Miracle-dz/UniFAR-FTUs huggingface dataset:
Nova: Value-based Negotiation of Norms

Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p-value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p-value and large effect size in particular settings) toward reaching an agreement.
Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction

Background: The 10-year Atherosclerotic Cardiovascular Disease risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of coronary artery disease (CAD) polygenic scores has been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction. Methods: The population-based Norwegian HUNT2 (Trøndelag Health Study 2) cohort of 51 036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372 410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards, and Harrell concordance index, sensitivity, and specificity were compared. Results: Inclusion of age and sex interactions of CAD polygenic score to the prediction models increased the C-index and sensitivity by accounting for nonadditive effects of CAD polygenic score and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a 2-step approach: (1) Atherosclerotic Cardiovascular Disease risk score (74.1%) and (2) the CAD polygenic score interaction model for those in low clinical risk (additional 8.5%). Conclusions: These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age- and sex-interaction terms with polygenic scores to optimize detection of individuals at high risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.
Flow-guided One-shot Talking Face Generation with a High-resolution Audio-visual Dataset

One-shot talking face generation should synthesize high visual quality facial videos with reasonable animations of expression and head pose, and just utilize arbitrary driving audio and arbitrary single face image as the source. Current works fail to generate over 256×256 resolution realistic-looking videos due to the lack of an appropriate high-resolution audio-visual dataset, and the limitation of the sparse facial landmarks in providing poor expression details. To synthesize high-definition videos, we build a large in-the-wild high-resolution audio-visual dataset and propose a novel flow-guided talking face generation framework. The new dataset is collected from youtube and consists of about 16 hours 720P or 1080P videos. We leverage the facial 3D morphable model (3DMM) to split the framework into two cascaded modules instead of learning a direct mapping from audio to video. In the first module, we propose a novel animation generator to produce the movements of mouth, eyebrow and head pose simultaneously. In the second module, we transform animation into dense flow to provide more expression details and carefully design a novel flow-guided video generator to synthesize videos. Our method is able to produce high-definition videos and outperforms state-of-the-art works in objective and subjective comparisons*.
Estimation of reproduction numbers in real time: Conceptual and statistical challenges

The reproduction number R$$ R $$ has been a central metric of the COVID‐19 pandemic response, published weekly by the UK government and regularly reported in the media. Here, we provide a formal definition and discuss the advantages and most common misconceptions around this quantity. We consider the intuition behind different formulations of R$$ R $$ , the complexities in its estimation (including the unavoidable lags involved), and its value compared to other indicators (e.g. the growth rate) that can be directly observed from aggregate surveillance data and react more promptly to changes in epidemic trend. As models become more sophisticated, with age and/or spatial structure, formulating R$$ R $$ becomes increasingly complicated and inevitably model‐dependent. We present some models currently used in the UK pandemic response as examples. Ultimately, limitations in the available data streams, data quality and time constraints force pragmatic choices to be made on a quantity that is an average across time, space, social structure and settings. Effectively communicating these challenges is important but often difficult in an emergency.
NSC – Named Service Calls, or a Remote Procedure Call for NDN

Named Data Networking (NDN) is a potential new internet architecture which has gained momentum due to its ability to enable applications in scenarios where the current IP networks have struggled. Among these scenarios, mobile ad-hoc networks or delay tolerant networks. Existing applications and systems make heavy use of remote procedure calls, or RPCs, to enable more complex applications. Designing a framework for remote code execution in NDN could ease development of applications and systems in NDN. Named Service Call (NSC) answers how to securely process client requests, and timely inform the client of their results within an NDN architecture.
Modification of the hydroxyl surface of potassium acetate intercalated halloysite between 25 and 300 °C

Abstract Changes in the hydroxyl surfaces of potassium acetate-intercalated halloysite were studied over ambient to predehydroxylation temperature range using a combination of X-ray diffraction and Raman spectroscopy. XRD shows that the halloysite is completely expanded to 13.80 Å. Upon heating the intercalation complex to 50 °C under nitrogen, two expanded phases are observed with d-spacings of 11.47 and 8.95 Å. Upon thermal treatment, the 11.47 Å phase is stable in both the heating and cooling cycles. The ~9.0 Å phase undergoes an expansion at 100 °C to 9.2 Å. Upon exposure to air, the intercalated kaolinite returns to a 13.80 Å phase. The completely intercalated halloysite showed a band at 3602 cm-1 attributed to the inner surface hydroxyl hydrogen bonded to the acetate ion together with bands at 3620 and 3695 cm-1 assigned to the inner hydroxyl and the inner surface hydroxyls, which do not react with the acetate. Mild heating of the intercalation complex to 50 °C caused a rearrangement of the surface structure with Raman bands being observed at 3606 and 3597 cm-1. Further thermal treatment at 100 °C caused these bands to shift to 3615 and 3601 cm-1. At the predehydroxylation temperature for potassium acetate intercalated halloysite (300 °C) two bands were observed at 3602 and 3612 cm-1. Above this temperature no hydroxyls are spectroscopically evident. Upon cooling to room temperature, the Raman spectra of the hydroxyl surfaces are altered with two bands observed at 3604 and 3596 cm-1
Supplement to Fourier Bases for Solving Permutation Puzzles

1.1 Preliminaries A compact way of denoting permutations of Sn is through cycle notation. Permutations can be expressed as a product of disjoint cycles. The cycle (a1, . . . ak) denotes the permutation that sends a1 7! a2, a2 7! a3, . , ak 7! a1. Without loss of generality, we can omit singleton cycles going forward. The representations of Sn are indexed by partitions of n, a set of positive integers that sum to n. The tuple of non-negative integers = ( 1, 2 . . . k) is a partition of n if Pk i=1 i = n. Conventionally, the parts of a partition are listed in weakly decreasing order: 1 . k. We use the notation ` n to indicate that is a partition of n. Partitions can be visualized by Ferrers diagrams, patterns of unfilled left aligned boxes where row i contains i boxes. Definition 1 A Young tableau is a assignment of the numbers 1, 2, . n to the n boxes of a Ferrers diagram. Definition 2 A Young standard tableau is a Young tableau where the entries are strictly increasing along each row and across each column. For convenience, we will also refer to Young standard tableaux as standard tableaux. The partition underlying a Ferrers diagram or a Young tableau as also referred to as its shape. Example 1 For n = 3, there are three possible partitions (3), (2, 1) and (1, 1, 1), which have the respective Ferrers diagrams: , , .
Explaining the Plans of Agents via Theory of Mind

For a plan to achieve some goal – to be valid – a set of suf-ﬁcient and necessary conditions must hold. In dynamic settings, agents may come to hold false beliefs about these conditions and, by extension, about the validity of their plans or the plans of other agents. Since different agents often believe different things about the world and about the beliefs of other agents, discrepancies may occur between agents’ beliefs about the validity of plans. In this work, we explore how agents can use their Theory in Mind to identify and correct other agents’ beliefs that give rise to discrepancies pertaining to plan validity. We appeal to an epistemic logic framework to allow agents to reason over the nested beliefs of other agents. We realize our approach using epistemic planning and demonstrate how an off-the-shelf epistemic planner can be used to resolve discrepancies regarding plan validity in a number of domains. A study showcases our approach’s ability to resolve misconceptions held by humans pertaining to plan validity.
Large Eddy Simulation of Ignition and Combustion of Ethylene/Air Turbulent Jet Diffusion Flame with Reduced Kinetic Mechanism

In this paper, unsteady process of ignition and combustion of turbulent plane-jet diffusion flame of ethylene/air at varied fuel/air ratios is numerically simulated with Large Eddy Simulation (LES) and a reduced kinetic mechanism of ethylene. The kinetic mechanism consisting of 25species and 131steps is reduced from a 71species/395steps detailed mechanism via the method of error-propagation-based directed relation graph (DRGEP) and sensitivity analysis. The LES results of height of flame lift-up and averaged temperature profiles at different downstream locations are compared with the DNS result of Yoo (2011) and satisfactory agreements are found. Unsteady processes of ignition and combustion of ethylene plane-jet diffusion flame are simulated with varied fuel injection velocities. Dynamic evolutions of temperature field as well as CH2 and OH radicals are obtained, which are found to be strongly related to turbulence eddies caused by jet/air mixing layer. The present numerical study shows that LES method with reduced mechanism of hydrocarbon fuels can effectively simulate temporal and spatial evolution of ignition and combustion process.
On the Turing complexity of learning finite families of algebraic structures

In previous work, we have combined computable structure theory and algorithmic learning theory to study which families of algebraic structures are learnable in the limit (up to isomorphism). In this paper, we measure the computational power that is needed to learn finite families of structures. In particular, we prove that, if a family of structures is both finite and learnable, then any oracle which computes the Halting set is able to achieve such a learning. On the other hand, we construct a pair of structures which is learnable but no computable learner can learn it.
Fully resonant magneto-elastic spin-wave excitation by surface acoustic waves under conservation of energy and linear momentum

We report on the resonant excitation of spin waves in micro-structured magnetic thin films by short-wavelength surface acoustic waves (SAWs). The spin waves as well as the acoustic waves are studied by micro-focused Brillouin light scattering spectroscopy. At low magnetic bias fields, a resonant phonon–magnon conversion is possible, which results in the excitation of short-wavelength spin waves. Using micromagnetic simulations, we verify that during this excitation both energy and linear momentum are conserved and fully transferred from the SAW to the spin wave. This conversion can already be detected after an interaction length of a few micrometers. Thus, our findings pave the way for miniaturized magneto-elastic spin-wave emitters for magnon computing.
Intent Disentanglement and Feature Self-Supervision for Novel Recommendation

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation methods over-emphasize the importance of tail items without differentiating the degree of users’ intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing studies has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experiments on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of the trade-off between accuracy and novelty.
Evolutionary Multi-Objective Virtual Network Function Placement: A Formal Model and Effective Algorithms

Data centers are critical to the commercial and social activities of modern society but are also major electricity consumers. To minimize their environmental impact, it is imperative to make data centers more energy efficient while maintaining a high quality of service (QoS). Bearing this consideration in mind, we develop an analytical model using queueing theory for evaluating the QoS of a data center. Furthermore, based on this model, we develop a domain-specific evolutionary optimization framework featuring a tailored solution representation and a constraint-aware initialization operator for finding the optimal placement of virtual network functions in a data center that optimizes multiple conflicting objectives with regard to energy consumption and QoS. In particular, our framework is applicable to any existing evolutionary multi-objective optimization algorithm in a plug-in manner. Extensive experiments validate the efficiency and accuracy of our QoS model as well as the effectiveness of our tailored algorithms for virtual network function placement problems at various scales.
Dynamic Planning and Learning under Recovering Rewards

Motivated by emerging applications such as live-streaming e-commerce, promotions and recommendations, we introduce a general class of multi-armed bandit problems that have the following two features: (i) the decision maker can pull and collect rewards from at most K out of N different arms in each time period; (ii) the expected reward of an arm immediately drops after it is pulled, and then non-parametrically recovers as the idle time increases. With the objective of maximizing expected cumulative rewards over T time periods, we propose, construct and prove performance guarantees for a class of “Purely Periodic Policies”. For the ofﬂine problem when all model parameters are known, our proposed policy obtains an approximation ratio that is at the order of 1 − O (1 / √ K ) , which is asymptotically optimal when K grows to inﬁnity. For the online problem when the model parameters are unknown and need to be learned, we design an Upper Con-ﬁdence Bound (UCB) based policy that approximately has (cid:101) O ( N √ T ) regret against the ofﬂine benchmark. Our framework and policy design may have the potential to be adapted into other ofﬂine planning and online learning applications with non-stationary and recovering rewards.
Edge-Unfolding Prismatoids: Tall or Rectangular Base

We show how to edge-unfold a new class of convex polyhedra, specifically a new class of prismatoids (the convex hull of two parallel convex polygons, called the top and base), by constructing a nonoverlapping"petal unfolding"in two new cases: (1) when the top and base are sufficiently far from each other; and (2) when the base is a rectangle and all other faces are nonobtuse triangles. The latter result extends a previous result by O'Rourke that the petal unfolding of a prismatoid avoids overlap when the base is a triangle (possibly obtuse) and all other faces are nonobtuse triangles. We also illustrate the difficulty of extending this result to a general quadrilateral base by giving a counterexample to our technique.
On a Finite-Size Neuronal Population Equation

Population equations for infinitely large networks of spiking neurons have a long tradition in theoretical neuroscience. In this work, we analyze a recent generalization of these equations to populations of finite size, which takes the form of a nonlinear stochastic integral equation. We prove that, in the case of leaky integrate-and-fire (LIF) neurons with escape noise and for a slightly simplified version of the model, the equation is well-posed and stable in the sense of Br\'emaud-Massouli\'e. The proof combines methods from Markov processes taking values in the space of positive measures and nonlinear Hawkes processes. For applications, we also provide efficient simulation algorithms.
Secure Reversible Data Hiding in Encrypted Images Using Cipher-Feedback Secret Sharing

Reversible data hiding in encrypted images (RDH-EI) has attracted increasing attention, since it can protect the privacy of original images while the embedded data can be exactly extracted. Recently, some RDH-EI schemes with multiple data hiders have been proposed using secret sharing technique. However, these schemes protect the contents of the original images with lightweight security level. In this paper, we propose a high-security RDH-EI scheme with multiple data hiders. First, we introduce a cipher-feedback secret sharing (CFSS) technique. It follows the cryptography standards by introducing the cipher-feedback strategy of AES. Then, using the CFSS technique, we devise a new (r,n)-threshold (r<=n) RDH-EI scheme with multiple data hiders called CFSS-RDHEI. It can encrypt an original image into n encrypted images with reduced size using an encryption key and sends each encrypted image to one data hider. Each data hider can independently embed secret data into the encrypted image to obtain the corresponding marked encrypted image. The original image can be completely recovered from r marked encrypted images and the encryption key. Performance evaluations show that our CFSS-RDHEI scheme has high embedding rate and its generated encrypted images are much smaller, compared to existing secret sharing-based RDH-EI schemes. Security analysis demonstrates that it can achieve high security to defense some commonly used security attacks.
The Transient Ultra-luminous X-ray Source, ULX-4, in M51

We present the results of a temporal and spectral analysis of the transient source ULX-4 in the galaxy M51. The data used were drawn from Chandra, XMM-Newton and Swift-XRT archives, spanning the years 2000-2019. The X-ray flux of the source is seen to vary by two orders of magnitudes within a month but a short-term variability was not observed over the time intervals of 100-2000 second in the 0.3−10 keV energy band. We find some evidence for the existence of bi-modality feature in the flux distribution of ULX-4. We identified two optical sources as possible counterparts within an error radius of 0. ′′18 at 95% confidence level for ULX-4 based on the archival HST/ACS and HST/WFC3 data. Blackbody fits of the spectral energy distributions (SEDs) indicate the spectral type to be B-type stars. One of these counterparts exhibits a low-amplitude optical periodicity of 264 ± 37 days in the F606W filter; if we assume this apparent periodicity is associated with the orbital motion of the donor, then it is more likely that the donor is a red supergiant satisfying the long periodicity and accretion via Roche-lobe overflow. Consequently, the SED would then have to be interpreted as a superposition of emissions from a cold donor and a hot flow component, most likely from an accretion disk. If, on the other hand, the periodicity is super orbital in nature i.e., due to possible interactions of the compact object with a circumstellar disk, the donor could then be a Be/X star hosting a neutron star.
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning

Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be transformed into a common supervised learning issue for solution. However, most mainstream data augmentation based approaches only consider the single modality information, which leads to the low diversity and quality of generated features. In this paper, we present a novel multi-modal data augmentation approach named Dizygotic Conditional Variational AutoEncoder (DCVAE) for addressing the aforementioned issue. DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner. Subsequently, the generated features of two CVAEs are adaptively combined to yield the final feature, which can be converted back into its paired conditions while ensuring these conditions are consistent with the original conditions not only in representation but also in function. DCVAE essentially provides a new idea of data augmentation in various multi-modal scenarios by exploiting the complement of different modality prior information. Extensive experimental results demonstrate our work achieves state-of-the-art performances on miniImageNet, CIFAR-FS and CUB datasets, and is able to work well in the partial modality absence case.