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Following initial work by JaJa and Ahlswede/Cai, and inspired by a recent renewed surge in interest in deterministic identification via noisy channels, we consider the problem in its generality for memoryless channels with finite output, but arbitrary input alphabets. Such a channel is essentially given by (the closure of) the subset of its output distributions in the probability simplex. Our main findings are that the maximum number of messages thus identifiable scales super-exponentially as $2^{R\,n\log n}$ with the block length $n$, and that the optimal rate $R$ is upper and lower bounded in terms of the covering (aka Minkowski, or Kolmogorov, or entropy) dimension $d$ of the output set: $\frac14 d \leq R \leq d$. Leading up to the general case, we treat the important special case of the so-called Bernoulli channel with input alphabet $[0;1]$ and binary output, which has $d=1$, to gain intuition. Along the way, we show a certain Hypothesis Testing Lemma (generalising an earlier insight of Ahlswede regarding the intersection of typical sets) that implies that for the construction of a deterministic identification code, it is sufficient to ensure pairwise reliable distinguishability of the output distributions. These results are then shown to generalise directly to classical-quantum channels with finite-dimensional output quantum system (but arbitrary input alphabet), and in particular to quantum channels on finite-dimensional quantum systems under the constraint that the identification code can only use tensor product inputs.

Transfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, our method searches for both the multiplets of variables that maximize information flow and those that minimize it. This provides a decomposition of TE into unique, redundant, and synergistic atoms. Our approach enables the quantification of the relative importance of high-order effects compared to pure two-body effects in information transfer between two processes, while also highlighting the processes that contribute to building these high-order effects alongside the driver. We demonstrate the application of our approach in climatology by analyzing data from El Ni\~{n}o and the Southern Oscillation.

Hazard ratios are frequently reported in time-to-event and epidemiological studies to assess treatment effects. In observational studies, the combination of propensity score weights with the Cox proportional hazards model facilitates the estimation of the marginal hazard ratio (MHR). The methods for estimating MHR are analogous to those employed for estimating common causal parameters, such as the average treatment effect. However, MHR estimation in the context of high-dimensional data remain unexplored. This paper seeks to address this gap through a simulation study that consider variable selection methods from causal inference combined with a recently proposed multiply robust approach for MHR estimation. Additionally, a case study utilizing stroke register data is conducted to demonstrate the application of these methods. The results from the simulation study indicate that the double selection covariate selection method is preferable to several other strategies when estimating MHR. Nevertheless, the estimation can be further improved by employing the multiply robust approach to the set of propensity score models obtained during the double selection process.

Diffusion and Poisson flow models have demonstrated remarkable success for a wide range of generative tasks. Nevertheless, their iterative nature results in computationally expensive sampling and the number of function evaluations (NFE) required can be orders of magnitude larger than for single-step methods. Consistency models are a recent class of deep generative models which enable single-step sampling of high quality data without the need for adversarial training. In this paper, we introduce a novel image denoising technique which combines the flexibility afforded in Poisson flow generative models (PFGM)++ with the, high quality, single step sampling of consistency models. The proposed method first learns a trajectory between a noise distribution and the posterior distribution of interest by training PFGM++ in a supervised fashion. These pre-trained PFGM++ are subsequently "distilled" into Poisson flow consistency models (PFCM) via an updated version of consistency distillation. We call this approach posterior sampling Poisson flow consistency models (PS-PFCM). Our results indicate that the added flexibility of tuning the hyperparameter D, the dimensionality of the augmentation variables in PFGM++, allows us to outperform consistency models, a current state-of-the-art diffusion-style model with NFE=1 on clinical low-dose CT images. Notably, PFCM is in itself a novel family of deep generative models and we provide initial results on the CIFAR-10 dataset.

We adopt the integral definition of the fractional Laplace operator and study an optimal control problem on Lipschitz domains that involves a fractional elliptic partial differential equation (PDE) as state equation and a control variable that enters the state equation as a coefficient; pointwise constraints on the control variable are considered as well. We establish the existence of optimal solutions and analyze first and, necessary and sufficient, second order optimality conditions. Regularity estimates for optimal variables are also analyzed. We develop two finite element discretization strategies: a semidiscrete scheme in which the control variable is not discretized, and a fully discrete scheme in which the control variable is discretized with piecewise constant functions. For both schemes, we analyze the convergence properties of discretizations and derive error estimates.

Reproducibility is widely acknowledged as a fundamental principle in scientific research. Currently, the scientific community grapples with numerous challenges associated with reproducibility, often referred to as the ''reproducibility crisis.'' This crisis permeated numerous scientific disciplines. In this study, we examined the factors in scientific practices that might contribute to this lack of reproducibility. Significant focus is placed on the prevalent integration of computation in research, which can sometimes function as a black box in published papers. Our study primarily focuses on highperformance computing (HPC), which presents unique reproducibility challenges. This paper provides a comprehensive review of these concerns and potential solutions. Furthermore, we discuss the critical role of reproducible research in advancing science and identifying persisting issues within the field of HPC.

Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be used to identify good potential grasps. However, due to modeling and sensing inaccuracies, local exploration is often needed to refine such grasps and successfully apply them in the real world. The recently proposed unscented Bayesian optimization technique can make such exploration safer by selecting grasps that are robust to uncertainty in the input space (e.g., inaccuracies in the grasp execution). Extending our previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to find safe grasps in a very efficient way: while by augmenting the search-space to 3D we are able to find better grasps, the collision penalty heuristic allows us to do so without increasing the number of exploration steps.

Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes -- i.e., articulatory-acoustic relation -- is of great importance in assessing speech quality and developing innovative treatment and rehabilitative strategies. This is especially important when evaluating and detecting abnormal articulatory features in patients with speech-related disorders. In this work, we aim to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics. This is achieved through the use of a deep cross-modal translator trained on data from healthy individuals only, which bridges the gap between 4D motion fields obtained from tagged MRI and 2D spectrograms derived from speech acoustic data. The trained translator is used as an anomaly detector, by measuring the spectrogram reconstruction quality on healthy individuals or patients. In particular, the cross-modal translator is likely to yield limited generalization capabilities on patient data, which includes unseen out-of-distribution patterns and demonstrates subpar performance, when compared with healthy individuals.~A one-class SVM is then used to distinguish the spectrograms of healthy individuals from those of patients. To validate our framework, we collected a total of 39 paired tagged MRI and speech waveforms, consisting of data from 36 healthy individuals and 3 tongue cancer patients. We used both 3D convolutional and transformer-based deep translation models, training them on the healthy training set and then applying them to both the healthy and patient testing sets. Our framework demonstrates a capability to detect abnormal patient data, thereby illustrating its potential in enhancing the understanding of the articulatory-acoustic relation for both healthy individuals and patients.

Physics-Informed Neural Networks (PINNs) have emerged as an iconic machine learning approach for solving Partial Differential Equations (PDEs). Although its variants have achieved significant progress, the empirical success of utilising feature mapping from the wider Implicit Neural Representations studies has been substantially neglected. We investigate the training dynamics of PINNs with a feature mapping layer via the limiting Conjugate Kernel and Neural Tangent Kernel, which sheds light on the convergence and generalisation of the model. We also show the inadequacy of commonly used Fourier-based feature mapping in some scenarios and propose the conditional positive definite Radial Basis Function as a better alternative. The empirical results reveal the efficacy of our method in diverse forward and inverse problem sets. This simple technique can be easily implemented in coordinate input networks and benefits the broad PINNs research.

We describe a force-controlled robotic gripper with built-in tactile and 3D perception. We also describe a complete autonomous manipulation pipeline consisting of object detection, segmentation, point cloud processing, force-controlled manipulation, and symbolic (re)-planning. The design emphasizes versatility in terms of applications, manufacturability, use of commercial off-the-shelf parts, and open-source software. We validate the design by characterizing force control (achieving up to 32N, controllable in steps of 0.08N), force measurement, and two manipulation demonstrations: assembly of the Siemens gear assembly problem, and a sensor-based stacking task requiring replanning. These demonstrate robust execution of long sequences of sensor-based manipulation tasks, which makes the resulting platform a solid foundation for researchers in task-and-motion planning, educators, and quick prototyping of household, industrial and warehouse automation tasks.