rotation period to 9.1736 $\pm$ 0.0002 hours and determine a geometric albedo of (6.5 $\pm$ 0.5) %, higher than previously determined by other methods. Finally, by comparing our findings with previous results and simulated rotational light curves, we analyze whether an irregular or contact-binary shape, the presence of an additional element such as a satellite, or significant albedo variations on Bienor's surface, may be present.

This article presents a formalism inspired by Dennett's notion of the intentional stance. Whereas Dennett's treatment of these concepts is informal, we aim to provide a more formal analogue. We introduce a framework based on stochastic processes with inputs and outputs, in which we can talk precisely about *interpreting* systems as having *normative-epistemic states*, which combine belief-like and desire-like features. Our framework is based on optimality but nevertheless allows us to model some forms of bounded cognition. One might expect that the systems that can be described in normative-epistemic terms would be some special subset of all systems, but we show that this is not the case: every system admits a (possibly trivial) normative-epistemic interpretation, and those that can be *uniquely specified* by a normative-epistemic description are exactly the deterministic ones. Finally, we show that there is a suitable notion of Bayesian updating for normative-epistemic states, which we call *value-laden filtering*, since it involves both normative and epistemic elements. For unbounded cognition it is always permissible to attribute beliefs that update in this way. This is not always the case for bounded cognition, but we give a sufficient condition under which it is. This paper gives an overview of our framework aimed at cognitive scientists, with a formal mathematical treatment given in a companion paper., Comment: The previous version of this document included the content of the companion paper, "Formalising the intentional stance 2: a coinductive approach". The paper has now been split into two, this one (which is an overview aimed at cognitive scientists) and the companion (which contains full mathematical detail). 16 pages, one figure with two subfigures

As Low Earth Orbit (LEO) satellite mega constellations continue to be deployed for satellite internet and recent successful experiments in satellite-based quantum entanglement distribution emerge, a natural question arises: How should we coordinate transmissions and design scalable scheduling policies for a quantum satellite internet? In this work, we consider the problem of transmission scheduling in quantum satellite networks subject to resource constraints at the satellites and ground stations. We show that the most general problem of assigning satellites to ground station pairs for entanglement distribution is NP-hard. We then propose four heuristic algorithms and evaluate their performance for Starlink mega constellation under various amount of resources and placements of the ground stations. We find that the maximum number of receivers necessary per ground station grows very slowly with the total number of deployed ground stations. Our proposed algorithms, leveraging optimal weighted b-matching and the global greedy heuristic, outperform others in entanglement distribution rate, entanglement fidelity, and handover cost metrics. While we develop these scheduling algorithms, we have also designed a software system to simulate, visualize, and evaluate satellite mega-constellations for entanglement distribution.

We consider the problem of reconstructing an undirected graph $G$ on $n$ vertices given multiple random noisy subgraphs or "traces". Specifically, a trace is generated by sampling each vertex with probability $p_v$, then taking the resulting induced subgraph on the sampled vertices, and then adding noise in the form of either (a) deleting each edge in the subgraph with probability $1-p_e$, or (b) deleting each edge with probability $f_e$ and transforming a non-edge into an edge with probability $f_e$. We show that, under mild assumptions on $p_v$, $p_e$ and $f_e$, if $G$ is selected uniformly at random, then $O(p_e^{-1} p_v^{-2} \log n)$ or $O((f_e-1/2)^{-2} p_v^{-2} \log n)$ traces suffice to reconstruct $G$ with high probability. In contrast, if $G$ is arbitrary, then $\exp(\Omega(n))$ traces are necessary even when $p_v=1, p_e=1/2$., Comment: 6 pages, to appear in ISIT 2024

Recent clinical xenotransplantation and human decedent studies demonstrate that clinical hyperacute rejection of genetically engineered porcine organs can be reliably avoided but that antibody mediated rejection continues to limit graft survival. We previously identified porcine glycans and proteins which are immunogenic after cardiac xenotransplantation in nonhuman primates, but the clinical immune response to antigens present in glycan depleted triple knockout (TKO) donor pigs is poorly understood. In this study we use fluorescence barcoded HEK cells and HEK cell lines expressing porcine glycans (Gal and SDa) or proteins (CD9, CD46, CD59, PROCR and ANXA2) to screen antibody reactivity in human serum from 160 swine veterinarians, a serum source with potential occupational immune challenge from porcine tissues and pathogens. High levels of anti-Gal IgM were present in all samples and lower levels of anti-SDa IgM were present in 41% of samples. IgM binding to porcine proteins, primarily CD9 and CD46, previously identified as immunogenic in pig to non-human primate cardiac xenograft recipients, was detected in 28 of the 160 swine veterinarian samples. These results suggest that barcoded HEK cell lines expressing porcine protein antigens can be useful for screening human patient serum. A comprehensive analysis of sera from clinical xenotransplant recipients to define a panel of commonly immunogenic porcine antigens will likely be necessary to establish an array of porcine non-Gal antigens for effective monitoring of patient immune responses and allow earlier therapies to reverse antibody mediated rejection., Comment: 13 pages, 2 Tables, 3 Figures

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.

Dimension reduction techniques are among the most essential analytical tools in the analysis of high-dimensional data. Generalized principal component analysis (PCA) is an extension to standard PCA that has been widely used to identify low-dimensional features in high-dimensional discrete data, such as binary, multi-category and count data. For microbiome count data in particular, the multinomial PCA is a natural counterpart of the standard PCA. However, this technique fails to account for the excessive number of zero values, which is frequently observed in microbiome count data. To allow for sparsity, zero-inflated multivariate distributions can be used. We propose a zero-inflated probabilistic PCA model for latent factor analysis. The proposed model is a fully Bayesian factor analysis technique that is appropriate for microbiome count data analysis. In addition, we use the mean-field-type variational family to approximate the marginal likelihood and develop a classification variational approximation algorithm to fit the model. We demonstrate the efficiency of our procedure for predictions based on the latent factors and the model parameters through simulation experiments, showcasing its superiority over competing methods. This efficiency is further illustrated with two real microbiome count datasets. The method is implemented in R., Comment: 2 figures, 3 tables

South Africa is currently facing a critical situation in its power generation landscape, which is plagued by frequent power outages and the need to move from fossil fuels to renewable energy sources. This period emphasizes the importance of having firm-dispatchable power to balance out the intermittent nature of wind and solar energy sources. The paper proposes to repurpose old coal-fired power plants to generate firm-dispatchable energy in line with the principles of a Just Transition. Eskom's coal plants are approaching the end of their economic life, and their declining energy availability factor is becoming a challenge in meeting the country's energy needs. The study suggests that a comprehensive strategy that integrates wind, solar, and firm-dispatchable power can be cost-effective and reliable compared to the traditional coal-based approach or the nuclear alternative. The study emphasizes the necessity of a 25-year plan that would invest in flexible and modular dispatchable generation. It also highlights the strategic location of this generating capacity, including repurposing decommissioned coal plant sites. The proposed model integrates private investment, adheres to established best practices, and emphasizes adaptability to changing demand dynamics. The study provides a roadmap for enabling firm-dispatchable capacity for South Africa's energy transition, emphasizing economic prudence, environmental sustainability, and alignment with the principles of the Just Transition program.

The maximum coverage problem is to select $k$ sets from a collection of sets such that the cardinality of the union of the selected sets is maximized. We consider $(1-1/e-\epsilon)$-approximation algorithms for this NP-hard problem in three standard data stream models. 1. {\em Dynamic Model.} The stream consists of a sequence of sets being inserted and deleted. Our multi-pass algorithm uses $\epsilon^{-2} k \cdot \text{polylog}(n,m)$ space. The best previous result (Assadi and Khanna, SODA 2018) used $(n +\epsilon^{-4} k) \text{polylog}(n,m)$ space. While both algorithms use $O(\epsilon^{-1} \log n)$ passes, our analysis shows that when $\epsilon$ is a constant, it is possible to reduce the number of passes by a $1/\log \log n$ factor without incurring additional space. 2. {\em Random Order Model.} In this model, there are no deletions and the sets forming the instance are uniformly randomly permuted to form the input stream. We show that a single pass and $k \text{polylog}(n,m)$ space suffices for arbitrary small constant $\epsilon$. The best previous result, by Warneke et al.~(ESA 2023), used $k^2 \text{polylog}(n,m)$ space. 3. {\em Insert-Only Model.} Lastly, our results, along with numerous previous results, use a sub-sampling technique introduced by McGregor and Vu (ICDT 2017) to sparsify the input instance. We explain how this technique and others used in the paper can be implemented such that the amortized update time of our algorithm is polylogarithmic. This also implies an improvement of the state-of-the-art insert only algorithms in terms of the update time: $\text{polylog}(m,n)$ update time suffices whereas the best previous result by Jaud et al.~(SEA 2023) required update time that was linear in $k$.

Many countries have commenced a transition from fossil fuel-based electricity generation systems to sustainable systems based on wind and solar generation. It is often noted that the least cost approach would involve a massive scale-up in the building of variable renewables, supported by battery storage and gas peaking plants. The required backup should be firm-dispatchable generation rather than peaking power. The wind and solar generation aspects for this system are clearly defined and understood, however, the term firm-dispatchable power is not defined and the specific requirements are poorly understood. This study seeks to define firm-dispatchable power in this context and its requirement in the sustainable generation system. The study compares 100% renewable generation scenarios from South Africa, Texas, and the UK to demonstrate the requirement for this firm-dispatchable generation. The results indicate that firm-dispatchable generation must be available to replace the renewable generation completely. The required installed capacity for this firm-dispatchable generation does not vary with the distinct demand profiles of the different locations or their comparative renewable generation profiles. It also does not change significantly with the use of energy storage. The usage for this firm-dispatchable generation will vary due to the comparative economics of its use, but the requirement for its installation does not change.