Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Function Market Maker (DFMM)

Decentralised automated market makers (AMMs) have gained significant attention recently. We propose an adaptive and automated Dynamic Function Market Maker (DFMM) that addresses challenges in this space. Our DFMM protocol includes a data aggregator and an order routing mechanism. It synchronises price-sensitive market information, asserting the principle of one price, and ensuring market efficiency. The data aggregator includes a virtual order book, asserting efficient asset pricing by staying synchronised with information from external venues, including competitors. The protocol's rebalancing and order routing method optimises inventory risk through arbitrageurs, who are more likely to assist DFMM, enhancing protocol stability. DFMM incorporates protective buffers with non-linear derivative instruments to manage risk and mitigate losses caused by market volatility. The protocol employs an algorithmic accounting-asset, connecting all pools and resolving the issue of segregated pools and risk transfer. The settlement process is entirely protocol-driven, maximising risk management efficiency, and eliminating subjective market risk assessments. In essence, DFMM offers a fully automated, decentralised, and robust solution for automated market making. It aims to provide long-term viability and stability in an asset class that demands robustness.
Continuous Time Evidential Distributions for Irregular Time Series

Prevalent in many real-world settings such as healthcare, irregular time series are challenging to formulate predictions from. It is difficult to infer the value of a feature at any given time when observations are sporadic, as it could take on a range of values depending on when it was last observed. To characterize this uncertainty we present EDICT, a strategy that learns an evidential distribution over irregular time series in continuous time. This distribution enables well-calibrated and flexible inference of partially observed features at any time of interest, while expanding uncertainty temporally for sparse, irregular observations. We demonstrate that EDICT attains competitive performance on challenging time series classification tasks and enabling uncertainty-guided inference when encountering noisy data.
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space

Counterfactual Explanations (CEs) are an important tool in Algorithmic Recourse for addressing two questions: 1. What are the crucial factors that led to an automated prediction/decision? 2. How can these factors be changed to achieve a more favorable outcome from a user's perspective? Thus, guiding the user's interaction with AI systems by proposing easy-to-understand explanations and easy-to-attain feasible changes is essential for the trustworthy adoption and long-term acceptance of AI systems. In the literature, various methods have been proposed to generate CEs, and different quality measures have been suggested to evaluate these methods. However, the generation of CEs is usually computationally expensive, and the resulting suggestions are unrealistic and thus non-actionable. In this paper, we introduce a new method to generate CEs for a pre-trained binary classifier by first shaping the latent space of an autoencoder to be a mixture of Gaussian distributions. CEs are then generated in latent space by linear interpolation between the query sample and the centroid of the target class. We show that our method maintains the characteristics of the input sample during the counterfactual search. In various experiments, we show that the proposed method is competitive based on different quality measures on image and tabular datasets -- efficiently returns results that are closer to the original data manifold compared to three state-of-the-art methods, which are essential for realistic high-dimensional machine learning applications.
Graphical model inference with unobserved variable via latent tree aggregation

Graphical network inference is used in many fields such as genomics or ecology to infer the conditional independence structure between variables, from measurements of gene expression or species abundances for instance. In many practical cases, it is likely that not all variables involved in the network have been actually observed. The observed samples are therefore drawn from a distribution where some unobserved variables have been marginalized out. In this context, the inference of underlying networks is compromised because marginalization in graphical models yields locally dense structures, which challenges the assumption that biological networks are sparse. In this article we present a procedure for inferring Gaussian graphical models in the presence of unobserved variables. We provide a model based on spanning trees and the EM algorithm which accounts both for the influence of unobserved variables and the low density of the network. We treat the graph structure and the unobserved nodes as latent variables and compute posterior probabilities of edge appearance. We also propose several model selection criteria to estimate the number of missing nodes, and compare our method to existing graph inference techniques on simulations and on a flow cytometry data illustration.
Interface Modification for Energy Level Alignment and Charge Extraction in CsPbI3 Perovskite Solar Cells

In perovskite solar cells (PSCs) energy level alignment and charge extraction at the interfaces are the essential factors directly affecting the device performance. In this work, we present a modified interface between all-inorganic CsPbI3 perovskite and its hole-selective contact (spiro-OMeTAD), realized by the dipole molecule trioctylphosphine oxide (TOPO), to align the energy levels. On a passivated perovskite film, with n-octylammonium iodide (OAI), we created an upward surface band-bending at the interface by TOPO treatment. This improved interface by the dipole molecule induces a better energy level alignment and enhances the charge extraction of holes from the perovskite layer to the hole transport material. Consequently, a Voc of 1.2 V and a high-power conversion efficiency (PCE) of over 19% were achieved for inorganic CsPbI3 perovskite solar cells. Further, to demonstrate the effect of the TOPO dipole molecule, we present a layer-by-layer charge extraction study by a transient surface photovoltage (trSPV) technique accomplished by a charge transport simulation.
Well-posed evolution of field theories with anisotropic scaling: the Lifshitz scalar field in a black hole space-time

Partial differential equations exhibiting an anisotropic scaling between space and time — such as those of Hořava-Lifshitz gravity — have a dispersive nature. They contain higher-order spatial derivatives, but remain second order in time. This is inconvenient for performing long-time numerical evolutions, as standard explicit schemes fail to maintain convergence unless the time step is chosen to be very small. In this work, we develop an implicit evolution scheme that does not suffer from this drawback, and which is stable and second-order accurate. As a proof of concept, we study the numerical evolution of a Lifshitz scalar field on top of a spherically symmetric black hole space-time. We explore the evolution of a static pulse and an (approximately) ingoing wave-packet for different strengths of the Lorentz-breaking terms, accounting also for the effect of the angular momentum eigenvalue and the resulting effective centrifugal barrier. Our results indicate that the dispersive terms produce a cascade of modes that accumulate in the region in between the Killing and universal horizons, indicating a possible instability of the latter.
A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies

The robust balancing capability of humanoid robots against disturbances has been considered as one of the crucial requirements for their practical mobility in real-world environments. In particular, many studies have been devoted to the efficient implementation of the three balance strategies, inspired by human balance strategies involving ankle, hip, and stepping strategies, to endow humanoid robots with human-level balancing capability. In this paper, a robust balance control framework for humanoid robots is proposed. Firstly, a novel Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum (CAM) damping control over the time horizon of MPC to improve the balancing performance. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through extensive simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated, particularly in the presence of disturbances, compared to a state-of-the-art Quadratic Programming (QP)-based CP controller that employs the ankle, hip, and stepping strategies. The supplementary video is available at https://youtu.be/CrD75UbYzdc
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping

Gradient clipping is a commonly used technique to stabilize the training process of neural networks. A growing body of studies has shown that gradient clipping is a promising technique for dealing with the heavy-tailed behavior that emerged in stochastic optimization as well. While gradient clipping is significant, its theoretical guarantees are scarce. Most theoretical guarantees only provide an in-expectation analysis and only focus on optimization performance. In this paper, we provide high probability analysis in the non-convex setting and derive the optimization bound and the generalization bound simultaneously for popular stochastic optimization algorithms with gradient clipping, including stochastic gradient descent and its variants of momentum and adaptive stepsizes. With the gradient clipping, we study a heavy-tailed assumption that the gradients only have bounded $\alpha$-th moments for some $\alpha \in (1, 2]$, which is much weaker than the standard bounded second-moment assumption. Overall, our study provides a relatively complete picture for the theoretical guarantee of stochastic optimization algorithms with clipping.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications.
Buoyant flow of $\text{CO}_{2}$ through and around a semi-permeable layer of finite extent

The buoyancy- and capillary-driven counter-current flow of $\text{CO}_{2}$ and brine through and around a semi-permeable layer is studied both numerically and theoretically. The continuities of the capillary pressure and the total flux at the interface between the permeable matrix and layer control the $\text{CO}_{2}$ saturation discontinuity at the interface and the balance between the buoyant and capillary diffusion fluxes on each side of the interface. This interface process is first studied in a one-dimensional (1-D) vertical column geometry using the concept of extended capillary pressure and a graphical representation of the continuity conditions in the ( $S_{L}$ , $S_{U}$ ) plane, where $S_{L}$ and $S_{U}$ are the lower and upper saturation traces at the interface, respectively. In two dimensions, we heuristically extend the two-phase gravity current model to the case where the current is bounded by a semi-permeable layer. Consequently, the current is not saturated with $\text{CO}_{2}$ , and its saturation and shape are derived from the flux and capillary pressure continuity conditions at the interface. This simplified model, which depends on $\text{CO}_{2}$ saturation only, is compared to fine grid simulations in the capillary-free and gravity-dominant cases. A good agreement is obtained in the second case; the current geometrical characteristics are accurately described. In the capillary-free case, we demonstrate that the local total velocity, which is, on average, zero because the flow is counter-current, must be considered in the total flux at the interface to obtain the same level of agreement.
Predicting Code Coverage without Execution

Code coverage is a widely used metric for quantifying the extent to which program elements, such as statements or branches, are executed during testing. Calculating code coverage is resource-intensive, requiring code building and execution with additional overhead for the instrumentation. Furthermore, computing coverage of any snippet of code requires the whole program context. Using Machine Learning to amortize this expensive process could lower the cost of code coverage by requiring only the source code context, and the task of code coverage prediction can be a novel benchmark for judging the ability of models to understand code. We propose a novel benchmark task called Code Coverage Prediction for Large Language Models (LLMs). We formalize this task to evaluate the capability of LLMs in understanding code execution by determining which lines of a method are executed by a given test case and inputs. We curate and release a dataset we call COVERAGEEVAL by executing tests and code from the HumanEval dataset and collecting code coverage information. We report the performance of four state-of-the-art LLMs used for code-related tasks, including OpenAI's GPT-4 and GPT-3.5-Turbo, Google's BARD, and Anthropic's Claude, on the Code Coverage Prediction task. Finally, we argue that code coverage as a metric and pre-training data source are valuable for overall LLM performance on software engineering tasks.
COMPARISON OF PISTON AND TANGENTIAL PNEUMATIC FLEXIBLE SHAFT COUPLINGS IN TERMS OF HIGH FLEXIBILITY

The optimal tuning of mechanical systems in terms of torsional vibration magnitude is a very important function in flexible shaft couplings. Therefore, a flexible coupling with suitable dynamic properties, particularly dynamic torsional stiffness, has to be carefully chosen for each specific application. The current trend in the field of flexible shaft couplings, and the most noticeable in the automotive industry, is the development and utilization of highly flexible couplings, which means flexible couplings with a very low value of relative torsional stiffness. The aim of this article is to introduce a new type of flexible shaft coupling: a piston pneumatic flexible shaft coupling. This coupling was developed to improve the properties of pneumatic flexible couplings, especially the maximum angle of twist, in order to create a highly flexible pneumatic coupling. For illustration purposes, the piston pneumatic coupling is compared with the tangential pneumatic flexible shaft coupling of Type 3-1/110T-C in terms of high flexibility characteristics, whereby the characteristic dimensions of both couplings are the same. Given that the piston pneumatic coupling has not been manufactured to date, only a computational model of this coupling was used. The results show that the design of the piston pneumatic flexible shaft coupling combines the advantages of a highly flexible and pneumatic shaft coupling.
Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimization

The stochastic compositional minimax problem has attracted a surge of attention in recent years since it covers many emerging machine learning models. Meanwhile, due to the emergence of distributed data, optimizing this kind of problem under the decentralized setting becomes badly needed. However, the compositional structure in the loss function brings unique challenges to designing efficient decentralized optimization algorithms. In particular, our study shows that the standard gossip communication strategy cannot achieve linear speedup for decentralized compositional minimax problems due to the large consensus error about the inner-level function. To address this issue, we developed a novel decentralized stochastic compositional gradient descent ascent with momentum algorithm to reduce the consensus error in the inner-level function. As such, our theoretical results demonstrate that it is able to achieve linear speedup with respect to the number of workers. We believe this novel algorithmic design could benefit the development of decentralized compositional optimization. Finally, we applied our methods to the imbalanced classification problem. The extensive experimental results provide evidence for the effectiveness of our algorithm.
Learning Autonomous Ultrasound via Latent Task Representation and Robotic Skills Adaptation

As medical ultrasound is becoming a prevailing examination approach nowadays, robotic ultrasound systems can facilitate the scanning process and prevent professional sonographers from repetitive and tedious work. Despite the recent progress, it is still a challenge to enable robots to autonomously accomplish the ultrasound examination, which is largely due to the lack of a proper task representation method, and also an adaptation approach to generalize learned skills across different patients. To solve these problems, we propose the latent task representation and the robotic skills adaptation for autonomous ultrasound in this paper. During the offline stage, the multimodal ultrasound skills are merged and encapsulated into a low-dimensional probability model through a fully self-supervised framework, which takes clinically demonstrated ultrasound images, probe orientations, and contact forces into account. During the online stage, the probability model will select and evaluate the optimal prediction. For unstable singularities, the adaptive optimizer fine-tunes them to near and stable predictions in high-confidence regions. Experimental results show that the proposed approach can generate complex ultrasound strategies for diverse populations and achieve significantly better quantitative results than our previous method.
Settling the Sample Complexity of Online Reinforcement Learning

A central issue lying at the heart of online reinforcement learning (RL) is data efficiency. While a number of recent works achieved asymptotically minimal regret in online RL, the optimality of these results is only guaranteed in a ``large-sample'' regime, imposing enormous burn-in cost in order for their algorithms to operate optimally. How to achieve minimax-optimal regret without incurring any burn-in cost has been an open problem in RL theory. We settle this problem for the context of finite-horizon inhomogeneous Markov decision processes. Specifically, we prove that a modified version of Monotonic Value Propagation (MVP), a model-based algorithm proposed by \cite{zhang2020reinforcement}, achieves a regret on the order of (modulo log factors) \begin{equation*} \min\big\{ \sqrt{SAH^3K}, \,HK \big\}, \end{equation*} where $S$ is the number of states, $A$ is the number of actions, $H$ is the planning horizon, and $K$ is the total number of episodes. This regret matches the minimax lower bound for the entire range of sample size $K\geq 1$, essentially eliminating any burn-in requirement. It also translates to a PAC sample complexity (i.e., the number of episodes needed to yield $\varepsilon$-accuracy) of $\frac{SAH^3}{\varepsilon^2}$ up to log factor, which is minimax-optimal for the full $\varepsilon$-range. Further, we extend our theory to unveil the influences of problem-dependent quantities like the optimal value/cost and certain variances. The key technical innovation lies in the development of a new regret decomposition strategy and a novel analysis paradigm to decouple complicated statistical dependency -- a long-standing challenge facing the analysis of online RL in the sample-hungry regime.
Leptohadronic multi-messenger modeling of 324 gamma-ray blazars

The origin of the diffuse astrophysical neutrino flux observed by the IceCube experiment is still under debate. Multiple associations have been reported between high-energy neutrino events and individual blazars, such as the source TXS 0506+056, which are active galaxies with relativistic jets pointing toward Earth. From a theoretical perspective, the properties of these sources as neutrino emitters are not yet well understood. By systematically modeling the effect of cosmic-ray protons on the multiwavelength data from the largest sample of bright gamma-ray blazars to date, we expect to learn about the multi-messenger nature of the active galaxy population as a whole, as well as the relationship between neutrino production and the multiwavelength spectrum of these sources. We predict the emitted multiwavelength and neutrino spectrum using a self-consistent numerical radiation model applied individually to each source in the sample. We then study the properties of the full population and identify empirical relations. We focus on public multiwavelength data from the radio to the gamma-ray bands from a sample of 324 blazars detected by the Fermi Large Area Telescope (LAT), most of which are flat-spectrum radio quasars (FSRQs). This amounts to 34 of all FSRQs in the latest Fermi catalog. We demonstrate that the optical and gigaelectronvolt gamma-ray broadband features are generally well described by electron emission, which helps for the location of the emission region relative to the central black hole to be constrained. For 33 of the blazars in our sample, a description of the observed X-ray spectrum benefits from an additional component from proton interactions, in agreement with recent studies of individual IceCube candidate blazars. We show that, on average, blazars that are brighter in gigaelectronvolt gamma rays have a higher neutrino production efficiency but a lower best-fit baryonic loading. The predicted neutrino luminosity shows a positive correlation both with the observed flux of gigaelectronvolt gamma rays and with the predicted flux of megaelectronvolt gamma rays. We also estimate the diffuse neutrino flux from gamma-ray blazars by extrapolating the result to the Fermi population, and we show that it may be at the level of sim 20 of the diffuse neutrino flux observed by IceCube, in agreement with current limits from stacking analyses. We discuss the implications of our results for future neutrino searches and suggest promising sources for potential detections.