Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameterizing the Angular Distribution of Emission: A Model for TOF-PET Low-Count Reconstruction

—Low-count reconstruction remains a challenge for Positron Emission Tomography (PET) even with the recent progress in time-of-ﬂight (TOF) resolution. In this context, the bias between the acquired histogram, consisting of low values or zeros, and the expected histogram, obtained from the forward projector, is propagated to the image, resulting in a biased reconstruction. This situation could be exacerbated with ﬁner resolution of the TOF information, which further sparsiﬁes the acquired histogram. We propose a new approach to circumvent this limitation of the classical reconstruction model. It consists of extending the description of the reconstruction scheme to also explicitly include the projection domain by Parameterizing the Angular Distribution of Emission (PADE). This parametrization has greater degrees of freedom than the log-likelihood model, which can not be harnessed in classical circumstances. We hypothesize that with ultra-fast TOF this new approach would not only be viable for low-count reconstruction, but also more adequate than the classical reconstruction model. As a proof of concept, an implementation of this approach is compared to the log-likelihood model by investigating two-dimensional simulations of a hot spots phantom. The proposed model achieves similar contrast recovery coefﬁcients as MLEM except for the smallest structures where the low-count nature of the simulations makes it difﬁcult to draw conclusions. However, this new model seems to converge toward less noisy solutions than MLEM. These results suggest that the PADE approach has potential for low-count reconstruction with ultra-fast TOF.
PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms

Path planning is a key component in mobile robotics. A wide range of path planning algorithms exist, but few attempts have been made to benchmark the algorithms holistically or unify their interface. Moreover, with the recent advances in deep neural networks, there is an urgent need to facilitate the development and benchmarking of such learning-based planning algorithms. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learned 2D and 3D path planning algorithms, while offering support for Robot Operating System (ROS). Many existing path planning algorithms are supported; e.g. A*, wavefront, rapidly-exploring random tree, value iteration networks, gated path planning networks; and integrating new algorithms is easy and clearly specified. We demonstrate the benchmarking capability of PathBench by comparing implemented classical and learned algorithms for metrics, such as path length, success rate, computational time and path deviation. These evaluations are done on built-in PathBench maps and external path planning environments from video games and real world databases. PathBench is open source 1.
Challenges and Outlook in Robotic Manipulation of Deformable Objects

Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and health-care sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex and is still an open research problem. Addressing DOM challenges demands breakthroughs in almost all aspects of robotics: hardware design, sensing, (deformation) modeling, planning, and control. In this article, we review recent advances and highlight the main challenges when considering deformation in each subfield. A particular focus of our article lies in the discussions of these challenges and proposing future directions of research.
Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have recently shown great promise as a way of incorporating physics-based domain knowledge, including fundamental governing equations, into neural network models for many complex engineering systems. They have been particularly effective in the area of inverse problems, where boundary conditions may be ill-defined, and data-absent scenarios, where typical supervised learning approaches will fail. Here, we further explore the use of this modeling methodology to surrogate modeling of a fluid dynamical system, and demonstrate additional undiscussed and interesting advantages of such a modeling methodology over conventional data-driven approaches: 1) improving the model's predictive performance even with incomplete description of the underlying physics; 2) improving the robustness of the model to noise in the dataset; 3) reduced effort to convergence during optimization for a new, previously unseen scenario by transfer optimization of a pre-existing model. Hence, we noticed the inclusion of a physics-based regularization term can substantially improve the equivalent data-driven surrogate model in many substantive ways, including an order of magnitude improvement in test error when the dataset is very noisy, and a 2-3x improvement when only partial physics is included. In addition, we propose a novel transfer optimization scheme for use in such surrogate modeling scenarios and demonstrate an approximately 3x improvement in speed to convergence and an order of magnitude improvement in predictive performance over conventional Xavier initialization for training of new scenarios.
Does the Goal Matter? Emotion Recognition Tasks Can Change the Social Value of Facial Mimicry Towards Artificial Agents

In this paper, we present a study aimed at understanding whether the embodiment and humanlikeness of an artificial agent can affect people’s spontaneous and instructed mimicry of its facial expressions. The study followed a mixed experimental design and revolved around an emotion recognition task. Participants were randomly assigned to one level of humanlikeness (between-subject variable: humanlike, characterlike, or morph facial texture of the artificial agents) and observed the facial expressions displayed by three artificial agents differing in embodiment (within-subject variable: video-recorded robot, physical robot, and virtual agent) and a human (control). To study both spontaneous and instructed facial mimicry, we divided the experimental sessions into two phases. In the first phase, we asked participants to observe and recognize the emotions displayed by the agents. In the second phase, we asked them to look at the agents’ facial expressions, replicate their dynamics as closely as possible, and then identify the observed emotions. In both cases, we assessed participants’ facial expressions with an automated Action Unit (AU) intensity detector. Contrary to our hypotheses, our results disclose that the agent that was perceived as the least uncanny, and most anthropomorphic, likable, and co-present, was the one spontaneously mimicked the least. Moreover, they show that instructed facial mimicry negatively predicts spontaneous facial mimicry. Further exploratory analyses revealed that spontaneous facial mimicry appeared when participants were less certain of the emotion they recognized. Hence, we postulate that an emotion recognition goal can flip the social value of facial mimicry as it transforms a likable artificial agent into a distractor. Further work is needed to corroborate this hypothesis. Nevertheless, our findings shed light on the functioning of human-agent and human-robot mimicry in emotion recognition tasks and help us to unravel the relationship between facial mimicry, liking, and rapport.
About the Convergence of a Family of Initial Boundary Value Problems for a Fractional Diffusion Equation with Robin Conditions

We consider a family of initial boundary value problems governed by a fractional diffusion equation with Caputo derivative in time, where the parameter is the Newton heat transfer coefficient linked to the Robin condition on the boundary. For each problem we prove existence and uniqueness of solution by a Fourier approach. This will enable us to also prove the convergence of the family of solutions to the solution of the limit problem, which is obtained by replacing the Robin boundary condition with a Dirichlet boundary condition.
Independent Spaces of q-Polymatroids

This paper is devoted to the study of independent spaces of q -polymatroids. With the aid of an auxiliary q -matroid it is shown that the collection of independent spaces satisﬁes the same properties as for q -matroids. However, in contrast to q -matroids, the rank value of an independent space does not agree with its dimension. Nonetheless, the rank values of the independent spaces fully determine the q -polymatroid, and this fact can be exploited to derive a cryptomorphism of q -polymatroids. Finally, the notions of minimal spanning spaces, maximally strongly independent spaces, and bases will be elaborated on.
Model‐free incremental adaptive dynamic programming based approximate robust optimal regulation

This article presents a new formulation for model‐free robust optimal regulation of continuous‐time nonlinear systems. The proposed reinforcement learning based approach, referred to as incremental adaptive dynamic programming (IADP), utilizes measured input‐state data to allow the design of the approximate optimal incremental control strategy, stabilizing the controlled system incrementally under model uncertainties, environmental disturbances, and input saturation. By leveraging the time delay estimation (TDE) technique, we first use sensor data to reduce the requirement of a complete dynamics, where input‐state data is adopted to construct an incremental dynamics which reflects the system evolution in an incremental form. Then, the resulting incremental dynamics serves to design the approximate optimal incremental control strategy based on adaptive dynamic programming, which is implemented as a simplified single critic structure to get the approximate solution to the value function of the Hamilton–Jacobi–Bellman equation. Furthermore, for the critic neural network, experience data are used to design an off‐policy weight update law with guaranteed weight convergence. Rather importantly, we incorporate a TDE error bound related term into the cost function, whereby the unintentionally introduced TDE error is attenuated during the optimization process. The proofs of system stability and weight convergence are provided. Numerical simulations are conducted to validate the effectiveness and superiority of our proposed IADP, especially regarding the reduced control energy expenditure and the enhanced robustness.
Model-free policy evaluation in Reinforcement Learning via upper solutions

In this work we present an approach for building tight model-free conﬁdence intervals for the optimal value function V (cid:63) in general inﬁnite horizon MDPs via the upper solutions. We suggest a novel upper value iterative procedure ( UVIP ) to construct upper solutions for a given agent’s policy. UVIP leads to a model free method of policy evaluation. We analyze convergence properties of the approximate UVIP under rather general assumptions and illustrate its performance on a number of benchmark RL problems.
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval

Deep hashing has gained growing popularity in approximate nearest neighbor search for large-scale image retrieval. Until now, the deep hashing for the image retrieval community has been dominated by convolutional neural network architectures, e.g. Resnet [22]. In this paper, inspired by the recent advancements of vision transformers, we present Transhash, a pure transformer-based framework for deep hashing learning. Concretely, our framework is composed of two major modules: (1) Based onVision Transformer (ViT), we design a siamese Multi-Granular Vision Tansformer backbone (MGVT) for image feature extraction. To learn fine-grained features, we innovate a dual-stream multi-granular feature learning on top of the transformer to learn discriminative global and local features. (2) Besides, we adopt a Bayesian learning scheme with a dynamically constructed similarity matrix to learn compact binary hash codes. The entire framework is jointly trained in an end-to-end manner. To the best of our knowledge, this is the first work to tackle deep hashing learning problems without convolutional neural networks (CNNs). We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUSWIDE and IMAGENET. The experiments have evidenced our superiority against the existing state-of-the-art deep hashing methods. Specifically, we achieve 8.2%, 2.6%, 12.7% performance gains in terms of average mAP for different hash bit lengths on three public datasets, respectively.
Solving Sokoban with backward reinforcement learning

In some puzzles, the strategy we need to use near the goal can be quite different from the strategy that is effective earlier on, e.g. due to a smaller branching factor near the exit state in a maze. A common approach in these cases is to apply both a forward and a backward search, and to try and align the two. In this work we propose an approach that takes this idea a step forward, within a reinforcement learning (RL) framework. Training a traditional forward-looking agent using RL can be difﬁcult because rewards are often sparse, e.g. given only at the goal. Instead, we ﬁrst train a backward-looking agent with a simple relaxed goal. We then augment the state representation of the puzzle with straightforward hint features that are extracted from the behavior of that agent. Finally, we train a forward looking agent with this informed augmented state. We demonstrate that this simple "access" to partial backward plans leads to a substantial performance boost. On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution. Impressively, we achieve these results while learning from only a small number of practice levels and using simple RL techniques.
Parametrized Hamiltonian simulation using quantum optimal control

Analog quantum simulation offers a hardware-specific approach to studying quantum dynamics, but mapping a model Hamiltonian onto the available device parameters requires matching the hardware dynamics. We introduce a paradigm for quantum Hamiltonian simulation that leverages digital decomposition techniques and optimal control to perform analog simulation. We validate this approach by constructing the optimal analog controls for a superconducting transmon device to emulate the dynamics of an extended Bose-Hubbard model. We demonstrate the role of control time, digital error, and pulse complexity, and we explore the accuracy and robustness of these controls. We conclude by discussing the opportunity for implementing this paradigm in near-term quantum devices.
When can accessibility help?: an exploration of accessibility feature recommendation on mobile devices

Numerous accessibility features have been developed and included in consumer operating systems to provide people with a variety of disabilities additional ways to access computing devices. Unfortunately, many users, especially older adults who are more likely to experience ability changes, are not aware of these features or do not know which combination to use. In this paper, we first quantify this problem via a survey with 100 participants, demonstrating that very few people are aware of built-in accessibility features on their phones. These observations led us to investigate accessibility recommendation as a way to increase awareness and adoption. We developed four prototype recommenders that span different accessibility categories, which we used to collect insights from 20 older adults. Our work demonstrates the need to increase awareness of existing accessibility features on mobile devices, and shows that automated recommendation could help people find beneficial accessibility features.
Real-time deep dynamic characters

We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery. In contrast to previous work, our controllable 3D character displays dynamics, e.g., the swing of the skirt, dependent on skeletal body motion in an efficient data-driven way, without requiring complex physics simulation. Our character model also features a learned dynamic texture model that accounts for photo-realistic motion-dependent appearance details, as well as view-dependent lighting effects. During training, we do not need to resort to difficult dynamic 3D capture of the human; instead we can train our model entirely from multi-view video in a weakly supervised manner. To this end, we propose a parametric and differentiable character representation which allows us to model coarse and fine dynamic deformations, e.g., garment wrinkles, as explicit spacetime coherent mesh geometry that is augmented with high-quality dynamic textures dependent on motion and view point. As input to the model, only an arbitrary 3D skeleton motion is required, making it directly compatible with the established 3D animation pipeline. We use a novel graph convolutional network architecture to enable motion-dependent deformation learning of body and clothing, including dynamics, and a neural generative dynamic texture model creates corresponding dynamic texture maps. We show that by merely providing new skeletal motions, our model creates motion-dependent surface deformations, physically plausible dynamic clothing deformations, as well as video-realistic surface textures at a much higher level of detail than previous state of the art approaches, and even in real-time.
Breaking and Fixing Unlinkability of the Key Agreement Protocol for 2nd Gen EMV Payments

—To address privacy problems with the EMV standard, EMVco proposed a Blinded Difﬁe-Hellman key establishment protocol. We point out that active attackers were not previously accounted for in the privacy requirements of this proposed protocol, despite the fact that an active attacker can compromise unlinkability. Here, we adopt a strong deﬁnition of unlinkability that does account for active attackers and propose an enhancement of the protocol proposed by EMVco where we make use of Verheul certiﬁcates. We prove that our protocol does satisfy strong unlinkability, while preserving authentication.
Attention-based Stylisation for Exemplar Image Colourisation

Exemplar-based colourisation aims to add plausible colours to a grayscale image using the guidance of a colour reference image. Most existing methods tackle the task as a style transfer problem, using a convolutional neural network (CNN) to obtain deep representations of the content of both inputs. Stylised outputs are then obtained by computing similarities between both feature representations in order to transfer the style of the reference to the content of the target input. However, in order to gain robustness towards dissimilar references, the stylised outputs need to be refined with a second colourisation network, which significantly increases the overall system complexity. This work reformulates the existing methodology introducing a novel end-to-end colourisation network that unifies the feature matching with the colourisation process. The proposed architecture integrates attention modules at different resolutions that learn how to perform the style transfer task in an unsupervised way towards decoding realistic colour predictions. Moreover, axial attention is proposed to simplify the attention operations and to obtain a fast but robust cost-effective architecture. Experimental validations demonstrate efficiency of the proposed methodology which generates high quality and visually appealing colourisation. Furthermore, the complexity of the proposed methodology is reduced compared to the state-of-the-art methods.
Lyman-alpha opacities at z = 4–6 require low mass, radiatively-suppressed galaxies to drive cosmic reionization

The high redshift Lyman-α forest, in particular the Gunn-Peterson trough, is the most unambiguous signature of the neutral to ionized transition of the intergalactic medium (IGM) taking place during the Epoch of Reionization. Recent studies have shown that reproducing the observed Lyman-α opacity distributions after overlap required a non-monotonous evolution of cosmic emissivity: rising, peaking at z ∼ 6, and then decreasing onwards to z = 4. Such an evolution is puzzling considering galaxy buildup and the cosmic star formation rate are still continously on the rise at these epochs. Here, we use new RAMSES-CUDATON simulations to show that such a peaked evolution may occur naturally in a fully coupled radiation-hydrodynamical framework. In our fiducial run, cosmic emissivity at z > 6 is dominated by a low mass (${\rm M_{DM}}\lt 2 \times 10^9 \rm \,M_{\odot }$), high escape fraction halo population, driving reionization, up to overlap. Approaching z = 6, this population is radiatively suppressed due to the rising ionizing UV background, and its emissivity drops. In the meantime, the high mass halo population builds up and its emissivity rises, but not fast enough to compensate the dimming of the low mass haloes, because of low escape fractions. The combined ionizing emissivity of these two populations therefore naturally results in a rise and fall of the cosmic emissivity, from z = 12 to z = 4, with a peak at z ∼ 6. An alternative run, which features higher escape fractions for the high mass haloes and later suppression at low mass, leads to overshooting the ionizing rate, over-ionizing the IGM and therefore too low Lyman-α opacities.
Predictive landscapes for biological cellular automata

Energy landscapes exemplify the predictive power of physics. Energy(-like) landscapes can explain biological processes, including some aspects of protein folding and gene expression. But for the most part, we cannot apply the familiar physics frameworks to predict behaviors of living systems. Instead, one predominantly models living systems with differential equations that are tailored to each system. Unfortunately, this approach faces the “parameter problem”: just a few biological processes can involve countless types of nonlinear, mathematical functions and unknown parameter values. As a complementary approach, one can view living systems as discrete dynamical systems governed by binary variables. Often, binary statements are accurate first-order approximations to biological features. Cellular automata are ideal for modeling discrete dynamics by incorporating such digital, verbal statements. While quantitative (Hamiltonian-based) rules can dictate cellular automata (e.g., Cellular Potts Model), numerous features of cells are more naturally summarized verbally than quantitatively (e.g., gene is (highly) expressed or not (highly) expressed). Cellular automata with verbal rules are underexplored in biology. They are usually difficult to understand because they produce complex, multi-agent dynamics that are – by design - difficult to quantify and because much of the mathematical tools (e.g., calculus) apply to continuous but not discrete dynamics. Promisingly, recent studies revealed that there can be unconventional, predictive landscapes - based on Lyapunov functions – that give an “equation of motion” that reveals all possible dynamics of a biologically motivated cellular automaton. Here we review these studies and predictive landscapes in general. Investigating biologically motivated cellular automata may reveal new links between living systems and theories of computation.