Miracle-dz/UniFAR-FTUs huggingface dataset:
TSP-Bot: Robotic TSP Pen Art using High-DoF Manipulators

TSP art is an art form for drawing an image using piecewise-continuous line segments. This paper presents a robotic pen drawing system capable of creating complicated TSP pen art on a planar surface using multiple colors. The system begins by converting a colored raster image into a set of points that represent the image's tone, which can be controlled by adjusting the point density. Next, the system finds a piecewise-continuous linear path that visits each point exactly once, which is equivalent to solving a Traveling Salesman Problem (TSP). The path is simplified with fewer points using bounded approximation and smoothed and optimized using Bezier spline curves with bounded curvature. Our robotic drawing system consisting of single or dual manipulators with fingered grippers and a mobile platform performs the drawing task by following the resulting complex and sophisticated path composed of thousands of TSP sites. As a result, our system can draw a complicated and visually pleasing TSP pen art.
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm

Regime shifts in high-dimensional time series arise naturally in many applications, from neuroimaging to finance. This problem has received considerable attention in low-dimensional settings, with both Bayesian and frequentist methods used extensively for parameter estimation. The EM algorithm is a particularly popular strategy for parameter estimation in low-dimensional settings, although the statistical properties of the resulting estimates have not been well understood. Furthermore, its extension to high-dimensional time series has proved challenging. To overcome these challenges, in this paper we propose an approximate EM algorithm for Markov-switching VAR models that leads to efficient computation and also facilitates the investigation of asymptotic properties of the resulting parameter estimates. We establish the consistency of the proposed EM algorithm in high dimensions and investigate its performance via simulation studies.
AFETM: Adaptive function execution trace monitoring for fault diagnosis

The high tracking overhead, the amount of up-front effort required to selecting the trace points, and the lack of effective data analysis model are the significant barriers to the adoption of intra-component tracking for fault diagnosis today. This paper introduces a novel method for fault diagnosis by combining adaptive function level dynamic tracking, target fault injection, and graph convolutional network. In order to implement this method, we introduce techniques for (i) selecting function level trace points, (ii) constructing approximate function call tree of program when using adaptive tracking, and (iii) constructing graph convolutional network with fault injection campaign. We evaluate our method using a web service benchmark composed of Redis, Nginx, Httpd, and SQlite. The experimental results show that this method outperforms log based method, full tracking method, and Gaussian influence method in the accuracy of fault diagnosis, overhead, and performance impact on the diagnosis target.
Engineering Optomechanically Induced Transparency by coupling a qubit to a spinning resonator

We theoretically study the spectral properties of a pump-probe driven hybrid spinning optomechanical ring resonator optically coupled with a two-level quantum emitter (QE or qubit). Recently we have shown [arXiv:1810.03709] that in the absence of the emitter the coupled cavity version of this setup is not only capable of nonreciprocal light propagation but can also exhibit slow&fast light propagation. In this work, we investigate in what ways the presence of a single QE coupled with the optical whispering gallery modes of the spinning optomechanical resonator can alter the probe light nonreciprocity. Under the weak-excitation assumption and mean-field approximation, we find that the interplay between the rotational/spinning Sagnac-effect and the qubit coupling can lead to the enhancement both in the optomechanically induced transparency (OMIT) peak value and in the width of the transparency window due to the opening of qubit-assisted back reflection channel. However, compared to the no-qubit case, we notice that such an enhancement comes at the cost of degrading the group delay in probe light transmission by a factor of 1/2 for clockwise rotary directions. The target applications of these results can be in the areas of quantum circuitry and in non-reciprocal quantum communication protocols where QEs are a key component.
Cultivating the Garden of Eden

Garden of Eden (GOE) states in cellular automata are grid configurations that have no precursors; that is, they can only occur as initial conditions. Finding individual configurations that minimize or maximize some criterion of interest (e.g., grid size, density, etc.) has been a popular sport in recreational mathematics, but systematic studies of the set of GOEs for a cellular automaton have been rare. This paper presents the current results of an ongoing computational study of GOE configurations in Conway’s Game of Life (GoL) cellular automaton. Specifically, we describe the current status of a map of the layout of GOEs and non-GOEs in 1-density/size space, characterize how the density-dependent structure of the number of precursors varies with increasing grid size as we approach the point where GOEs begin to occur, provide a catalog of all known GOE configurations up to a grid size of 11×11, and initiate a study of the structure of the network of constraints that characterize GOE versus non-GOE configurations.
A Location-Based Global Authorization Method for Underwater Security

National or international maritime authorities are used to handle requests for licenses for all kinds of marine activities. These licenses constitute authorizations limited in time and space, but there is no technical security service to check for the authorization of a wide range of marine assets. We have noted secure AIS solutions suitable for more or less constantly internet-connected assets such as ships with satellite connections. The additional constraints posed by underwater autonomous assets, namely less power and connectivity, can be mitigated by using symmetric cryptography. We propose a security service that allows the automatized check of asset authorization status based on large symmetric keys. Key generation can take place at a central authority according to the time and space limitations of a license, i.e. timestamped and geocoded. Our solution harnesses the exceptionally large key size of the RC5 cipher and the standardized encoding of geocells in the Open Location Code system. While we developed and described our solution for offshore underwater use, aerial and terrestrial environments could also make use of it if they are similarly bandwidth constrained or want to rely on quantum-resistant and computationally economic symmetric methods.
Dense-ATOMIC: Construction of Densely-connected and Multi-hop Commonsense Knowledge Graph upon ATOMIC

A TOMIC is a large-scale commonsense knowledge graph (CSKG) containing everyday if-then knowledge triplets, i.e., { head event , relation, tail event }. The one-hop annotation manner made A TOMIC a set of independent bipartite graphs, which ignored the numerous missing links between events in different bipartite graphs and consequently caused shortcomings in knowledge coverage and multi-hop reasoning. To address these issues, we propose a CSKG completion approach by training a relation prediction model based on a set of existing triplets, and infer the missing links on A TOMIC . On this basis, we construct Dense-A TOMIC , a densely-connected and multi-hop commonsense knowledge graph. The experimental results on an annotated dense subgraph demonstrate the effectiveness of our CSKG completion approach upon A TOMIC . The evaluation on a downstream commonsense reasoning task also proves the advantage of Dense-A TOMIC against conventional A TOMIC .
Comparison of Extended Irreversible Thermodynamics with thermodynamics based on a distribution containing the first-passage time.

An analogy is drawn between version of non-equilibrium thermodynamics a distribution-based containing an additional thermodynamic first-passage time parameter and Extended Irreversible Thermodynamics with flows as an additional thermodynamic parameter. Thermodynamics containing an additional thermodynamic first-passage time parameter maps to Extended Irreversible Thermodynamics. Various conditions for the dependence of the distribution parameters of the first-passage time on the random value of energy, the first thermodynamic parameter, are considered. It is shown that more meaningful results are recorded when the time parameter relaxation time τ of Extended Irreversible Thermodynamics is replaced by the average first-passage time. Expressions are obtained for the average first-passage time through the flows. Extended Irreversible Thermodynamics ( EIT ) [1-4] goes beyond the local equilibrium hypothesis. The paradox of propagation of signals with infinite speed is eliminated. The laws of Fourier, Fick, Stokes and Newton are generalized. Considerations include: memory effects (for fast processes and polymers); non-local effects (micro- and nanodevices); non-linear effects (high powers). The main innovations of the theory are: raising dissipative flows to the status of state variables; the central role is given to the generalized entropy, assumed to be a given function of the entire set of variables, and the rate of production of which is always positive definite. The EIT [1-4] uses an additional thermodynamic parameter, flows of thermodynamic quantities. For simplicity, we restrict ourselves to the case of heat flow. The change in entropy in this case is
Space-Efficient STR-IC-LCS Computation

One of the most fundamental method for comparing two given strings A and B is the longest common subsequence (LCS), where the task is to ﬁnd (the length) of the longest common subsequence. In this paper, we address the STR-IC-LCS problem which is one of the constrained LCS problems proposed by Chen and Chao [J. Comb. Optim, 2011]. A string Z is said to be an STR-IC-LCS of three given strings A , B , and P , if Z is one of the longest common subsequences of A and B that contains P as a substring. We present a space eﬃcient solution for the STR-IC-LCS problem. Our algorithm computes the length of an STR-IC-LCS in O ( n 2 ) time and O (( (cid:96) + 1)( n − (cid:96) + 1)) space where (cid:96) is the length of a longest common subsequence of A and B of length n . When (cid:96) = O (1) or n − (cid:96) = O (1), then our algorithm uses only linear O ( n ) space.
Let's Talk Through Physics! Covert Cyber-Physical Data Exfiltration on Air-Gapped Edge Devices

Although organizations are continuously making concerted efforts to harden their systems against network attacks by air-gapping critical systems, attackers continuously adapt and uncover covert channels to exfiltrate data from air-gapped systems. For instance, attackers have demonstrated the feasibility of exfiltrating data from a computer sitting in a Faraday cage by exfiltrating data using magnetic fields. Although a large body of work has recently emerged highlighting various physical covert channels, these attacks have mostly targeted open-loop cyber-physical systems where the covert channels exist on physical channels that are not being monitored by the victim. Network architectures such as fog computing push sensitive data to cyber-physical edge devices--whose physical side channels are typically monitored via state estimation. In this paper, we formalize covert data exfiltration that uses existing cyber-physical models and infrastructure of individual devices to exfiltrate data in a stealthy manner, i.e., we propose a method to circumvent cyber-physical state estimation intrusion detection techniques while exfiltrating sensitive data from the network. We propose a generalized model for encoding and decoding sensitive data within cyber-physical control loops. We evaluate our approach on a distributed IoT network that includes computation nodes residing on physical drones as well as on an industrial control system for the control of a robotic arm. Unlike prior works, we formalize the constraints of covert cyber-physical channel exfiltration in the presence of a defender performing state estimation.
A Non-iterative Spatio-temporal Multi-task Assignments based Collision-free Trajectories for Music Playing Robots

This paper addresses the music-playing robot problem, which is a benchmark problem for the spatio-temporal multi-task assignment problem. In the music-playing robot problem, an algorithm needs to compute the trajectories for a dynamically-sized team of robots who will play musical notes by traveling through the specific locations associated with musical notes at their respective specific times. A two-step dynamic resource allocation based on a spatio-temporal multi-task assignment problem (DREAM) has been implemented to assign robots to play a musical tune. The algorithm computes the number of robots required to play the music in the first step. In the second step, optimal assignments are computed for the updated team of robots, which minimizes the total distance traveled by the team. Even for individual feasible trajectories, the multi-robot execution may fail if robots encounter a collision. As some time will be utilized for this conflict resolution, robots may not be able to reach the desired location on time. This paper analyses and proves that if robots are operating in a convex region, the solution of the DREAM approach provides collision-free trajectories. The working of the DREAM approach is illustrated using high-fidelity simulations in a Gazebo operated using ROS2. The result clearly shows that the DREAM approach computes the required number of robots and assigns multiple tasks to robots in at most two steps. A simulation of the robots playing the ‘happy birthday’ is available at https://youtu.be/XToicNm-CO8.
Latent process models for functional network data

Network data are often sampled with auxiliary information or collected through the observation of a complex system over time, leading to multiple network snapshots indexed by a continuous variable. Many methods in statistical network analysis are traditionally designed for a single network, and can be applied to an aggregated network in this setting, but that approach can miss important functional structure. Here we develop an approach to estimating the expected network explicitly as a function of a continuous index, be it time or another indexing variable. We parameterize the network expectation through low dimensional latent processes, whose components we represent with a fixed, finite-dimensional functional basis. We derive a gradient descent estimation algorithm, establish theoretical guarantees for recovery of the low-dimensional structure, compare our method to competitors, and apply it to a dataset of international political interactions over time, showing our proposed method to adapt well to data, outperform competitors, and provide interpretable and meaningful results.
Spectral and steady-state properties of fermionic random quadratic Liouvillians

We study spectral and steady-state properties of generic Markovian dissipative systems described by quadratic fermionic Liouvillian operators of the Lindblad form. The Hamiltonian dynamics is modeled by a generic random quadratic operator, i.e., as a featureless superconductor of class D, whereas the Markovian dissipation is described by MM random linear jump operators. By varying the dissipation strength and the ratio of dissipative channels per fermion, m=M/(2N_F)m=M/(2NF), we find two distinct phases where the support of the single-particle spectrum has one or two connected components. In the strongly dissipative regime, this transition occurs for m=1/2m=1/2 and is concomitant with a qualitative change in both the steady-state and the spectral gap that rules the large-time dynamics. Above this threshold, the spectral gap and the steady-state purity qualitatively agree with the fully generic (i.e., non-quadratic) case studied recently. Below m=1/2m=1/2, the spectral gap closes in the thermodynamic limit and the steady-state decouples into an ergodic and a nonergodic sector yielding a non-monotonic steady-state purity as a function of the dissipation strength. Our results show that some of the universal features previously observed for fully random Liouvillians are generic for a sufficiently large number of jump operators. On the other hand, if the number of dissipation channels is decreased the system can exhibit nonergodic features, rendering it possible to suppress dissipation in protected subspaces even in the presence of strong system-environment coupling.
Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity

Similarity search is a popular technique for seismic signal processing, with template matching, matched filters, and subspace detectors being utilized for a wide variety of tasks, including both signal detection and source discrimination. Traditionally, these techniques rely on the cross‐correlation function as the basis for measuring similarity. Unfortunately, seismogram correlation is dominated by path effects, essentially requiring a distinct waveform template along each path of interest. To address this limitation, we propose a novel measure of seismogram similarity that is explicitly invariant to path. Using Earthscope’s USArray experiment, a path‐rich dataset of 207,291 regional seismograms across 8452 unique events is constructed, and then employed via the batch‐hard triplet loss function, to train a deep convolutional neural network that maps raw seismograms to a low‐dimensional embedding space, where nearness on the space corresponds to nearness of source function, regardless of path or recording instrumentation. This path‐agnostic embedding space forms a new representation for seismograms, characterized by robust, source‐specific features, which we show to be useful for performing both pairwise event association as well as template‐based source discrimination with a single template.
Shadfa 0.1: The Iranian Movie Knowledge Graph and Graph-Embedding-Based Recommender System

— Movies are a great source of entertainment. However, the problem arises when one is trying to find the desired content within this vast amount of data which is significantly increasing every year. Recommender systems (RSs) can provide appropriate algorithms to solve this problem. The content-based technique has found popularity due to the lack of available user data in most cases. Content-based RSs are based on similarity of items demographic information; Term Frequency – Inverse Document Frequency (TF-IDF) and Knowledge Graph Embedding (KGE) are two approaches used to vectorize data to calculate these similarities. In this paper, we propose a weighted content-based movie RS by combining TF-IDF which is an appropriate approach for embedding textual data such as plot/description and KGE which is used to embed named entities such as director’s name. The weights between features are determined using a Genetic algorithm. Additionally, Iranian movies dataset is created by scraping data from movie-related websites. This dataset and the structure of the FarsBase KG are used to create the MovieFarsBase KG which is a component in the implementation process of the proposed content-based RS. Using precision, recall, and F1 score metrics, this study shows that the proposed approach outperforms the conventional approach that uses TF-IDF for embedding all attributes.
Robustness of cosmic birefringence measurement against Galactic foreground emission and instrumental systematics

The polarization of the cosmic microwave background (CMB) can be used to search for parity-violating processes like that predicted by a Chern-Simons coupling to a light pseudoscalar field. Such an interaction rotates E modes into E modes in the observed CMB signal through an effect known as cosmic birefringence. Even though isotropic birefringence can be confused with the rotation produced by a miscalibration of the detectors' polarization angles, the degeneracy between both effects is broken when Galactic foreground emission is used as a calibrator. In this work, we use realistic simulations of the High-Frequency Instrument of the Planck mission to test the impact that Galactic foreground emission and instrumental systematics have on the recent birefringence measurements obtained through this technique. Our results demonstrate the robustness of the methodology against the miscalibration of polarization angles and other systematic effects, like intensity-to-polarization leakage, beam leakage, or cross-polarization effects. However, our estimator is sensitive to the EB correlation of polarized foreground emission. Here we propose to correct the bias induced by dust EB by modeling the foreground signal with templates produced in Bayesian component-separation analyses that fit parametric models to CMB data. Acknowledging the limitations of currently available dust templates like that of the Commander sky model, high-precision CMB data and a characterization of dust beyond the modified blackbody paradigm are needed to obtain a definitive measurement of cosmic birefringence in the future.
A Scalable Finite Difference Method for Deep Reinforcement Learning

Several low-bandwidth distributable black-box optimization algorithms have re-cently been shown to perform nearly as well as more reﬁned modern methods in some Deep Reinforcement Learning domains. In this work we investigate a core problem with the use of distributed workers in such systems. Further, we investigate the dramatic differences in performance between the popular Adam gradient descent algorithm and the simplest form of stochastic gradient descent. These investigations produce a stable, low-bandwidth learning algorithm that achieves 100% usage of all connected CPUs under typical conditions.
Decentralized Coverage Path Planning with Reinforcement Learning and Dual Guidance

Planning coverage path for multiple robots in a decentralized way enhances robustness to coverage tasks handling uncertain malfunctions. To achieve high efficiency in a distributed manner for each single robot, a comprehensive understanding of both the complicated environments and cooperative agents intent is crucial. Unfortunately, existing works commonly consider only part of these factors, resulting in imbalanced subareas or unnecessary overlaps. To tackle this issue, we introduce a Decentralized reinforcement learning framework with dual guidance to train each agent to solve the decentralized multiple coverage path planning problem straightly through the environment states. As distributed robots require others intentions to perform better coverage efficiency, we utilize two guidance methods, artificial potential fields and heuristic guidance, to include and integrate others intentions into observations for each robot. With our constructed framework, results have shown our agents successfully learn to determine their own subareas while achieving full coverage, balanced subareas and low overlap rates. We then implement spanning tree cover within those subareas to construct actual routes for each robot and complete given coverage tasks. Our performance is also compared with the state of the art decentralized method showing at most 10 percent lower overlap rates while performing high efficiency in similar environments.