Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of advanced Riemann solvers in a neutrino-radiation magnetohydrodynamics code in numerical relativity and its application to a binary neutron star merger

We implement advanced Riemann solvers HLLC and HLLD [1, 2] together with an advanced constrained transport scheme [3] in a numerical-relativity neutrino-radiation magnetohydrodynamics code. We validate our implementation by performing a series of one- and multi-dimensional test problems for relativistic hydrodynamics and magnetohydrodynamics in both Minkowski spacetime and a static black hole spacetime. We ﬁnd that the numerical solutions with the advanced Riemann solvers are more accurate than those with the HLLE solver [4], which was originally implemented in our code. As an application to numerical relativity, we simulate an asymmetric binary neutron star merger leading to a short-lived massive neutron star both with and without magnetic ﬁelds. We ﬁnd that the lifetime of the rotating massive neutron star formed after the merger and also the amount of the tidally-driven dynamical ejecta are overestimated when we employ the diﬀusive HLLE solver. We also ﬁnd that the magnetorotational instability is less resolved when we employ the HLLE solver because of the solver’s large numerical diﬀusivity. This causes a spurious enhancement both of magnetic winding resulting from large scale poloidal magnetic ﬁelds, and also of the energy of the outﬂow induced by magnetic pressure.
On Nested Justification Systems (full version)

Justiﬁcation theory is a general framework for the deﬁnition of semantics of rule-based languages that has a high explanatory potential. Nested justiﬁcation systems, ﬁrst introduced by Denecker et al. (2015), allow for the composition of justiﬁcation systems. This notion of nesting thus enables the modular deﬁnition of semantics of rule-based languages, and increases the representational capacities of justiﬁcation theory. As we show in this paper, the original characterization of semantics for nested justiﬁcation systems leads to the loss of information relevant for explanations. In view of this problem, we provide an alternative characterization of their semantics and show that it is equivalent to the original one. Furthermore, we show how nested justiﬁcation systems allow representing ﬁxpoint deﬁnitions. This is an extended version of our paper that will be presented at ICLP 2022 and will appear in the special issue of TPLP with the ICLP proceedings. argumentation, and their various semantics. By establishing a precise correspondence between semantics of diﬀerent logics, justiﬁcation theory sheds light on the common semantic mechanisms underlying these diﬀerent logics.
Universal Caching

In learning theory, the performance of an online policy is commonly measured in terms of the static regret metric, which compares the cumulative loss of an online policy to that of an optimal benchmark in hindsight. In the definition of static regret, the action of the benchmark policy remains fixed throughout the time horizon. Naturally, the resulting regret bounds become loose in non-stationary settings where fixed actions often suffer from poor performance. In this paper, we investigate a stronger notion of regret minimization in the context of online caching. In particular, we allow the action of the benchmark at any round to be decided by a finite state machine containing any number of states. Popular caching policies, such as LRU and FIFO, belong to this class. Using ideas from the universal prediction literature in information theory, we propose an efficient online caching policy with a sub-linear regret bound. To the best of our knowledge, this is the first data-dependent regret bound known for the caching problem in the universal setting. We establish this result by combining a recently-proposed online caching policy with an incremental parsing algorithm, namely Lempel-Ziv ‘78. Our methods also yield a simpler learning-theoretic proof of the improved regret bound as opposed to the involved problem-specific combinatorial arguments used in the earlier works.
Political Propagation of Social Botnets: Policy Consequences

The 2016 US election was a watershed event where an electoral intervention by an adversarial state made extensive use of networks of software robots and data driven communications which transformed the interference into a goal driven functionality of man-machine collaboration. Reviewing the debates post the debacle, we reflect upon the policy consequences of the use of Social Botnets and understand the impact of their adversarial operation in terms of catalysing institutional decay, growing infrastructural anxieties, increased industry regulations, more vulnerable Individuals and more distorted ideas, and most importantly, the emergence of an unintended constituency in form of the bot agency itself. The article first briefly introduces the nature and evolution of Social Botnets, and then moves over to discussing the policy consequences. For future work, it is important to understand the agency and collective properties of these software robots, in order to design the institutional and socio-technical mechanisms which mitigate the risk of adversarial social engineering using these bots from interfering into democratic processes.
Estimating the impact of foregrounds on the future detection of Rayleigh scattering

Rayleigh scattering of the cosmic microwave background (CMB) by neutral hydrogen shortly after recombination leaves frequency-dependent imprints on intensity and polarization fluctuations. High signal-to-noise observations of CMB Rayleigh scattering would provide additional insight into the physics of recombination, including greater constraining power for parameters like the primordial helium fraction, the light relic density, and the sum of neutrino masses. However, such a measurement of CMB Rayleigh scattering is challenging due to the presence of astrophysical foregrounds, which are more intense at the high frequencies, where the effects of Rayleigh scattering are most prominent. Here we forecast the detectability of CMB Rayleigh scattering including foreground removal using blind internal linear combination methods for a set of near-future surveys. We show that atmospheric effects for ground-based observatories and astrophysical foregrounds pose a significant hindrance to detecting CMB Rayleigh scattering with experiments planned for this decade, though a high-significance measurement should be possible with a future CMB satellite.
Towards Optimal VPU Compiler Cost Modeling by using Neural Networks to Infer Hardware Performances

Calculating the most efficient schedule of work in a neural network compiler is a difficult task. There are many parameters to be accounted for that can positively or adversely affect that schedule depending on their configuration - How work is shared between distributed targets, the subdivision of tensors to fit in memory, toggling the enablement of optimizations, etc. Traditionally, neural network compilers determine how to set these values by building a graph of choices and choosing the path with minimal 'cost'. These choices and their corresponding costs are usually determined by an algorithm crafted by engineers with a deep knowledge of the target platform. However, when the amount of options available to a compiler is large, it is very difficult to ensure that these models consistently produce an optimal schedule for all scenarios, whilst still completing compilation in an acceptable timeframe. This paper presents 'VPUNN' - a neural network-based cost model trained on low-level task profiling that consistently outperforms the state-of-the-art cost modeling in Intel's line of VPU processors.
Designing an Interactive, Notebook-Embedded, Tree Visualization to Support Exploratory Performance Analysis

Interactive visualization via direct manipulation has inherent design trade-offs in flexibility, discoverability, and ease-of-use. Scripting languages can support a vast range of user queries and tasks, but may be more cumbersome for free-form exploration. Embedding interactive visualization in a scripting environment, such as a computational notebook, provides an opportunity for leveraging the strengths of both direct manipulation and scripting. We conduct a design study investigating this opportunity in the context of calling context trees as used for performance analysis of parallel software. Our collaborators make new performance analysis functionality available to users via Jupyter notebook examples, making the project setting conducive to such an investigation. Through a series of semi-structured interviews and regular meetings with project stakeholders, we produce a formal task analysis grounded in the expectation that tasks may be supported by scripting, interactive visualization, or both paradigms. We then design an interactive bivariate calling context tree visualization for embedding in Jupyter notebooks with features to pass data and state between the scripting and visualization contexts. We evaluated our embedded design with seven high performance computing experts. The experts were able to complete tasks and provided further feedback on the visualization and the notebook-embedded interactive visualization paradigm. We reflect upon the project and discuss factors in both the process and the design of the embedded visualization.
JCSP: Joint Caching and Service Placement for Edge Computing Systems

With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle incoming requests. However, current systems separate the contents and services without considering the latency interplay of caching and queueing. Therefore, in this paper, we propose a novel class of stochastic models that enable the optimization of content caching and service placement decisions jointly. We first explain how to apply layered queueing networks (LQNs) models for edge service placement and show that combining this with genetic algorithms provides higher accuracy in resource allocation than an established baseline. Next, we extend LQNs with caching components to establish a joint modeling method for content caching and service placement (JCSP) and present analytical methods to analyze the resulting model. Finally, we simulate real-world Azure traces to evaluate the JCSP method and find that JCSP achieves up to 35% improvement in response time and 500MB reduction in memory usage than baseline heuristics for edge caching resource allocation.
Integrating fuzzy trajectory data and artificial intelligence methods for multi-style lane-changing behavior prediction

Artificial intelligence algorithms have been extensively applied in the field of intelligent transportation, especially for driving behavior analysis and prediction. This study proposes a novel framework by integrating fuzzy trajectory data, unsupervised learning and supervised learning methods to predict lane-changing behaviors taking multi driving styles into account. The microscopic trajectory data from the Highway Drone Dataset (HighD) are employed to construct two types of datasets including precise trajectory datasets and fuzzy trajectory datasets for lane-changing prediction models. The fuzzy trajectory data are developed based on different driving styles, which are clustered by the K-means algorithm. Two typical supervised learning methods, including random forest and long-short-term memory combined with convolutional neural network, are further applied for lane-changing behavior prediction. Results indicate that (1) the proposed integration approach performs better than the conventional lane-change prediction; (2) the relative speed-related features have a greater contribution to the lane-changing prediction after being processed by fuzzy rules based on driving styles; and (3) the difference among driving styles is more reflected from the state of lateral movement rather than the lane-changing duration.
Expansion in Supercritical Random Subgraphs of Expanders and its Consequences

In 2004, Frieze, Krivelevich and Martin [17] established the emergence of a giant component in random subgraphs of pseudo-random graphs. We study several typical properties of the giant component, most notably its expansion characteristics. We establish an asymptotic vertex expansion of connected sets in the giant by a factor of $\tilde{O}\left(\epsilon^2\right)$. From these expansion properties, we derive that the diameter of the giant is typically $O_{\epsilon}\left(\log n\right)$, and that the mixing time of a lazy random walk on the giant is asymptotically $O_{\epsilon}\left(\log^2 n\right)$. We also show similar asymptotic expansion properties of (not necessarily connected) linear sized subsets in the giant, and the typical existence of a large expander as a subgraph.
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction

In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to capture the relationship between data of different dimensions.Recent research shows that the Informer model based on Transformer has achieved excellent performance in long time series prediction.However, this model still has some deficiencies in multidimensional prediction,it cannot capture the relationship between different dimensions well. We improved Informer to address its shortcomings in multidimensional forecasting. First,we introduce an adaptive graph neural network to capture hidden dimension dependencies in mostly time series prediction. Secondly,we integrate adaptive graph convolutional networks into various spatio-temporal series prediction models to solve the defect that they cannot capture the relationship between different dimensions. Thirdly,After experimental testing with multiple data sets, the accuracy of our framework improved by about 10\% after being introduced into the model.
The Compound Information Bottleneck Outlook

We formulate and analyze the compound information bottleneck programming . In this problem, a Markov chain X → Y → Z is assumed with ﬁxed marginal distributions P X and P Y , and the mutual information between X and Z is sought to be maximized over the choice of conditional probability of Z given Y from a given class, under the worst choice of the joint probability of the pair ( X , Y ) from a different class. We consider several classes based on extremes of: mutual information; minimal correlation; total variation; and the relative entropy class. We provide values, bounds, and various characterizations for speciﬁc instances of this problem: the binary symmetric case, the scalar Gaussian case, the vector Gaussian case and the symmetric modulo-additive case. Finally, for the general case, we propose a Blahut-Arimoto type of alternating iterations algorithm to ﬁnd a consistent solution to this problem.
Fully packed quantum loop model on the triangular lattice: Hidden vison plaquette phase and cubic phase transitions

Quantum loop and dimer models are prototypical correlated systems with local constraints, whose generic solutions for different lattice geometries and parameter regimes are still missing due to the lack of controlled methods to solve such systems in the thermodynamic limit. Here, we obtain, via the newly developed sweeping cluster quantum Monte Carlo algorithm, the phase diagram of the triangular-lattice fully packed quantum loop model (quantum dimer model with two dimers per site). We find that as the interaction turns from attractive to repulsive, the system has a first-order transition from a lattice nematic (LN) solid to a novel vison plaquette (VP) solid without dimer density modulations but with off-diagonal order. Further increasing the dimer repulsion towards the Rokhsar-Kivelson point, the VP phase experiences a continuous transition to an even $\mathbb{Z}_2$ quantum spin liquid (QSL) phase. Our results reveal the LN-to-VP first-order transition is triggered by the intricate change from face- to corner-cubic anisotropy of the O($3$) vison order parameter, and the VP-to-QSL continuous transition is of the cubic universality class, which is very close to the $(2+1)$D O($3$) one, in full consistency with the recent conformal bootstrap findings on the cubic fixed point.
Global attractor for the weakly damped forced Kawahara equation on the torus

We study the long time behaviour of solutions for the weakly damped forced Kawahara equation on the torus. More precisely, we prove the existence of a global attractor in L 2 , to which as time passes all solutions draw closer. In fact, we show that the global attractor turns out to lie in a smoother space H 2 and be bounded therein. Further, we give an upper bound of the size of the attractor in H 2 that depends only on the damping parameter and the norm of the forcing term.
Towards better reconciling randomized controlled trial and observational study ﬁndings: Eﬃcient algorithms for building representative matched samples with enhanced external validity

: Many recent eﬀorts center on assessing the ability of real-world evidence (RWE) generated from nonrandomized observational data to provide results that are compatible with those from randomized controlled trials (RCTs). One noticeable endeavor is the RCT DUPLICATE initiative (Franklin et al., 2020). To better reconcile ﬁndings from observational and trial data, it is desirable to eliminate differences between the RCT and corresponding observational study populations. We outline an eﬃcient, network-ﬂow-based statistical matching algorithm that designs well-matched pairs from observational data that mimic the covariates’ distribution of a target population, e.g., the RCT study population or a population of scientiﬁc interest. We demonstrate the usefulness of the method by revisiting the inconsistency regarding a cardioprotective eﬀect of the hormone replacement therapy (HRT) in the Women’s Health Initiative (WHI) clinical trial and corresponding observational study. We found that the discrepancy between the trial and observational study persisted in a design that adjusted for study populations’ cardiovascular risk proﬁle, but seemed to disappear in a study design that further adjusted for the HRT initiation age and previous estrogen-plus-progestin use. The proposed method is integrated into the R package match2C .
A Specification Logic for Programs in the Probabilistic Guarded Command Language (Extended Version)

The semantics of probabilistic languages has been extensively studied, but specification languages for their properties have received little attention. This paper introduces the probabilistic dynamic logic pDL, a specification logic for programs in the probabilistic guarded command language (pGCL) of McIver and Morgan. The proposed logic pDL can express both first-order state properties and probabilistic reachability properties, addressing both the non-deterministic and probabilistic choice operators of pGCL. In order to precisely explain the meaning of specifications, we formally define the satisfaction relation for pDL. Since pDL embeds pGCL programs in its box-modality operator, pDL satisfiability builds on a formal MDP semantics for pGCL programs. The satisfaction relation is modeled after PCTL, but extended from propositional to first-order setting of dynamic logic, and also embedding program fragments. We study basic properties of pDL, such as weakening and distribution, that can support reasoning systems. Finally, we demonstrate the use of pDL to reason about program behavior.
TinyGenius: Intertwining Natural Language Processing with Microtask Crowdsourcing for Scholarly Knowledge Graph Creation

As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles. CCS CONCEPTS • Information systems → Crowdsourcing; • Applied computing → Digital libraries and archives; • Human-centered computing → Graphical user interfaces.
Reliable Monte Carlo localization for mobile robots

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two‐dimensional LiDAR. Results show that reliable MCL that performs robust localization, self‐failure detection, and quick failure recovery can be realized.
DNS based In-Browser Cryptojacking Detection

The metadata aspect of Domain Names (DNs) enables us to perform a behavioral study of DNs and detect if a DN is involved in in-browser cryptojacking. Thus, we are motivated to study different temporal and behavioral aspects of DNs involved in cryptojacking. We use temporal features such as query frequency and query burst along with graphbased features such as degree and diameter, and non-temporal features such as the string-based to detect if a DNs is suspect to be involved in the in-browser cryptojacking. Then, we use them to train the Machine Learning (ML) algorithms over different temporal granularities such as 2 hours datasets and complete dataset. Our results show DecisionTrees classifier performs the best with 59.5% Recall on cryptojacked DN, while for unsupervised learning, K-Means with K=2 perform the best. Similarity analysis of the features reveals a minimal divergence between the cryptojacking DNs and other already known malicious DNs. It also reveals the need for improvements in the feature set of state-of-the-art methods to improve their accuracy in detecting in-browser cryptojacking. As added analysis, our signature-based analysis identifies that none-of-the Indian Government websites were involved in cryptojacking during October-December 2021. However, based on the resource utilization, we identify 10 DNs with different properties than others.