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The functional renormalization group (FRG) is a non-perturbative method that considers quantum and thermal fluctuations. Using the FRG flow equations, the critical region of the two-flavor quark-meson model in a finite isospin chemical potential with omega and rho vector mesons interactions is investigated in this work. We also use the traditional mean-field method to calculate the phase diagram in the chiral limit for comparison. The results show that the influences of the omega meson and rho meson on the phase structure are quite different. The existence of the isospin chemical potential also causes significant changes in the phase structure.

For any cssc-crossed module a category is constructed, equipped with a structure and proved that this is a coherent categorical group. Together with a result of the previous paper, where to any categorical group the cssc-crossed module is associated, this construction will enable us to prove an equivalence between the categories of categorical groups and of cssc-crossed modules in the sequel to this paper., Comment: 22 pages, research paper, LaTeX2e, xypic

One of today's most significant societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacting agents (human and artificial), aligns with human values. To address this challenge, we detail a formal model of human values for their explicit computational representation. To our knowledge, this has not been attempted as yet, which is surprising given the growing volume of research integrating values within AI. Taking as our starting point the wealth of research investigating the nature of human values from social psychology over the last few decades, we set out to provide such a formal model. We show how this model can provide the foundational apparatus for AI-based reasoning over values, and demonstrate its applicability in real-world use cases. We illustrate how our model captures the key ideas from social psychology research and propose a roadmap for future integrated, and interdisciplinary, research into human values in AI. The ability to automatically reason over values not only helps address the value alignment problem but also facilitates the design of AI systems that can support individuals and communities in making more informed, value-aligned decisions. More and more, individuals and organisations are motivated to understand their values more explicitly and explore whether their behaviours and attitudes properly reflect them. Our work on modelling human values will enable AI systems to be designed and deployed to meet this growing need.

We consider real, Gauss-divisible matrices $A_{t}=A+\sqrt{t}B$, where $B$ is from the real Ginibre ensemble. We prove that the bulk correlation functions converge to a universal limit for $t=O(N^{-1/3+\epsilon})$ if $A$ satisfies certain local laws. If $A=\frac{1}{\sqrt{N}}(\xi_{jk})_{j,k=1}^{N}$ with $\xi_{jk}$ independent and identically distributed real random variables having zero mean, unit variance and finite moments, the Gaussian component can be removed using local laws proven by Bourgade--Yau--Yin, Alt--Erd\H{o}s--Kr\"{u}ger and Cipolloni--Erd\H{o}s--Schr\"{o}der and the four moment theorem of Tao--Vu., Comment: Revised version

We present geomagnetic storms (GSs) selected from three solar cycles, spanning the years 1995 to 2022. We studied the development of the main phase of storms within disturbance storm time (Dst) amplitudes ranging from Dst =-64 nT to Dst=- 422 nT. In order to determine the solar wind (SW) parameters that mainly influence the main phase development of a GS, which can best describe the SW-magnetosphere coupling, we divided our selected GSs into four groups based on main phase duration. Superposed epoch analysis was performed on the selected geomagnetic indices, SW plasma and field parameters, and their derivatives separately for each group. To that end, the dynamics of GS main phase development is mainly guided by interplanetary driver magnetic field southward component, (-Bz). It has been determined that there is a temporal difference between the peak values of Bz and Dst. As a result, Dst is delayed from Bz by 1-4 hours, which is crucial for space weather forecasting. The peak of Dst has a direct relationship with the amplitude of storm sudden commencement (SSC) and an inverse relationship with the duration of SSC. The inter-relationship between the peaks of the three indices (Dst, AE, and ap) during GS, is also obtained. Dst is found to be more closely related to ap than AE. To determine the best fit SW parameter to the geomagnetic activity indices, we used a linear correlation between the peak values of individual geomagnetic indices and SW plasma and field parameters and their derivatives. An electric field related function involving speed and IMF (v$^{4/3}$Bz) when coupled with a viscous term ($\rho^{1/2}$) correlates very well with the intensity of the GS (Dst$ _{min} $ or $ \Delta$Dst) and the magnitude of (ap$_{max}$) and (AE$_{max}$) during storms. However, a related function (v$^{4/3}$B$\rho^{1/2}$) represents slightly better the peak of AE$_{max}$ during the storms., Comment: Accepted for publication in Advances in Space Research

We study the management of product transitions in a semiconductor manufacturing firm that requires the coordination of resource allocation decisions by multiple, autonomous Product Divisions using a multi-follower bilevel model to capture the hierarchical and decentralized nature of this decision process. Corporate management, acting as the leader, seeks to maximize the firm's total profit over a finite horizon. The followers consist of multiple Product Divisions that must share manufacturing and engineering resources to develop, produce and sell products in the market. Each Product Division needs engineering capacity to develop new products, and factory capacity to produce products for sale while also producing the prototypes and samples needed for the product development process. We model this interdependency between Product Divisions as a generalized Nash equilibrium problem at the lower level and propose a reformulation where Corporate Management acts as the leader to coordinate the resource allocation decisions. We then derive an equivalent single-level reformulation and develop a cut-and-column generation algorithm. Extensive computational experiments evaluate the performance of the algorithm and provide managerial insights on how key parameters and the distribution of decision authority affect system performance.

Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models. To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection. Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation. In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures. When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging from 0.75 to 0.87. Our synthetic data-driven method, informed by social factors, offers consistent F1-scores of 0.82 for both models, suggesting that the richness of topics in synthetic data can bridge the performance gap across different model complexities. Most impressively, when we combined a mere 30% of the UMD dataset with our synthetic data, we witnessed a substantial increase in performance, achieving an F1-score of 0.88 on the UMD test set. Such results underscore the cost-effectiveness and potential of our approach in confronting major challenges in the field, such as data scarcity and the quest for diversity in data representation.

To meet the requirements of modern production, industrial communication increasingly shifts from wired fieldbus to wireless 5G communication. Besides tremendous benefits, this shift introduces severe novel risks, ranging from limited reliability over new security vulnerabilities to a lack of accountability. To address these risks, we present approaches to (i) prevent attacks through authentication and redundant communication, (ii) detect anomalies and jamming, and (iii) respond to detected attacks through device exclusion and accountability measures., Comment: Accepted to the poster session of the 22nd International Conference on Applied Cryptography and Network Security (ACNS 2024)

Bayesian optimization (BO) protocol based on Active Learning (AL) principles has garnered significant attention due to its ability to optimize black-box objective functions efficiently. This capability is a prerequisite for guiding autonomous and high-throughput materials design and discovery processes. However, its application in materials science, particularly for novel alloy designs with multiple targeted properties, remains limited. This limitation is due to the computational complexity and the lack of reliable and robust acquisition functions for multiobjective optimization. In recent years, expected hypervolume-based geometrical acquisition functions have demonstrated superior performance and speed compared to other multiobjective optimization algorithms, such as Thompson Sampling Efficient Multiobjective Optimization (TSEMO), Pareto Efficient Global Optimization (parEGO), etc. This work compares several state-of-the-art multiobjective BO acquisition functions, i.e., parallel expected hypervolume improvement (qEHVI), noisy parallel expected hypervolume improvement (qNEHVI), parallel Pareto efficient global optimization (parEGO), and parallel noisy Pareto efficient global optimization (qNparEGO) for the multiobjective optimization of physical properties in multi-component alloys. We demonstrate the impressive performance of the qEHVI acquisition function in finding the optimum Pareto front in 1-, 2-, and 3-objective Aluminium alloy optimization problems within a limited evaluation budget and reasonable computational cost. In addition, we discuss the role of different surrogate model optimization methods from a computational cost and efficiency perspective.

Generative systems are becoming a crucial part of current design practice. There exist gaps however, between the digital processes, field data and designer's input. To solve this problem, multiple processes were developed in order to generate emergent and self-organizing design solutions that combine the designer's input with surface models acquired via photogrammetry and generative design tools. Different generative design methods were utilized for trials, including surface scattering based on UV coordinates, animation snapshots (similar to long exposure photography) and a particle swarm algorithm on arbitrary data, interpolated within GIS software. A large volume of adaptive forms that are complex, yet responsive to changes in parameters, user input, topography and/or various spatial data were acquired. Resulting outputs were rendered and projection mapped onto the original physical model and evaluated for further iterations., Comment: 10 pages, 10 figures. 4th International Symposium - Formal Methods in Architecture, April 3-5, 2017 Porto, Portugal

Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating 3D MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. Purpose: To build a deep learning method exploring sparse annotations, namely only a single 2D slice label for each 3D training MR image. Population: 3D MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1,377 image slices) are for prostate segmentation. The other 100 (8,800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing. Assessment: A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled non-central slices. Segmentation performance on testing set was reported quantitatively and qualitatively. Results: Compared to FS-LCS, MT, UA-MT, DCT-Seg, ICT, and AC-MT, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% vs. ICT B-IoU: 60.3%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% vs. ICT B-IoU: 60.1%)., Comment: Paper accepted by Journal of Magnetic Resonance Imaging

This paper explores the estimation of a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model. The log-volatility term in this model can depend on (i) the spatial lag of the log-squared outcome variable, (ii) the time-lag of the log-squared outcome variable, (iii) the spatiotemporal lag of the log-squared outcome variable, (iv) exogenous variables, and (v) the unobserved heterogeneity across regions and time, i.e., the regional and time fixed effects. We examine the small and large sample properties of two quasi-maximum likelihood estimators and a generalized method of moments estimator for this model. We first summarize the theoretical properties of these estimators and then compare their finite sample properties through Monte Carlo simulations.

Sustainability in high performance computing (HPC) is a major challenge not only for HPC centers and their users, but also for society as the climate goals become stricter. A lot of effort went into reducing the energy consumption of systems in general. Even though certain efforts to optimize the energy-efficiency of HPC workloads exist, most such efforts propose solutions targeting CPUs. As HPC systems shift more and more to GPU-centric architectures, simulation codes increasingly adopt GPU-programming models. This leads to an urgent need to increase the energy-efficiency of GPU-enabled codes. However, studies for reducing the energy consumption of large-scale simulations executing on CPUs and GPUs have received insufficient attention. In this work, we enable accurate power and energy measurements using an open-source toolkit across a range of CPU+GPU node architectures. We use this approach in SPH-EXA, an open-source GPU-centric astrophysical and cosmological simulation framework. We show that with simple code instrumentation, users can accurately measure power and energy related data about their application, beyond data provided by HPC systems alone. The accurate power and energy data provide significant insight to users for conducting energy-aware computational experiments and future energy-aware code development., Comment: Appeared in Proceedings of the SC 23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis November 2023

Lead halide perovskites have recently been reported to demonstrate an exceptionally high nonlinear (Kerr) refractive index n$_2$ of up to 10$^{-8}$ cm$^2$/W in CH$_3$NH$_3$PbBr$_3$. Other researchers however observe different, substantially more conservative numbers. In order to resolve this disagreement the nonlinear Kerr index of a bulk sample of lead halide perovskite was measured directly by means of an interferometer. This approach has many advantages as compared to the more standard z-scan technique. In particular this method allows studying the induced changes to the refractive index in a time-resolved manner, thus enabling to separate the different contributions to $n_2$. The extracted n$_2$ values for CsPbBr$_3$ and MAPbBr$_3$ at $\lambda \approx 1\mu m$ are $n_2=+2.1\times$ 10$^{-14}$ cm$^2$/W and $n_2=+6\times$ 10$^{-15}$ cm$^2$/W respectively hence substantially lower than what has been indicated in most of the previous reports implying the latter should be regarded with a great care.

Open-ended image understanding tasks gained significant attention from the research community, particularly with the emergence of Vision-Language Models. Open-Vocabulary Segmentation (OVS) methods are capable of performing semantic segmentation without relying on a fixed vocabulary, and in some cases, they operate without the need for training or fine-tuning. However, OVS methods typically require users to specify the vocabulary based on the task or dataset at hand. In this paper, we introduce \textit{Auto-Vocabulary Semantic Segmentation (AVS)}, advancing open-ended image understanding by eliminating the necessity to predefine object categories for segmentation. Our approach, \ours, presents a framework that autonomously identifies relevant class names using enhanced BLIP embeddings, which are utilized for segmentation afterwards. Given that open-ended object category predictions cannot be directly compared with a fixed ground truth, we develop a Large Language Model-based Auto-Vocabulary Evaluator (LAVE) to efficiently evaluate the automatically generated class names and their corresponding segments. Our method sets new benchmarks on datasets such as PASCAL VOC and Context, ADE20K, and Cityscapes for AVS and showcases competitive performance to OVS methods that require specified class names.

Given a map $f:X \to M$ from a topological space $X$ to a metric space $M$, a decorated Reeb space consists of the Reeb space, together with an attribution function whose values recover geometric information lost during the construction of the Reeb space. For example, when $M=\mathbb{R}$ is the real line, the Reeb space is the well-known Reeb graph, and the attributions may consist of persistence diagrams summarizing the level set topology of $f$. In this paper, we introduce decorated Reeb spaces in various flavors and prove that our constructions are Gromov-Hausdorff stable. We also provide results on approximating decorated Reeb spaces from finite samples and leverage these to develop a computational framework for applying these constructions to point cloud data., Comment: V2: Full version of the paper to appear in SOCG 24

We provide a summary of the sixth edition of the CASE workshop that is held in the scope of RANLP 2023. The workshop consists of regular papers, three keynotes, working papers of shared task participants, and shared task overview papers. This workshop series has been bringing together all aspects of event information collection across technical and social science fields. In addition to contributing to the progress in text based event extraction, the workshop provides a space for the organization of a multimodal event information collection task., Comment: https://aclanthology.org/2023.case-1.22