Miracle-dz/UniFAR-FTUs huggingface dataset:
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.
Deeper and Wider Networks for Performance Metrics Prediction in Communication Networks

In today's era, users have increasingly high expectations regarding the performance and efficiency of communication networks. Network operators aspire to achieve efficient network planning, operation, and optimization through Digital Twin Networks (DTN). The effectiveness of DTN heavily relies on the network model, with graph neural networks (GNN) playing a crucial role in network modeling. However, existing network modeling methods still lack a comprehensive understanding of communication networks. In this paper, we propose DWNet (Deeper and Wider Networks), a heterogeneous graph neural network modeling method based on data-driven approaches that aims to address end-to-end latency and jitter prediction in network models. This method stands out due to two distinctive features: firstly, it introduces deeper levels of state participation in the message passing process; secondly, it extensively integrates relevant features during the feature fusion process. Through experimental validation and evaluation, our model achieves higher prediction accuracy compared to previous research achievements, particularly when dealing with unseen network topologies during model training. Our model not only provides more accurate predictions but also demonstrates stronger generalization capabilities across diverse topological structures.
Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction

Although sparse-view computed tomography (CT) has significantly reduced radiation dose, it also introduces severe artifacts which degrade the image quality. In recent years, deep learning-based methods for inverse problems have made remarkable progress and have become increasingly popular in CT reconstruction. However, most of these methods suffer several limitations: dependence on high-quality training data, weak interpretability, etc. In this study, we propose a fully unsupervised framework called Deep Radon Prior (DRP), inspired by Deep Image Prior (DIP), to address the aforementioned limitations. DRP introduces a neural network as an implicit prior into the iterative method, thereby realizing cross-domain gradient feedback. During the reconstruction process, the neural network is progressively optimized in multiple stages to narrow the solution space in radon domain for the under-constrained imaging protocol, and the convergence of the proposed method has been discussed in this work. Compared with the popular pre-trained method, the proposed framework requires no dataset and exhibits superior interpretability and generalization ability. The experimental results demonstrate that the proposed method can generate detailed images while effectively suppressing image artifacts.Meanwhile, DRP achieves comparable or better performance than the supervised methods.
Detachment fold vs. ramp anticline: a gravity survey in the southern Pyrenean front (External Sierras)

The Santo Domingo Anticline (External Sierras, Southern Pyrenees), which separates the Jaca piggyback basin from the Ebro foreland basin, is a key structure of the Pyrenees. Its geometry has been interpreted both as a detachment fold and as a hangingwall anticline associated with an underlying thrust. In this paper, we present the results from a gravity survey and 2.5D gravity modelling carried out around the Santo Domingo Anticline. Density measurements indicate a sharp density contrast between the Triassic evaporites‐mudstones in the core of the anticline and the sedimentary sequence (limestones and sandstones) at its limbs. Gravity anomalies together with 2.5D gravity models allow to discern the along‐strike structural changes. From east to west, we document a change from the ramp‐associated fold to the detachment anticline. The capabilities and limitations of the gravimetric method for the determination of fold geometry are also discussed. Copyright © 2017 John Wiley & Sons, Ltd.
On the breathing of spectral bands in periodic quantum waveguides with inflating resonators

We are interested in the lower part of the spectrum of the Dirichlet Laplacian $A^\varepsilon$ in a thin waveguide $\Pi^\varepsilon$ obtained by repeating periodically a pattern, itself constructed by scaling an inner field geometry $\Omega$ by a small factor $\varepsilon>0$. The Floquet-Bloch theory ensures that the spectrum of $A^\varepsilon$ has a band-gap structure. Due to the Dirichlet boundary conditions, these bands all move to $+\infty$ as $O(\varepsilon^{-2})$ when $\varepsilon\to0^+$. Concerning their widths, applying techniques of dimension reduction, we show that the results depend on the dimension of the so-called space of almost standing waves in $\Omega$ that we denote by $\mathrm{X}_\dagger$. Generically, i.e. for most $\Omega$, there holds $\mathrm{X}_\dagger=\{0\}$ and the lower part of the spectrum of $A^\varepsilon$ is very sparse, made of bands of length at most $O(\varepsilon)$ as $\varepsilon\to0^+$. For certain $\Omega$ however, we have $\mathrm{dim}\,\mathrm{X}_\dagger=1$ and then there are bands of length $O(1)$ which allow for wave propagation in $\Pi^\varepsilon$. The main originality of this work lies in the study of the behaviour of the spectral bands when perturbing $\Omega$ around a particular $\Omega_\star$ where $\mathrm{dim}\,\mathrm{X}_\dagger=1$. We show a breathing phenomenon for the spectrum of $A^\varepsilon$: when inflating $\Omega$ around $\Omega_\star$, the spectral bands rapidly expand before shrinking. In the process, a band dives below the normalized threshold $\pi^2/\varepsilon^2$, stops breathing and becomes extremely short as $\Omega$ continues to inflate.
Exploring the Synergy: A Review of Dual-Functional Radar Communication Systems

This review paper examines the concept and advancements in the evolving landscape of Dual-functional Radar Communication (DFRC) systems. Traditionally, radar and communication systems have functioned independently, but current research is actively investigating the integration of these functionalities into a unified platform. This paper discusses the motivations behind the development of DFRC systems, the challenges involved, and the potential benefits they offer. A discussion on the performance bounds for DFRC systems is also presented. The paper encompasses a comprehensive analysis of various techniques, architectures, and technologies used in the design and optimization of DFRC systems, along with their performance and trade-offs. Additionally, we explore potential application scenarios for these joint communication and sensing systems, offering a comprehensive perspective on the multifaceted landscape of DFRC technology.
Reynolds number dependence of length scales governing turbulent flow separation with application to wall-modeled large-eddy simulations

This article proposes a Reynolds number scaling of the required grid points to perform wall-modeled LES of turbulent flows encountering separation off a solid surface. Based on comparisons between the various time scales in a non-equilibrium (due to the action of an external pressure gradient) turbulent boundary layer, a simple definition of the near-wall ``under-equilibrium"and ``out-of-equilibrium"scales is put forward (where ``under-equilibrium"refers to scales governed by a quasi-balance between the viscous and the pressure gradient terms). It is shown that the former length scale varies with Reynolds number as lp Re^(-2/3). The same scaling is obtained from a simplified Green's function solution of the Poisson equation in the vicinity of the separation point. A-priori analysis demonstrates that the resolution required to reasonably predict the wall-shear stress (for example, errors lower than approximately 10-15% in the entire domain) in several nonequilibrium flows is at least O(10) lp irrespective of the Reynolds number and the Clauser parameter. Further, a series of a-posteriori validation studies are performed to determine the accuracy of this scaling including the flow over the Boeing speed bump, Song-Eaton diffuser, Notre-Dame Ramp, and the backward-facing step. The results suggest that for these flows, scaling the computational grids () such that / lp is independent of the Reynolds number results in accurate predictions of flow separation at the same ``nominal"grid resolution across different Reynolds numbers. Finally, it is suggested that in the vicinity of the separation and reattachment points, the grid-point requirements for wall-modeled large eddy simulations may scale as Re^4/3, which is more restrictive than the previously proposed flat-plate boundary layer-based estimates (Re1) of Choi and Moin (Phys. Fluids, 2012) and Yang and Griffin (Phys. Fluids, 2021).
Periodically Driven Open Quantum Systems: Spectral Properties and Non-Equilibrium Steady States

In this article, we investigate periodically driven open quantum systems within the framework of Floquet-Lindblad master equations. Specifically, we discuss Lindblad master equations in the presence of a coherent, time-periodic driving and establish their general spectral features. We also clarify the notions of transient and non-decaying solutions from this spectral perspective, and then prove that any physical system described by a Floquet-Lindblad equation must have at least one \textit{physical} non-equilibrium steady state (NESS), corresponding to an eigenoperator of the Floquet-Lindblad evolution superoperator $\mathcal{U}_F$ with unit eigenvalue. Since the Floquet-Lindblad formalism encapsulates the entire information regarding the NESS, it in principle enables us to obtain non-linear effects to all orders at once. The Floquet-Lindblad formalism thus provides a powerful tool for studying driven-dissipative solid-state systems, which we illustrate by deriving the nonlinear optical response of a simple two-band model of an insulating solid and comparing it with prior results established through Keldysh techniques.
Spinterface Mediated Magnetic Properties of Co20Fe60B20/Alq3 Heterostructures

Organic semiconductors (OSCs) are suitable materials for spintronics applications as they form a spinterface when placed next to a ferromagnet, which in turn leads to novel functionalities. The evolution of spinterface can tune the global magnetic anisotropy, magnetization reversal, magnetization dynamics, etc. Planar tris-(8-hydroxyquinoline)aluminum (Alq3) OSC has shown tremendous potential for spintronics applications, thanks to its efficient spin-polarized current transport ability. Here, we establish the spinterface when the Alq3 molecules are deposited on amorphous ferromagnet Co20Fe60B20(CFB). The $\pi$-d hybridization in CFB/Alq3 enhances the coercive field and significantly modifies the shape and size of the magnetic domains. A $\sim$100% increase in uniaxial anisotropic energies and a reduction in magnetic damping are also evident owing to the strong interfacial hybridization.
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training

Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed makeup of these datasets is often not disclosed, leading to concerns about data security and potential misuse. This is particularly relevant when copyrighted material, still under legal protection, is used inappropriately, either intentionally or unintentionally, infringing on the rights of the authors. In this paper, we introduce a detailed framework designed to detect and assess the presence of content from potentially copyrighted books within the training datasets of LLMs. This framework also provides a confidence estimation for the likelihood of each content sample's inclusion. To validate our approach, we conduct a series of simulated experiments, the results of which affirm the framework's effectiveness in identifying and addressing instances of content misuse in LLM training processes. Furthermore, we investigate the presence of recognizable quotes from famous literary works within these datasets. The outcomes of our study have significant implications for ensuring the ethical use of copyrighted materials in the development of LLMs, highlighting the need for more transparent and responsible data management practices in this field.
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training

In the evolution of Vision-Language Pre-training, shifting from short-text comprehension to encompassing extended textual contexts is pivotal. Recent autoregressive vision-language models like \cite{flamingo, palme}, leveraging the long-context capability of Large Language Models, have excelled in few-shot text generation tasks but face challenges in alignment tasks. Addressing this gap, we introduce the contrastive loss into text generation models, presenting the COntrastive-Streamlined MultimOdal framework (\ModelName), strategically partitioning the language model into dedicated unimodal text processing and adept multimodal data handling components. \ModelName, our unified framework, merges unimodal and multimodal elements, enhancing model performance for tasks involving textual and visual data while notably reducing learnable parameters. However, these models demand extensive long-text datasets, yet the availability of high-quality long-text video datasets remains limited. To bridge this gap, this work introduces \VideoDatasetName, an inaugural interleaved video-text dataset featuring comprehensive captions, marking a significant step forward. Demonstrating its impact, we illustrate how \VideoDatasetName{} enhances model performance in image-text tasks. With 34% learnable parameters and utilizing 72\% of the available data, our model demonstrates significant superiority over OpenFlamingo~\cite{openflamingo}. For instance, in the 4-shot flickr captioning task, performance notably improves from 57.2% to 65. \%. The contributions of \ModelName{} and \VideoDatasetName{} are underscored by notable performance gains across 14 diverse downstream datasets encompassing both image-text and video-text tasks.
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control

Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, making their detection difficult. Consequently, the adaptive optics (AO) system's control algorithm must be carefully designed to distinguish the exoplanet from the residual light produced by the host star. A new promising avenue of research to improve AO control builds on data-driven control methods such as Reinforcement Learning (RL). RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment. Thus, RL can be seen as an automated approach to AO control, where its usage is entirely a turnkey operation. In particular, model-based reinforcement learning (MBRL) has been shown to cope with both temporal and misregistration errors. Similarly, it has been demonstrated to adapt to non-linear wavefront sensing while being efficient in training and execution. In this work, we implement and adapt an RL method called Policy Optimization for AO (PO4AO) to the GHOST test bench at ESO headquarters, where we demonstrate a strong performance of the method in a laboratory environment. Our implementation allows the training to be performed parallel to inference, which is crucial for on-sky operation. In particular, we study the predictive and self-calibrating aspects of the method. The new implementation on GHOST running PyTorch introduces only around 700 microseconds in addition to hardware, pipeline, and Python interface latency. We open-source well-documented code for the implementation and specify the requirements for the RTC pipeline. We also discuss the important hyperparameters of the method, the source of the latency, and the possible paths for a lower latency implementation.
Critical point in heavy-quark region of QCD on fine lattices

We perform a finite-size scaling analysis of the critical point in the heavy-quark region of QCD at nonzero temperature. Our previous analysis on the Binder cumulant at $N_t=4$ is extended to finer lattices with $N_t=6$ and $8$. The aspect ratio is also extended up to $15$ to suppress the non-singular contribution. High-precision analysis of the Binder cumulant is realized by an efficient Monte-Carlo simulation with the hopping-parameter expansion (HPE). Effects of higher-order terms in the HPE are incorporated by the reweighting method.
Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation

The Segment Anything Model (SAM) represents a significant breakthrough into foundation models for computer vision, providing a large-scale image segmentation model. However, despite SAM's zero-shot performance, its segmentation masks lack fine-grained details, particularly in accurately delineating object boundaries. We have high expectations regarding whether SAM, as a foundation model, can be improved towards highly accurate object segmentation, which is known as dichotomous image segmentation (DIS). To address this issue, we propose DIS-SAM, which advances SAM towards DIS with extremely accurate details. DIS-SAM is a framework specifically tailored for highly accurate segmentation, maintaining SAM's promptable design. DIS-SAM employs a two-stage approach, integrating SAM with a modified IS-Net dedicated to DIS. Despite its simplicity, DIS-SAM demonstrates significantly enhanced segmentation accuracy compared to SAM and HQ-SAM.
CycleGAN Models for MRI Image Translation

Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class is limited. From the learning perspective, this process contributes to data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features. In the case of generating additional neuroimages, it is advantageous to obtain unidentifiable medical data and augment smaller annotated datasets. This study proposes the development of a CycleGAN model for translating neuroimages from one field strength to another (e.g., 3 Tesla to 1.5). This model was compared to a model based on DCGAN architecture. CycleGAN was able to generate the synthetic and reconstructed images with reasonable accuracy. The mapping function from the source (3 Tesla) to target domain (1.5 Tesla) performed optimally with an average PSNR value of 25.69 $\pm$ 2.49 dB and an MAE value of 2106.27 $\pm$ 1218.37.
A Rapid Scoping Review and Conceptual Analysis of the Educational Metaverse in the Global South: Socio-Technical Perspectives

This paper presents a conceptual insight into the Design of the Metaverse to facilitate educational transformation in selected developing nations within the Global South regions, e.g., India. These regions are often afflicted with socio-economic challenges but rich in cultural diversity. By utilizing a socio-technical design approach, this study explores the specific needs and opportunities presented by these diverse settings. A rapid scoping review of the scant existing literature is conducted to provide fundamental insights. A novel design methodology was formulated that utilized ChatGPT for ideation, brainstorming, and literature survey query generation. This paper aims not only to shed light on the educational possibilities enabled by the Metaverse but also to highlight design considerations unique to the Global South.
Binary AdS black holes coupled to a bath in Type IIB

We construct Type IIB string theory setups which, via double holography, realize two gravitational systems in separate AdS spaces which interact with each other and with a non-gravitational bath. We employ top-down string theory solutions with concrete field theory duals in the form of 4d $\mathcal N=4$ SYM BCFTs and a first-principles notion of double holography. The setups are used to realize pairs of `near' and `far' black holes from the perspective of the bath, which exchange Hawking radiation with each other and radiate into the bath. We identify three phases for the entropy in the bath characterized as no island, partial island and full island, and discuss the entropy curves. The setups differ from the black hole binaries observed in gravitational wave experiments but may capture certain aspects.
Shape-programmable Adaptive Multi-material Microrobots for Biomedical Applications

Flagellated microorganisms can swim at low Reynolds numbers and adapt to changes in their environment. Specifically, the flagella can switch their shapes or modes through gene expression. In the past decade, efforts have been made to fabricate and investigate rigid types of microrobots without any adaptation to the environments. More recently, obtaining adaptive microrobots mimicking real microorganisms is getting more attention. However, even though some adaptive microrobots achieved by hydrogels have emerged, the swimming behaviors of the microrobots before and after the environment-induced deformations are not predicted in a systematic standardized way. In this work, experiments, finite element analysis, and dynamic modeling are presented together to realize a complete understanding of these adaptive microrobots. The above three parts are cross-verified proving the success of using such methods, facilitating the bio-applications with shape-programmable and even swimming performance-programmable microrobots. Moreover, an application of targeted object delivery using the proposed microrobot has been successfully demonstrated. Finally, cytotoxicity tests are performed to prove the potential for using the proposed microrobot for biomedical applications.