Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of radiation sensitive normoxic polymer gels for radiotherapy dosimetry

The overall objective of this study was to develop and characterise new normoxic polymer gel formulations for evaluation of complex 3-D treatment volumes for application in radiotherapy dosimetry. Throughout this thesis, the essential characteristics of normoxic polymer gels have been extensively investigated. Studies were performed on the chemical components of the MAGIC gel and an improved formulation was proposed. Various anti-oxidants were studied and different versions of the MAGIC gel with fewer chemicals were developed and named MAGAS and MAGAT gel dosimeters. The ascorbic acid anti-oxidant was found to have a slow oxygen scavenging rate and therefore a delay period between manufacture and irradiation of the MAGAS gel was necessary before the gel became radiation sensitive. Vacuum pumping on the MAGAS gel solution to remove dissolved oxygen was shown to initially increase the R2-dose response and sensitivity of the dosimeter, reducing the time between manufacture and irradiation. Studies of the MAGAS gel for measurement of depth dose showed that MAGAS gel has potential as a clinical radiotherapy dosimetry tool. The radiological properties of MAGIC, MAGAS and MAGAT gels were investigated. Due to their high gelatine and monomer concentration, differences with water were observed for the cross-section ratios for attenuation, energy absorption and collision stopping power coefficient ratios through the therapeutic energy range. It was determined that when using and developing normoxic polymer gels the most important consideration for radiological water equivalence are the mass and relative electron densities. A preliminary study was performed with the hypoxic PAG gel dosimeter combined with tetrakis (hydroxymethyl) phosphonium chloride anti-oxidant to form a normoxic PAG gel dosimeter named PAGAT gel. It was found PAGAT gel compared favourably with previous studies of the hypoxic PAG gel. An extensive study was subsequently undertaken in which PAGAT gel was investigated for a number of essential characteristics. The PAGAT gel formulation showed potential as a normoxic polymer gel for clinical radiotherapy dosimetry, which has a significantly reduced manufacturing time and procedure compared with the hypoxic PAG gel dosimeter. The radiological attenuation properties of the PAGAT and MAGAT gels were investigated as a feasibility study for using x-ray computerised tomography (CT) as an evaluation technique of normoxic polymer gels. CT was shown to have potential as an evaluation tool for measuring the dose response of normoxic polymer gel dosimeters. An investigation was performed on the CT diagnostic dose response of normoxic polymer gels. Normoxic polymer gels were found to have potential for use as a specialised tool in measuring computerised tomography dose index (CTDI) for acceptance testing and quality assurance of CT scanners in diagnostic radiology. These findings provide a significant contribution toward the development and successful implementation of normoxic polymer gel dosimetry to clinical radiotherapy.
An ingestible self-propelling device for intestinal reanimation

Postoperative ileus (POI) is the leading cause of prolonged hospital stay after abdominal surgery and is characterized by a functional paralysis of the digestive tract, leading to symptoms such as constipation, vomiting, and functional obstruction. Current treatments are mainly supportive and inefficacious and yield acute side effects. Although electrical stimulation studies have demonstrated encouraging pacing and entraining of the intestinal slow waves, no devices exist today to enable targeted intestinal reanimation. Here, we developed an ingestible self-propelling device for intestinal reanimation (INSPIRE) capable of restoring peristalsis through luminal electrical stimulation. Optimizing mechanical, material, and electrical design parameters, we validated optimal deployment, intestinal electrical luminal contact, self-propelling capability, safety, and degradation of the device in ex vivo and in vivo swine models. We compared the INSPIRE’s effect on motility in models of normal and depressed motility and chemically induced ileus. Intestinal contraction improved by 44% in anesthetized animals and up to 140% in chemically induced ileus cases. In addition, passage time decreased from, on average, 8.6 days in controls to 2.5 days with the INSPIRE device, demonstrating significant improvement in motility. Luminal electrical stimulation of the intestine via the INSPIRE efficaciously restored peristaltic activity. This noninvasive option offers a promising solution for the treatment of ileus and other motility disorders. An ingestible self-propelling device neurally stimulates intestines to restore peristalsis and alleviate ileus.
Refined Absorption: A New Proof of the Existence Conjecture

The study of combinatorial designs has a rich history spanning nearly two centuries. In a recent breakthrough, the notorious Existence Conjecture for Combinatorial Designs dating back to the 1800s was proved in full by Keevash via the method of randomized algebraic constructions. Subsequently Glock, K\"{u}hn, Lo, and Osthus provided an alternate purely combinatorial proof of the Existence Conjecture via the method of iterative absorption. We introduce a novel method of refined absorption for designs; here as our first application of the method we provide a new alternate proof of the Existence Conjecture (assuming the existence of $K_q^r$-absorbers by Glock, K\"{u}hn, Lo, and Osthus).
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games -- a public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.0029 II interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem.
Weak Scale Supersymmetry Emergent from the String Landscape

Superstring flux compactifications can stabilize all moduli while leading to an enormous number of vacua solutions, each leading to different 4−d laws of physics. While the string landscape provides at present the only plausible explanation for the size of the cosmological constant, it may also predict the form of weak scale supersymmetry which is expected to emerge. Rather general arguments suggest a power-law draw to large soft terms, but these are subject to an anthropic selection of a not-too-large value for the weak scale. The combined selection allows one to compute relative probabilities for the emergence of supersymmetric models from the landscape. Models with weak scale naturalness appear most likely to emerge since they have the largest parameter space on the landscape. For finetuned models such as high-scale SUSY or split SUSY, the required weak scale finetuning shrinks their parameter space to tiny volumes, making them much less likely to appear compared to natural models. Probability distributions for sparticle and Higgs masses from natural models show a preference for Higgs mass mh∼125 GeV, with sparticles typically beyond the present LHC limits, in accord with data. From these considerations, we briefly describe how natural SUSY is expected to be revealed at future LHC upgrades. This article is a contribution to the Special Edition of the journal Entropy, honoring Paul Frampton on his 80th birthday.
Flux Quantization

Flux- and charge-quantization laws for higher gauge fields of Maxwell type -- e.g. the common electromagnetic field (the"A-field") but also the B-, RR-, and C-fields considered in string/M-theory -- specify non-perturbative completions of these fields by encoding their solitonic behaviour and hence by specifying the discrete charges carried by the individual branes (higher-dimensional monopoles or solitons) that source the field fluxes. This article surveys the general (rational-)homotopy theoretic understanding of flux- and charge-quantization via the Chern-Dold character map generalized to the non-linear (self-sourcing) Bianchi identities that appear in higher-dimensional supergravity theories, notably for B&RR-fields in D=10, for the C-field in D=11 supergravity, and for the B-field on fivebrane worldvolumes.
Anisotropic Dynamical Planckian Scaling of the Quasi-Kagome Kondo Lattice CeRhSn with Strong Valence Fluctuation

The anisotropic electronic structure and Drude response of the quasi-kagome Kondo lattice CeRhSn have been investigated using polarized optical conductivity measurements and first-principles density-functional-theory calculations. Along the hexagonal $a$- and $c$-axes, the peaks in the middle-infrared region originating from the hybridization between conduction bands and Ce~$4f$ states appear even at room temperature, indicating strong valence fluctuation. Renormalized Drude structures observed at the photon energy below 100~meV, on the other hand, obey the dynamical Planckian scaling below 80~K for $E \parallel a$ and below 200~K for $E \parallel c$, suggesting the non-Fermi liquid behavior due to the quantum criticality. These results indicate that the coexistence of the valence fluctuation and quantum criticality is a consequence of anisotropic magnetic fluctuations based on the Ce quasi-kagome lattice structure
Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes

Improving the speed and fidelity of quantum logic gates is essential to reach quantum advantage with future quantum computers. However, fast logic gates lead to increased leakage errors in superconducting quantum processors based on qubits with low anharmonicity, such as transmons. To reduce leakage errors, we propose and experimentally demonstrate two new analytical methods, Fourier ansatz spectrum tuning derivative removal by adiabatic gate (FAST DRAG) and higher-derivative (HD) DRAG, both of which enable shaping single-qubit control pulses in the frequency domain to achieve stronger suppression of leakage transitions compared to previously demonstrated pulse shapes. Using the new methods to suppress the $ef$-transition of a transmon qubit with an anharmonicity of -212 MHz, we implement $R_X(\pi/2)$-gates with a leakage error below $3.0 \times 10^{-5}$ down to a gate duration of 6.25 ns, which corresponds to a 20-fold reduction in leakage compared to a conventional Cosine DRAG pulse. Employing the FAST DRAG method, we further achieve an error per gate of $(1.56 \pm 0.07)\times 10^{-4}$ at a 7.9-ns gate duration, outperforming conventional pulse shapes both in terms of error and gate speed. Furthermore, we study error-amplifying measurements for the characterization of temporal microwave control pulse distortions, and demonstrate that non-Markovian coherent errors caused by such distortions may be a significant source of error for sub-10-ns single-qubit gates unless corrected using predistortion.
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning

Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learning for decomposing joint action spaces instead of directly conducting a collective search in the entire action-observation space. However, they often face challenges obtaining specific joint action sequences to reach successful states in long-horizon tasks. To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios. IIE employs a transformer model to imagine how the agents reach a critical state that can influence each other's transition functions. Then, we initialize the environment at this state using a simulator before the exploration phase. We formulate the imagination as a sequence modeling problem, where the states, observations, prompts, actions, and rewards are predicted autoregressively. The prompt consists of timestep-to-go, return-to-go, influence value, and one-shot demonstration, specifying the desired state and trajectory as well as guiding the action generation. By initializing agents at the critical states, IIE significantly increases the likelihood of discovering potentially important under-explored regions. Despite its simplicity, empirical results demonstrate that our method outperforms multi-agent exploration baselines on the StarCraft Multi-Agent Challenge (SMAC) and SMACv2 environments. Particularly, IIE shows improved performance in the sparse-reward SMAC tasks and produces more effective curricula over the initialized states than other generative methods, such as CVAE-GAN and diffusion models.
Wavelet Scattering Transform for Bioacustics: Application to Watkins Marine Mammal Sound Database

Marine mammal communication is a complex field, hindered by the diversity of vocalizations and environmental factors. The Watkins Marine Mammal Sound Database (WMMD) is an extensive labeled dataset used in machine learning applications. However, the methods for data preparation, preprocessing, and classification found in the literature are quite disparate. This study first focuses on a brief review of the state-of-the-art benchmarks on the dataset, with an emphasis on clarifying data preparation and preprocessing methods. Subsequently, we propose the application of the Wavelet Scattering Transform (WST) in place of standard methods based on the Short-Time Fourier Transform (STFT). The study also tackles a classification task using an ad-hoc deep architecture with residual layers. We outperform the existing classification architecture by $6\%$ in accuracy using WST and $8\%$ using Mel spectrogram preprocessing, effectively reducing by half the number of misclassified samples, and reaching a top accuracy of $96\%$.
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness

In this paper, we present an empirical study on image recognition fairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We experimentally demonstrate that classes are not equal and the fairness issue is prevalent for image classification models across various datasets, network architectures, and model capacities. Moreover, several intriguing properties of fairness are identified. First, the unfairness lies in problematic representation rather than classifier bias. Second, with the proposed concept of Model Prediction Bias, we investigate the origins of problematic representation during optimization. Our findings reveal that models tend to exhibit greater prediction biases for classes that are more challenging to recognize. It means that more other classes will be confused with harder classes. Then the False Positives (FPs) will dominate the learning in optimization, thus leading to their poor accuracy. Further, we conclude that data augmentation and representation learning algorithms improve overall performance by promoting fairness to some degree in image classification. The Code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.
Dynamic Explanation Selection Towards Successful User-Decision Support with Explainable AI

This paper addresses the problem of how to select explanations for XAI (Explainable AI)-based Intelligent Decision Support Systems (IDSSs). IDSSs have shown promise in improving user decisions through XAI-generated explanations along with AI predictions. As the development of XAI made various explanations available, we believe that IDSSs can be greatly improved if they can strategically select explanations that guide users to better decisions. This paper proposes X-Selector, a method for dynamically selecting explanations. X-Selector aims to guide users to better decisions by predicting the impact of different combinations of explanations on user decisions. We compared X-Selector's performance with two naive strategies (all possible explanations and explanations only for the most likely prediction) and two baselines (no explanation and no AI support). The results suggest the potential of X-Selector to guide users to recommended decisions and improve the performance when AI accuracy is high and a challenge when it is low.
Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards

Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that dynamics-aware reward functions improve the sample efficiency of PbRL by an order of magnitude. In our experiments we iterate between: (1) learning a dynamics-aware state-action representation (z^{sa}) via a self-supervised temporal consistency task, and (2) bootstrapping the preference-based reward function from (z^{sa}), which results in faster policy learning and better final policy performance. For example, on quadruped-walk, walker-walk, and cheetah-run, with 50 preference labels we achieve the same performance as existing approaches with 500 preference labels, and we recover 83\% and 66\% of ground truth reward policy performance versus only 38\% and 21\%. The performance gains demonstrate the benefits of explicitly learning a dynamics-aware reward model. Repo: \texttt{https://github.com/apple/ml-reed}.
Autoencoder-based General Purpose Representation Learning for Customer Embedding

In recent years, exploiting the domain-specific underlying structure of data and its generative factors for representation learning has shown success in various use-case agnostic applications. However, the diversity and complexity of tabular data have made it challenging to represent these structures in a latent space through multi-dimensional vectors. We design an autoencoder-based framework for building general purpose embeddings, we assess the performance of different autoencoder architectures, and show simpler models outperform complex ones in embedding highly complex tabular data. We apply our framework to produce plug-and-play, rich, and anonymized embeddings representing AWS customers for usage in any model, saving up to 45% of development time, and observe significant improvements in downstream models. Moreover, we propose a significant improvement to the calculation of reconstruction loss for multi-layer contractive autoencoders (CAE) by calculating the Jacobian of the entire encoder leading to a 15% improvement in reconstruction quality when compared to a stacked CAE.
Retrieval-based Full-length Wikipedia Generation for Emergent Events

In today's fast-paced world, the growing demand to quickly generate comprehensive and accurate Wikipedia documents for emerging events is both crucial and challenging. However, previous efforts in Wikipedia generation have often fallen short of meeting real-world requirements. Some approaches focus solely on generating segments of a complete Wikipedia document, while others overlook the importance of faithfulness in generation or fail to consider the influence of the pre-training corpus. In this paper, we simulate a real-world scenario where structured full-length Wikipedia documents are generated for emergent events using input retrieved from web sources. To ensure that Large Language Models (LLMs) are not trained on corpora related to recently occurred events, we select events that have taken place recently and introduce a new benchmark Wiki-GenBen, which consists of 309 events paired with their corresponding retrieved web pages for generating evidence. Additionally, we design a comprehensive set of systematic evaluation metrics and baseline methods, to evaluate the capability of LLMs in generating factual full-length Wikipedia documents. The data and code are open-sourced at WikiGenBench.
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning

People often give instructions whose meaning is ambiguous without further context, expecting that their actions or goals will disambiguate their intentions. How can we build assistive agents that follow such instructions in a flexible, context-sensitive manner? This paper introduces cooperative language-guided inverse plan search (CLIPS), a Bayesian agent architecture for pragmatic instruction following and goal assistance. Our agent assists a human by modeling them as a cooperative planner who communicates joint plans to the assistant, then performs multimodal Bayesian inference over the human's goal from actions and language, using large language models (LLMs) to evaluate the likelihood of an instruction given a hypothesized plan. Given this posterior, our assistant acts to minimize expected goal achievement cost, enabling it to pragmatically follow ambiguous instructions and provide effective assistance even when uncertain about the goal. We evaluate these capabilities in two cooperative planning domains (Doors, Keys&Gems and VirtualHome), finding that CLIPS significantly outperforms GPT-4V, LLM-based literal instruction following and unimodal inverse planning in both accuracy and helpfulness, while closely matching the inferences and assistive judgments provided by human raters.