Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses

Vehicular metaverse, which is treated as the future continuum between automotive industry and metaverse, is envisioned as a blended immersive domain as the digital twins of intelligent transportation systems. Vehicles access the vehicular metaverses by their own Vehicle Twins (VTs) (e.g., avatars) that resource-limited vehicles offload the tasks of building VTs to their nearby RoadSide Units (RSUs). However, due to the limited coverage of RSUs and the mobility of vehicles, VTs have to be migrated from one RSU to other RSUs to ensure uninterrupted metaverse services for users within vehicles. This process requires the next RSUs to contribute sufficient bandwidth resources for VT migrations under asymmetric information. To this end, in this paper, we design an efficient incentive mechanism framework for VT migrations. We first propose a novel metric named Age of Migration Task (AoMT) to quantify the task freshness of the VT migration. AoMT measures the time elapsed from the first collected sensing data of the freshest avatar migration task to the last successfully processed data at the next RSU. To incentivize the contribution of bandwidth resources among the next RSUs, we propose an AoMT-based contract model, where the optimal contract is derived to maximize the expected utility of the RSU that provides metaverse services. Numerical results demonstrate the efficiency of the proposed incentive mechanism for VT migrations.
A High-Sensitivity Radon Emanation Detector System for Future Low-Background Experiments

Radioactive radon atoms originating from the long-lived primordial $^{238}\mathrm{U}$ and $^{232}\mathrm{Th}$ decay chains are constantly emanated from the surfaces of most materials. The radon atoms and their radioactive daughter isotopes can significantly contribute to the background of low-background experiments. The $^{222}\mathrm{Rn}$ progeny $^{214}\mathrm{Pb}$, for example, dominates the background of current liquid xenon-based direct dark matter detectors. We report on a new detector system to directly quantify the $^{222}\mathrm{Rn}$ surface emanation rate of materials. Using cryogenic physisorption traps, emanated radon atoms are transferred from an independent emanation vessel and concentrated within the dedicated detection vessel. The charged daughter isotopes are collected electrostatically on a silicon PIN photodiode to spectrometrically measure the alpha decays of $^{218}\mathrm{Po}$ and $^{214}\mathrm{Po}$. The overall detection efficiency is $\sim 36\,\%$ for both polonium channels. The radon emanation activity of the emanation vessel was measured to be $(0.16\pm 0.03)\,\mathrm{mBq}$, resulting in a detection sensitivity of $\sim 0.06\,\mathrm{mBq}$ at $90\,\%\,\mathrm{C.L. }$.
Local vs. Cooperative: Unraveling Glass Transition Mechanisms with SEER

Which phenomenon slows down the dynamics in super-cooled liquids and turns them into glasses is a long-standing question of condensed-matter. Most popular theories posit that as the temperature decreases, many events must occur in a coordinated fashion on a growing length scale for relaxation to occur. Instead, other approaches consider that local barriers associated with the elementary rearrangement of a few particles or `excitations' govern the dynamics. To resolve this conundrum, our central result is to introduce an algorithm, SEER, which can systematically extract hundreds of excitations and their energy from any given configuration. We also provide a novel measurement of the activation energy, characterizing the liquid dynamics, based on fast quenching and reheating. We use these two methods in a popular liquid model of polydisperse particles. Such polydisperse models are known to capture the hallmarks of the glass transition and can be equilibrated efficiently up to millisecond time scales. The analysis reveals that cooperative effects do not control the fragility of such liquids: the change of energy of local barriers determines the change of activation energy. More generally, these methods can now be used to measure the degree of cooperativity of any liquid model.
Editing 3D Scenes via Text Prompts without Retraining

Numerous diffusion models have recently been applied to image synthesis and editing. However, editing 3D scenes is still in its early stages. It poses various challenges, such as the requirement to design specific methods for different editing types, retraining new models for various 3D scenes, and the absence of convenient human interaction during editing. To tackle these issues, we introduce a text-driven editing method, termed DN2N, which allows for the direct acquisition of a NeRF model with universal editing capabilities, eliminating the requirement for retraining. Our method employs off-the-shelf text-based editing models of 2D images to modify the 3D scene images, followed by a filtering process to discard poorly edited images that disrupt 3D consistency. We then consider the remaining inconsistency as a problem of removing noise perturbation, which can be solved by generating training data with similar perturbation characteristics for training. We further propose cross-view regularization terms to help the generalized NeRF model mitigate these perturbations. Our text-driven method allows users to edit a 3D scene with their desired description, which is more friendly, intuitive, and practical than prior works. Empirical results show that our method achieves multiple editing types, including but not limited to appearance editing, weather transition, material changing, and style transfer. Most importantly, our method generalizes well with editing abilities shared among a set of model parameters without requiring a customized editing model for some specific scenes, thus inferring novel views with editing effects directly from user input. The project website is available at https://sk-fun.fun/DN2N
HIGH RESOLUTION MONSOON RECORDS FROM LAND AND OCEAN: WHAT HAVE WE LEARNT DURING THE LAST DECADE?

Impact of global climate change on the future water resources of India is intimately coupled with the variation of monsoon wind and monsoon rainfall. In this paper, the differences in paleo records from different areas have been discussed. It is felt that the climate models which can reproduce observed spatial distribution of monsoon are required to further understand the recorded paleomonsoon fluctuations.
Cloud-mediated self-triggered synchronization of a general linear multi-agent system over a directed graph

This paper proposes a self-triggered synchronization control method of a general high-order linear time-invariant multi-agent system through a cloud repository. In the cloud-mediated self-triggered control, each agent asynchronously accesses the cloud repository to get past information on its neighboring agents. Then, the agent predicts future behaviors of its neighbors as well as of its own, and locally determines its next access time to the cloud repository. In the case of a general high-order linear agent dynamics, each agent has to estimate exponential evolution of its trajectory characterized by eigenvalues of a system matrix, which is different from single/double integrator or first-order linear agents. Our proposed method deals with exponential behaviors of the agents by tightly evaluating the bounds on matrix exponentials. Based on these bound, we design the self-triggered controller through a cloud which achieves bounded state synchronization of the closed-loop system without exhibiting any Zeno behaviors. The effectiveness of the proposed method is demonstrated through the numerical simulation.
Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)

Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Causal discovery extensively involves conditional independence (CI) tests. Hence, its output quality highly depends on the performance of CI tests, which can often be unreliable in practice. Moreover, privacy concerns arise when excessive CI tests are performed. Despite the distinct nature between unreliable and excessive CI tests, this paper identifies a unified and principled approach to addressing both of them. Generally, CI statements, the outputs of CI tests, adhere to Pearl's axioms, which are a set of well-established integrity constraints on conditional independence. Hence, we can either detect erroneous CI statements if they violate Pearl's axioms or prune excessive CI statements if they are logically entailed by Pearl's axioms. Holistically, both problems boil down to reasoning about the consistency of CI statements under Pearl's axioms (referred to as CIR problem). We propose a runtime verification tool called CICheck, designed to harden causal discovery algorithms from reliability and privacy perspectives. CICheck employs a sound and decidable encoding scheme that translates CIR into SMT problems. To solve the CIR problem efficiently, CICheck introduces a four-stage decision procedure with three lightweight optimizations that actively prove or refute consistency, and only resort to costly SMT-based reasoning when necessary. Based on the decision procedure to CIR, CICheck includes two variants: ED-CICheck and ED-CICheck, which detect erroneous CI tests (to enhance reliability) and prune excessive CI tests (to enhance privacy), respectively. [abridged due to length limit]
Vibration suppression through stiffness variation and modal disparity

VIBRATION SUPPRESSION THROUGH STIFFNESS VARIATION AND MODAL DISPARITY By Jimmy Issa Vibration suppression is the main objective of this study. A semi-active and an active vibration control strategy based on stiffness variation are proposed for removing energy from vibrating structures. For the semi-active vibration control strategy, the notion of modal disparity is introduced and exploited as a new method of vibration suppression. For a given structure, modal disparity is a measure of the difference in the mode shapes of the structure in two stiffness states. Modal disparity is validated experimentally in a beam where stiffness variation is induced by application and removal of constraints. In dynamical systems modeled with finite degrees-of-freedom, the application of constraints transfers energy to the unmodeled high-frequency modes, where it is dissipated naturally and quickly. The removal of constraints does not dissipate energy but resets the system for the constraints to be applied again for further reduction of energy. Thus sequential application and removal of constraints eventually dissipates the energy of the system completely. It is shown that energy removal is always possible, even with a random switching schedule, except in one case where the energy is trapped in modes that span invariant subspaces with certain orthogonality properties. The optimal locations and timing of constraint application is investigated with the goal of maximizing energy dissipation through maximal energy transfer to the unmodeled high-frequency modes. For the active control strategy, cable actuators are proposed for removing energy from three-dimensional framed structures. The tension in the cables has two effects on the structure; it increases the stiffness of the structure and applies an external load on the structure. Both effects are used in the design of the active control strategy in which the cable tension is essentially switched between different levels to do negative work. Experimental results are presented to validate the efficacy of the control strategy.
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning

In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model to address the challenge of achieving a common goal of multiple agents interacting in the same environment with reduced sample complexity. Due to scalability and non-stationarity problems posed by multi-agent systems, model-free methods rely on a considerable number of samples for training. In contrast, we use a modularized world model, composed of action-conditioned, action-free, and static branches, to unravel the environment dynamics and produce imagined outcomes based on past experience, without sampling directly from the real environment. We employ variational auto-encoders and variational graph auto-encoders to learn the latent representations for the world model, which is merged with a value-based framework to predict the joint action-value function and optimize the overall training objective. We present experimental results in Easy, Hard, and Super-Hard StarCraft II micro-management challenges to demonstrate that our method achieves high sample efficiency and exhibits superior performance in defeating the enemy armies compared to other baselines.
Generalization error bounds for iterative learning algorithms with bounded updates

This paper explores the generalization characteristics of iterative learning algorithms with bounded updates for non-convex loss functions, employing information-theoretic techniques. Our key contribution is a novel bound for the generalization error of these algorithms with bounded updates. Our approach introduces two main novelties: 1) we reformulate the mutual information as the uncertainty of updates, providing a new perspective, and 2) instead of using the chaining rule of mutual information, we employ a variance decomposition technique to decompose information across iterations, allowing for a simpler surrogate process. We analyze our generalization bound under various settings and demonstrate improved bounds. To bridge the gap between theory and practice, we also examine the previously observed scaling behavior in large language models. Ultimately, our work takes a further step for developing practical generalization theories.
Cyclotomic function fields over finite fields with irreducible quadratic modulus

Let $\mathbb{F}_q$ be the finite field of order $q$ and $F=\mathbb{F}_q(x)$ the rational function field. In this paper, we give a characterization of the cyclotomic function fields $F(\Lambda_M)$ with modulus $M$, where $M \in \mathbb{F}_q[T]$ is a monic and irreducible polynomial of degree two. We also provide the full automorphism group of $F(\Lambda_M)$ in odd characteristic, extending results of \cite{MXY2016} where the automorphism group of $F(\Lambda_M)$ over $\mathbb{F}_q$ was computed.
Influence Maximization in Social Networks: A Survey

Online social networks have become an important platform for people to communicate, share knowledge and disseminate information. Given the widespread usage of social media, individuals' ideas, preferences and behavior are often influenced by their peers or friends in the social networks that they participate in. Since the last decade, influence maximization (IM) problem has been extensively adopted to model the diffusion of innovations and ideas. The purpose of IM is to select a set of k seed nodes who can influence the most individuals in the network. In this survey, we present a systematical study over the researches and future directions with respect to IM problem. We review the information diffusion models and analyze a variety of algorithms for the classic IM algorithms. We propose a taxonomy for potential readers to understand the key techniques and challenges. We also organize the milestone works in time order such that the readers of this survey can experience the research roadmap in this field. Moreover, we also categorize other application-oriented IM studies and correspondingly study each of them. What's more, we list a series of open questions as the future directions for IM-related researches, where a potential reader of this survey can easily observe what should be done next in this field.
The Price of Indoor Air Pollution: Evidence from Risk Maps and the Housing Market

This study uses the housing market to examine the costs of indoor air pollution. We focus on radon, a common indoor air pollutant which is the leading cause of lung cancer after smoking. For identification, we exploit a natural experiment whereby a risk map update in England induces exogenous variation in published pollution risk levels. We find a significant negative relationship between changes in published pollution risk levels and residential property prices. Interestingly, we do not find a symmetric effect for decreasing risk. We also show that the update of the risk map led higher socioeconomic groups (SEGs) to move away from affected areas, attracting lower SEG residents via lower prices. Overall, our results demonstrate that indoor air quality has material economic effects on the housing market and provide novel policy-relevant insights into how the market responds to information on environmental risks.
Time to complementary feeding initiation and its predictors among children aged 9–23 months in Meket District, Northeast Ethiopia: a Cox Weibull regression

Abstract . Globally and nationally, only 64⋅5 and 49⋅2 % of infants received solid or semi-solid foods, respectively. The available evidence indicates that the time to initiate complementary feeding practices is still poor and varies by region. The aim of the present study was to assess the time to initiation of complementary feeding and its predictors among children aged 9–23 months in Meket District, Amhara Region, Ethiopia. A community-based retrospective cohort study was conducted from June to July 2022 among 459 systematically selected mothers/caregivers with their children from 9 to 23 months of age. The result of descriptive statistics was reported by table, frequency, Kaplan-Meier curve and percent. The proportional hazard model assumption was checked, and a Weibull regression model was used to see the predictors of timely initiation of complementary feeding. An adjusted hazard ratio with a 95 % confidence interval and a P-value of 0⋅05 were used to declare the significant predictors. The median time of complementary feeding initiation was 6 months. Attending primary education (adjusted hazard ration (AHR) 1⋅8; 95 % CI 1⋅16, 2⋅78), occupation of the mother (AHR 1⋅43; 95 % CI 1⋅04, 1⋅95), home delivery (AHR 1⋅61; 95 % CI 1⋅09, 2⋅37) and birth preparedness (AHR 1⋅37; 95 % CI 1⋅03, 1⋅81) were the predictors of time to complementary feeding initiation. The median time to complementary feeding initiation was consistent with the WHO recommendation. Maternal education, maternal employment, place of delivery and birth preparedness were the predictors of time to initiation of complementary feeding. Therefore, working with the education sector, increasing the delivery rate in health facilities, strengthening counselling on birth preparation, increasing maternity leave until 6 months of age and initiating corner feeding should be part of the complementary feeding practices promotion agenda.
Intermittent Collusive Agreements: Antitrust Policy and Business Cycles

Abstract In this article we study collusive strategies and the optimal level of fines when firms face random demand fluctuations. Collusive firms can choose to alternate collusive periods with more competitive periods: such an intermittent strategy can be implemented particularly if demand variability is high. Firms then set competitive prices during recessions to cancel the risk of cartel detection and keep the ability to cartelize for the future. If the maximum fine is too low to fully deter cartels, the antitrust authority can influence the choice of collusive agreement by varying the level of fines according to demand states. If the demand is highly variable, the antitrust authority may induce firms to collude in all demand states (by decreasing the fine during recessions), in order to detect and break up cartels more easily. On the other hand, if the demand variability is low the optimal policy may be to reduce the fine when demand is high.
Advanced ovarian yolk sac tumor: upfront surgery or neoadjuvant chemotherapy followed by interval debulking?

Objective To compare surgery and survival outcomes between neoadjuvant chemotherapy and primary debulking surgery in patients with advanced ovarian yolk sac tumor. Methods In this retrospective cohort analysis, patients with stage III to IV ovarian yolk sac tumor or mixed germ cell tumors containing yolk sac tumor elements, and who underwent surgery at Peking Union Medical College Hospital between January 2011 and December 2021, were identified. Patient characteristics, treatment, and survival data were analyzed between the two groups. Results A total of 40 patients were enrolled: 19 patients received neoadjuvant chemotherapy followed by interval surgery, and 21 patients were treated with primary debulking surgery. After neoadjuvant chemotherapy, the surgical conditions of patients were improved. All patients achieved cytoreduction to R0 or R1 at interval surgery. No statistical difference was found in 3-year disease-free survival and overall survival between the neoadjuvant chemotherapy group and the primary debulking surgery group (log rank p=0.4 and 0.94). Patients had less blood loss (328.4 vs 1285.7 mL, p=0.029), lower transfusion volume (1044.4 vs 3066.7 mL, p=0.011), and fewer peri-operative complications (15.8% vs 47.6%, p=0.032) at the interval debulking surgery after neoadjuvant chemotherapy compared with patients who underwent primary debulking surgery. Conclusion For patients with advanced-stage ovarian yolk sac tumor, neoadjuvant chemotherapy followed by interval surgery is an alternative option, especially for those who cannot tolerate the primary debulking surgery because of high tumor burden and vulnerable status.
On Class and Elitism in Archaeology

Abstract While archaeology is certainly a politically conscious discipline, with various members involved in political and activist movements, especially focusing on gender and race issues, little has been said, discussed, or done with regards to class and elitism. In fact, it seems that since the advent of postmodernity in the 1970s, class and elitism have become moot topics. The aim of this article is to reflect upon class-based discrimination and elitism in archaeology, first by tracing the changes they have undergone, especially during late capitalist times, and how class has become sublimated to fit the neoliberal agenda; second, the article focuses on several issues concerning class and elitism in the university context in general, and in archaeology in particular, and how these serve as barriers to those less wealthy and fortunate; third, this work highlights how these issues concerning class and elitism in archaeology then affect scientific discourse on one hand, which has become more technologically advanced, and consequently more expensive, and archaeological theory on the other, which in its hectic search for novelty has lost its capacity to truly contribute anything new to our understanding of past and present societies.