Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

Designing effective routing strategies for mobile wireless networks is challenging due to the need to seamlessly adapt routing behavior to spatially diverse and temporally changing network conditions. In this work, we use deep reinforcement learning (DeepRL) to learn a scalable and generalizable single-copy routing strategy for such networks. We make the following contributions: i) we design a reward function that enables the DeepRL agent to explicitly trade-off competing network goals, such as minimizing delay vs. the number of transmissions per packet; ii) we propose a novel set of relational neighborhood, path, and context features to characterize mobile wireless networks and model device mobility independently of a specific network topology; and iii) we use a flexible training approach that allows us to combine data from all packets and devices into a single offline centralized training set to train a single DeepRL agent. To evaluate generalizeability and scalability, we train our DeepRL agent on one mobile network scenario and then test it on other mobile scenarios, varying the number of devices and transmission ranges. Our results show our learned single-copy routing strategy outperforms all other strategies in terms of delay except for the optimal strategy, even on scenarios on which the DeepRL agent was not trained.
Testing dark energy models with gamma-ray bursts calibrated from the observational H(z) data through a gaussian process

We use a cosmology-independent method to calibrate gamma-ray burst (GRB) from the observational Hubble data (OHD) with the cosmic chronometers method. By using Gaussian Process to reconstruct OHD, we calibrate the Amati relation (Ep–Eiso) to construct a GRB Hubble diagram with the A118 data set, and constrain Dark Energy models in a flat space with the Markov Chain Monte Carlo numerical method. With the cosmology-independent GRBs at 1.4 < z ≤ 8.2 in the A118 data set and the Pantheon sample of type Ia supernovae (SNe Ia) at 0.01 < z ≤ 2.3, we obtained Ωm = $0.379^{+0.033}_{-0.024}$, h = $0.701^{+0.0035}_{-0.0035}$, w = $-1.25^{+0.14}_{-0.12}$, wa = $-0.84^{+0.81}_{-0.38}$ for the flat CPL model at the 1σ confidence level. We find no significant evidence supporting deviations from the standard ΛCDM model.
Imitating Graph-Based Planning with Goal-Conditioned Policies

Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoal-conditioned policies. However, the sample-efficiency of such RL schemes still remains a challenge, particularly for long-horizon tasks. To address this issue, we present a simple yet effective self-imitation scheme which distills a subgoal-conditioned policy into the target-goal-conditioned policy. Our intuition here is that to reach a target-goal, an agent should pass through a subgoal, so target-goal- and subgoal- conditioned policies should be similar to each other. We also propose a novel scheme of stochastically skipping executed subgoals in a planned path, which further improves performance. Unlike prior methods that only utilize graph-based planning in an execution phase, our method transfers knowledge from a planner along with a graph into policy learning. We empirically show that our method can significantly boost the sample-efficiency of the existing goal-conditioned RL methods under various long-horizon control tasks.
A Pervasive Framework for Human Detection and Tracking

The advent of the Edge Computing (EC) leads to a huge ecosystem where numerous nodes can interact with data collection devices located close to end users. Human detection and tracking can be realized at edge nodes that perform the surveillance of an area under consideration through the assistance of a set of sensors (e.g., cameras). Our target is to incorporate the discussed functionalities to embedded devices present at the edge keeping their size limited while increasing their processing capabilities. In this paper, we propose two models for human detection accompanied by algorithms for tracing the corresponding trajectories. We provide the description of the proposed models and extend them to meet the challenges of the problem. Our evaluation aims at identifying models' accuracy while presenting their requirements to have them executed in embedded devices.
Physical Evolution of Dark Matter Halo around the Depletion Boundary

We investigate the buildup of the halo profile out to large scale in a cosmological simulation, focusing on the roles played by the recently proposed depletion radii. We explicitly show that halo growth is accompanied by the depletion of the environment, with the inner depletion radius demarcating the two. This evolution process is also observed via the formation of a trough in the bias profile, with the two depletion radii identifying key scales in the evolution. The ratio between the inner depletion radius and the virial radius is approximately a constant factor of 2 across redshifts and halo masses. The ratio between their enclosed densities is also close to a constant of 0.18. These simple scaling relations reflect the largely universal scaled mass profile on these scales, which only evolves weakly with redshift. The overall picture of the boundary evolution can be broadly divided into three stages according to the maturity of the depletion process, with cluster halos lagging behind low-mass ones in the evolution. We also show that the traditional slow and fast accretion dichotomy of halo growth can be identified as accelerated and decelerated depletion phases, respectively.
Beyond independent error assumptions in large GNN atomistic models.

The calculation of relative energy difference has significant practical applications, such as determining adsorption energy, screening for optimal catalysts with volcano plots, and calculating reaction energies. Although Density Functional Theory (DFT) is effective in calculating relative energies through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To address this, we analyzed ∼483 × 106 pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020-Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, challenging the assumption of independent errors in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio. Our findings suggest that state-of-the-art GNN models can achieve error reduction of up to 77% in these subgroups, which is comparable to the error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual energy predictions and distinguish subtle energy differences. We propose the marginal correct sign ratio as a metric to evaluate this performance. Additionally, our results show that the similarity in local embeddings is related to the magnitude of error cancellation, indicating the need for a proper training method that can augment the embedding similarity for chemically similar adsorbate-catalyst systems.
Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning

A robot operating in unstructured environments must be able to discriminate between different grasping styles depending on the prospective manipulation task. Having a system that allows learning from remote non-expert demonstrations can very feasibly extend the cognitive skills of a robot for task-oriented grasping. We propose a novel two-step framework towards this aim. The first step involves grasp area estimation by segmentation. We receive grasp area demonstrations for a new task via interactive segmentation, and learn from these few demonstrations to estimate the required grasp area on an unseen scene for the given task. The second step is autonomous grasp estimation in the segmented region. To train the segmentation network for few-shot learning, we built a grasp area segmentation (GAS) dataset with 10089 images grouped into 1121 segmentation tasks. We benefit from an efficient meta learning algorithm for training for few-shot adaptation. Experimental evaluation showed that our method successfully detects the correct grasp area on the respective objects in unseen test scenes and effectively allows remote teaching of new grasp strategies by non-experts.
VPU-EM: An Event-based Modeling Framework to Evaluate NPU Performance and Power Efficiency at Scale

State-of-art NPUs are typically architected as a self-contained sub-system with multiple heterogeneous hardware computing modules, and a dataflow-driven programming model. There lacks well-established methodology and tools in the industry to evaluate and compare the performance of NPUs from different architectures. We present an event-based performance modeling framework, VPU-EM, targeting scalable performance evaluation of modern NPUs across diversified AI workloads. The framework adopts high-level event-based system-simulation methodology to abstract away design details for speed, while maintaining hardware pipelining, concurrency and interaction with software task scheduling. It is natively developed in Python and built to interface directly with AI frameworks such as Tensorflow, PyTorch, ONNX and OpenVINO, linking various in-house NPU graph compilers to achieve optimized full model performance. Furthermore, VPU-EM also provides the capability to model power characteristics of NPU in Power-EM mode to enable joint performance/power analysis. Using VPU-EM, we conduct performance/power analysis of models from representative neural network architecture. We demonstrate that even though this framework is developed for Intel VPU, an Intel in-house NPU IP technology, the methodology can be generalized for analysis of modern NPUs.
Efficient Public Good Provision in a Multipolar World

We model a public goods game with groups, position uncertainty, and observational learning. Contributions are simultaneous within groups, but groups play sequentially based on their observation of an incomplete sample of past contributions. We show that full cooperation between and within groups is possible with self-interested players on a fixed horizon. Position uncertainty implies the existence of an equilibrium where groups of players conditionally cooperate in the hope of influencing further groups. Conditional cooperation implies that each group member is pivotal, so that efficient simultaneous provision within groups is an equilibrium.
Standard errors when a regressor is randomly assigned

We examine asymptotic properties of the OLS estimator when the values of the regressor of interest are assigned randomly and independently of other regressors. We find that the OLS variance formula in this case is often simplified, sometimes substantially. In particular, when the regressor of interest is independent not only of other regressors but also of the error term, the textbook homoskedastic variance formula is valid even if the error term and auxiliary regressors exhibit a general dependence structure. In the context of randomized controlled trials, this conclusion holds in completely randomized experiments with constant treatment effects. When the error term is heteroscedastic with respect to the regressor of interest, the variance formula has to be adjusted not only for heteroscedasticity but also for correlation structure of the error term. However, even in the latter case, some simplifications are possible as only a part of the correlation structure of the error term should be taken into account. In the context of randomized control trials, this implies that the textbook homoscedastic variance formula is typically not valid if treatment effects are heterogenous but heteroscedasticity-robust variance formulas are valid if treatment effects are independent across units, even if the error term exhibits a general dependence structure. In addition, we extend the results to the case when the regressor of interest is assigned randomly at a group level, such as in randomized control trials with treatment assignment determined at a group (e.g., school/village) level.
Chiral topological light for detecting robust enantio-sensitive observables

The topological response of matter to electromagnetic fields is a property in high demand in materials design and metrology due to its robustness against noise and decoherence, stimulating recent advances in ultrafast photonics. Embedding topological properties into the enantio-sensitive optical response of chiral molecules could therefore enhance the efficiency and robustness of chiral optical discrimination. Here we achieve such a topological embedding by introducing the concept of chiral topological light~-- a light beam which displays chirality locally, with an azimuthal distribution of its handedness described globally by a topological charge. The topological charge is mapped onto the azimuthal intensity modulation of the non-linear optical response, where enantio-sensitivity is encoded into its spatial rotation. The spatial rotation is robust against intensity fluctuations and imperfect local polarization states of the driving field. Our theoretical results show that chiral topological light enables detection of percentage-level enantiomeric excesses in randomly oriented mixtures of chiral molecules, opening a way to new, extremely sensitive and robust chiro-optical spectroscopies with attosecond time resolution.
Retrieving Multimodal Information for Augmented Generation: A Survey

As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world. However, there lacks a unified perception of at which stage and how to incorporate different modalities. In this survey, we review methods that assist and augment generative models by retrieving multimodal knowledge, whose formats range from images, codes, tables, graphs, to audio. Such methods offer a promising solution to important concerns such as factuality, reasoning, interpretability, and robustness. By providing an in-depth review, this survey is expected to provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs.
Application of high T/sub c/ SQUID magnetometer to biological immunoassays

A high T/sub c/ SQUID system is developed for the application to biological immunoassay. In this application, magnetic nanoparticles are used as magnetic markers to perform immunoassay, i.e., to detect binding reaction between an antigen and its antibody. Design and set up of the system is described. Minimum detectable amplitude of the magnetic flux is 0.6 m/spl Phi//sub 0/ for the measurement bandwidth from 0.2 Hz to 5 Hz when we use a magnetometer. The system noise does not increase when the magnetic field of 0.8 mT is applied in parallel to the SQUID. An experiment to measure the antigen-antibody reaction shows that the sensitivity of the present system is 10 times better than that of the conventional method using an optical marker. When a gradiometer is used, the system noise decreased by a factor of 5, compared to the case of the magnetometer. This improvement indicates the usefulness of the gradiometer to suppress the residual environmental noise in the present system. Magnetic markers that have remanent magnetic moment are also studied in order to increase the signal.
Proxy Forecasting to Avoid Stochastic Decision Rules in Decision Markets

Information that is of relevance for decision-making is often distributed, and held by self-interested agents. Decision markets are well-suited mechanisms to elicit such information and aggregate it into conditional forecasts that can be used for decision-making. However, for incentive-compatible elicitation, decision markets rely on stochastic decision rules which entails that sometimes actions have to be taken that have been predicted to be sub-optimal. In this work, we propose three closely related mechanisms that elicit and aggregate information similar to a decision market, but are incentive compatible despite using a deterministic decision rule. Following ideas from peer prediction mechanisms, proxies rather than observed future outcomes are used to score predictions. The first mechanism requires the principal to have her own signal, which is then used as a proxy to elicit information from a group of self-interested agents. The principal then deterministically maps the aggregated forecasts and the proxy to the best possible decision. The second and third mechanisms expand the first to cover a scenario where the principal does not have access to her own signal. The principal offers a partial profit to align the interest of one agent and retrieve its signal as a proxy; or alternatively uses a proper peer prediction mechanism to elicit signals from two agents. Aggregation and decision-making then follow the first mechanism. We evaluate our first mechanism using a multi-agent bandit learning system. The result suggests that the mechanism can train agents to achieve a performance similar to a Bayesian inference model with access to all information held by the agents.
Investigate the strong coupling of $g_{X J/\psi\phi}$ in $X(4500) \to J/\psi \phi$ by using the three-point sum rules and the light-cone sum rules

We assign $X(4500)$ as a D-wave tetraquark state and study the decay of $X(4500)$ $\to$ $J/\psi \phi$. The mass and the decay constant of $X(4500)$ are calculated by using the SVZ sum rules. For the decay width of $X(4500)$ $\to$ $J/\psi \phi$, we present the calculation within the framework of both the three-point sum rules and the light-cone sum rules. The strong coupling $g_{X J/\psi \phi}$ is obtained by considering the soft-meson approximation when we use the light-cone sum rules calculation. Both calculations show that the decay of $X(4500)$ $\to$ $J/\psi\phi$ close to the total width of $X(4500)$ if we assign $X$(4500) as a D-wave tetraquark. In this paper, only the hidden-charm decay channel is considered. With these results and those from the open-charm decay channel, we are able to give a more rational conclusion when comparing with the total width of $X(4500)$. In the future, experiments will be more helpful in determining whether or not this structure of $X(4500)$ is appropriate.
Dynamical Hyperspectral Unmixing With Variational Recurrent Neural Networks

Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers) and of their proportions (abundances) in a given scene. However, adequately accounting for the spatial and temporal variability of the endmembers in MTHU is challenging, and has not been fully addressed so far in unsupervised frameworks. In this work, we propose an unsupervised MTHU algorithm based on variational recurrent neural networks. First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process. Moreover, a new model based on a low-dimensional parametrization is used to represent spatial and temporal endmember variability, significantly reducing the amount of variables to be estimated. We propose to formulate MTHU as a Bayesian inference problem. However, the solution to this problem does not have an analytical solution due to the nonlinearity and non-Gaussianity of the model. Thus, we propose a solution based on deep variational inference, in which the posterior distribution of the estimated abundances and endmembers is represented by using a combination of recurrent neural networks and a physically motivated model. The parameters of the model are learned using stochastic backpropagation. Experimental results show that the proposed method outperforms state of the art MTHU algorithms.
Adaptive Modified RISE-based Quadrotor Trajectory Tracking with Actuator Uncertainty Compensation

This paper presents an adaptive robust nonlinear control method, which achieves reliable trajectory tracking control for a quadrotor unmanned aerial vehicle in the presence of gyroscopic effects, rotor dynamics, and external disturbances. Through novel mathematical manipulation in the error system development, the quadrotor dynamics are expressed in a control-oriented form, which explicitly incorporates the uncertainty in the gyroscopic term and control actuation term. An adaptive robust nonlinear control law is then designed to stabilize both the position and attitude loops of the quadrotor system. A rigorous Lyapunov-based analysis is utilized to prove asymptotic trajectory tracking, where the region of convergence can be made arbitrarily large through judicious control gain selection. Moreover, the stability analysis formally addresses gyroscopic effects and actuator uncertainty. To illustrate the performance of the control law, comparative numerical simulation results are provided, which demonstrate the improved closed-loop performance achieved under varying levels of parametric uncertainty and disturbance magnitudes.
Feedback stabilization of parabolic coupled system and its numerical study

In the first part of this article, we study feedback stabilization of a parabolic coupled system by using localized interior controls. The system is feedback stabilizable with exponential decay $-\omega<0$ for any $\omega>0$. A stabilizing control is found in feedback form by solving a suitable algebraic Riccati equation. In the second part, a conforming finite element method is employed to approximate the continuous system by a finite dimensional discrete system. The approximated system is also feedback stabilizable (uniformly) with exponential decay $-\omega+\epsilon$, for any $\epsilon>0$ and the feedback control is obtained by solving a discrete algebraic Riccati equation. The error estimate of stabilized solutions as well as stabilizing feedback controls are obtained. We validate the theoretical results by numerical implementations.
Plasmonic photothermal response of a phantom embedded with gold nanorod aggregates on broadband near-infrared irradiation

Longer near-infrared wavelengths provide better penetration depth in biological tissues, so these are useful for plasmonic photothermal cancer therapeutics. In the context of nanoparticles for such applications, the absorption can be tuned for longer NIR wavelengths. However, on increasing the size of the nanoparticle, the scattering is enhanced and thus is not suitable for plasmonic therapeutics. Therefore, to overcome this issue, different types of small gold nanorods were synthesized and converted into stable aggregates to red-shift the plasmonic resonance wavelength to longer near-infrared wavelengths. The gold nanorod aggregates were embedded into the agarose gel phantoms mimicking the tumor-tissue-like structure. The photothermal response was measured through the prepared phantoms using a broadband near-infrared light source. It was shown that even in an extremely dilute concentration of gold nanorods, the photothermal heat generation could increase after the aggregation and also give gives deeper penetration of thermal energy. The observed photothermal response was also verified through numerical simulation. The current study shows better performance by increasing the plasmonic coupling, reducing the mismatch issue of plasmonic resonance shift in the second biological therapeutic window for the aggregates without increasing the size of individual nanoparticles. The aggregates provide better light penetration at deeper tissue by red-shifting the absorbance resonance wavelength which is useful for plasmonic photothermal cancer therapy.