Miracle-dz/UniFAR-FTUs huggingface dataset:
MetroSets: Visualizing Sets as Metro Maps

We propose MetroSets, a new, flexible online tool for visualizing set systems using the metro map metaphor. We model a given set system as a hypergraph <inline-formula><tex-math notation="LaTeX">$\mathcal{H}=(V,\ \mathcal{S})$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-jacobsen-3030475-eqinline-1-small.tif"/></alternatives></inline-formula>, consisting of a set <inline-formula><tex-math notation="LaTeX">$V$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-jacobsen-3030475-eqinline-2-small.tif"/></alternatives></inline-formula> of vertices and a set <inline-formula><tex-math notation="LaTeX">$\mathcal{S}$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-jacobsen-3030475-eqinline-3-small.tif"/></alternatives></inline-formula>, which contains subsets of <inline-formula><tex-math notation="LaTeX">$V$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-jacobsen-3030475-eqinline-4-small.tif"/></alternatives></inline-formula> called hyperedges. Our system then computes a metro map representation of <inline-formula><tex-math notation="LaTeX">$\mathcal{H}$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-jacobsen-3030475-eqinline-5-small.tif"/></alternatives></inline-formula>, where each hyperedge <inline-formula><tex-math notation="LaTeX">$E$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-jacobsen-3030475-eqinline-6-small.tif"/></alternatives></inline-formula> in <inline-formula><tex-math notation="LaTeX">$\mathcal{S}$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-jacobsen-3030475-eqinline-7-small.tif"/></alternatives></inline-formula> corresponds to a metro line and each vertex corresponds to a metro station. Vertices that appear in two or more hyperedges are drawn as interchanges in the metro map, connecting the different sets. MetroSets is based on a modular 4-step pipeline which constructs and optimizes a path-based hypergraph support, which is then drawn and schematized using metro map layout algorithms. We propose and implement multiple algorithms for each step of the MetroSet pipeline and provide a functional prototype with easy-to-use preset configurations. Furthermore, using several real-world datasets, we perform an extensive quantitative evaluation of the impact of different pipeline stages on desirable properties of the generated maps, such as octolinearity, monotonicity, and edge uniformity.
IoT Network Security: Requirements, Threats, and Countermeasures

IoT devices are increasingly utilized in critical infrastructure, enterprises, and households. There are several sophisticated cyber-attacks that have been reported and many networks have proven vulnerable to both active and passive attacks by leaking private information, allowing unauthorized access, and being open to denial of service attacks. This paper aims firstly, to assist network operators to understand the need for an IoT network security solution, and then secondly, to survey IoT network attack vectors, cyber threats, and countermeasures with a focus on improving the robustness of existing security solutions. Our first contribution highlights viewpoints on IoT security from the perspective of stakeholders such as manufacturers, service providers, consumers, and authorities. We discuss the differences between IoT and IT systems, the need for IoT security solutions, and we highlight the key components required for IoT network security system architecture. For our second contribution, we survey the types of IoT attacks by grouping them based on their impact. We discuss various attack techniques, threats, and shortfalls of existing countermeasures with an intention to enable future research into improving IoT network security.
Spatial Language Representation with Multi-Level Geocoding

We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations. The Earth's surface is represented using space-filling curves that decompose the sphere into a hierarchy of similarly sized, non-overlapping cells. MLG balances generalization and accuracy by combining losses across multiple levels and predicting cells at each level simultaneously. Without using any dataset-specific tuning, we show that MLG obtains state-of-the-art results for toponym resolution on three English datasets. Furthermore, it obtains large gains without any knowledge base metadata, demonstrating that it can effectively learn the connection between text spans and coordinates - and thus can be extended to toponymns not present in knowledge bases.
Thermodynamics and overcharging problem in extended phase space of charged AdS black holes with cloud of strings and quintessence under charged particle absorption *

The thermodynamics and overcharging problem in the RN-AdS black hole with the cloud of strings and quintessence are investigated by absorption of scalar particle and fermion in the extend phase space. The cosmological constant is treated as the pressure with a conjugate volume. Besides, the parameters related to quintessence and cloud of strings are treated as thermodynamic variables. Finally we find the first law of thermodynamics is satisfied and the second law of thermodynamics is indefinite. Furthermore, the near-extremal and extremal black holes can not be overcharged.
Sparse Grids based Adaptive Noise Reduction strategy for Particle-In-Cell schemes

We propose a sparse grids based adaptive noise reduction strategy for electrostatic particle-in-cell (PIC) simulations. Our approach is based on the key idea of relying on sparse grids instead of a regular grid in order to increase the number of particles per cell for the same total number of particles, as first introduced in Ricketson and Cerfon (Plasma Phys. and Control. Fusion, 59(2), 024002). Adopting a new filtering perspective for this idea, we construct the algorithm so that it can be easily integrated into high performance large-scale PIC code bases. Unlike the physical and Fourier domain filters typically used in PIC codes, our approach automatically adapts to mesh size, number of particles per cell, smoothness of the density profile and the initial sampling technique. Thanks to the truncated combination technique, we can reduce the larger grid-based error of the standard sparse grids approach for non-aligned and non-smooth functions. We propose a heuristic based on formal error analysis for selecting the optimal truncation parameter at each time step, and develop a natural framework to minimize the total error in sparse PIC simulations. We demonstrate its efficiency and performance by means of two test cases: the diocotron instability in two dimensions, and the three-dimensional electron dynamics in a Penning trap. Our run time performance studies indicate that our new scheme can provide significant speedup and memory reduction as compared to regular PIC for achieving comparable accuracy in the charge density deposition.
Defending Distributed Classifiers Against Data Poisoning Attacks

Support Vector Machines (SVMs) are vulnerable to targeted training data manipulations such as poisoning attacks and label flips. By carefully manipulating a subset of training samples, the attacker forces the learner to compute an incorrect decision boundary, thereby cause misclassifications. Considering the increased importance of SVMs in engineering and life-critical applications, we develop a novel defense algorithm that improves resistance against such attacks. Local Intrinsic Dimensionality (LID) is a promising metric that characterizes the outlierness of data samples. In this work, we introduce a new approximation of LID called K-LID that uses kernel distance in the LID calculation, which allows LID to be calculated in high dimensional transformed spaces. We introduce a weighted SVM against such attacks using K-LID as a distinguishing characteristic that de-emphasizes the effect of suspicious data samples on the SVM decision boundary. Each sample is weighted on how likely its K-LID value is from the benign K-LID distribution rather than the attacked K-LID distribution. We then demonstrate how the proposed defense can be applied to a distributed SVM framework through a case study on an SDR-based surveillance system. Experiments with benchmark data sets show that the proposed defense reduces classification error rates substantially (10% on average).
On the Optimal 3D Placement of a UAV Base Station for Maximal Coverage of UAV Users

Unmanned aerial vehicles (UAVs) can be users that support new applications, or be communication access points that serve terrestrial and/or aerial users. In this paper, we focus on the connectivity problem of aerial users when they are exclusively served by aerial base stations (BS), i.e., UAVBSs. Specifically, the 3D placement problem of a directionalantenna equipped UAV-BS, aiming to maximize the number of covered aerial users under a spectrum sharing policy with terrestrial networks, is investigated. Given a known spectrum sharing policy between the aerial and terrestrial networks, we propose a 3D placement algorithm that achieves optimality. Simulation results show the performance of our approach, in terms of number of covered aerial users for different configurations and parameters, such as the spectrum sharing policy, antenna beamwidth, transmit power, and aerial users density. These results represent novel guidelines for exclusive aerial networks deployment and applications, distinctively for orthogonal and non-orthogonal spectrum sharing policies with terrestrial networks.
A Survey on Assessing the Generalization Envelope of Deep Neural Networks at Inference Time for Image Classification

Deep Neural Networks (DNNs) achieve state-of-the-art performance on numerous applications. However, it is difficult to tell beforehand if a DNN receiving an input will deliver the correct output since their decision criteria are usually nontransparent. A DNN delivers the correct output if the input is within the area enclosed by its generalization envelope. In this case, the information contained in the input sample is processed reasonably by the network. It is of large practical importance to assess at inference time if a DNN generalizes correctly. Currently, the approaches to achieve this goal are investigated in different problem set-ups rather independently from one another, leading to three main research and literature fields: predictive uncertainty, out-of-distribution detection and adversarial example detection. This survey connects the three fields within the larger framework of investigating the generalization performance of machine learning methods and in particular DNNs. We underline the common ground, point at the most promising approaches and give a structured overview of the methods that provide at inference time means to establish if the current input is within the generalization envelope of a DNN.
Spitz

Databases in the past have helped businesses maintain and extract insights from their data. Today, it is common for a business to involve multiple independent, distrustful parties. This trend towards decentralization introduces a new and important requirement to databases: the integrity of the data, the history, and the execution must be protected. In other words, there is a need for a new class of database systems whose integrity can be verified (or verifiable databases). In this paper, we identify the requirements and the design challenges of verifiable databases. We observe that the main challenges come from the need to balance data immutability, tamper evidence, and performance. We first consider approaches that extend existing OLTP and OLAP systems with support for verification. We next examine a clean-slate approach, by describing a new system, Spitz, specifically designed for efficiently supporting immutable and tamper-evident transaction management. We conduct a preliminary performance study of both approaches against a baseline system, and provide insights on their performance.
Defending Regression Learners Against Poisoning Attacks

Regression models, which are widely used from engineering applications to financial forecasting, are vulnerable to targeted malicious attacks such as training data poisoning, through which adversaries can manipulate their predictions. Previous works that attempt to address this problem rely on assumptions about the nature of the attack/attacker or overestimate the knowledge of the learner, making them impractical. We introduce a novel Local Intrinsic Dimensionality (LID) based measure called N-LID that measures the local deviation of a given data point's LID with respect to its neighbors. We then show that N-LID can distinguish poisoned samples from normal samples and propose an N-LID based defense approach that makes no assumptions of the attacker. Through extensive numerical experiments with benchmark datasets, we show that the proposed defense mechanism outperforms the state of the art defenses in terms of prediction accuracy (up to 76% lower MSE compared to an undefended ridge model) and running time.
Finding the strongest stable weightless column with a follower load and relocatable concentrated masses

We consider a problem of optimal placement of concentrated masses along a weightless elastic column (rod) that is clamped at one end and loaded by a nonconservative follower force at the free end. The goal is to find the largest possible interval such that the variation in the loading parameter within this interval preserves stability of the structure. When the number of masses is two we find the optimal load analytically. For the case of three or more masses, we obtain conjectures for the globally optimal load that are strongly supported by extensive computational results. To obtain these, we use techniques from nonsmooth optimization, employing the recently developed open-source software package GRANSO (GRadient-based Algorithm for Non-Smooth Optimization) to maximize the load subject to appropriate nonsmooth stability constraints.
Differences in phytoplankton dynamics and community structure between a wet year and dry year in the Zhoucun Reservoir

Rainfall-induced nutrient fluctuations in surface water affect phytoplankton communities and cause algal blooms. To examine this influence in the Zhoucun Reservoir, China, field surveys were performed weekly or biweekly from February 2013 (a wet year) to December 2014 (a dry year). Physical conditions, nutrient levels, and phytoplankton functional groups were investigated during monitoring. Nutrient levels increased in the summer of 2013 (TN: 0.85–2.66 mg/L; TP: 0.037–0.138 mg/L), while nitrogen deficiency occurred in the summer of 2014 (TN: 0.38–0.73 mg/L, TP: 0.044–0.079 mg/L). A cyanobacteria bloom that appeared in the summer of 2013 had the dominant phytoplankton functional groups M and Tc. In the summer of 2014, algal cell density and biomass were much lower than those during the corresponding period in 2013, and the co-dominant phytoplankton groups were F, L0, Tc, and S2. Non-N-fixing cyanobacteria were not abundant in summer 2013 probably because of the low ratio of NO3− to NH4+, although nitrogen was also deficient. In contrast to these summer trends, little difference was noted between phytoplankton dynamics and community structure in the two winters and two springs, regardless of nutrient conditions. Redundancy analysis indicated that rainfall-induced nitrogen fluctuations were the primary factor causing phytoplankton dynamics to differ between the wet and dry year. The effects of water temperature, stratification, and light on the phytoplankton community were independent of nutrient fluctuations. This study shows that rainfall fluctuations change summer nutrient conditions and that cyanobacteria blooms are likely to occur in wet years in the Zhoucun Reservoir.
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been increasingly deployed to train deep learning models. These accelerators exhibit heterogeneous performance behavior across model architectures. Existing schedulers for clusters of accelerators, which are used to arbitrate these expensive training resources across many users, have shown how to optimize for various multi-job, multi-user objectives, like fairness and makespan. Unfortunately, existing schedulers largely do not consider performance heterogeneity. In this paper, we propose Gavel, a heterogeneity-aware scheduler that systematically generalizes a wide range of existing scheduling policies. Gavel expresses these policies as optimization problems, making it easy to optimize for objectives in a heterogeneity-aware way, while also being cognizant of performance optimizations like space sharing. Gavel then uses a round-based scheduling mechanism to ensure jobs receive their ideal allocation given the target scheduling policy. Gavel's heterogeneity-aware policies allow a heterogeneous cluster to sustain higher input load, and improve end objectives such as average job completion time and makespan by up to 3.5x compared to heterogeneity-agnostic policies.
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

<italic>Speech enhancement</italic> and <italic>speech separation</italic> are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, <italic>visual information</italic> from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically <italic>deep learning</italic>, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: <italic>acoustic features</italic>; <italic>visual features</italic>; <italic>deep learning methods</italic>; <italic>fusion techniques</italic>; <italic>training targets</italic> and <italic>objective functions</italic>. In addition, we review deep-learning-based methods for <italic>speech reconstruction from silent videos</italic> and <italic>audio-visual sound source separation for non-speech signals</italic>, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed <italic>audio-visual speech datasets</italic>, given their central role in the development of data-driven approaches, and <italic>evaluation methods</italic>, because they are generally used to compare different systems and determine their performance.
The Canonical Interval Forest (CIF) Classifier for Time Series Classification

Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval methods, and has demonstrated strong performance as well as relative speed in training and predictions. However, recent advances in other approaches have left TSF behind. TSF originally summarises intervals using three simple summary statistics. The ‘catch22' feature set of 22 time series features was recently proposed to aid time series analysis through a concise set of diverse and informative descriptive characteristics. We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF). We outline additional enhancements to the training procedure, and extend the classifier to include multivariate classification capabilities. We demonstrate a large and significant improvement in accuracy over both TSF and catch22, and show it to be on par with top performers from other algorithmic classes. By upgrading the interval-based component from TSF to CIF, we also demonstrate a significant improvement in the hierarchical vote collective of transformation-based ensembles (HIVE-COTE) that combines different time series representations. HIVE-COTE using CIF is significantly more accurate on the UCR archive than any other classifier we are aware of and represents a new state of the art for TSC.
Chandra Monitoring of the J1809–1917 Pulsar Wind Nebula and Its Field

PSR J1809–1917 is a young (τ = 51 kyr) and energetic ( erg s−1) radio pulsar powering an X-ray pulsar wind nebula (PWN) that exhibits morphological variability. We report on the results of a new monitoring campaign by the Chandra X-ray Observatory (Chandra), carried out across six epochs with a ∼7 week cadence. The compact nebula can be interpreted as a jet-dominated outflow along the pulsar’s spin axis. Its variability can be the result of Doppler boosting in the kinked jet, whose shape changes with time (akin to the Vela pulsar jet). The deep X-ray image, composed of 405 ks of new and 131 ks of archival Chandra data, reveals an arcminute-scale extended nebula (EN) whose axis of symmetry aligns with both the axis of the compact nebula and the direction toward the peak of the nearby TeV source HESS J1809–193. The EN’s morphology and extent suggest that the pulsar is likely moving through the ambient medium at a transonic velocity. We also resolved a faint 7′ long nonthermal collimated structure protruding from the PWN. It is possibly another instance of a “misaligned outflow” (also known as a “kinetic jet”) produced by high-energy particles escaping the PWN’s confinement and tracing the interstellar magnetic field lines. Finally, taking advantage of the 536 ks exposure, we analyzed the point sources in the J1809 field and classified them using multiwavelength data. None of the classified sources in the field can reasonably be expected to produce the extended TeV flux in the region, suggesting that PSR J1809–1917 is indeed the counterpart to HESS/eHWC J1809–193.
Precollapse Properties of Superluminous Supernovae and Long Gamma-Ray Burst Progenitor Models

We analyze the properties of 42 rapidly rotating, low-metallicity, quasi-chemically homogeneously evolving stellar models in the mass range between 4 and 45 at the time of core collapse. Such models were proposed as progenitors for both superluminous supernovae (SLSNe) and long-duration gamma-ray bursts (lGRBs) and the Type Ic-BL supernovae (SNe) that are associated with them. Our findings suggest that whether these models produce a magnetar-driven SLSN explosion or a near-critically rotating black hole is not a monotonic function of the initial mass. Rather, their explodability varies nonmonotonically depending on the late core evolution, once chemical homogeneity is broken. Using different explodability criteria, we find that our models have a clear preference to produce SLSNe at lower masses and lGRBs at higher masses, but we find several exceptions, expecting lGRBs to form from stars as low as 10 and SLSNe with progenitors as massive as 30 . In general, our models reproduce the predicted angular momenta, ejecta masses, and magnetic field strengths at core collapse inferred for SLSNe and lGRBs and suggest significant interaction with their circumstellar medium, particularly for explosions with low ejecta mass.