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The sum-rank metric is a hybrid between the Hamming metric and the rank metric and suitable for error correction in multishot network coding and distributed storage as well as for the design of quantum-resistant cryptosystems. In this work, we consider the construction and decoding of folded linearized Reed-Solomon (FLRS) codes, which are shown to be maximum sum-rank distance (MSRD) for appropriate parameter choices. We derive an efficient interpolation-based decoding algorithm for FLRS codes that can be used as a list decoder or as a probabilistic unique decoder. The proposed decoding scheme can correct sum-rank errors beyond the unique decoding radius with a computational complexity that is quadratic in the length of the unfolded code. We show how the error-correction capability can be optimized for high-rate codes by an alternative choice of interpolation points. We derive a heuristic upper bound on the decoding failure probability of the probabilistic unique decoder and verify its tightness by Monte Carlo simulations. Further, we study the construction and decoding of folded skew Reed-Solomon codes in the skew metric. Up to our knowledge, FLRS codes are the first MSRD codes with different block sizes that come along with an efficient decoding algorithm.

Epilepsy is a chronic neurological disorder with a significant prevalence. However, there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing is an interesting alternative for wearable devices, characterized by a much simpler learning process and also lower memory requirements. In this work, we demonstrate a few additional aspects in which HD computing, and the way its models are built and stored, can be used for further understanding, comparing, and creating more advanced machine learning models for epilepsy detection. These possibilities are not feasible with other state-of-the-art models, such as random forests or neural networks. We compare inter-subject similarity of models per different classes (seizure and non-seizure), then study the process of creation of generalized models from personalized ones, and in the end, how to combine personalized and generalized models to create hybrid models. This results in improved epilepsy detection performance. We also tested knowledge transfer between models created on two different datasets. Finally, all those examples could be highly interesting not only from an engineering perspective to create better models for wearables, but also from a neurological perspective to better understand individual epilepsy patterns.

Humanoids are versatile robotic platforms because of their limbs with multiple degrees of freedom. Although humanoids can walk like humans, the speed is relatively slow, and they cannot run over large barriers. To address these problems, we aim to achieve rapid terrestrial locomotion ability and simultaneously expand the domain of locomotion to the air by utilizing thrust for propulsion. In this paper, we first describe an optimized construction method of a humanoid robot equipped with wheels and a flight unit to achieve these abilities. Then, we describe the integrated control framework of the proposed flying humanoid for each mode of locomotion: aerial locomotion, leg locomotion, and wheel locomotion. Finally, we achieved multimodal locomotion and aerial manipulation experiments using the robot platform proposed in this work. To the best of our knowledge, it is the first time to achieve three different types of locomotion, including flight, by a single humanoid.

In the study of sparse stochastic block model (SBM) one needs to analyze a distributional recursion, known as belief propagation (BP) on a tree. Uniqueness of the fixed point of this recursion implies several results about the SBM, including optimal recovery algorithms for SBM (Mossel et al. (2016)) and SBM with side information (Mossel and Xu (2016)), and a formula for SBM mutual information (Abbe et al. (2021)). The 2-community case corresponds to an Ising model, for which Yu and Polyanskiy (2022) established uniqueness for all cases. Here, we analyze broadcasting of $q$-ary spins on a Galton-Watson tree with expected offspring degree $d$ and Potts channels with second-largest eigenvalue $\lambda$. We allow for the intermediate vertices to be observed through noisy channels (side information) We prove BP uniqueness holds with and without side information when $d\lambda^2 \ge 1 + C \max\{\lambda, q^{-1}\}\log q$ for some absolute constant $C>0$ independent of $q,d,\lambda$. For large $q$ and $\lambda = o(1/\log q)$, this is asymptotically achieving the Kesten-Stigum threshold $d\lambda^2=1$. These results imply mutual information formula and optimal recovery algorithms for the $q$-community SBM in the corresponding ranges. For $q\ge 4$, Sly (2011); Mossel et al. (2022) shows that there exist choices of $q,d,\lambda$ below Kesten-Stigum (i.e. $d\lambda^2 < 1$) but reconstruction is possible. Somewhat surprisingly, we show that in such regimes BP uniqueness \textit{does not hold} at least in the presence of weak side information. Our technical tool is a theory of q-ary symmetric channels, that we initiate here, generalizing the classical and widely-utilized information-theoretic characterization of BMS (binary memoryless symmetric) channels.

Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller's capability to reduce muscle effort across different tasks.

High-throughput DFT calculations are key to screening existing/novel materials, sampling potential energy surfaces, and generating quantum mechanical data for machine learning. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local DFT and furnish a more accurate description of the underlying electronic structure, albeit at a high computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed SeA (SeA=SCDM+exx+ACE), a robust, accurate, and efficient framework for high-throughput condensed-phase hybrid DFT in the PWSCF module of Quantum ESPRESSO (QE) by combining: (1) the non-iterative selected columns of the density matrix (SCDM) orbital localization scheme, (2) a black-box and linear-scaling EXX algorithm (exx), and (3) adaptively compressed exchange (ACE). Across a diverse set of non-equilibrium (H$_2$O)$_{64}$ configurations (with densities spanning 0.4 g/cm$^3$$-$1.7 g/cm$^3$), SeA yields a one$-$two order-of-magnitude speedup (~8X$-$26X) in the overall time-to-solution compared to PWSCF(ACE) in QE (~78X$-$247X speedup compared to the conventional EXX implementation) and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ~8,700 (H$_2$O)$_{64}$ configurations. Using an out-of-sample set of (H$_2$O)$_{512}$ configurations (at non-ambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing > 1,500 atoms.

Graphic layout designs play an essential role in visual communication. Yet handcrafting layout designs is skill-demanding, time-consuming, and non-scalable to batch production. Generative models emerge to make design automation scalable but it remains non-trivial to produce designs that comply with designers' multimodal desires, i.e., constrained by background images and driven by foreground content. We propose LayoutDETR that inherits the high quality and realism from generative modeling, while reformulating content-aware requirements as a detection problem: we learn to detect in a background image the reasonable locations, scales, and spatial relations for multimodal foreground elements in a layout. Our solution sets a new state-of-the-art performance for layout generation on public benchmarks and on our newly-curated ad banner dataset. We integrate our solution into a graphical system that facilitates user studies, and show that users prefer our designs over baselines by significant margins. Our code, models, dataset, graphical system, and demos are available at //github.com/salesforce/LayoutDETR.

In this letter, we investigate a novel quadrature spatial scattering modulation (QSSM) transmission technique based on millimeter wave (mmWave) systems, in which the transmitter generates two orthogonal beams targeting candidate scatterers in the channel to carry the real and imaginary parts of the conventional signal, respectively. Meanwhile, the maximum likelihood (ML) detector is adopted at the receiver to recover the received beams and signals. Based on the ML detector, we derive the closed-form average bit error probability (ABEP) expression of the QSSM scheme. Furthermore, we evaluate the asymptotic ABEP expression of the proposed scheme. Monte Carlo simulations verify the exactness and tightness of the derivation results. It is shown that the ABEP performance of QSSM is better than that of traditional spatial scattering modulation.

Human mesh recovery (HMR) provides rich human body information for various real-world applications such as gaming, human-computer interaction, and virtual reality. Compared to single image-based methods, video-based methods can utilize temporal information to further improve performance by incorporating human body motion priors. However, many-to-many approaches such as VIBE suffer from motion smoothness and temporal inconsistency. While many-to-one approaches such as TCMR and MPS-Net rely on the future frames, which is non-causal and time inefficient during inference. To address these challenges, a novel Diffusion-Driven Transformer-based framework (DDT) for video-based HMR is presented. DDT is designed to decode specific motion patterns from the input sequence, enhancing motion smoothness and temporal consistency. As a many-to-many approach, the decoder of our DDT outputs the human mesh of all the frames, making DDT more viable for real-world applications where time efficiency is crucial and a causal model is desired. Extensive experiments are conducted on the widely used datasets (Human3.6M, MPI-INF-3DHP, and 3DPW), which demonstrated the effectiveness and efficiency of our DDT.

Often in Software Engineering, a modeling formalism has to support scenarios of inconsistency in which several requirements either reinforce or contradict each other. Paraconsistent transition systems are proposed in this paper as one such formalism: states evolve through two accessibility relations capturing weighted evidence of a transition or its absence, respectively. Their weights come from a specific residuated lattice. A category of these systems, and the corresponding algebra, is defined as providing a formal setting to model different application scenarios. One of them, dealing with the effect of quantum decoherence in quantum programs, is used for illustration purposes.