Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling

Field-of-view and resolution trade-offs in X-Ray micro-computed tomography (micro-CT) imaging limit the characterization, analysis and model development of multi-scale porous systems. To this end, we developed an applied methodology utilising deep learning to enhance low resolution images over large sample sizes and create multi-scale models capable of accurately simulating experimental fluid dynamics from the pore (microns) to continuum (centimetres) scale. We develop a 3D Enhanced Deep Super Resolution (EDSR) convolutional neural network to create super resolution (SR) images from low resolution images, which alleviates common micro-CT hardware/reconstruction defects in high-resolution (HR) images. When paired with pore-network simulations and parallel computation, we can create large 3D continuum-scale models with spatially varying flow&material properties. We quantitatively validate the workflow at various scales using direct HR/SR image similarity, pore-scale material/flow simulations and continuum scale multiphase flow experiments (drainage immiscible flow pressures and 3D fluid volume fractions). The SR images and models are comparable to the HR ground truth, and generally accurate to within experimental uncertainty at the continuum scale across a range of flow rates. They are found to be significantly more accurate than their LR counterparts, especially in cases where a wide distribution of pore-sizes are encountered. The applied methodology opens up the possibility to image, model and analyse truly multi-scale heterogeneous systems that are otherwise intractable.
Quality factor for zero-bias conductance peaks in Majorana nanowire

Despite recent experimental progress towards observing large zero-bias conductance peaks (ZBCPs) as signatures of Majorana modes, confusion remains about whether Majorana modes have been observed. This is in part due to the theoretical prediction of ﬁne-tuned trivial (i.e., non-topological) zero-bias peaks that occur because of uncontrolled quantum dots or disorder potentials. While many aspects of the topological phase can be somewhat ﬁne-tuned because the topological phase space is often small, the quantized height of the ZBCP associated with a Majorana mode is known to be robust at suﬃciently low temperatures even as the tunnel barrier is pinched oﬀ to vanishingly small normal-state conductance. The key shortcoming of the existing experimental works is an acute lack of stability of the putative Majorana mode features, indicating the probable absence of a topological phase, and the current paper suggests speciﬁc experimentally accessible measures for a careful quantitative analysis of the measured ZBCP stability. In this paper, we study how the counter-intuitive robustness of the ZBCP height to the tunnel barrier strength can be used to distinguish Majorana modes from non-topological ZBCPs. To this end, we introduce a dimensionless quality factor F to quantify the robustness of the ZBCP height based on the range of normal-state (i.e. above-gap) conductance (which depends crucially on the tunnel barrier height) over which the ZBCP height remains within a pre-speciﬁed range of quantization. By computing this quality factor F together with the topological characteristics for a wide range of models and parameters, we ﬁnd that Majoranas are signiﬁcantly more robust (i.e., have a higher value of F ) compared with non-topological ZBCPs in the ideal low-temperature limit. Even at a temperature as high as the experimentally used 20 mK, we ﬁnd that we can set a threshold value of F ∼ 2 . 5 (for (cid:15) = 0 . 1) so that ZBCPs associated with a quality factor F > 2 . 5 are likely topological and F (cid:28) 2 . 5 are topologically trivial. More precisely, the value of F is operationally related to the degree of separation of the Majorana modes in the system, although F uses only the experimentally measured tunnel conductance properties. Finally, we discuss how the quality factor F measured in a transport setup can help estimate the quality of topological qubits made from Majorana modes. In particular, we show that if the induced gap can be enhanced somehow beyond the currently available ∼ 30 µ eV in InAs/Al samples, large (small) values of F could easily distinguish between stable topological (unstable trivial) ZBCPs with the quantum dot induced quasi-Majorana bound states occasionally behaving similar to topological Majorana modes in short wires.
Learning Eye-in-Hand Camera Calibration from a Single Image

Eye-in-hand camera calibration is a fundamental and long-studied problem in robotics. We present a study on using learning-based methods for solving this problem online from a single RGB image, whilst training our models with entirely synthetic data. We study three main approaches: one direct regression model that directly predicts the extrinsic matrix from an image, one sparse correspondence model that regresses 2D keypoints and then uses PnP, and one dense correspondence model that uses regressed depth and segmentation maps to enable ICP pose estimation. In our experiments, we benchmark these methods against each other and against well-established classical methods, to find the surprising result that direct regression outperforms other approaches, and we perform noise-sensitivity analysis to gain further insights into these results.
Instructive artificial intelligence (AI) for human training, assistance, and explainability

We propose a novel approach to explainable AI (XAI) based on the concept of "instruction" from neural networks. In this case study, we demonstrate in a human subject experiment how a superhuman neural network might instruct human trainees as an alternative to traditional approaches to XAI. Specifically, an AI examines human actions and calculates variations on the human strategy that lead to better performance. Experiments with a JHU/APL-developed AI player for the cooperative card game Hanabi suggest this technique makes unique contributions to explainability while improving human performance. One area of focus for Instructive AI is in the significant discrepancies that can arise between a human's actual strategy and the strategy they profess to use. This inaccurate self-assessment presents a barrier for XAI, since explanations of an AI's strategy may not be properly understood or implemented by human recipients. As an alternative, we propose a method of translating insights from an AI into corrections on human decision making. With neural networks, this allows a direct calculation of the changes in network weights needed to improve the human strategy to better emulate an AI that outperforms humans on certain metrics. Subjected to constraints (e.g. sparsity) these weight changes can be interpreted as recommended changes to human strategy (e.g. "value A more, and value B less"). Instruction from AI such as these functions both to help humans perform better at tasks, but also to better understand, anticipate, and correct the actions of an AI.
Free energy calculation of crystalline solids using normalizing flows

Taking advantage of the advances in generative deep learning, particularly normalizing flow, a framework, called Boltzmann generator, has recently been proposed to generate equilibrium atomic configurations from the canonical ensemble and determining the associated free energy. In this work, we revisit Boltzmann generator to motivate the construction of the loss function from the statistical mechanical point of view and to cast the training of the normalizing flow model in a purely unsupervised manner that does not require any sample of the atomic configurations from the equilibrium ensemble. We further show that the normalizing flow framework furnishes a reference thermodynamic system, very close to the real thermodynamic system under consideration, that is suitable for the well-established free energy perturbation methods to determine accurate free energy of solids. We then apply the normalizing flow to two problems: temperature-dependent Gibbs free energy of perfect crystal and formation free energy of monovacancy defect in a model system of diamond cubic Si. The results obtained from the normalizing flow are shown to be in good agreement with that obtained from independent well-established free energy methods.
Fundamental Physics Searches with IACTs

Michele Doro1, M.A. Sánchez-Conde2, M. Hütten3 1 University of Padova, Department of Physics and Astronomy, I-35131 Padova (Italy) michele. doro@unipd.it 2 Instituto de F́ısica Teórica, IFT UAM-CSIC, Departamento de F́ısica Teórica, Universidad Autónoma de Madrid, ES-28049 Madrid (Spain) miguel.sanchezconde@uam.es 3 Institute for Cosmic Ray Research, the University of Tokyo (Japan) huetten@icrr.u-tokyo.ac. jp
Competitive Algorithms for Online Weighted Bipartite Matching and its Variants

Online bipartite matching has been extensively studied. In the unweighted setting, Karp et al. gave an optimal $(1 - 1/e)$-competitive randomized algorithm. In the weighted setting, optimal algorithms have been achieved only under assumptions on the edge weights. For the general case, little was known beyond the trivial $1/2$-competitive greedy algorithm. Recently, Fahrbach et al. have presented an 0.5086-competitive algorithm (for the problem in a model, namely free-disposal), overcoming the long-standing barrier of 1/2. Besides, in designing competitive algorithms for the online matching problem and its variants, several techniques have been developed, in particular the primal-dual method. Specifically, Devanur et al. gave a primal-dual framework, unifying previous approaches and Devanur and Jain provided another scheme for a generalization of the online matching problem. In this paper, we present competitive algorithms for the online weighted bipartite matching in different models; in particular we achieve the optimal $(1-1/e)$ competitive ratio in the free-disposal model and in other model, namely stochastic reward. Our work also unifies the previous approaches by the mean of the primal-dual technique with configuration linear programs.
Dynamic Geometric Set Cover, Revisited

Geometric set cover is a classical problem in computational geometry, which has been extensively studied in the past. In the dynamic version of the problem, points and ranges may be inserted and deleted, and our goal is to efficiently maintain a set cover solution (satisfying certain quality requirement). In this paper, we give a plethora of new dynamic geometric set cover data structures in 1D and 2D, which significantly improve and extend the previous results: 1. The first data structure for $(1+\varepsilon)$-approximate dynamic interval set cover with polylogarithmic amortized update time. Specifically, we achieve an update time of $O(\log^3 n/\varepsilon)$, improving the $O(n^\delta/\varepsilon)$ bound of Agarwal et al. [SoCG'20], where $\delta>0$ denotes an arbitrarily small constant. 2. A data structure for $O(1)$-approximate dynamic unit-square set cover with $2^{O(\sqrt{\log n})}$ amortized update time, substantially improving the $O(n^{1/2+\delta})$ update time of Agarwal et al. [SoCG'20]. 3. A data structure for $O(1)$-approximate dynamic square set cover with $O(n^{1/2+\delta})$ randomized amortized update time, improving the $O(n^{2/3+\delta})$ update time of Chan and He [SoCG'21]. 4. A data structure for $O(1)$-approximate dynamic 2D halfplane set cover with $O(n^{17/23+\delta})$ randomized amortized update time. The previous solution for halfplane set cover by Chan and He [SoCG'21] is slower and can only report the size of the approximate solution. 5. The first sublinear results for the \textit{weighted} version of dynamic geometric set cover. Specifically, we give a data structure for $(3+o(1))$-approximate dynamic weighted interval set cover with $2^{O(\sqrt{\log n \log\log n})}$ amortized update time and a data structure for $O(1)$-approximate dynamic weighted unit-square set cover with $O(n^\delta)$ amortized update time.
Nucleation efficiency of nuclear recoils in bubble chambers

Bubble chambers using liquid xenon (and liquid argon) have been operated (resp. planned) by the Scintillating Bubble Chamber (SBC) collaboration for GeV-scale dark matter searches and CEνNS from reactors. This will require a robust calibration program of the nucleation efficiency of low-energy nuclear recoils in these target media. Such a program has been carried out by the PICO collaboration, which aims to directly detect dark matter using C3F8 bubble chambers. Neutron calibration data from mono-energetic neutron beam and Am-Be source has been collected and analyzed, leading to a global fit of a generic nucleation efficiency model for carbon and fluorine recoils, at thermodynamic thresholds of 2.45 and 3.29 keV. Fitting the many-dimensional model to the data (34 free parameters) is a non-trivial computational challenge, addressed with a custom Markov Chain Monte Carlo approach, which will be presented. Parametric MC studies undertaken to validate this methodology are also discussed. This fit paradigm demonstrated for the PICO calibration will be applied to existing and future scintillating bubble chamber calibration data.
Antenna Optimization for WBAN Based on Spherical Wave Functions De-Embedding

Antennas for wireless body area networks (WBANs) need to be modeled with adapted methods because the coupling with the body tissue does not allow for a clear separation between antenna and channel. Especially for dynamically varying on-body channels due to changing body poses, e.g., with head-worn antennas, modeling is challenging and design goals for optimal antennas are difficult to determine. Therefore, in this article, the modeling of WBAN channels using spherical wave functions (SWFs) is utilized for antenna de-embedding and for deriving optimal antenna characteristics that maximize the transmission coefficient for the respective channel. It is evaluated how typical factors influencing WBAN channels (different body anatomies, body postures, and varying positions of the communication nodes) can be modeled statistically with SWF. An optimized antenna design is developed based on the derived optimization method, specifically adapted to the channel of on-body links with eye-wear applications. The results with the optimized antenna are compared to other standard antenna designs and validated against measurements.
Safe PDE Backstepping QP Control With High Relative Degree CBFs: Stefan Model With Actuator Dynamics

High-relative-degree control barrier functions play a prominent role in automotive safety and in robotics. In this article, we launch a generalization of this concept for PDE control, treating a specific, physically relevant model of thermal dynamics where the boundary of the PDE moves due to a liquid–solid phase change—the so-called Stefan model. The familiar quadratic programming (QP) design is employed to ensure safety but with CBFs that are infinite-dimensional (including one control barrier “functional”) and with safe sets that are infinite-dimensional as well. Since, in the presence of actuator dynamics, at the boundary of the Stefan system, this system's main CBF is of relative degree 2, an additional CBF is constructed, by backstepping design, which ensures the positivity of all the CBFs without any additional restrictions on the initial conditions. It is shown that the “safety filter” designed in this article guarantees safety in the presence of an arbitrary operator input. This is similar to an automotive system in which a safety feedback law overrides—but only when necessary—the possibly unsafe steering, acceleration, or braking by a vigorous but inexperienced driver. Simulations have been performed for a process in metal additive manufacturing, which show that the operator's heat-and-cool commands to the Stefan model are being obeyed but without the liquid ever freezing.
Simulation of Parallel-Jaw Grasping using Incremental Potential Contact Models

—Soft compliant jaw tips are almost universally used with parallel-jaw robot grippers due to their ability to increase contact area and friction between the jaws and the object to be manipulated. However, interactions between the compliant surfaces and rigid objects are notoriously difﬁcult to model. We introduce IPC-GraspSim, a novel simulator using Incremental Potential Contact (IPC) — a deformation model developed in 2020 for computer graphics — that models both the dynamics and the deformation of compliant jaw tips during grasping. IPC-GraspSim is evaluated using a set of 2,000 physical grasps across 16 adversarial objects where standard analytic models perform poorly. In comparison to both analytic quasistatic contact models (soft point contact, REACH, 6DFC) and dynamic grasp simulators (Isaac Gym with FleX backend), results suggest that IPC-GraspSim more accurately models real-world grasps, increasing F1 score by 9%. All data, code, videos, and supplementary material are available at https://sites.google. com/berkeley.edu/ipcgraspsim.
Household Cloth Object Set: Fostering Benchmarking in Deformable Object Manipulation

Benchmarking of robotic manipulations is one of the open issues in robotic research. An important factor that has enabled progress in this area in the last decade is the existence of common object sets that have been shared among different research groups. However, the existing object sets are very limited when it comes to cloth-like objects that have unique particularities and challenges. This letter is a first step towards the design of a cloth object set to be distributed among research groups from the robotics cloth manipulation community. We present a set of household cloth objects and related tasks that serve to expose the challenges related to gathering such an object set and propose a roadmap to the design of common benchmarks in cloth manipulation tasks, with the intention to set the grounds for a future debate in the community that will be necessary to foster benchmarking for the manipulation of cloth-like objects. Some RGB-D and object scans are collected as examples for the relevant configurations and shared in http://www.iri. upc.edu/groups/perception/ClothObjectSet/.
StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN

Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space. However, additional architectures or task-specific training paradigms are usually required for different tasks. In this work, we take a deeper look at the spatial properties of StyleGAN. We show that with a pretrained StyleGAN along with some operations, without any additional architecture, we can perform comparably to the state-of-the-art methods on various tasks, including image blending, panorama generation, generation from a single image, controllable and local multimodal image to image translation, and attributes transfer. The proposed method is simple, effective, efficient, and applicable to any existing pretrained StyleGAN model.
A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots

In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. Dialogue systems are increasingly being designed to move beyond just imitating conversation and also improve from such interactions over time. In this survey, we present a broad overview of methods developed to build dialogue systems over the years. Different use cases for dialogue systems ranging from task-based systems to open domain chatbots motivate and necessitate specific systems. Starting from simple rule-based systems, research has progressed towards increasingly complex architectures trained on a massive corpus of datasets, like deep learning systems. Motivated with the intuition of resembling human dialogues, progress has been made towards incorporating emotions into the natural language generator, using reinforcement learning. While we see a trend of highly marginal improvement on some metrics, we find that limited justification exists for the metrics, and evaluation practices are not uniform. To conclude, we flag these concerns and highlight possible research directions.
Optimized Passive Optical Networks with Cascaded-AWGRs for Data Centers

The use of Passive Optical Networks (PONs) in modern and future data centers can provide energy efficiency, high capacity, low cost, scalability, and elasticity. This paper introduces a passive optical network design with 2-tier cascaded Arrayed Waveguide Grating Routers (AWGRs) to connect groups of racks (i.e. cells) within a data center. This design employs a Software-Defined Networking (SDN) controller to manage the routing and assignment of the networking resource while introducing multiple paths between any two cells to improve routing, load balancing and resilience. We provide benchmarking results for the power consumption to compare the energy efficiency of this design to state-of-the-art data centers. The results indicate that the cascaded AWGRs architecture can achieve up to 43% saving in the networking power consumption compared to a Fat-Tree data center architecture.
Nonstochastic Bandits and Experts with Arm-Dependent Delays

We study nonstochastic bandits and experts in a delayed setting where delays depend on both time and arms. While the setting in which delays only depend on time has been extensively studied, the arm-dependent delay setting better captures real-world applications at the cost of introducing new technical challenges. In the full information (experts) setting, we design an algorithm with a first-order regret bound that reveals an interesting trade-off between delays and losses. We prove a similar first-order regret bound also for the bandit setting, when the learner is allowed to observe how many losses are missing. These are the first bounds in the delayed setting that depend on the losses and delays of the best arm only. When in the bandit setting no information other than the losses is observed, we still manage to prove a regret bound through a modification to the algorithm of Zimmert and Seldin (2020). Our analyses hinge on a novel bound on the drift, measuring how much better an algorithm can perform when given a look-ahead of one round.
Ramsey numbers of graphs with most degrees bounded in random graphs

For graphs F and G, let F → G signify that any red/blue edge coloring of F contains a monochromatic G. Denote by G(N, p) the random graph space of order N and edge probability p. Using the regularity method, one can show that for any fixed p ∈ (0, 1], almost all graphs F ∈ G(cn, p) have F → G for any graph G of order n and all but at most m degrees bounded, where c is an integer depending on p and m. Note that r(Km,n) ∼ 2 n and r(Km+Kn) ∼ 2 n as n → ∞, for which we investigate the relation between c and p. Let N = ⌊c 2n⌋ with c > 1 and pu, pl = 1 c1/m (1 ± √ M log n n ), where M = M(c,m) > 0. It is shown that pu and pl are Ramsey thresholds of Km,n in G(N, p). Namely, almost all F ∈ G(N, pu) and almost no F ∈ G(N, pl) have F → Km,n, respectively. Moreover, it is shown that pu and pl are (ordinary) upper threshold and lower threshold of Km + Kn to appear in G(N, p/2), respectively. We show that G(N, p/2) can be identified as the set of red (or blue) graphs obtained from F ∈ G(N, p) by red/blue edge coloring of F with probability 1/2 for each color, which leads to the definition of the weak Ramsey thresholds.
Variance bounds for disc-polygons

We prove asymptotic lower bounds on the variance of the number of vertices and missed area of random disc-polygons in convex discs whose boundary is $C_+^2$ smooth. The established lower bounds are of the same order as the upper bounds proved previously by Fodor and V\'{\i}gh (2018).