Miracle-dz/UniFAR-FTUs huggingface dataset:
System Architecture and Communication Infrastructure for the RoboVaaS Project

Current advancements in waterborne autonomous systems, together with the development of cloud-based service-oriented architectures and the recent availability of low-cost underwater acoustic modems and long-range above-water wireless devices, enable the development of new applications to support ships and port activities. Autonomous surface vehicles can, for instance, be used to perform bathymetry and environmental data collection tasks to ensure under keel clearance and to monitor the quality of the water. Similarly, remotely operated vehicles can be deployed to inspect ship hulls and typical port infrastructure elements, such as quay and sheet pilling walls. In this article, we present the complete system deployed for the small-scale demonstrations of the Robotic Vessels as-a-Service project, which introduces an on-demand service-based cloud system that dispatches unmanned vehicles capable of performing the required service either autonomously or piloted. These vessels are able to interact with sensors deployed in the port and with the shore station through an integrated underwater and above-water network. The developed system has been validated through sea trials and showcased through an underwater sensor data collection use case. The results of the test presented in this article provide a proof of concept of the system design and indicate its technical feasibility. It also shows the need for further developments for a mature technology allowing on-demand robotic maritime assistance services in real operational scenarios.
Robust Bayesian Recourse

Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnostic recourse that minimizes the posterior probability odds ratio. Further, we present its min-max robust counterpart with the goal of hedging against future changes in the machine learning model parameters. The robust counterpart explicitly takes into account possible perturbations of the data in a Gaussian mixture ambiguity set prescribed using the optimal transport (Wasserstein) distance. We show that the resulting worst-case objective function can be decomposed into solving a series of two-dimensional optimization subproblems, and the min-max recourse finding problem is thus amenable to a gradient descent algorithm. Contrary to existing methods for generating robust recourses, the robust Bayesian recourse does not require a linear approximation step. The numerical experiment demonstrates the effective-ness of our proposed robust Bayesian recourse facing model shifts. Our code is available at https://github . com/VinAIResearch/ robust-bayesian-recourse .
Organizational Failure and Decline - A Bibliometric Study of the Scientific Frontend

In today’s difficult economic times, the number of failing firms remains high. Theories of why companies fail have mainly been developed in a research field traditionally summarized as “Organizational Failure and Decline”. Unfortunately, this research field suffers from strong fragmentation and an unclear research agenda. The aim of this study is to structure existing research with the help of bibliometric methods and present developments in research between 1982 and 2014. Concretely, we perform a co-citation analysis and visualize existing sub-clusters of organizational failure research. We also highlight the most frequently cited publications based on a citation analysis and analyze citation patterns in equally sized periods. Seven sub- clusters define the intellectual foundation of organizational failure and decline research. While an organizational ecology and industrial organization perspective dominates before 2000, the strategic choice literature gains relative attention after 2000. In recent years.
Cold Posteriors through PAC-Bayes

We investigate the cold posterior effect through the lens of PAC-Bayes generalization bounds. We argue that in the non-asymptotic setting, when the number of training samples is (relatively) small, discussions of the cold posterior effect should take into account that approximate Bayesian inference does not readily provide guarantees of performance on out-of-sample data. Instead, out-of-sample error is better described through a generalization bound. In this context, we explore the connections between the ELBO objective from variational inference and the PAC-Bayes objectives. We note that, while the ELBO and PAC-Bayes objectives are similar, the latter objectives naturally contain a temperature parameter $\lambda$ which is not restricted to be $\lambda=1$. For both regression and classification tasks, in the case of isotropic Laplace approximations to the posterior, we show how this PAC-Bayesian interpretation of the temperature parameter captures the cold posterior effect.
Providers-Clients-Robots: Framework for spatial-semantic planning for shared understanding in human-robot interaction

This paper develops a novel framework called Providers-Clients-Robots (PCR), applicable to socially assistive robots that support research on shared understanding in human-robot interactions. Providers, Clients, and Robots share an actionable and intuitive representation of the environment to create plans that best satisfy the combined needs of all parties. The plans are formed via interaction between the Client and the Robot based on a previously built multi-modal navigation graph. The explainable environmental representation in the form of a navigation graph is constructed collaboratively between Providers and Robots prior to interaction with Clients. We develop a realization of the proposed framework to create a spatial-semantic representation of an indoor environment autonomously. Moreover, we develop a planner that takes in constraints from Providers and Clients of the establishment and dynamically plans a sequence of visits to each area of interest. Evaluations show that the proposed realization of the PCR framework can successfully make plans while satisfying the specified time budget and sequence constraints and outperforming the greedy baseline.
Effect of Phonons and Impurities on the Quantum Transport in XXZ Spin-Chains

Numerical and analytic results have been used to characterize quantum transport in spin chains, showing the existence of both ballistic and diffusive motion. Experiments have shown that heat transfer is surprisingly always diffusive. The scattering from phonons and impurities have been postulated to be the two factors critical in causing the diffusive transport. In this work, we evaluate the transport process by incorporating a bath of phonons and impurities in order to understand the role played by each of the factors. While methods like time-dependent density matrix renormalization group (tDMRG) can be used to simulate isolated spin chains, the coupling with phonons make simulations significantly more challenging. The recently developed multisite tensor network path integral (MS-TNPI) method builds a framework for simulating the dynamics in extended open quantum systems by combining ideas from tDMRG and Feynman-Vernon influence functional. This MS-TNPI is used to characterize dynamics in open, extended quantum systems. Simulations are done with the commonly used sub-Ohmic, Ohmic and super-Ohmic spectral densities describing the phononic bath. We show that while the transport in presence of impurities eventually becomes diffusive, the exact details are dependent on the specifics of the interactions and amount of impurities. In contrast, the presence of a bath makes the transport diffusive irrespective of the parameters characterizing the bath.
I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation

In this paper, we present the Intra- and Inter-Human Relation Networks I²R-Net for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relation between multiple instances and focuses on capturing Inter-Human interactions. The Inter-Human Relation Module can be designed very lightweight by reducing the resolution of feature map, yet learn useful relation information to significantly boost the performance of the Intra-Human Relation Module. Even without bells and whistles, our method can compete or outperform current competition winners. We conduct extensive experiments on COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the proposed model surpasses all the state-of-the-art methods. Concretely, the proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman dataset respectively, outperforming existing methods by a large margin. Additionally, the ablation study and visualization analysis also prove the effectiveness of our model.
Subleading asymptotics of link spectral invariants and homeomorphism groups of surfaces

In previous work, we deﬁned “link spectral invariants” for any compact surface and used these to study the algebraic structure of the group of area-preserving homeomorphisms; in particular, we showed that the kernel of Fathi’s mass-ﬂow homomorphism is never simple. A key idea for this was a kind of Weyl law, showing that asymptotically the link spectral invariants recover the classical Calabi invariant. In the present work, we use the subleading asymptotics in this Weyl law to learn more about the algebraic structure of these homeomorphism groups in the genus zero case. In particular, when the surface has boundary, we show that the kernel of the Calabi homomorphism on the group of hameomorphisms is not simple, answering an old question of Oh and M¨uller; this contrasts the smooth case, where the kernel of Calabi is simple. We similarly show that the group of hameomorphisms of the two-sphere is not simple. Related considerations allow us to extend the Calabi homomorphism to the full group of compactly supported area-preserving homeomorphisms, answering a longstanding question of Fathi. In fact, we produce inﬁnitely many distinct extensions. Central to the applications is that we show that the subleading asymptotics for smooth, possibly time-dependent, Hamiltonians are always O p 1 q , and for certain autonomous maps recover the Ruelle invariant. The construction of a hameomorphism with “inﬁnite Ruelle invariant” then shows that a normal subgroup with O p 1 q subleading asymptotics is proper.
Towards Ground Truth for Single Image Deraining

We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As there exists no real-world dataset for deraining, current state-of-the-art methods rely on synthetic data and thus are limited by the sim2real domain gap; moreover, rigorous evaluation remains a challenge due to the absence of a real paired dataset. We fill this gap by collecting the first real paired deraining dataset through meticulous control of non-rain variations. Our dataset enables paired training and quantitative evaluation for diverse real-world rain phenomena (e.g. rain streaks and rain accumulation). To learn a representation invariant to rain phenomena, we propose a deep neural network that reconstructs the underlying scene by minimizing a rain-invariant loss between rainy and clean images. Extensive experiments demonstrate that the proposed dataset benefits existing derainers, and our model can outperform the state-of-the-art deraining methods on real rainy images under various conditions.
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with sparse reward. To tackle this problem, we propose a novel method named MASER: MARL with subgoals generated from experience replay buffer. Under the widely-used assumption of centralized training with decentralized execution and consistent Q-value decomposition for MARL, MASER automatically generates proper subgoals for multiple agents from the experience replay buffer by considering both individual Q-value and total Q-value. Then, MASER designs individual intrinsic reward for each agent based on actionable representation relevant to Q-learning so that the agents reach their subgoals while maximizing the joint action value. Numerical results show that MASER significantly outperforms StarCraft II micromanagement benchmark compared to other state-of-the-art MARL algorithms.
Non-Hermitian skin effect and self-acceleration

Non-Hermitian systems exhibit nontrivial band topology and a strong sensitivity of the energy spectrum on the boundary conditions. Remarkably, a macroscopic number of bulk states get squeezed toward the lattice edges under open boundary conditions, an eﬀect dubbed the non-Hermitian skin eﬀect (NHSE). A well-established dynamical signature of the NHSE in real space is the directional bulk ﬂow (or persistent current) for arbitrary initial excitation of the system, which is observed at long times. Here we unravel a diﬀerent dynamical signature of the NHSE in real space that mani-fests itself in the early-time dynamics of the system, namely self-acceleration of the wave function. Self-acceleration is demonstrated to occur rather generally in single–band lattice models probed by single-site excitation, where the acceleration turns out to be proportional to the area enclosed by the energy spectrum of the Bloch Hamiltonian under periodic boundary conditions. The observation of wave packet self-acceleration at early times is a clear signature of the NHSE and should be experimentally accessible using synthetic non-Hermitian matter, for example in discrete-time photonic quantum walks.
Particle‐resolved simulations of four‐way coupled, polydispersed, particle‐laden flows

We present a collocated‐grid framework for direct numerical simulations of polydisperse particles submerged in a viscous fluid. The fluid‐particle forces are coupled with the immersed boundary method (IBM) while the particle‐particle forces are modeled with a combination of contact and lubrication models, adapted for collocated grids. Our method is modified from the staggered‐grid IBM of previous authors to a collocated‐grid IBM by adapting the fluid and particle solvers. The method scales well on high‐performance parallel computing platforms. It has been validated against various cases and is able to reproduce experimental results. Tuning parameters have been thoroughly calibrated to ensure the accuracy of the method. Finally, we demonstrate the capability of the method to simulate both monodispersed and bidispersed fluidized beds and reproduce the power law relationship between the inflow velocity and the porosity.
FedorAS: Federated Architecture Search under system heterogeneity

Federated learning (FL) has recently gained considerable attention due to its ability to learn on decentralised data while preserving client privacy. However, it also poses additional challenges related to the heterogeneity of the participating devices, both in terms of their computational capabilities and contributed data. Meanwhile, Neural Architecture Search (NAS) has been successfully used with centralised datasets, producing state-of-the-art results in constrained or unconstrained settings. However, such centralised datasets may not be always available for training. Most recent work at the intersection of NAS and FL attempts to alleviate this issue in a cross-silo federated setting, which assumes homogeneous compute environments with datacenter-grade hardware. In this paper we explore the question of whether we can design architectures of different footprints in a cross-device federated setting, where the device landscape, availability and scale are very different. To this end, we design our system, FedorAS, to discover and train promising architectures in a resource-aware manner when dealing with devices of varying capabilities holding non-IID distributed data. We present empirical evidence of its effectiveness across different settings, spanning across three different modalities (vision, speech, text), and showcase its better performance compared to state-of-the-art federated solutions, while maintaining resource efficiency.
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN architectures as well as their basic theoretical properties are still largely unexplored. In this paper, we study the most prominent form of subgraph methods, which employs node-based subgraph selection policies such as ego-networks or node marking and deletion. We address two central questions: (1) What is the upper-bound of the expressive power of these methods? and (2) What is the family of equivariant message passing layers on these sets of subgraphs? Our first step in answering these questions is a novel symmetry analysis which shows that modelling the symmetries of node-based subgraph collections requires a significantly smaller symmetry group than the one adopted in previous works. This analysis is then used to establish a link between Subgraph GNNs and Invariant Graph Networks (IGNs). We answer the questions above by first bounding the expressive power of subgraph methods by 3-WL, and then proposing a general family of message-passing layers for subgraph methods that generalises all previous node-based Subgraph GNNs. Finally, we design a novel Subgraph GNN dubbed SUN, which theoretically unifies previous architectures while providing better empirical performance on multiple benchmarks.
Numerical Simulations of Dark Matter Admixed Neutron Star Binaries

Multi-messenger observations of compact binary mergers provide a new way to constrain the nature of dark matter that may accumulate in and around neutron stars. In this article, we extend the infrastructure of our numerical-relativity code BAM to enable the simulation of neutron stars that contain an additional mirror dark matter component. We perform single star tests to verify our code and the first binary neutron star simulations of this kind. We find that the presence of dark matter reduces the lifetime of the merger remnant and favors a prompt collapse to a black hole. Furthermore, we find differences in the merger time for systems with the same total mass and mass ratio, but different amounts of dark matter. Finally, we find that electromagnetic signals produced by the merger of binary neutron stars admixed with dark matter are very unlikely to be as bright as their dark matter-free counterparts. Given the increased sensitivity of multi-messenger facilities, our analysis gives a new perspective on how to probe the presence of dark matter.
Gaining Insights on U.S. Senate Speeches Using a Time Varying Text Based Ideal Point Model

Estimating political positions of lawmakers has a long tradition in political science. We present the time varying text based ideal point model to study the political positions of lawmakers based on text data. In addition to identifying political positions, our model also provides insights into topical contents and their change over time. We use our model to analyze speeches given in the U.S. Senate between 1981 and 2017 and demonstrate how the results allow to conclude that partisanship between Republicans and Democrats increased in recent years. Further we investigate the political positions of speakers over time as well as at a speciﬁc point in time to identify speakers which are positioned at the extremes of their political party based on their speeches. The topics extracted are inspected to assess how their term compositions diﬀer in dependence of the political position as well as how these term compositions change over time.
Enhancement of the Residual Linear Term in the Thermal Conductivity of Kondo Insulator SmB6 under Uniaxial Tensile Strain

Recent experiments on Kondo insulator SmB6 have revealed bulk excitations that could be Fermions without charge. This would lead to a residual linear term in the thermal conductivity that was not observed universally in experiments. To solve this mystery, we introduce a novel symmetric geometry for performing thermal transport measurements under strains. Magnetic-field-independent residual linear terms were found in flux-grown SmB6 at nominal zero strain, and they grew under a tensile strain, which is known to modify Sm valence. A small growth-dependent difference in the Sm valence might explain the observed discrepancies. We discuss the constraints on the theories of bulk excitations in SmB6.
Ordered-Statistics Decoding with Adaptive Gaussian Elimination Reduction for Short Codes

In this paper, we propose an efficient ordered-statistics decoding (OSD) algorithm with an adaptive Gaussian elimination (GE) reduction technique. The proposed decoder utilizes two decoding conditions to adaptively remove GE in OSD. The first condition determines whether GE could be skipped in the OSD process by estimating the decoding error probability. Then, the second condition is utilized to identify the correct decoding result during the decoding process without GE. The proposed decoder can break the “complexity floor” in OSD decoders introduced by the GE overhead. Simulation results advise that when compared with the latest schemes in the literature, the proposed approach can significantly reduce the decoding complexity at high SNRs without any degradation in the error-correction capability.
Prototypical Contrastive Language Image Pretraining

Contrastive Language Image Pretraining (CLIP) received widespread attention since its learned representations can be transferred well to various downstream tasks. During CLIP training, the InfoNCE objective aims to align positive image-text pairs and separate negative ones. In this paper, we show a representation grouping effect during this process: the InfoNCE objective indirectly groups semantically similar representations together via randomly emerged within-modal anchors. We introduce Prototypical Contrastive Language Image Pretraining (ProtoCLIP) to enhance such grouping by boosting its efficiency and increasing its robustness against modality gap. Specifically, ProtoCLIP sets up prototype-level discrimination between image and text spaces, which efficiently transfers higher-level structural knowledge. We further propose Prototypical Back Translation (PBT) to decouple representation grouping from representation alignment, resulting in effective learning of meaningful representations under large modality gap. PBT also enables us to introduce additional external teachers with richer prior knowledge. ProtoCLIP is trained with an online episodic training strategy, which makes it can be scaled up to unlimited amounts of data. We train our ProtoCLIP on Conceptual Captions and achieved an +5.81% ImageNet linear probing improvement and an +2.01% ImageNet zero-shot classification improvement. On larger YFCC dataset, ProtoCLIP matches the performance of CLIP with 4$\times$fewer pretraining epochs. Codes are available at https://github.com/megvii-research/protoclip.