Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agent Deep Reinforcement Learning Based Resource Management in SWIPT Enabled Cellular Networks with H2H/M2M Co-Existence

Machine-to-Machine (M2M) communication is crucial in developing Internet of Things (IoT). As it is well known that cellular networks have been considered as the primary infrastructure for M2M communications, there are several key issues to be addressed in order to deploy M2M communications over cellular networks. Notably, the rapid growth of M2M traffic dramatically increases energy consumption, as well as degrades the performance of existing Human-to-Human (H2H) traffic. Sustainable operation technology and resource management are efficacious ways for solving these issues. In this paper, we investigate a resource management problem in cellular networks with H2H/M2M coexistence. First, considering the energy-constrained nature of machine type communication devices (MTCDs), we propose a novel network model enabled by simultaneous wireless information and power transfer (SWIPT), which empowers MTCDs with the ability to simultaneously perform energy harvesting (EH) and information decoding. Given the diverse characteristics of IoT devices, we subdivide MTCDs into critical and tolerable types, further formulating the resource management problem as an energy efficiency (EE) maximization problem under divers Quality-of-Service (QoS) constraints. Then, we develop a multi-agent deep reinforcement learning (DRL) based scheme to solve this problem. It provides optimal spectrum, transmit power and power splitting (PS) ratio allocation policies, along with efficient model training under designed behaviour-tracking based state space and common reward function. Finally, we verify that with a reasonable training mechanism, multiple M2M agents successfully work cooperatively in a distributed way, resulting in network performance that outperforms other intelligence approaches in terms of convergence speed and meeting the EE and QoS requirements.
Hierarchical Beam Training for Extremely Large-Scale MIMO: From Far-Field to Near-Field

Extremely large-scale MIMO (XL-MIMO) is a promising technique for future 6G communications. The sharp increase in the number of antennas causes electromagnetic propagation to change from far-field to near-field. Due to the near-field effect, the exhaustive near-field beam training at all angles and distances requires very high overhead. The improved fast near-field beam training scheme based on time-delay structure can reduce the overhead, but it suffers from very high hardware costs and energy consumption caused by time-delay circuits. In this paper, we propose a near-field two dimension (2D) hierarchical beam training scheme to reduce the overhead without the need for extra hardware circuits. Specifically, we first formulate the multi-resolution near-field codewords design problem covering different angle and distance coverages. Next, inspired by phase retrieval problems in digital holography imaging technology, we propose a Gerchberg-Saxton (GS)-based algorithm to acquire the theoretical codeword by considering the ideal fully digital architecture. Based on the theoretical codeword, an alternating optimization algorithm is then proposed to acquire the practical codeword by considering the hybrid digital-analog architecture. Finally, with the help of multi-resolution codebooks, we propose a near-field 2D hierarchical beam training scheme to significantly reduce the training overhead, which is verified by extensive simulation results.
Visual observation of optical Floquet-Bloch oscillations

: Bloch oscillations, an important transport phenomenon, have extensively been studied in static systems but remain largely unexplored in Floquet systems. Here, we propose a new type of Bloch oscillations, namely the “Floquet–Bloch oscillations,” which refer to rescaled Bloch oscillations with a period of extended least common multiple of the modulation and Bloch periods. We report the first visual observation of such Floquet–Bloch oscillations in femtosecond-laser-written waveguide arrays by using waveguide fluorescence microscopy. These Floquet–Bloch oscillations exhibit exotic properties, such as fractal spectrum and fractional Floquet tunneling. This new transport mechanism offers an intriguing method of wave manipulation, which has significant applications in coherent quantum transport.
High reflectance membrane-based distributed Bragg reflectors for GaN photonics

Preparation of highly reflective distributed Bragg reflectors (DBRs) from III-nitrides is an important building block for cavity photonics. In this work, we report the fabrication of a membrane-based GaN/air-gap DBR for blue/green light emitting devices. The formation of membrane DBRs relies on a recently discovered electrochemical procedure in which selective etch is achieved by adjusting the conductivity rather than chemical composition, thus relieving greatly the burden in creating epitaxial DBRs. Micro-reflectance measurement shows over 98% peak reflectance and a wide stopband with only four pairs of GaN/air-gap layers. Micro-photoluminescence spectra of InGaN multiple quantum wells (MQWs) on DBRs show reduced linewidth and improved emission efficiency. After capping the MQWs on DBRs with silver, a significant linewidth narrowing indicates the modification of spontaneous emission due to the presence of a planar microcavity.
On Transforming Reinforcement Learning by Transformer: The Development Trajectory

—Transformer, originally devised for natural language processing, has also attested signiﬁcant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances on transforming RL by transformer (transformer-based RL or TRL), in order to explore its development trajectory and future trend. We group existing developments in two categories: architecture enhancement and trajectory optimization, and examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving. For architecture enhancement, these methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, which model agents and environments much more precisely than deep RL methods, but they are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and “deadly triad”. For trajectory optimization, these methods treat RL problems as sequence modeling and train a joint state-action model over entire trajectories under the behavior cloning framework, which are able to extract policies from static datasets and fully use the long-sequence modeling capability of the transformer. Given these advancements, extensions and challenges in TRL are reviewed and proposals about future direction are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing ﬁeld.
AttEntropy: Segmenting Unknown Objects in Complex Scenes using the Spatial Attention Entropy of Semantic Segmentation Transformers

Vision transformers have emerged as powerful tools for many computer vision tasks. It has been shown that their features and class tokens can be used for salient object segmentation. However, the properties of segmentation transformers remain largely unstudied. In this work we conduct an in-depth study of the spatial attentions of different backbone layers of semantic segmentation transformers and uncover interesting properties. The spatial attentions of a patch intersecting with an object tend to concentrate within the object, whereas the attentions of larger, more uniform image areas rather follow a diffusive behavior. In other words, vision transformers trained to segment a fixed set of object classes generalize to objects well beyond this set. We exploit this by extracting heatmaps that can be used to segment unknown objects within diverse backgrounds, such as obstacles in traffic scenes. Our method is training-free and its computational overhead negligible. We use off-the-shelf transformers trained for street-scene segmentation to process other scene types.
Novel Spring Mechanism Enables Iterative Energy Accumulation under Force and Deformation Constraints

Springs can provide force at zero net energy cost by recycling negative mechanical work to benefit motor-driven robots or spring-augmented humans. However, humans have limited force and range of motion, and motors have a limited ability to produce force. These limits constrain how much energy a conventional spring can store and, consequently, how much assistance a spring can provide. In this paper, we introduce an approach to accumulating negative work in assistive springs over several motion cycles. We show that, by utilizing a novel floating spring mechanism, the weight of a human or robot can be used to iteratively increase spring compression, irrespective of the potential energy stored by the spring. Decoupling the force required to compress a spring from the energy stored by a spring advances prior works, and could enable spring-driven robots and humans to perform physically demanding tasks without the use of large actuators.
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?

We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a direct latent model learning approach, where a dynamic model in some latent state space is learned by predicting quantities directly related to planning (e.g., costs) without reconstructing the observations. In particular, we focus on an intuitive cost-driven state representation learning method for solving Linear Quadratic Gaussian (LQG) control, one of the most fundamental partially observable control problems. As our main results, we establish finite-sample guarantees of finding a near-optimal state representation function and a near-optimal controller using the directly learned latent model. To the best of our knowledge, despite various empirical successes, prior to this work it was unclear if such a cost-driven latent model learner enjoys finite-sample guarantees. Our work underscores the value of predicting multi-step costs, an idea that is key to our theory, and notably also an idea that is known to be empirically valuable for learning state representations.
Active Planning for Cooperative Localization: A Fisher Information Approach

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.
Investigating SINDy as a Tool for Causal Discovery in Time Series Signals

The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.
An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards

In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties. Existing task coordination algorithms either ignore the stochastic process or suffer from the computational intensity. Taking advantage of the weakly coupled feature of the problem and the opportunity for coordination in advance, we propose a decentralized auction-based coordination strategy using a newly formulated score function which is generated by forming the problem into task-constrained Markov decision processes (MDPs). The proposed method guarantees convergence and at least 50% optimality in the premise of a submodular reward function. Furthermore, for the implementation on large-scale applications, an approximate variant of the proposed method, namely Deep Auction, is also suggested with the use of neural networks, which is evasive of the troublesome for constructing MDPs. Inspired by the well-known actor-critic architecture, two Transformers are used to map observations to action probabilities and cumulative rewards respectively. Finally, we demonstrate the performance of the two proposed approaches in the context of drone deliveries, where the stochastic planning for the drone league is cast into a stochastic price-collecting Vehicle Routing Problem (VRP) with time windows. Simulation results are compared with state-of-the-art methods in terms of solution quality, planning efficiency and scalability.
Simulating neutrino oscillations on a superconducting qutrit

Precise measurements of parameters in the PMNS framework might lead to new physics beyond the Standard Model. However, they are incredibly challenging to determine in neutrino oscillation experiments. Quantum simulations can be a powerful supplementary tool to study these phenomenologies. In today's noisy quantum hardware, encoding neutrinos in a multi-qubit system requires a redundant basis and tricky entangling gates. We encode a three-flavor neutrino in a superconducting qutrit and study its oscillations using PMNS theory with time evolution expressed in terms of single qutrit gates. The qutrit is engineered from the multi-level structure of IBM transmon devices. High-fidelity gate control and readout are fine-tuned using programming microwave pulses using a high-level language. Our quantum simulations on real hardware match well to analytical calculations in three oscillation cases: vacuum, interaction with matter, and CP-violation.
Learning to mask: Towards generalized face forgery detection

Generalizability to unseen forgery types is crucial for face forgery detectors. Recent works have made significant progress in terms of generalization by synthetic forgery data augmentation. In this work, we explore another path for improving the generalization. Our goal is to reduce the features that are easy to learn in the training phase, so as to reduce the risk of overfitting on specific forgery types. Specifically, in our method, a teacher network takes as input the face images and generates an attention map of the deep features by a diverse multihead attention ViT. The attention map is used to guide a student network to focus on the low-attended features by reducing the highly-attended deep features. A deep feature mixup strategy is also proposed to synthesize forgeries in the feature domain. Experiments demonstrate that, without data augmentation, our method is able to achieve promising performances on unseen forgeries and highly compressed data.
Overmassive Black Holes in Dwarf Galaxies Out to z ∼ 0.9 in the VIPERS Survey

Supermassive black holes (SMBHs) are thought to originate from early universe seed black holes of mass M BH ∼ 102–105 M ⊙ and grown through cosmic time. Such seeds could be powering the active galactic nuclei (AGN) found in today’s dwarf galaxies. However, probing a connection between the early seeds and local SMBHs has not yet been observationally possible. Massive black holes hosted in dwarf galaxies at intermediate redshifts, on the other hand, may represent the evolved counterparts of the seeds formed at very early times. We present a sample of seven broad-line AGN in dwarf galaxies with a spectroscopic redshift ranging from z = 0.35 to z = 0.93. The sources are drawn from the VIPERS survey as having an Large Magellanic Cloud (LMC) like stellar mass (M ∗) derived from spectral energy distribution fitting, and they are all star-forming galaxies. Six of these sources are also X-ray AGN. The AGN are powered by SMBHs of >107 M ⊙, more massive than expected from the M BH–M ∗ scaling relation of AGN. Based on semianalytical simulations, we find that these objects are likely overmassive with respect to their hosts since early times (z > 4), independently of whether they formed as heavy (∼105 M ⊙) or light (∼102 M ⊙) seed black holes. In our simulations, these objects tend to grow faster than their host galaxies, contradicting models of synchronized growth. The host galaxies are found to possibly evolve into massive systems by z ∼ 0, indicating that local SMBHs in massive galaxies could originate in dwarf galaxies hosting seed black holes at higher z.
Multimodal Wildland Fire Smoke Detection

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires.
From the binomial reshuffling model to Poisson distribution of money

We present a novel reshuffling exchange model and investigate its long time behavior. In this model, two individuals are picked randomly, and their wealth $ X_i $ and $ X_j $ are redistributed by flipping a sequence of fair coins leading to a binomial distribution denoted $ B\circ (X_i+X_j) $. This dynamics can be considered as a natural variant of the so-called uniform reshuffling model in econophysics. May refer to Cao, Jabin and Motsch (2023), Dragulescu and Yakovenko (2000). As the number of individuals goes to infinity, we derive its mean-field limit, which links the stochastic dynamics to a deterministic infinite system of ordinary differential equations. Our aim of this work is then to prove (using a coupling argument) that the distribution of wealth converges to the Poisson distribution in the $ 2 $-Wasserstein metric. Numerical simulations illustrate the main result and suggest that the polynomial convergence decay might be further improved.
Refinements of asymptotics at zero of Brownian self-intersection local times

In this article we establish some estimates related to the Gaussian densities and to Hermite polynomials in order to obtain an almost sure estimate for each term of the It\^{o}-Wiener expansion of the self-intersection local times of the Brownian motion. In dimension $d\geqslant 4$ the self-intersection local times of the Brownian motion can be considered as a family of measures on the classical Wiener space. We provide some asymptotics relative to these measures. Finally, we try to estimate the quadratic Wasserstein distance between these measures and the Wiener measure.
(1 − (cid:15) )-Approximate Maximum Weighted Matching in poly(1 /(cid:15), log n ) Time in the Distributed and Parallel Settings

The maximum weighted matching ( mwm ) problem is one of the most well-studied combinatorial optimization problems in distributed graph algorithms. Despite a long development on the problem, and the recent progress of Fischer, Mitrovic, and Uitto [FMU22] who gave a poly(1 /(cid:15), log n )-round algorithm for obtaining a (1 − (cid:15) )-approximate solution for the unweighted maximum matching, it had been an open problem whether a (1 − (cid:15) )-approximate mwm can be obtained in poly(log n, 1 /(cid:15) ) rounds in the CONGEST model. Algorithms with such running times were only known for special graph classes such as bipartite graphs [AKO18] and minor-free graphs [CS22]. For general graphs, the previously known algorithms require exponential in (1 /(cid:15) ) rounds for obtaining a (1 − (cid:15) )-approximate solution [FFK21] or achieve an approximation factor of at most 2/3 [AKO18]. In this work, we settle this open problem by giving a deterministic poly(1 /(cid:15), log n )-round algorithm for computing a (1 − (cid:15) )-approximate mwm for general graphs in the CONGEST model. Our proposed solution extends the algorithm of Fischer, Mitrovic, and Uitto [FMU22], blends in the sequential algorithm from Duan and Pettie [DP14] and the work of Faour, Fuchs, and Kuhn [FFK21]. Interestingly, this solution also implies a CREW PRAM algorithm with poly(1 /(cid:15), log n ) span using only O ( m ) processors.
A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators

Kernels are efficient in representing nonlocal dependence and they are widely used to design operators between function spaces. Thus, learning kernels in operators from data is an inverse problem of general interest. Due to the nonlocal dependence, the inverse problem can be severely ill-posed with a data-dependent singular inversion operator. The Bayesian approach overcomes the ill-posedness through a non-degenerate prior. However, a fixed non-degenerate prior leads to a divergent posterior mean when the observation noise becomes small, if the data induces a perturbation in the eigenspace of zero eigenvalues of the inversion operator. We introduce a data-adaptive prior to achieve a stable posterior whose mean always has a small noise limit. The data-adaptive prior's covariance is the inversion operator with a hyper-parameter selected adaptive to data by the L-curve method. Furthermore, we provide a detailed analysis on the computational practice of the data-adaptive prior, and demonstrate it on Toeplitz matrices and integral operators. Numerical tests show that a fixed prior can lead to a divergent posterior mean in the presence of any of the four types of errors: discretization error, model error, partial observation and wrong noise assumption. In contrast, the data-adaptive prior always attains posterior means with small noise limits.