Miracle-dz/UniFAR-FTUs huggingface dataset:
Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation

Radio signals are used broadly as navigation aids, and current and future terrestrial wireless communication systems have properties that make their dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable widespread coverage for data communication and navigation, but typically offer smaller bandwidths and limited resolution for precise estimation of geometries, particularly in environments where propagation channels are diffuse in time and/or space. Non-parametric methods have been employed with some success for such scenarios both commercially and in literature, but often with an emphasis on low-cost hardware and simple models of propagation, or with simulations that do not fully capture hardware impairments and complex propagation mechanisms. In this article, we make opportunistic observations of downlink signals transmitted by commercial cellular networks by using a software-defined radio and massive antenna array mounted on a passenger vehicle in an urban non line-of-sight scenario, together with a ground truth reference for vehicle pose. With these observations as inputs, we employ artificial neural networks to generate estimates of vehicle location and heading for various artificial neural network architectures and different representations of the input observation data, which we call wiometrics, and compare the performance for navigation. Position accuracy on the order of a few meters, and heading accuracy of a few degrees, are achieved for the best-performing combinations of networks and wiometrics. Based on the results of the experiments we draw conclusions regarding possible future directions for wireless navigation using statistical methods.
Prompt-based Effective Input Reformulation for Legal Case Retrieval

Legal case retrieval plays an important role for legal practitioners to effectively retrieve relevant cases given a query case. Most existing neural legal case retrieval models directly encode the whole legal text of a case to generate a case representation, which is then utilised to conduct a nearest neighbour search for retrieval. Although these straightforward methods have achieved improvement over conventional statistical methods in retrieval accuracy, two significant challenges are identified in this paper: (1) Legal feature alignment: the usage of the whole case text as the input will generally incorporate redundant and noisy information because, from the legal perspective, the determining factor of relevant cases is the alignment of key legal features instead of whole text matching; (2) Legal context preservation: furthermore, since the existing text encoding models usually have an input length limit shorter than the case, the whole case text needs to be truncated or divided into paragraphs, which leads to the loss of the global context of legal information. In this paper, a novel legal case retrieval framework, PromptCase, is proposed to tackle these challenges. Firstly, legal facts and legal issues are identified and formally defined as the key features facilitating legal case retrieval based on a thorough study of the definition of relevant cases from a legal perspective. Secondly, with the determining legal features, a prompt-based encoding scheme is designed to conduct an effective encoding with language models. Extensive zero-shot experiments have been conducted on two benchmark datasets in legal case retrieval, which demonstrate the superior retrieval effectiveness of the proposed PromptCase. The code has been released on https://github.com/yanran-tang/PromptCase.
Generalised Mutual Information: a Framework for Discriminative Clustering

In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely discussed for improvements, little attention has been dedicated to the relevance of MI as a clustering objective. In this paper, we first highlight how the maximisation of MI does not lead to satisfying clusters. We identified the Kullback-Leibler divergence as the main reason of this behaviour. Hence, we generalise the mutual information by changing its core distance, introducing the Generalised Mutual Information (GEMINI): a set of metrics for unsupervised neural network training. Unlike MI, some GEMINIs do not require regularisations when training as they are geometry-aware thanks to distances or kernels in the data space. Finally, we highlight that GEMINIs can automatically select a relevant number of clusters, a property that has been little studied in deep discriminative clustering context where the number of clusters is a priori unknown.
Black-Box Attacks against Signed Graph Analysis via Balance Poisoning

Signed graphs are well-suited for modeling social networks as they capture both positive and negative relationships. Signed graph neural networks (SGNNs) are commonly employed to predict link signs (i.e., positive and negative) in such graphs due to their ability to handle the unique structure of signed graphs. However, real-world signed graphs are vulnerable to malicious attacks by manipulating edge relationships, and existing adversarial graph attack methods do not consider the specific structure of signed graphs. SGNNs often incorporate balance theory to effectively model the positive and negative links. Surprisingly, we find that the balance theory that they rely on can ironically be exploited as a black-box attack. In this paper, we propose a novel black-box attack called balance-attack that aims to decrease the balance degree of the signed graphs. We present an efficient heuristic algorithm to solve this NP-hard optimization problem. We conduct extensive experiments on five popular SGNN models and four real-world datasets to demonstrate the effectiveness and wide applicability of our proposed attack method. By addressing these challenges, our research contributes to a better understanding of the limitations and resilience of robust models when facing attacks on SGNNs. This work contributes to enhancing the security and reliability of signed graph analysis in social network modeling. Our PyTorch implementation of the attack is publicly available on GitHub: https://github.com/JialongZhou666/Balance-Attack.git.
Logical states for fault-tolerant quantum computation with propagating light

To harness the potential of a quantum computer, quantum information must be protected against error by encoding it into a logical state that is suitable for quantum error correction. The Gottesman-Kitaev-Preskill (GKP) qubit is a promising candidate because the required multiqubit operations are readily available at optical frequency. To date, however, GKP qubits have been demonstrated only at mechanical and microwave frequencies. We realized a GKP state in propagating light at telecommunication wavelength and verified it through homodyne measurements without loss corrections. The generation is based on interference of cat states, followed by homodyne measurements. Our final states exhibit nonclassicality and non-Gaussianity, including the trident shape of faint instances of GKP states. Improvements toward brighter, multipeaked GKP qubits will be the basis for quantum computation with light. Editor’s summary Quantum computers under development are at the intermediate size scale and are already demonstrating quantum advantage over classical systems for specific tasks. Going to larger scale systems is challenging for some solid-state platforms. Optics provides a possible route, but optical systems require engineered photonic states to mitigate for loss and to run error correction codes. Konno et al. demonstrate the generation of Gottesman-Kitaev-Preskill states, or grid states, in which the wavefunction resembles a sharp-peaked two-dimensional array (see the Perspective by Pfister). These states have been predicted to be fault tolerant, allowing quantum error correction codes to be readily implemented. Realizing such engineered photonic states will be important in the development of large-scale optical quantum computers. —Ian S. Osborne Engineered photonic states required for fault-tolerant optical quantum computing were generated.
Design principles for>90% efficiency and>99% indistinguishability broadband quantum dot cavities

Quantum dots have the potential to be the brightest deterministic single photon source with plausible high end applications in quantum computing and cluster state generation. In this work, we re-examine the design of simple micropillars by meticulously examining the structural effects of the decay into leaky channels beyond the atom-like cavity estimation. We show that precise control of the side losses with the diameter and avoidance of propagating Bloch modes in the DBR structure can result in easy to manufacture broadband (Q$\approx750-2500$) micropillars and demonstrate extremely high internal efficiency ($90.5\%-96.4\%$). We also demonstrate that such cavities naturally decouple from the phonon sideband, with the phonon sideband reducing by a factor of $5-33$ allowing us to predict that the photons should show $99.2\%-99.8\%$ indistinguishability.
Time delays in anisotropic systems

Scattering properties and time delays for general (non-symmetric) potentials in terms of the respective S-matrices are discussed paradigmatically in one dimension and in comparison to symmetric potentials. Only for the latter the Wigner and Smith time delays coincide. Con- sidering asymmetric potentials also reveals that only one version of S-matrices used in the literature (the one with reflection coefficients on the diagonal) generalizes to the asymmetric case. Finally, we give a criterion how to identify a potential with intrinsic symmetry which behaves like an asymmetric one if it is merely offset from the scattering center.
Exact Inference for Continuous-Time Gaussian Process Dynamics

Many physical systems can be described as a continuous-time dynamical system. In practice, the true system is often unknown and has to be learned from measurement data. Since data is typically collected in discrete time, e.g. by sensors, most methods in Gaussian process (GP) dynamics model learning are trained on one-step ahead predictions. While this scheme is mathematically tempting, it can become problematic in several scenarios, e.g. if measurements are provided at irregularly-sampled time steps or physical system properties have to be conserved. Thus, we aim for a GP model of the true continuous-time dynamics. We tackle this task by leveraging higher-order numerical integrators. These integrators provide the necessary tools to discretize dynamical systems with arbitrary accuracy. However, most higher-order integrators require dynamics evaluations at intermediate time steps, making exact GP inference intractable. In previous work, this problem is often addressed by approximate inference techniques. However, exact GP inference is preferable in many scenarios, e.g. due to its mathematical guarantees. In order to enable direct inference, we propose to leverage multistep and Taylor integrators. We demonstrate how exact inference schemes can be derived for these types of integrators. Further, we derive tailored sampling schemes that allow one to draw consistent dynamics functions from the posterior. The learned model can thus be integrated with arbitrary integrators, just like a standard dynamical system. We show empirically and theoretically that our approach yields an accurate representation of the continuous-time system.
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation~(ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication~(ISAC) devices to sense the targets and offload the data to the base station~(BS) for further processing. However, as the number of ISAC devices growing, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game~(MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which models the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.
Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments

In the area of learning-driven artificial intelligence advancement, the integration of machine learning (ML) into self-driving (SD) technology stands as an impressive engineering feat. Yet, in real-world applications outside the confines of controlled laboratory scenarios, the deployment of self-driving technology assumes a life-critical role, necessitating heightened attention from researchers towards both safety and efficiency. To illustrate, when a self-driving model encounters an unfamiliar environment in real-time execution, the focus must not solely revolve around enhancing its anticipated performance; equal consideration must be given to ensuring its execution or real-time adaptation maintains a requisite level of safety. This study introduces an algorithm for online meta-reinforcement learning, employing lookahead symbolic constraints based on \emph{Neurosymbolic Meta-Reinforcement Lookahead Learning} (NUMERLA). NUMERLA proposes a lookahead updating mechanism that harmonizes the efficiency of online adaptations with the overarching goal of ensuring long-term safety. Experimental results demonstrate NUMERLA confers the self-driving agent with the capacity for real-time adaptability, leading to safe and self-adaptive driving under non-stationary urban human-vehicle interaction scenarios.
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges

In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behaviors manually or defining their behavior through reward functions (as done in reinforcement learning (RL)) has become exceedingly difficult. This is because such environments require a high degree of flexibility and adaptability, making it challenging to specify an optimal set of rules or reward signals that can account for all possible situations. In such environments, learning from an expert's behavior through imitation is often more appealing. This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert's behavior, which is provided through demonstrations. This paper aims to provide an introduction to IL and an overview of its underlying assumptions and approaches. It also offers a detailed description of recent advances and emerging areas of research in the field. Additionally, the paper discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of the paper is to provide a comprehensive guide to the growing field of IL in robotics and AI.
A Lightweight and Transferable Design for Robust LEGO Manipulation

LEGO is a well-known platform for prototyping pixelized objects. However, robotic LEGO prototyping (i.e. manipulating LEGO bricks) is challenging due to the tight connections and accuracy requirement. This paper investigates safe and efficient robotic LEGO manipulation. In particular, this paper reduces the complexity of the manipulation by hardware-software co-design. An end-of-arm tool (EOAT) is designed, which reduces the problem dimension and allows large industrial robots to easily manipulate LEGO bricks. In addition, this paper uses evolution strategy to safely optimize the robot motion for LEGO manipulation. Experiments demonstrate that the EOAT performs reliably in manipulating LEGO bricks and the learning framework can effectively and safely improve the manipulation performance to a 100% success rate. The co-design is deployed to multiple robots (i.e. FANUC LR-mate 200id/7L and Yaskawa GP4) to demonstrate its generalizability and transferability. In the end, we show that the proposed solution enables sustainable robotic LEGO prototyping, in which the robot can repeatedly assemble and disassemble different prototypes.
Emergence of Exotic Spin Texture in Supramolecular Metal Complexes on a 2D Superconductor

Designer heterostructures, where the desired physics emerges from the controlled interactions between different components, represent one of the most powerful strategies to realize unconventional electronic states. This approach has been particularly fruitful in combining magnetism and superconductivity to create exotic superconducting states. In this work, we use a heterostructure platform combining supramolecular metal complexes (SMCs) with a quasi-2D van der Waals (vdW) superconductor NbSe$_2$. Our scanning tunneling microscopy (STM) measurements demonstrate the emergence of Yu-Shiba-Rusinov (YSR) bands arising from the interaction between the SMC magnetism and the NbSe$_2$ superconductivity. Using X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) measurements, we show the presence of antiferromagnetic coupling between the SMC units. These result in the emergence of an unconventional $3\times3$ reconstruction in the magnetic ground state that is directly reflected in real space modulation of the YSR bands. The combination of flexible molecular building blocks, frustrated magnetic textures, and superconductivity in heterostructures establishes a fertile starting point to fabricating tunable quantum materials, including unconventional superconductors and quantum spin liquids.
Forgotten treasures in the HST/FOC UV imaging polarimetric archives of active galactic nuclei. I. Pipeline and benchmarking against NGC 1068 and exploring IC 5063

Over its 13 years of operation (1990 -- 2002), the Faint Object Camera (FOC) on board the Hubble Space Telescope (HST) observed 26 individual active galactic nuclei (AGNs) in ultraviolet (UV) imaging polarimetry. However, not all of the observations have been reduced and analyzed or set within a standardized framework. We plan to reduce and analyze the AGN observations that have been neglected in the FOC archives using a consistent, novel, and open-access reduction pipeline of our own. We then extend the method to the full AGN sample, thus leading to potential discoveries in the near future. We developed a new pipeline in Python that will be able to reduce all the FOC observations in imaging polarimetry in a homogeneous way. Most of the previously published reduced observations are dispersed throughout the literature, with the range of different analyses and approaches making it difficult to fully interpret the FOC AGN sample. By standardizing the method, we have enabled a coherent comparison among the different observational sets. In this first paper of a series exploring the full HST/FOC AGN sample, we present an exhaustively detailed account of how to properly reduce the observational data. Current progress in data-analysis is implemented in and has provided state-of-the-art UV polarimetric maps. We compare our new maps to the benchmark AGN case of NGC~1068 and successfully reproduce the main results previously published, while pushing the polarimetric exploration of this AGN futher, thanks to a finer resolution and a higher signal-to-noise ratio (S/N) than previously reported. We also present, for the first time, an optical polarimetric map of the radio-loud AGN IC~5063 and we examine the complex interactions between the AGN outflows and the surrounding interstellar medium (ISM).
On computing the jump condition of the dissipation rate in the two-equation turbulence models for two-phase flow and application to air-water waves

Traditional turbulence models are derived for single-phase flow. Extension of the family of two-equation turbulence models for two-phase flow is obtained via scaling the transport equations by the density. In the special case of two-phase flow with a sharp interface, jump conditions exist. Two types of jump conditions are found: (1) jump in the partial differential equation (PDE) physical quantities such as density and viscosity and (2) jump in the turbulence frequency. We first derive and clarify the jump in the equations. The jump in the turbulence frequency is proportional to the kinematic viscosity ratio, which is approximately $10$ in the case of air-water. Then a new field, the inverse turbulence area, is considered to model the turbulence effects instead of the turbulence frequency. For the system of air and water, the effect of the jump of the kinematic viscosity is always greater than the effect arising from the jump of velocity gradient. This approximation leads to the assumption of a continuous inverse turbulence area scale. Validation versus experimental measurements from the literature is then presented to demonstrate the improvement of the model. In particular, the wave breaking phenomenon is simulated in two conditions: spilling and plunging wave breakers. The proposed model shows its ability to predict the turbulence in the surf zone accurately. Finally, it explains the low values of the time-averaged turbulent kinetic energy in the surf zone which is caused by the increase of the turbulence frequency in the air.