Miracle-dz/UniFAR-FTUs huggingface dataset:
A Grouping-based Scheduler for Efficient Channel Utilization under Age of Information Constraints

We consider a status information updating system where a fusion center collects the status information from a large number of sources and each of them has its own age of information (AoI) constraints. A novel grouping-based scheduler is proposed to solve this complex large-scale problem by dividing the sources into different scheduling groups. The problem is then transformed into deriving the optimal grouping scheme. A two-step grouping algorithm (TGA) is proposed: 1) Given AoI constraints, we first identify the sources with harmonic AoI constraints, then design a fast grouping method and an optimal scheduler for these sources. Under harmonic AoI constraints, each constraint is divisible by the smallest one and the sum of reciprocals of the constraints with the same value is divisible by the reciprocal of the smallest one. 2) For the other sources without such a special property, we pack the sources which can be scheduled together with minimum update rates into the same group. Simulations show the channel usage of the proposed TGA is significantly reduced as compared to a recent work and is 0.42% larger than a derived lower bound when the number of sources is large.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports.
Comparison of deep-learning data fusion strategies in mandibular osteoradionecrosis prediction modelling using clinical variables and radiation dose distribution volumes

Purpose. NTCP modelling is rapidly embracing DL methods as the need to include spatial dose information is acknowledged. Finding the most appropriate way of combining radiation dose distribution images and clinical data involves technical challenges and requires domain knowledge. We propose different data fusion strategies that we hope will serve as a starting point for future DL NTCP studies. Methods. Early, joint and late DL multi-modality fusion strategies were compared using clinical variables and mandibular radiation dose distribution volumes. The discriminative performance of the multi-modality models was compared to that of single-modality models. All the experiments were conducted on a control-case matched cohort of 92 ORN cases and 92 controls from a single institution. Results. The highest ROC AUC score was obtained with the late fusion model (0.70), but no statistically significant differences in discrimination performance were observed between strategies. While late fusion was the least technically complex strategy, its design did not model the inter-modality interactions that are required for NTCP modelling. Joint fusion involved the most complex design but resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation. Conclusions. This is the first study that compares different strategies for including image data into DL NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. We encourage future DL NTCP studies to report on different fusion strategies to better justify their choice of DL pipeline.
Hierarchical Multi-Marginal Optimal Transport for Network Alignment

Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning networks in pairs, the literature on multi-network alignment is sparse due to the exponentially growing solution space and lack of high-order discrepancy measures. To fill this gap, we propose a hierarchical multi-marginal optimal transport framework named HOT for multi-network alignment. To handle the large solution space, multiple networks are decomposed into smaller aligned clusters via the fused Gromov-Wasserstein (FGW) barycenter. To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly. A fast proximal point method is further developed with guaranteed convergence to a local optimum. Extensive experiments and analysis show that our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability.
From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Self

In the rapidly evolving landscape of AI-mediated communication (AIMC), tools powered by Large Language Models (LLMs) are becoming integral to interpersonal communication. Employing a mixed-methods approach, we conducted a one-week diary and interview study to explore users' perceptions of these tools' ability to: 1) support interpersonal communication in the short-term, and 2) lead to potential long-term effects. Our findings indicate that participants view AIMC support favorably, citing benefits such as increased communication confidence, and finding precise language to express their thoughts, navigating linguistic and cultural barriers. However, the study also uncovers current limitations of AIMC tools, including verbosity, unnatural responses, and excessive emotional intensity. These shortcomings are further exacerbated by user concerns about inauthenticity and potential overreliance on the technology. Furthermore, we identified four key communication spaces delineated by communication stakes (high or low) and relationship dynamics (formal or informal) that differentially predict users' attitudes toward AIMC tools. Specifically, participants found the tool is more suitable for communicating in formal relationships than informal ones and more beneficial in high-stakes than low-stakes communication.
Developing and Refining a Multifunctional Facial Recognition System for Older Adults with Cognitive Impairments: A Journey Towards Enhanced Quality of Life

In an era where the global population is aging significantly, cognitive impairments among the elderly have become a major health concern. The need for effective assistive technologies is clear, and facial recognition systems are emerging as promising tools to address this issue. This document discusses the development and evaluation of a new Multifunctional Facial Recognition System (MFRS), designed specifically to assist older adults with cognitive impairments. The MFRS leverages face_recognition [1], a powerful open-source library capable of extracting, identifying, and manipulating facial features. Our system integrates the face recognition and retrieval capabilities of face_recognition, along with additional functionalities to capture images and record voice memos. This combination of features notably enhances the system's usability and versatility, making it a more user-friendly and universally applicable tool for end-users. The source code for this project can be accessed at https://github.com/Li-8023/Multi-function-face-recognition.git.
Pika: Empowering Non-Programmers to Author Executable Governance Policies in Online Communities

Internet users have formed a wide array of online communities with nuanced and diverse community goals and norms. However, most online platforms only offer a limited set of governance models in their software infrastructure and leave little room for customization. Consequently, technical proficiency becomes a prerequisite for online communities to build governance policies in code, excluding non-programmers from participation in designing community governance. In this paper, we present Pika, a system that empowers non-programmers to author a wide range of executable governance policies. At its core, Pika incorporates a declarative language that decomposes governance policies into modular components, thereby facilitating expressive policy authoring through a user-friendly, form-based web interface. Our user studies with 17 participants show that Pika can empower non-programmers to author governance policies approximately 2.5 times faster than programmers who author in code. We also provide insights about Pika's expressivity in supporting diverse policies that online communities want.
Sequential Tag Recommendation

With the development of Internet technology and the expansion of social networks, online platforms have become an important way for people to obtain information. The introduction of tags facilitates information categorization and retrieval. Meanwhile, the development of tag recommendation systems not only enables users to input tags more efficiently, but also improves the quality of tags. However, current tag recommendation methods only consider the content of the current post and do not take into account the influence of user preferences. Since the main body of tag recommendation is the user, it is very necessary to obtain the user's tagging habits. Therefore, this paper proposes a tag recommendation algorithm (MLP4STR) based on the dynamic preference of user's behavioral sequence, which models the user's historical post information and historical tag information to obtain the user's dynamic interest changes. A pure MLP structure across feature dimensions is used in sequence modeling to model the interaction between tag content and post content to fully extract the user's interests. Finally tag recommendation is performed.
Manipulation of magnetic topological textures via perpendicular strain and polarization in van der Waals magnetoelectric heterostructures

Multi-functional manipulation of magnetic topological textures such as skyrmions and bimerons in energy-efficient ways is of great importance for spintronic applications, but still being a big challenge. Here, by first-principles calculations and atomistic simulations, the creation and annihilation of skyrmions/bimerons, as key operations for the reading and writing of information in spintronic devices, are achieved in van der Waals magnetoelectric CrISe/In2Se3 heterostructure via perpendicular strain or electric field without external magnetic field. Besides, the bimeron-skyrmion conversion, size modulation and the reversible magnetization switching from in-plane to out-of-plane could also be realized in magnetic-field-free ways. Moreover, the topological charge and morphology can be precisely controlled by a small magnetic field. The strong Dzyaloshinskii-Moriya interaction and tunable magnetic anisotropy energy in a wide window are found to play vital roles in such energy efficient multi-functional manipulation, and the underlying physical mechanisms are elucidated. Our work predicts the CrISe/In2Se3 heterostructure being an ideal platform to address this challenge in spintronic applications, and theoretically guides the low-dissipation multi-functional manipulation of magnetic topological textures.
Integration of Fractional Order Black-Scholes Merton with Neural Network

This study presents a novel approach to enhance option pricing accuracy by introducing the Fractional Order Black-Scholes-Merton (FOBSM) model. FOBSM combines elements of the traditional Black-Scholes-Merton (BSM) model with the flexibility of neural networks (NN). American options pose unique pricing challenges due to free boundary difficulties. On the other hand, traditional models like BSM struggle to accurately represent market pricing. The challenge is to develop a pricing model that better captures the tail behavior, memory effects, volatility clustering, long-term dependencies, and skewness inherent in financial data, while simultaneously utilizing the theoretical underpinnings of BSM and fractional calculus. The research gap arises from the absence of a comprehensive framework that integrates fractional calculus and neural networks to enhance option pricing accuracy in complex diffusion dynamics scenarios. Since FOBSM captures memory characteristics in sequential data, it is better at simulating real-world systems than integer-order models. The findings reveal that in complex diffusion dynamics, this hybridization approach in option pricing improves the accuracy of price predictions.
Convergent ADMM Plug and Play PET Image Reconstruction

In this work, we investigate hybrid PET reconstruction algorithms based on coupling a model-based variational reconstruction and the application of a separately learnt Deep Neural Network operator (DNN) in an ADMM Plug and Play framework. Following recent results in optimization, fixed point convergence of the scheme can be achieved by enforcing an additional constraint on network parameters during learning. We propose such an ADMM algorithm and show in a realistic [18F]-FDG synthetic brain exam that the proposed scheme indeed lead experimentally to convergence to a meaningful fixed point. When the proposed constraint is not enforced during learning of the DNN, the proposed ADMM algorithm was observed experimentally not to converge.
Nontrivial solutions for the Laplace equation with a nonlinear Goldstein-Wentzell boundary condition

<abstract><p>The paper deals with the existence and multiplicity of nontrivial solutions for the doubly elliptic problem</p> <p><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{cases} \Delta u = 0 \qquad &\text{in}~~ \Omega , \\ u = 0 &\text{on}~~ \Gamma_0 , \\ -\Delta_\Gamma u +\partial_\nu u = |u|^{p-2}u\qquad &\text{on}~~ \Gamma_1 , \end{cases} $\end{document} </tex-math></disp-formula></p> <p>where $ \Omega $ is a bounded open subset of $ \mathbb R^N $ ($ N\ge 2 $) with $ C^1 $ boundary $ \partial\Omega = \Gamma_0\cup\Gamma_1 $, $ \Gamma_0\cap\Gamma_1 = \emptyset $, $ \Gamma_1 $ being nonempty and relatively open on $ \Gamma $, $ \mathcal{H}^{N-1}(\Gamma_0) > 0 $ and $ p > 2 $ being subcritical with respect to Sobolev embedding on $ \partial\Omega $. </p> <p>We prove that the problem admits nontrivial solutions at the potential-well depth energy level, which is the minimal energy level for nontrivial solutions. We also prove that the problem has infinitely many solutions at higher energy levels. </p></abstract>
The glauconite–Fe‐illite–Fe‐smectite problem: a critical review

Iron silicate minerals are a significant component of sedimentary systems but their modes of formation remain controversial. Our analysis of published data identifies end‐member compositions and mixtures and allows us to recognize controls of formation of different mineral species. The compositional fields of glaucony, Fe‐illite, Fe–Al smectites are determined in the M+/4Si vs. Fe/Sum of octahedral cations (M+ = interlayer charge). Solid solutions could exist between these phases. The Fe–Al and Fe‐rich clay minerals form two distinct solid solutions. The earliest phases to be formed are Fe–Al smectites or berthierine depending on the sedimentation rate. Reductive microsystems appear in the vicinity of organic debris in unconsolidated sediments. The Fe is incorporated first in pyrite and then in silicates after oxidation. Potassium ions diffuse from the sea‐water–sediment interface. If not interrupted, the diffusion process is active until reaction completion is reached, i.e. formation of Fe‐illite or glauconite or a mineral assemblage (berthierine–nontronite) according to the available Al ion amounts in the microsystem. Mixed‐layer minerals are formed when the diffusion process is interrupted because of sedimentation, compaction or cementation. Despite the common belief of their value as palaeoenvironment indicators, these minerals can form in a variety of environments and over a period of millions of years during sediment burial.
A MKID-readout based on a heterogeneous, closely coupled architecture

Within this proceeding, we introduce the U-Board platform, a versatile platform for signal generation, acquisition and processing, based on a heterogenous processing architecture. Based on this platform we present a readout for Microwave Kinetic Inductance Detectors (MKIDs) for the A-MKID camera for APEX. In addition to the implementation of the readout on this heterogenous architecture, we also present a first comparison of the performance of the readout compared to the currently used readout of the A-MKID camera. Last but not least, we discuss how we plan to miniaturize the current prototype, which is based on commercial off the shelf components.
Aspect of Clusters Correlation at Light Nuclei Excited State

The correlation of $\alpha\alpha$ was probed via measuring the transverse momentum $p_{T}$ and width $\delta p_{T}$ of one $\alpha$, for the first time, which represents the spatial and dynamical essentialities of the initial coupling state in $^{8}$Be nucleus. The weighted interaction vertex of 3$\alpha$ reflected by the magnitudes of their relative momentums and relative emission angles proves the isosceles triangle configuration for 3$\alpha$ at the high excited energy analogous Hoyle states.
Prediction of flow and drawdown for the site characterization and validation site in the Stripa Mine

Geophysical and hydrologic data from a location in the Stripa Mine in Sweden, called the Site Characterization and Validation (SCV) block, has been used to create a series of models for flow through the fracture network. The models can be characterized as ``equivalent discontinuum`` models. Equivalent discontinuum models are derived starting from a specified lattice or 6 ``template``. An inverse analysis called ``Simulated Annealing`` is used to make a random search through the elements of the lattice to find a configuration that can reproduce the measured responses. Evidence at Stripa points to hydrology which is dominated by fracture zones. These have been identified and located through extensive characterization efforts. Lattice templates were arranged to lie on the fracture zones identified by Black and Olsson. The fundamental goal of this project was to build a fracture flow model based an initial data set, and use this model to make predictions of the flow behavior during a new test. Then given data from the new test, predict a second test, etc. The first data set was an interference test called C1-2. Both a two-dimensional and a three-dimensional model were annealed to the C1-2 data and use this model to predict the behaviormore » of the Simulated Drift Experiment (SDE). The SDE measured the flow into, and drawdown due to reducing the pressure in a group of 6 parallel boreholes. Then both the C1-2 and SDE data were used to predict the flow into and drawdown due to an excavation, the Validation Drift (VD), made through the boreholes. Finally, all the data was used to predict the hydrologic response to opening another hole, T1. « less
Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers

To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity. However, the thresholding functions of these nonconvex regularizers may not have closed-form expressions and thus iterations are needed, which increases the computational loads. To solve this issue, we devise a framework to generate sparsity-inducing regularizers with closed-form thresholding functions. These regularizers are applied to low-tubal-rank tensor completion, and efficient algorithms based on the alternating direction method of multipliers are developed. Furthermore, convergence of our methods is analyzed and it is proved that the generated sequences are bounded and any limit point is a stationary point. Experimental results using synthetic and real-world datasets show that the proposed algorithms outperform the state-of-the-art methods in terms of restoration performance.
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models

An increasing number of vision-language tasks can be handled with little to no training, i.e., in a zero and few-shot manner, by marrying large language models (LLMs) to vision encoders, resulting in large vision-language models (LVLMs). While this has huge upsides, such as not requiring training data or custom architectures, how an input is presented to a LVLM can have a major impact on zero-shot model performance. In particular, inputs phrased in an underspecified way can result in incorrect answers due to factors like missing visual information, complex implicit reasoning, or linguistic ambiguity. Therefore, adding visually grounded information to the input as a preemptive clarification should improve model performance by reducing underspecification, e.g., by localizing objects and disambiguating references. Similarly, in the VQA setting, changing the way questions are framed can make them easier for models to answer. To this end, we present Rephrase, Augment and Reason (RepARe), a gradient-free framework that extracts salient details about the image using the underlying LVLM as a captioner and reasoner, in order to propose modifications to the original question. We then use the LVLM's confidence over a generated answer as an unsupervised scoring function to select the rephrased question most likely to improve zero-shot performance. Focusing on two visual question answering tasks, we show that RepARe can result in a 3.85% (absolute) increase in zero-shot performance on VQAv2 and a 6.41% point increase on A-OKVQA. Additionally, we find that using gold answers for oracle question candidate selection achieves a substantial gain in VQA accuracy by up to 14.41%. Through extensive analysis, we demonstrate that outputs from RepARe increase syntactic complexity, and effectively utilize vision-language interaction and the frozen language model in LVLMs.