Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified and Modular Model Predictive Control Framework for Soft Continuum Manipulators under Internal and External Constraints

Fluidically actuated soft robots have promising capabilities such as inherent compliance and user safety. The control of soft robots needs to properly handle nonlinear actuation dynamics, motion constraints, workspace limitations, and variable shape stiffness, so having a unique algorithm for all these issues would be extremely beneficial. In this work, we adapt Model Predictive Control (MPC), popular for rigid robots, to a soft robotic arm called SoPrA. We address the challenges that current control methods are facing, by proposing a frame-work that handles these in a modular manner. While previous work focused on Joint-Space formulations, we show through simulation and experimental results that Task-Space MPC can be successfully implemented for dynamic soft robotic control. We provide a way to couple the Piece-wise Constant Curvature and Augmented Rigid Body Model assumptions with internal and external constraints and actuation dynamics, delivering an algorithm that unites these aspects and optimizes over them. We believe that a MPC implementation based on our approach could be the way to address most of model-based soft robotics control issues within a unified and modular framework, while allowing to include improvements that usually belong to other control domains such as machine learning techniques.
Soliton trapping and orthogonal Raman scattering in a birefringent photonic crystal fiber

We report on trapped pulse generation in birefringent microstructured optical fiber. Linearly polarized fs pulses are injected into the microstructured fiber in anomalous dispersion regime. We observed experimentally that soliton pulse polarized along the fast fiber axis partially transfers its energy to the orthogonal polarization. The generated pulse is amplified through the orthogonal Raman gain. The two polarization components are located at group-velocity matched wavelengths. The experimental works are supported with numerical simulations. The obtained results are important for the light sources using self-frequency shifted solitons in applications demanding high polarization purity.
CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models.
Outage Performance of Uplink Rate Splitting Multiple Access With Randomly Deployed Users

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users’ rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user’s diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region.
Guarded Kleene Algebra with Tests: Automata Learning

Guarded Kleene Algebra with Tests (GKAT) is the fragment of Kleene Algebra with Tests (KAT) that arises by replacing the union and iteration operations of KAT with predicate-guarded variants. GKAT is more efficiently decidable than KAT and expressive enough to model simple imperative programs, making it attractive for applications to e.g. network verification. In this paper, we further explore GKAT's automata theory, and present GL*, an algorithm for learning the GKAT automaton representation of a black-box, by observing its behaviour. A complexity analysis shows that it is more efficient to learn a representation of a GKAT program with GL* than with Angluin's existing L* algorithm. We implement GL* and L* in OCaml and compare their performances on example programs.
Eyes on the Prize: Improved Perception for Robust Dynamic Grasping

—This paper is concerned with perception challenges for robust grasping in the presence of clutter and unpredictable relative motion between robot and object. Traditional perception systems developed for static grasping are unable to provide feedback during the ﬁnal phase of a grasp due to sensor minimum range, occlusion, and a limited ﬁeld of view. A multi-camera eye-in-hand perception system is presented that has advantages over commonly used camera conﬁgurations. We quantitatively evaluate the performance on a real robot with an image-based visual servoing grasp controller and show a signiﬁcantly improved success rate on a dynamic grasping task. A fully reproducible open-source testing system is described to encourage benchmarking of dynamic grasping system performance.
On global randomized block Kaczmarz algorithm for solving large-scale matrix equations

The randomized Kaczmarz algorithm is one of the most popular approaches for solving large-scale linear systems due to its simplicity and eﬃciency. In this paper, we propose two classes of global randomized Kaczmarz methods for solving large-scale linear matrix equations AXB = C , the global randomized block Kaczmarz algorithm and global randomized average block Kaczmarz algorithm. The feature of global randomized block Kaczmarz algorithm is the fact that the current iterate is projected onto the solution space of the sketched matrix equation at each iteration, while the global randomized average block Kaczmarz approach is pseudoinverse-free and therefore can be deployed on parallel computing units to achieve signiﬁcant improvements in the computational time. We prove that these two methods linearly converge in the mean square to the minimum norm solution X ∗ = A † CB † of a given linear matrix equation. The convergence rates depend on the geometric properties of the data matrices and their submatrices and on the size of the blocks. Numerical results reveal that our proposed algorithms are eﬃcient and eﬀective for solving large-scale matrix equations. In particular, they can also achieve satisfying performance when applied to image deblurring problems.
Efficient, High-purity, Robust Sound Frequency Conversion with a Linear Metasurface

The intrinsic limitation of the material nonlinearity inevitably results in the poor mode purity, conversion efficiency and real-time reconfigurability of the generated harmonic waves, both in optics and acoustics. Rotational Doppler effect provides us an intuitive paradigm to shifting the frequency in a linear system, which needs to be facilitated by a spiraling phase change upon the wave propagation. Here we numerically and experimentally present a rotating linear vortex metasurface and achieve close-to-unity mode purity (above 95%) and conversion efficiency (above 65%) in audible sound frequency as low as 3000 Hz. The topological charge of the transmitted sound is almost immune from the rotational speed and transmissivity, demonstrating the mechanical robustness and stability in adjusting the high-performance frequency conversion in situ. These features enable us to cascade multiple vortex metasurfaces to further enlarge and diversify the extent of sound frequency conversion, which are experimentally verified. Our strategy takes a step further towards the freewheeling sound manipulation at acoustic frequency domain, and may have far-researching impacts in various acoustic communications, signal processing, and contactless detection.
Network Topology Optimization via Deep Reinforcement Learning

Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely difficult to obtain an optimal solution, especially since topology planning in networks also often comes with management-specific constraints. As a result, local optimization with hand-tuned heuristic methods from human experts is often adopted in practice. Yet, heuristic methods cannot cover the global topology design space while taking into account constraints, and cannot guarantee to find good solutions. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm for graph searching, called DRL-GS, for network topology optimization. DRL-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL agent to conduct a topology search. DRL-GS can efficiently search over relatively large topology space and output topology with satisfactory performance. We conduct a case study based on a real-world network scenario, and our experimental results demonstrate the superior performance of DRL-GS in terms of both efficiency and performance.
greed: An R Package for Model-Based Clustering by Greedy Maximization of the Integrated Classification Likelihood

The greed package implements the general and ﬂexible framework of Côme, Jouvin, Latouche, and Bouveyron (2021) for model-based clustering in the R language. Based on the direct maximization of the exact Integrated Classiﬁcation Likelihood with respect to the partition, it allows jointly performing clustering and selection of the number of groups. This combinatorial problem is handled through an eﬃcient hybrid genetic algorithm, while a ﬁnal hierarchical step allows accessing coarser partitions and extract an ordering of the clusters. This methodology is applicable in a wide variety of latent variable models and, hence, can handle various data types as well as heterogeneous data. Classical models for continuous, count, categorical and graph data are implemented, and new models may be incorporated thanks to S4 class abstraction. This paper introduces the package, the design choices that guided its development and illustrates its usage on practical use-cases.
Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics

The absence of a conventional association between the cell–cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate, differentiate, and compete (i.e., the cell ecology). With the recent advancement of single-cell RNA sequencing (RNA-seq), we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs and analyze these graphs systematically using the summary statistics given by the algebraic topology. We propose single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38,731 cells, 25 cell types, and 12 time steps, our approach highlights gastrulation as the most critical stage, consistent with the consensus in developmental biology. As a nonlinear, model-independent, and unsupervised framework, our approach can also be applied to tracing multi-scale cell lineage, identifying critical stages, or creating pseudo-time series.
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams

Sequences of group interactions, such as emails, online discussions, and co-authorships, are ubiquitous; and they are naturally represented as a stream of hyperedges (i.e., sets of nodes). Despite its broad potential applications, anomaly detection in hypergraphs (i.e., sets of hyperedges) has received surprisingly little attention, compared to anomaly detection in graphs. While it is tempting to reduce hypergraphs to graphs and apply existing graph-based methods, according to our experiments, taking higher-order structures of hypergraphs into consideration is worthwhile. We propose HashNWalk, an incremental algorithm that detects anomalies in a stream of hyperedges. It maintains and updates a constant-size summary of the structural and temporal information about the input stream. Using the summary, which is the form of a proximity matrix, HashNWalk measures the anomalousness of each new hyperedge as it appears. HashNWalk is (a) Fast: it processes each hyperedge in near real-time and billions of hyperedges within a few hours, (b) Space Efficient: the size of the maintained summary is a user-specific constant, (c) Effective: it successfully detects anomalous hyperedges in real-world hypergraphs.
Differentially Private Load Restoration for Microgrids with Distributed Energy Storage

Distributed energy storage systems (ESSs) can be efficiently leveraged for load restoration (LR) for a microgrid (MG) in island mode. When the ESSs are owned by third parties rather than the MG operator (MGO), the ESS operating setpoints may be considered as private information of their respective owners. Therefore, efforts must be put forth to avoid the disclosure through adversarial analysis of load setpoints. In this paper, we consider a scenario where LR takes place in a MG by determining load and ESS power injections through the solution of an AC optimal power flow (AC-OPF) problem. Since the charge/discharge mode at any given time is assumed to be private, we develop a differentially-private mechanism which restores load while maintaining privacy of ESS mode data. The performance of the proposed mechanism is demonstrated for a 33-bus MG.
Information-Theoretic Reduction of Markov Chains

We survey information-theoretic approaches to the reduction of Markov chains. Our survey is structured in two parts: The first part considers Markov chain coarse graining, which focuses on projecting the Markov chain to a process on a smaller state space that is informative}about certain quantities of interest. The second part considers Markov chain model reduction, which focuses on replacing the original Markov model by a simplified one that yields similar behavior as the original Markov model. We discuss the practical relevance of both approaches in the field of knowledge discovery and data mining by formulating problems of unsupervised machine learning as reduction problems of Markov chains. Finally, we briefly discuss the concept of lumpability, the phenomenon when a coarse graining yields a reduced Markov model.
Designing for Responsible Trust in AI Systems: A Communication Perspective

Current literature and public discourse on “trust in AI” are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems’ affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users’ cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.
Coordinated Charging Station Search in Stochastic Environments: A Multi-Agent Approach

Range and charge anxiety remain essential barriers to a faster electric vehicle market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an electric vehicle uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real-time availability data but struggle with data inaccuracy, e.g., due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize a driver's detour until reaching an available charging station. So far, both practical and theoretical approaches ignore driver coordination enabled by charging requests centralization or sharing of data, e.g., sharing observations of charging stations' availability or visit intentions between drivers. Against this background, we study coordinated stochastic search algorithms, which help to reduce station visit conflicts and improve the drivers' charging experience. We model a multi-agent stochastic charging station search problem as a finite-horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies. In contrast to static policies, dynamic policies account for information updates during policy planning and execution. We present a hierarchical implementation of a single-agent heuristic for decentralized decision making and a rollout algorithm for centralized decision making. Extensive numerical studies show that compared to an uncoordinated setting, a decentralized setting with visit-intentions sharing decreases the system cost by 26%, which is nearly as good as the 28% cost decrease achieved in a centralized setting, and saves up to 23% of a driver's search time while increasing her search reliability.
Pre-training helps Bayesian optimization too

Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive real-world functions. Contrary to a common belief that BO is suited to optimizing black-box functions, it actually requires domain knowledge on characteristics of those functions to deploy BO successfully. Such domain knowledge often manifests in Gaussian process priors that specify initial beliefs on functions. However, even with expert knowledge, it is not an easy task to select a prior. This is especially true for hyperparameter tuning problems on complex machine learning models, where landscapes of tuning objectives are often difficult to comprehend. We seek an alternative practice for setting these functional priors. In particular, we consider the scenario where we have data from similar functions that allow us to pre-train a tighter distribution a priori. To verify our approach in realistic model training setups, we collected a large multi-task hyperparameter tuning dataset by training tens of thousands of configurations of near-state-of-the-art models on popular image and text datasets, as well as a protein sequence dataset. Our results show that on average, our method is able to locate good hyperparameters at least 3 times more efficiently than the best competing methods.
The surface geometry and shadow of a Schwarzschild black hole with halo

We have studied the surface geometry and shadows of Schwarzschild black hole with a halo containing the quadrupolar and octopolar terms. We found the quadrupole term makes the Schwarzschild black hole prolate for the quadrupole strength $\mathcal{Q}<0$ and oblate for $\mathcal{Q}>0$, and the octopole term makes shadow stretch upward for the octopolar strength $\mathcal{O}<0$ and stretch downward for $\mathcal{O}>0$. The shadow of Schwarzschild black hole with halo stretches and squeezes along the horizontal direction for $\mathcal{Q}<0$ and $\mathcal{Q}>0$ respectively. Meanwhile, black hole shadow shifts upward for $\mathcal{O}<0$ and shifts downward for $\mathcal{O}>0$. We exhibited the light rays that form the shadow boundary to explain the emergence of the extraordinary patterns of black hole shadow with the quadrupole and octopole terms. From the observable width $W$, height $H$, oblateness $K$ and distortion parameter $\delta_{c}$ of black hole shadow, one can determine the quadrupole and octopolar strengths of Schwarzschild black hole with halo. Our results show that the quadrupolar and octopolar terms yield a series of interesting patterns for the shadow of a Schwarzschild black hole with halo.