ethicalabs/Research-Intent-Collab huggingface dataset:
Hardware-Aware Quantum Error Correction and Architectural Designs for Quantum Computers

Quantum Error Correction (QEC) is often idealized as hardware-agnostic, yet its practical implementations must be tailored to the underlying system. The nuclear and electron spins of donor atoms in silicon constitute a physical platform that offers long coherence times, a well-characterized Hamiltonian, precise quantum control, and a readout mechanism that uses the electron as an ancilla. In this thesis, we reconsider the fundamentals of QEC, from gates to architecture, showing how they can be tailored to donor spins, while exploring different architectural paradigms using donor spins. Motivated by the hardware’s native Hamiltonian and ancilla-assisted readout, we design Single-Step Parity Check (SSPC) gates that perform parity checks directly, bypassing 1- and 2-qubit decompositions and improving circuit error tolerance. We developed a numerical workflow using experimental process matrices to evaluate these operations and quantified cumulative losses in a two-body parity-check circuit by tracking 1- and 2-qubit gate error rates on a two-phosphorus, one-electron silicon chip. GRAPE-optimized pulses were generated for both standard and SSPC-based circuits on the same platform, showing SSPC gates outperform and offer hardware-aware QEC implementations. We next explore antimony, a high-spin donor, as a candidate for photonic quantum computing. With a nuclear spin of 7/2, antimony has eight energy levels which, when coupled to a microwave cavity, can emit a single-photon across eight modes. This supports third quantization, where (near-)deterministic photon entanglement is achieved by spreading single photons across many modes and sending each mode to a party, creating entanglement without photon–photon interactions or probabilistic gates. We proposed an experiment for the simplest realization of third quantization, where an antimony donor produces two independent 8-mode photons, distributed to parties, creates a Bell state between 56 random pairs with 87.5% efficiency. We then explored high-dimensional fusion operations for fusing qudit cluster states. Focusing on Type-II fusion, a central component of measurement-based quantum computing, we derived the most efficient high-dimensional protocol known to date — a 723-fold improvement over the best previously known protocol in the case of d = 5. To further enhance success probability of fusion, we introduced extra-dimensional corrections framework. We unified these insights to design two approaches for qudit graph state generation using antimony donors. One is for the direct generation of arbitrary graph states from coupled antimony donors, where two antimony atoms share a single electron, forming 128-dimensional Hilbert space, while the other focuses on the generation of linear graph states from a single antimony donor, followed by high-dimensional fusion operations to construct graph states in arbitrary shapes. We compared the two schemes in terms of their capabilities and potential for scalability.
Hydrologic Modelling of an Agricultural Drained Micro-Watershed: Performance Analysis of Coupled Surface Water/Groundwater Models

The objective of this study is to quantitatively assess the impact of subsurface drainage and soil properties on the hydrological behaviour of a headwater micro-watershed located in the Bras d’Henri watershed, Quebec (Canada). The studied 2.4-km² micro-watershed is characterized by intensive livestock production supported by forages and annual crops such as corn grain or soybeans. It is one of Agriculture and Agri-Food Canada’s WEBs watersheds (Water Evaluation of Beneficial management practices). Hydrometeorological monitoring has shown that soil properties and subsurface drainage could negatively affect the expected behaviour of beneficial management practices at the watershed scale. Therefore there is a need to understand the influence of these properties on hydrology and one way to study this problem is to set up a physically-based hydrological modelling investigation. Specifically, this project focuses on evaluating the ability of two or three coupled hydrological models (surface flow/subsurface flow) to simulate flows at the micro-watershed outlet and water table depth fluctuations while quantifying the surface and subsurface contributions to flows. The models under consideration are: CATHY (Camporese et al., 2010), DRAINMOD (Skaggs, 1978) and PESTDRAIN (Branger et al. 2009).
The Path to a Modular and Standards-based Digital Health Ecosystem

Software engineering for digital health applications entails several challenges, including heterogeneous data acquisition, data standardization, software reuse, security, and privacy considerations. We explore these challenges and how our Stanford Spezi ecosystem addresses these challenges by providing a modular and standards-based open-source digital health ecosystem. Spezi enables developers to select and integrate modules according to their needs and facilitates an open-source community to democratize access to building digital health innovations.
Low Cost Semi-Autonomous Agricultural Robots In Pakistan-Vision Based Navigation Scalable methodology for wheat harvesting

Robots have revolutionized our way of life in recent years.One of the domains that has not yet completely benefited from the robotic automation is the agricultural sector. Agricultural Robotics should complement humans in the arduous tasks during different sub-domains of this sector. Extensive research in Agricultural Robotics has been carried out in Japan, USA, Australia and Germany focusing mainly on the heavy agricultural machinery. Pakistan is an agricultural rich country and its economy and food security are closely tied with agriculture in general and wheat in particular. However, agricultural research in Pakistan is still carried out using the conventional methodologies. This paper is an attempt to trigger the research in this modern domain so that we can benefit from cost effective and resource efficient autonomous agricultural methodologies. This paper focuses on a scalable low cost semi-autonomous technique for wheat harvest which primarily focuses on the farmers with small land holdings. The main focus will be on the vision part of the navigation system deployed by the proposed robot.
The Use of Digital Phenotyping in Mental Health Services: Opportunities, Ethical and Legal Issues

The widespread use of smartphones, mobile devices, wearable technology, and the increase in time online has provided the opportunity to collect data about users continuously. Several sectors such as health, economy, and entertainment have benefitted from the digital traces left by users due to the tight interactions on digital platforms. Today, it is likely to determine and predict users' moods, behavioral patterns, habits, and personality traits with the use of digital traces that are processed using artificial intelligence techniques. Such use of digital data offers new opportunities for mental health services. Today with this method, it is possible to obtain simultaneous data on the course of the psychological disorder and create a complete and more holistic picture of the disorder by accessing data that can not be obtained from self-report assessment techniques. This new approach, which is called digital phenotyping, can improve the objectivity in diagnosis. Studies have proven digital phenotyping’s potential to determine disorders' recurrence risk and make psychometric predictions. The studies have indicated the promising future of digital phenotyping in mental health services since the initial discussions by Jain et al. in 2015. It is reported that digital phenotyping can be used to diagnose and follow certain mental health disorders such as depression, anxiety, and schizophrenia at an early stage. However, ethical concerns such as privacy, autonomy, data security, and data confidentiality are among the critical issues surrounding the use of digital phenotyping. This paper includes essential information about the digital phenotyping method, discussions about the practical, legal and ethical concerns regarding the use of digital phenotyping in mental health services, and suggestions for future research. Keywords: Digital phenotyping, mental health, smartphone, digital data, mental health services
The Juridification of Uncertainty: Observations on the Ambivalence of the Precautionary Principle within the EU and the WTO

Abstract                Few legal concepts have achieved the notoriety of the precautionary principle. Praised by some, disparaged by others, the principle is deeply ambivalent and infinitely malleable. The principle has found its way from national (German) law to transnational law, and from statute book to high politics. This chapter explores the precautionary principle in the context of risk regulation, against a backdrop of integrating markets, and within a framework of multi-leveled, overlapping governance. It highlights the ambiguities inherent in the principle, both in terms of legal status and substantive application, and the tensions to which it gives rise in the legitimation of power.
Phase transition in quantum tunneling and exact statistical mechanics for a model of parametrized double-well potential

A model for one-dimensional bistable systems characterized by a deformable double-well energy landscape, is introduced in order to investigate the effect of shape deformability on the order of phase transition in quantum tunneling, and on the quasi-exact integrability of the classical statistical mechanics of these systems. The deformable double-well energy landscape is modelled by a parametrized double-well potential possessesing two fixed degenerate minima and a constant barrier height, but a tunable shape of its walls which affects the confinement of the two wells. It is found that unlike bistable models involving the standard $\phi^4$-field model for which the transition in quantum tunneling is predicted to be strictly of second order, a parametrization of the double-well potential also favors a first-order transition occurring above a universal critical value of the shape deformability parameter. The partition function of the model is constructed within the framework of the transfer-integral formalism, with emphasis on low-lying eigenstates of the transfer-integral operator. A criteria for quasi-exact integrability of the partition function is formulated, in terms of the condition for possible existence of exact eigenstates of the transfer-integral operator. The quasi-exact solvability condition is obtained analytically and from this, some exact eigenstates are derived at several temperatures. The exact probability densities obtained from the analytical expressions of the groundstate wavefunctions at different temperatures, are found to be in excellent agreement with the probability density obtained from numerical simulations of the Langevin equation.
Directions for improving the regulatory framework for the use of discretionary powers by law enforcement agencies in administrative legal relations

The article notes that limiting the discretionary powers of law enforcement agencies through the implementation of legislative provisions which would detail and set the limits for the application of these powers is seen as one of the primary tasks for these institutions to comply with the rule of law and is considered to be the main leitmotif of the process of improving the regulatory framework for the use of discretionary powers by law enforcement agencies in administrative legal relations.  The emphasis is placed on the fact that recently, in the activities of law enforcement agencies, including administrative law enforcement, there have been such negative trends as concealment of offences, corruption, bribery, and dishonest behaviour of officials. Such a situation, which is the result of sometimes unlimited use of discretionary powers by these entities, creates a huge public outcry, reduces the level of public confidence in the law enforcement system of the State as a whole, and destabilises society, which is especially dangerous in wartime. It is noted that the primary task in the process of solving the described problems and minimising the adverse effects of decisions made by law enforcement officials in the course of administrative discretion is to develop a targeted legislative policy aimed at optimising the limits of discretionary powers by establishing the necessary legal framework for their exercise.  It is also emphasised that in order to implement these initiatives, it is necessary to increase the effectiveness of measures to improve the rulemaking technique, i.e., the set of technical and legal means, methods, techniques and methods by which a draft legal act is created. The paper proposes that when developing and adopting new acts, it is mandatory to introduce the most complete and accurate definition of the terms (concepts) used, especially those involving the use of discretionary powers. It is concluded that it is advisable to provide for the level of bodies (officials) which are granted the relevant discretionary powers in the regulations.
Beta-delayed neutron spectra for application in reactor technology, nuclear physics and astrophysics

Abstract Recent developments in s-delayed neutron (DN) spectroscopy are reviewed, and the importance of DN energy spectra for various problems in reactor physics, nuclear physics and astrophysics is discussed.
Polynomial-time computing over quadratic maps I: sampling in real algebraic sets

Given a quadratic map Q : K^n -> K^k defined over a computable subring D of a real closed field K, and a polynomial p(Y_1,...,Y_k) of degree d, we consider the zero set Z=Z(p(Q(X)),K^n) of the polynomial p(Q(X_1,...,X_n)). We present a procedure that computes, in (dn)^O(k) arithmetic operations in D, a set S of (real univariate representations of) sampling points in K^n that intersects nontrivially each connected component of Z. As soon as k=o(n), this is faster than the standard methods that all have exponential dependence on n in the complexity. In particular, our procedure is polynomial-time for constant k. In contrast, the best previously known procedure (due to A.Barvinok) is only capable of deciding in n^O(k^2) operations the nonemptiness (rather than constructing sampling points) of the set Z in the case of p(Y)=sum_i Y_i^2 and homogeneous Q.   A by-product of our procedure is a bound (dn)^O(k) on the number of connected components of Z.   The procedure consists of exact symbolic computations in D and outputs vectors of algebraic numbers. It involves extending K by infinitesimals and subsequent limit computation by a novel procedure that utilizes knowledge of an explicit isomorphism between real algebraic sets.
Combining Envelope Methodology and Aster Models for Variance Reduction in Life History Analyses

Precise estimation of expected Darwinian fitness, the expected lifetime number of offspring of organism, is a central component of life history analysis. The aster model serves as a defensible statistical model for distributions of Darwinian fitness. The aster model is equipped to incorporate the major life stages an organism travels through which separately may effect Darwinian fitness. Envelope methodology reduces asymptotic variability by establishing a link between unknown parameters of interest and the asymptotic covariance matrices of their estimators. It is known both theoretically and in applications that incorporation of envelope methodology reduces asymptotic variability. We develop an envelope framework, including a new envelope estimator, that is appropriate for aster analyses. The level of precision provided from our methods allows researchers to draw stronger conclusions about the driving forces of Darwinian fitness from their life history analyses than they could with the aster model alone. Our methods are illustrated on a simulated dataset and a life history analysis of \emph{Mimulus guttatus} flowers is provided. Useful variance reduction is obtained in both analyses.
Mitigating Cold Start Problem in Serverless Computing with Function Fusion

As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.
Synergising learning sciences, learning analytics, and educational technologies

The adoption of educational technologies in modern educational systems has significantly advanced the field of learning sciences. This shift, particularly evident within digital learning environments, has enriched pedagogical strategies and redefined educational evaluation methodologies by leveraging sophisticated developments in learning analytics. Despite these advancements, a notable gap persists in effectively applying learning theories within digital environments and in the design of learning analytics. This shortfall partly stems from the ongoing development of empirical evidence supporting these theories and a prevalent reliance on software engineering and data science perspectives, which may not fully integrate learning theory insights. To this end, this thesis addresses this gap by proposing two triadic relationships among learning theories, educational technologies, and learning analytics. The overarching aim is to leverage these relationships to enhance learning understanding and learning optimisation. First, I leverage the dyadic relationship between learning theory and educational technologies to influence the development of the third actor of the triad, learning analytics, to enhance learning understanding. I demonstrate the application of this relationship through two approaches using two authentic educational platforms with real-life course data. The first approach, LA-exam, uses an e-exam platform and Self-Regulated Learning (SRL) theory to develop analytics for e-exams on two levels: student level and item level. The second approach, LA-sourcing, uses a learnersourcing platform and SRL theory to develop analytics about student tactics and strategies when engaging with the platform activities. Second, I leverage the dyadic relationship between learning theory and learning analytics to inform the design choices of the third actor of the triad, educational technologies, to enhance learning optimisation. I demonstrate the application of this relationship through two approaches that report the results of randomised controlled experiments conducted on a learnersourcing platform. The first approach, ET-create, uses a set of learning analytics and SRL theory to inform the design choices of SRL scaffolding strategies for content creation. The second approach, ET-review, uses a set of learning analytics and SRL and scripting theories to inform the design choices of scaffolding strategies for peer review.
Dynamic memory to alleviate catastrophic forgetting in continuous learning settings

In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance. Scanner manufacturer, reconstruction kernel, dose, other protocol specific settings or administering of contrast agents are examples that influence image content independent of the scanned biology. Such domain and task shifts limit the applicability of machine learning algorithms in the clinical routine by rendering models obsolete over time. Here, we address the problem of data shifts in a continuous learning scenario by adapting a model to unseen variations in the source domain while counteracting catastrophic forgetting effects. Our method uses a dynamic memory to facilitate rehearsal of a diverse training data subset to mitigate forgetting. We evaluated our approach on routine clinical CT data obtained with two different scanner protocols and synthetic classification tasks. Experiments show that dynamic memory counters catastrophic forgetting in a setting with multiple data shifts without the necessity for explicit knowledge about when these shifts occur.
Integrating histopathology and genomic data: a comparative study of fusion methods for breast cancer survival prediction

Abstract                   Accurate breast cancer survival prediction using multi-modal data is vital for enhancing clinical decisions. This study evaluates deep learning based fusion strategies, early, intermediate, late, and a hybrid approach, to integrate histopathology images and genomic data for one year survival prediction. We developed a robust evaluation framework, employing tailored deep learning architectures and metrics including accuracy, precision, recall, F1 score, and AUC. Model performance was validated using Kaplan–Meier curves and log-rank tests, with SHAP-based feature importance analysis enhancing interpretability. Results highlight the strengths and limitations of each fusion strategy, offering insights into optimal multi-modal learning approaches for breast cancer prognosis. Our findings underscore the importance of selecting task specific fusion methods, providing a reproducible, interpretable framework to advance survival prediction. All code and configurations are publicly available.