Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost Problems for Parametric Time Petri Nets

We investigate the problem of parameter synthesis for time Petri nets with a cost variable that evolves both continuously with time, and discretely when firing transitions. More precisely, parameters are rational symbolic constants used for time constraints on the firing of transitions and we want to synthesise all their values such that some marking is reachable, with a cost that is either minimal or simply less than a given bound. We first prove that the mere existence of values for the parameters such that the latter property holds is undecidable. We nonetheless provide symbolic semi-algorithms for the two synthesis problems and we prove them both sound and complete when they terminate. We also show how to modify them for the case when parameter values are integers. Finally, we prove that these modified versions terminate if parameters are bounded. While this is to be expected since there are now only a finite number of possible parameter values, our algorithms are symbolic and thus avoid an explicit enumeration of all those values. Furthermore, the results are symbolic constraints representing finite unions of convex polyhedra that are easily amenable to further analysis through linear programming. We finally report on the implementation of the approach in Romeo, a software tool for the analysis of time Petri nets.
CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems

Given the success of reinforcement learning (RL) in various domains, it is promising to explore the application of its methods to the development of intelligent and autonomous cyber agents. Enabling this development requires a representative RL training environment. To that end, this work presents CyGIL: an experimental testbed of an emulated RL training environment for network cyber operations. CyGIL uses a stateless environment architecture and incorporates the MITRE ATT&CK framework to establish a high fidelity training environment, while presenting a sufficiently abstracted interface to enable RL training. Its comprehensive action space and flexible game design allow the agent training to focus on particular advanced persistent threat (APT) profiles, and to incorporate a broad range of potential threats and vulnerabilities. By striking a balance between fidelity and simplicity, it aims to leverage state of the art RL algorithms for application to real-world cyber defence.
Thermostat effect on water transport dynamics across CNT membranes

Abstract Whereas numerous experimental and simulation studies have been contributed to the investigation of fluid transport across membranes in the past decades, there is a significant discrepancy between experiments and simulations in the magnitude of fluid permeability and the degree of flow rate enhancement. Here, we show that one of the causes of the discrepancy is the variety of thermostating object, via which the temperature of fluid flow in non-equilibrium molecular simulations is controlled. By thermostating either the water system or the membrane material with Langevin method, we examine the temperatures of water flows in two types of membranes, the amounts of absorbed water molecules, fluid velocities, slip lengths and water fluxes. We show that thermostating the CNT membrane brings overall enhanced water flow than directly thermostating the water system. Moreover, increasing the temperature coupling time in the thermostat gives rise to an enhancement of water flux, while weakens the stability of system temperature. In addition to explaining the disparate simulation results on fluid transport in nanopores, this work provides guidelines for diagnosing the setting of NEMD simulations.
DeepZensols: Deep Natural Language Processing Framework

Reproducing results in publications by distributing publicly available source code is becoming ever more popular. Given the difficulty of reproducing machine learning (ML) experiments, there have been significant efforts in reducing the variance of these results. As in any science, the ability to consistently reproduce results effectively strengthens the underlying hypothesis of the work, and thus, should be regarded as important as the novel aspect of the research itself. The contribution of this work is a framework that is able to reproduce consistent results and provides a means of easily creating, training, and evaluating natural language processing (NLP) deep learning (DL) models.
Multi-center decomposition of molecular densities: a mathematical perspective

The aim of this article is to analyze from a mathematical perspective some existing schemes to partition a molecular density into several atomic contributions with a specific focus on Iterative Stockholder Atom (ISA) methods. We provide a unified mathematical framework to describe the latter family of methods and propose a new scheme, named L-ISA (for linear approximation of ISA), which generalizes the so-called additive variational Hirshfeld method. We prove several important mathematical properties of the ISA and L-ISA minimization problems and show that the so-called ISA algorithms can be viewed as alternating minimization schemes, which, in turn, enables us to obtain new convergence results for these numerical methods. Specific mathematical properties of the ISA decomposition for diatomic systems are also presented. Numerical results on diatomic systems illustrate the proven mathematical properties.
Generalized dilaton gravity in 2d

Generalized dilaton gravity in 2d is the most general consistent deformation of the Jackiw–Teitelboim model that maintains local Lorentz invariance. The action is generically not power-counting renormalizable, thus going beyond the class of models typically studied. Nevertheless, all these models are exactly soluble. We focus on a subclass of dilaton scale invariant models. Within this subclass, we identify a 3-parameter family of models that describe black holes asymptoting to AdS_22 in the UV and to dS_22 in the IR. Since these models could be interesting for holography, we address thermodynamics and boundary issues, including boundary charges, asymptotic symmetries and holographic renormalization.
Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion

Voice conversion (VC) is an effective approach to electrolaryngeal (EL) speech enhancement, a task that aims to improve the quality of the artificial voice from an electrolarynx device. In frame-based VC methods, time alignment needs to be performed prior to model training, and the dynamic time warping (DTW) algorithm is widely adopted to compute the best time alignment between each utterance pair. The validity is based on the assumption that the same phonemes of the speakers have similar features and can be mapped by measuring a pre-defined distance between speech frames of the source and the target. However, the special characteristics of the EL speech can break the assumption, resulting in a sub-optimal DTW alignment. In this work, we propose to use lip images for time alignment, as we assume that the lip movements of laryngectomee remain normal compared to healthy people. We investigate two naive lip representations and distance metrics, and experimental results demonstrate that the proposed method can significantly outperform the audio-only alignment in terms of objective and subjective evaluations.
Worldtube excision method for intermediate-mass-ratio inspirals: Scalar-field toy model

The computational cost of inspiral and merger simulations for black-hole binaries increases in inverse proportion to the square of the mass ratio $q:=m_2/m_1\leq 1$. One factor of $q$ comes from the number of orbital cycles, which is proportional to $1/q$, and another is associated with the required number of time steps per orbit, constrained (via the Courant-Friedrich-Lewy condition) by the need to resolve the two disparate length scales. This problematic scaling makes simulations progressively less tractable at smaller $q$. Here we propose and explore a method for alleviating the scale disparity in simulations with mass ratios in the intermediate astrophysical range ($10^{-4} \lesssim q\lesssim 10^{-2}$), where purely perturbative methods may not be adequate. A region of radius much larger than $m_2$ around the smaller object is excised from the numerical domain, and replaced with an analytical model approximating a tidally deformed black hole. The analytical model involves certain a priori unknown parameters, associated with unknown bits of physics together with gauge-adjustment terms; these are dynamically determined by matching to the numerical solution outside the excision region. In this paper we develop the basic idea and apply it to a toy model of a scalar charge in a circular geodesic orbit around a Schwarzschild black hole, solving for the massless Klein-Gordon field in a 1+1D framework. Our main goal here is to explore the utility and properties of different matching strategies, and to this end we develop two independent implementations, a finite-difference one and a spectral one. We discuss the extension of our method to a full 3D numerical evolution and to gravity.
Approximately counting independent sets in bipartite graphs via graph containers

By implementing algorithmic versions of Sapozhenko's graph container methods, we give new algorithms for approximating the number of independent sets in bipartite graphs. Our first algorithm applies to d$$ d $$ ‐regular, bipartite graphs satisfying a weak expansion condition: when d$$ d $$ is constant, and the graph is a bipartite Ω(log2d/d)$$ \Omega \left({\log}^2d/d\right) $$ ‐expander, we obtain an FPTAS for the number of independent sets. Previously such a result for d>5$$ d>5 $$ was known only for graphs satisfying the much stronger expansion conditions of random bipartite graphs. The algorithm also applies to weighted independent sets: for a d$$ d $$ ‐regular, bipartite α$$ \alpha $$ ‐expander, with α>0$$ \alpha >0 $$ fixed, we give an FPTAS for the hard‐core model partition function at fugacity λ=Ω(logd/d1/4)$$ \lambda =\Omega \left(\log d/{d}^{1/4}\right) $$ . Finally we present an algorithm that applies to all d$$ d $$ ‐regular, bipartite graphs, runs in time expOn·log3dd$$ \exp \left(O\left(n\cdotp \frac{\log^3d}{d}\right)\right) $$ , and outputs a (1+o(1))$$ \left(1+o(1)\right) $$ ‐approximation to the number of independent sets.
Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs

Contrastive Learning (CL) is one of the most popular self-supervised learning frameworks for graph representation learning, which trains a Graph Neural Network (GNN) by discriminating positive and negative node pairs. However, there are two challenges for CL on graphs. On the one hand, traditional CL methods will unavoidably introduce semantic errors since they will treat some semantically similar nodes as negative pairs. On the other hand, most of the existing CL methods ignore the multiplexity nature of the real-world graphs, where nodes are connected by various relations and each relation represents a view of the graph. To address these challenges, we propose a novel Graph Multi-View Prototypical (Graph-MVP) framework to extract node embeddings on multiplex graphs. Firstly, we introduce a Graph Proto-typical Contrastive Learning (Graph-PCL) framework to capture both node-level and semantic-level information for each view of multiplex graphs. Graph-PCL captures the node-level information by a simple yet effective data transformation technique. It captures the semantic-level information by an Expectation-Maximization (EM) algorithm, which alternatively performs clustering over node embeddings and parameter updating for GNN. Next, we introduce Graph-MVP based on Graph-PCL to jointly model different views of the multiplex graphs. Our key insight behind Graph-MVP is that different view-speciﬁc embeddings of the same node should have similar underlying semantic, based on which we propose two versions of Graph-MVP: Graph-MVP hard and Graph-MVP soft to align embeddings across views. Finally, we evaluate the proposed Graph-PCL and Graph-MVP on a variety of real-world datasets and downstream tasks. The experimental results demonstrate the effectiveness of the proposed Graph-PCL and Graph-MVP frameworks.
Any-Horizon Uniform Random Sampling and Enumeration of Constrained Scenarios for Simulation-Based Formal Verification

<italic>Model-based</italic> approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on <italic>numerical simulation</italic> of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given <italic>constraints</italic>. Such constraints typically stem from <italic>requirements</italic> (or <italic>assumptions</italic>) on the SUV inputs and its <italic>operational environment</italic> as well as from the enforcement of <italic>additional conditions</italic> aiming at, e.g., <italic>prioritising</italic> the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising <italic>selected</italic> requirements, or avoiding <italic>vacuity</italic> in their satisfaction. In this setting, the possibility to <italic>efficiently sample at random</italic> (with a known distribution, e.g., uniformly) within, or to efficiently <italic>enumerate</italic> (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general <italic>fail</italic> in extracting scenarios according to a given distribution (e.g., uniformly), and can be <italic>very inefficient</italic> to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple <italic>finite memory monitors</italic>, a data structure (<italic>scenario generator</italic>) can be synthesised, from which <italic>any-horizon scenarios</italic> satisfying the input constraints can be <italic>efficiently</italic> extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables <italic>seamless support to virtually all simulation-based approaches to CPS verification</italic>, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking.
SIHR: Statistical Inference in High-Dimensional Linear and Logistic Regression Models

We introduce the R package \CRANpkg{SIHR} for statistical inference in high-dimensional generalized linear models with continuous and binary outcomes. The package provides functionalities for constructing confidence intervals and performing hypothesis tests for low-dimensional objectives in both one-sample and two-sample regression settings. We illustrate the usage of \CRANpkg{SIHR} through numerical examples and present real data applications to demonstrate the package's performance and practicality.
C-MinHash: Rigorously Reducing K Permutations to Two

Minwise hashing (MinHash) is an important and practical algorithm for generating random hashes to approximate the Jaccard (resemblance) similarity in massive binary (0/1) data. The basic theory of MinHash requires applying hundreds or even thousands of independent random permutations to each data vector in the dataset, in order to obtain reliable results for (e.g.,) building large-scale learning models or approximate near neighbor search in massive data. In this paper, we propose {\bf Circulant MinHash (C-MinHash)} and provide the surprising theoretical results that we just need \textbf{two} independent random permutations. For C-MinHash, we first conduct an initial permutation on the data vector, then we use a second permutation to generate hash values. Basically, the second permutation is re-used $K$ times via circulant shifting to produce $K$ hashes. Unlike classical MinHash, these $K$ hashes are obviously correlated, but we are able to provide rigorous proofs that we still obtain an unbiased estimate of the Jaccard similarity and the theoretical variance is uniformly smaller than that of the classical MinHash with $K$ independent permutations. The theoretical proofs of C-MinHash require some non-trivial efforts. Numerical experiments are conducted to justify the theory and demonstrate the effectiveness of C-MinHash.
Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to bench-mark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the pro-posed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.
Towards Natural Language Interfaces for Data Visualization: A Survey

Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than having to worry about how to operate visualization tools on the interface. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed in academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each article, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query interpretation, data transformation, visual mapping, view transformation, human interaction, dialogue management, and presentation. Finally, we also shed light on several promising directions for future work in the V-NLI community.
Vertical distribution of epiphytic bryophytes depends on phorophyte type; a case study from windthrows in Kampinoski National Park (Central Poland)

The vertical distribution of epiphytic bryophytes in European forests are still relatively poorly understood. The aim of the study was to analyse the diversity and vertical zonation of epiphytic mosses and liverworts on selected tree types (Quercus petraea, Betula pendula and Pinus sylvestris) within windthrow areas in the Kampinoski National Park (Central Poland). The investigations were performed in five parts of the trees: the tree base, lower trunk, upper trunk, lower crown, and upper crown. Deciduous trees have more species than pine trees (13 on Quercus and Betula, 8 on Pinus). The type of phorophyte was crucial for the differences in the species composition from the tree base to the upper crown that was observed. The highest richness of bryophytes was recorded on the tree bases, while the lowest was recorded in the upper parts of the crowns. The variability of the habitat conditions in the vertical gradient on the trunk that affected the patterns of the occurrence of species with different ecological preferences was determined using the Ellenberg indicator values. An increase in the value of the light and acidity indicators from the base of the trunk upwards and decreasing tendency in the case of moisture indicator was noted.
CREBH normalizes dyslipidemia and halts atherosclerosis in diabetes by decreasing circulating remnant lipoproteins.

Loss-of-function mutations in the transcription factor CREB3L3 (CREBH) associate with severe hypertriglyceridemia in humans. CREBH is believed to lower plasma triglycerides by augmenting the action of lipoprotein lipase (LPL). However, by using a mouse model of type 1 diabetes mellitus (T1DM), we found that greater liver expression of active CREBH normalized both elevated plasma triglycerides and cholesterol. Residual triglyceride-rich lipoprotein (TRL) remnants were enriched in apolipoprotein E (APOE) and impoverished in APOC3, an apolipoprotein composition indicative of increased hepatic clearance. The underlying mechanism was independent of LPL as CREBH reduced both triglycerides and cholesterol in LPL-deficient mice. Instead, APOE was critical for CREBH's ability to lower circulating remnant lipoproteins because it failed to reduce TRL cholesterol in Apoe-/- mice. Importantly, humans with CREB3L3 loss-of-function mutations exhibited increased levels of remnant lipoproteins that were deprived of APOE. Recent evidence suggests that impaired clearance of TRL remnants promotes cardiovascular disease in patients with T1DM. Consistently, we found that hepatic expression of CREBH prevented the progression of diabetes-accelerated atherosclerosis. Our results support the proposal that CREBH acts through an APOE-dependent pathway to increase hepatic clearance of remnant lipoproteins. They also implicate elevated levels of remnants in the pathogenesis of atherosclerosis in T1DM.
Comparative proteome analyses of rhizomania resistant transgenic sugar beets based on RNA silencing mechanism

ABSTRACT Rhizomania is an economically important disease of sugar beet, which is caused by Beet necrotic yellow vein virus (BNYVV). As previously shown, RNA silencing mechanism effectively inhibit the viral propagation in transgenic sugar beet plants. To investigate possible proteomic changes induced by gene insertion and/or RNA silencing mechanism, the root protein profiles of wild type sugar beet genotype 9597, as a control, and transgenic events named 6018-T3:S6-44 (S6) and 219-T3:S3-13.2 (S3) were compared by two-dimensional gel electrophoresis. The accumulation levels of 25 and 24 proteins were differentially regulated in S3 and S6 plants, respectively. The accumulation of 15 spots were increased or decreased more than 2-fold. Additionally, 10 spots repressed or induced in both, while seven spots showed variable results in two events. All the differentially expressed spots were analyzed by MALDI-TOF-TOF mass spectrometry. The functional analysis of differentially accumulated proteins showed that most of them are related to the metabolism and defense/stress response. None of these recognized proteins were allergens or toxic proteins except for a spot identified as phenylcoumaran benzylic ether reductase, Pyrc5, which was decreased in the genetically modified S6 plant. These data are in favor of substantial equivalence of the transgenic plants in comparison to their related wild type cultivar since the proteomic profile of sugar beet root was not remarkably affected by gene transfer and activation RNA silencing mechanism.