Miracle-dz/UniFAR-FTUs huggingface dataset:
Provable Near-Optimal Low-Multilinear-Rank Tensor Recovery

We consider the problem of recovering a low-multilinear-rank tensor from a small amount of linear measurements. We show that the Riemannian gradient algorithm initialized by one step of iterative hard thresholding can reconstruct an order-$d$ tensor of size $n\times\ldots\times n$ and multilinear rank $(r,\ldots,r)$ with high probability from only $O(nr^2 + r^{d+1})$ measurements, assuming $d$ is a constant. This sampling complexity is optimal in $n$, compared to existing results whose sampling complexities are all unnecessarily large in $n$. The analysis relies on the tensor restricted isometry property (TRIP) and the geometry of the manifold of all tensors with a fixed multilinear rank. High computational efficiency of our algorithm is also achieved by doing higher order singular value decomposition on intermediate small tensors of size only $2r\times \ldots\times 2r$ rather than on tensors of size $n\times \ldots\times n$ as usual.
A dichotomy theorem for nonuniform CSPs simplified

In a non-uniform Constraint Satisfaction problem CSP(G), where G is a set of relations on a finite set A, the goal is to find an assignment of values to variables subject to constraints imposed on specified sets of variables using the relations from G. The Dichotomy Conjecture for the non-uniform CSP states that for every constraint language G the problem CSP(G) is either solvable in polynomial time or is NP-complete. It was proposed by Feder and Vardi in their seminal 1993 paper. In this paper we confirm the Dichotomy Conjecture.
Using Data Mining for Infrastructure and Safety Violations Discovery in Cities

In city planning and maintenance, the ability to quickly identify infrastructure violations - such as missing or misplaced fire hydrants - can be crucial for maintaining a safe city; it can even save lives. In this work, we aim to provide an analysis of such violations, and to demonstrate the potential of data-driven approaches for quickly locating and addressing them. We conduct an analytical study based upon data from the city of Beer-Sheva’s public records of fire hydrants, bomb shelters, and other public facilities. The results of our analysis are presented along with an interactive exploration tool, which allows for easy exploration and identification of the different facilities around the city that violate regulations.
Planar Distance Oracles with Better Time-Space Tradeoffs

In a recent breakthrough, Charalampopoulos, Gawrychowski, Mozes, and Weimann (STOC 2019) showed that exact distance queries on planar graphs could be answered in $n^{o(1)}$ time by a data structure occupying $n^{1+o(1)}$ space, i.e., up to $o(1)$ terms, optimal exponents in time (0) and space (1) can be achieved simultaneously. Their distance query algorithm is recursive: it makes successive calls to a point-location algorithm for planar Voronoi diagrams, which involves many recursive distance queries. The depth of this recursion is non-constant and the branching factor logarithmic, leading to $(\log n)^{\omega(1)} = n^{o(1)}$ query times. In this paper we present a new way to do point-location in planar Voronoi diagrams, which leads to a new exact distance oracle. At the two extremes of our space-time tradeoff curve we can achieve either $n^{1+o(1)}$ space and $\log^{2+o(1)}n$ query time, or $n\log^{2+o(1)}n$ space and $n^{o(1)}$ query time. All previous oracles with $\tilde{O}(1)$ query time occupy space $n^{1+\Omega(1)}$, and all previous oracles with space $\tilde{O}(n)$ answer queries in $n^{\Omega(1)}$ time.
Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning

Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. First, a highway driving environment is founded, wherein the ego vehicle aims to pass through the surrounding vehicles with an efficient and safe maneuver. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions, and the lower-level cares about the supervision of vehicle speed and acceleration. Then, the particular DRL method named dueling deep Q-network (DDQN) algorithm is applied to derive the highway decision-making strategy. The exhaustive calculative procedures of deep Q-network and DDQN algorithms are discussed and compared. Finally, a series of estimation simulation experiments are conducted to evaluate the effectiveness of the proposed highway decision-making policy. The advantages of the proposed framework in convergence rate and control performance are illuminated. Simulation results reveal that the DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely.
PThammer: Cross-User-Kernel-Boundary Rowhammer through Implicit Accesses

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to memory can induce bit flips in neighboring memory locations. Being a hardware vulnerability, rowhammer bypasses all of the system memory protection, allowing adversaries to compromise the integrity and confidentiality of data. Rowhammer attacks have shown to enable privilege escalation, sandbox escape, and cryptographic key disclosures.Recently, several proposals suggest exploiting the spatial proximity between the accessed memory location and the location of the bit flip for a defense against rowhammer. These all aim to deny the attacker’s permission to access memory locations near sensitive data.In this paper, we question the core assumption underlying these defenses. We present PThammer, a confused-deputy attack that causes accesses to memory locations that the attacker is not allowed to access. Specifically, PThammer exploits the address translation process of modern processors, inducing the processor to generate frequent accesses to protected memory locations. We implement PThammer, demonstrating that it is a viable attack, resulting in a system compromise (e.g., kernel privilege escalation). We further evaluate the effectiveness of proposed software-only defenses showing that PThammer can overcome those.
Exploiting Centrality: Attacks in Payment Channel Networks with Local Routing

Payment channel networks (PCN) enable scalable blockchain transactions without fundamentally changing the underlying distributed ledger algorithm. However, routing a payment via multiple channels in a PCN requires locking collateral for potentially long periods of time. Adversaries can abuse this mechanism to conduct denial-of-service attacks. Previous work on denial-of-service attacks focused on source routing, which is unlikely to remain a viable routing approach as these networks grow. In this work we examine the effectiveness of attacks in PCNs that use routing algorithms based on local knowledge, where compromised intermediate nodes delay or drop transactions to create denial-of-service. We focus on SpeedyMurmurs as a representative of such protocols. Our attack simulations show that SpeedyMurmurs is resilient to attacks by randomly selected intermediate nodes because it dynamically adjusts using local knowledge. We further consider attackers that control a significant fractions of paths and we show that this ability to route around problematic regions becomes insufficient for such attackers. We propose methods to incentivize payment channel networks with less central nodes and more diverse paths and show through simulation that these methods effectively mitigate the identified denial-of-service attacks.
ezLDA: Efficient and Scalable LDA on GPUs

Latent Dirichlet Allocation (LDA) is a statistical approach for topic modeling with a wide range of applications. Attracted by the exceptional computing and memory throughput capabilities, this work introduces ezLDA which achieves efficient and scalable LDA training on GPUs with the following three contributions: First, ezLDA introduces three-branch sampling method which takes advantage of the convergence heterogeneity of various tokens to reduce the redundant sampling task. Second, to enable sparsity-aware format for both D and W on GPUs with fast sampling and updating, we introduce hybrid format for W along with corresponding token partition to T and inverted index designs. Third, we design a hierarchical workload balancing solution to address the extremely skewed workload imbalance problem on GPU and scale ezLDA across multiple GPUs. Taken together, ezLDA achieves superior performance over the state-of-the-art attempts with lower memory consumption.
Branching process descriptions of information cascades on Twitter

A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading.
The Galactic population and properties of young, highly energetic pulsars

The population of young, non-recycled pulsars with spin down energies Edot >10^35 erg/s is sampled predominantly at gamma-ray and radio wavelengths. A total of 137 such pulsars are known, with partial overlap between the sources detectable in radio and gamma-rays. We use a very small set of assumptions in an attempt to test whether the observed pulsar sample can be explained by a single underlying population of neutron stars. For radio emission we assume a canonical conal beam with a fixed emission height of 300~km across all spin periods and a luminosity law which depends on Edot^{0.25}. For gamma-ray emission we assume the outer-gap model and a luminosity law which depends on Edot^{0.5}. We synthesise a population of fast-spinning pulsars with a birth rate of one per 100 years. We find that this simple model can reproduce most characteristics of the observed population with two caveats. The first is a deficit of gamma-ray pulsars at the highest Edot which we surmise to be an observational selection effect due to the difficulties of finding gamma-ray pulsars in the presence of glitches without prior knowledge from radio frequencies. The second is a deficit of radio pulsars with interpulse emission, which may be related to radio emission physics. We discuss the implications of these findings.
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression

Obtaining training data for multi-document Summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this paper, we propose SummPip: an unsupervised method for multi-document summarization, in which we convert the original documents to a sentence graph, taking both linguistic and deep representation into account, then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary. Experiments on Multi-News and DUC-2004 datasets show that our method is competitive to previous unsupervised methods and is even comparable to the neural supervised approaches. In addition, human evaluation shows our system produces consistent and complete summaries compared to human written ones.
The Energy of an Arbitrary Electrical Circuit, Classical and Quantum

In this Note, I show an algorithmic method to find the energy and, thus, the Hamiltonian of an arbitrary electrical circuit based on the so-called incidence matrix and the circuit's total power. This method does not require to find any Lagrangian; instead, it is based on the concept of generalized linear momenta for the kinetic and co-kinetic energy of a circuit. The method can account for superconducting loops by a simple extension of Faraday-Henry-Neumann's law. Auxiliary (i.e., parasitic) circuit elements are required to deal with circuits with an incomplete set of generalized velocities resulting in an incomplete set of canonical coordinates. This method can be readily automatized to obtain the Hamiltonian of arbitrarily complicated circuits. I also show how to quantize the circuit associated with a resonator capacitively coupled with a qubit.
Local Genetic Differentiation and Diversity of the Oita Salamander (Hynobius dunni) in Kyushu Revealed by Mitochondrial and Microsatellite DNA Analyses

Abstract The Oita salamander Hynobius dunni Tago, 1931, endemic to eastern Kyushu and western Shikoku of southwestern Japan, is a lowland lentic breeder and has declined throughout its distribution range. To contribute to the future conservation of this salamander, current population genetic structures and genetic diversities were examined for 12 populations of eastern Kyushu, using a mitochondrial cytochrome b gene and three microsatellite loci. Populations were found to be genetically separated into northern and southern groups, and microsatellite analysis showed some genetic differences even in the northern regions. The southern group was restricted to a narrow area and had low genetic diversity in both mitochondrial and microsatellite DNAs. In the northern group, the mitochondrial and microsatellite DNA diversities were also low in some peripheral populations. For the accurate genetic management of this species, we need to pay more detailed attention to such genetic differentiation and diversity.
Fire History of a Douglas-Fir-Oregon White Oak Woodland, Waldron Island, Washington

Abstract The last 100 years have seen a marked decline in Oregon white oak woodlands in the Puget Sound region. Efforts to restore the woodlands cannot hope to be successful unless the role fire has played in maintaining them in the past is understood. A fire scar chronology was constructed from a Pseudotsuga menziesii—Quercus garryana community within a 155 ha study site on southeast Waldron Island, Washington. Sixty-two scars were identified on 15 crossdated Pseudotsuga samples that documented fire events between 1530 and 1908. A master tree-ring chronology was created for the period 1685 to 2004. Composite fire intervals and individual-tree fire intervals were used to characterize the fire history. Seasons of past fires were determined by analyzing fire scar position within annual ring structure. For the historical period 1700–1879, the composite mean fire return interval (FRI) was 7.4 yrs, and the mean individual-tree FRI was 18.4 yrs. The historic period mean individual-tree FRI was 18.4 yrs. In contrast, only three fires were recorded during the settlement/modern period (1880–2004), resulting in a mean individual-tree FRI of 103.8 yrs. Seasonality of past fires indicates that most fires occurred during late summer and fall. No evidence of spring or early summer burning was detected. Our study of the fire history for a site on Waldron Island shows a marked reduction in fire frequency between the historical and settlement/modern period, which we interpret as reflecting declines in Native American population size and activity and the eventual cessation of deliberate ignitions by Native Americans.
A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects

Background: Meeting the growing industry demand for Data Science requires cross-disciplinary teams that can translate machine learning research into production-ready code. Software engineering teams value adherence to coding standards as an indication of code readability, maintainability, and developer expertise. However, there are no large-scale empirical studies of coding standards focused specifically on Data Science projects. Aims: This study investigates the extent to which Data Science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? Method: We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity. Results: Data Science projects suffer from a significantly higher rate of functions that use an excessive numbers of parameters and local variables. Data Science projects also follow different variable naming conventions to non-Data Science projects. Conclusions: The differences indicate that Data Science codebases are distinct from traditional software codebases and do not follow traditional software engineering conventions. Our conjecture is that this may be because traditional software engineering conventions are inappropriate in the context of Data Science projects.
Diversity-by-Design for Dependable and Secure Cyber-Physical Systems: A Survey

Diversity-based security approaches have been studied for several decades since the 1970s. The concept of diversity-by-design emerged in the 1980s. Since then, diversity-based system design research has been explored to provide more secure and dependable services in cyber-physical systems (CPSs). In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, their insights, and associated future work directions for building secure and dependable CPSs. This will allow us to provide promising ways of providing quality network and services based on key diversity-by-design principles for those who want to conduct research on developing secure and dependable CPSs using diversity as a system design feature. This survey paper mainly provides: (i) The common concept of diversity based on its multidisciplinary nature along with the historical evolution of the concept of diversity-by-design for providing secure and dependable services; (ii) the key diversity-by-design principles; (iii) the key benefits and caveats of using the diversity-by-design; (iv) the main concerns of CPS environments utilizing the diversity-by-design; (v) an extensive survey and discussions of existing diversity-based approaches based on five different classifications; (vi) the types of attacks considered by diversity-based approaches; (vii) the overall trends of evaluation methodologies used for diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) the insights, lessons, and gaps identified from this extensive survey and future work directions.
Unsupervised Representation Learning For Context of Vocal Music

In this paper we aim to learn meaningful representations of sung intonation patterns derived from surrounding data without supervision. We focus on two facets of context in which a vocal line is produced: 1) within the short-time context of contiguous vocalizations, and 2) within the larger context of a recording. We propose two unsupervised deep learning methods, pseudo-task learning and slot filling, to produce latent encodings of these con-textual representations. To evaluate the quality of these representations and their usefulness as meaningful feature space, we conduct classification tasks on recordings sung by both professional and amateur singers. Initial results indicate that the learned representations enhance the performance of downstream classification tasks by several points, as compared to learning directly from the intonation contours alone. Larger increases in performance on classification of technique and vocal phrase patterns suggest that the representations encode short-time temporal context learned directly from the original recordings. Additionally, their ability to improve singer and gender identification suggest the learning of more broad contextual pat-terns. The growing availability of large unlabeled datasets makes this idea of contextual representation learning additionally promising, with larger amounts of meaningful samples often yielding better performance
Invisible Backdoor Attacks on Deep Neural Networks Via Steganography and Regularization

Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific input (called triggers), trick the model into producing unexpected behavior. In this article, we create covert and scattered triggers for backdoor attacks, invisible backdoors, where triggers can fool both DNN models and human inspection. We apply our invisible backdoors through two state-of-the-art methods of embedding triggers for backdoor attacks. The first approach on Badnets embeds the trigger into DNNs through steganography. The second approach of a trojan attack uses two types of additional regularization terms to generate the triggers with irregular shape and size. We use the Attack Success Rate and Functionality to measure the performance of our attacks. We introduce two novel definitions of invisibility for human perception; one is conceptualized by the Perceptual Adversarial Similarity Score (PASS) and the other is Learned Perceptual Image Patch Similarity (LPIPS). We show that the proposed invisible backdoors can be fairly effective across various DNN models as well as four datasets MNIST, CIFAR-10, CIFAR-100, and GTSRB, by measuring their attack success rates for the adversary, functionality for the normal users, and invisibility scores for the administrators. We finally argue that the proposed invisible backdoor attacks can effectively thwart the state-of-the-art trojan backdoor detection approaches.
An Eddy-Zonal Flow Feedback Model for Propagating Annular Modes and their Dynamics

<p>There is strong evidence that a positive feedback between the zonal-mean wind anomalies and the eddies (i.e. a positive feedback of EOF1 onto itself) is important for maintaining the wind anomalies associated with the annular modes. However, a recent study by Lubis and Hassanzadeh, (2021, JAS) shows that under some circumstances, EOF1 and EOF2 can interact and exert feedbacks on each other at some lag times, affecting the time scale of the annular modes. &#160;Building upon the seminal work of Lorenz and Hartmann (2001, JAS),&#160;we introduced a reduced-order model for coupled EOF1 and EOF2 that accounts for potential cross-EOF eddy-zonal flow feedbacks. Using the analytical solution of this model, we derive conditions for the existence of the propagating regime based on the feedback strengths. Using this model, and idealized GCMs and stochastic prototypes, we show that cross-EOF feedbacks play an important role in controlling the persistence of the annular modes by setting the frequency of the oscillation. We find that stronger cross-EOF feedbacks lead to less persistent annular modes. &#160;The underlying dynamics of the cross-EOF feedbacks for propagating annular modes in both reanalysis and an idealized GCM are also investigated. Using a finite-amplitude wave activity (FAWA) framework, we show that the cross-EOF feedbacks result from the out-of-phase oscillations of EOF1 (north-south jet displacement) and EOF2 (jet pulsation) leading to an orchestrated combination of equatorward propagation of wave activity (a baroclinic process) and nonlinear wave breaking (a barotropic process), which altogether act to reduce the total eddy forcings. &#160;The results highlight the importance of considering the coupling of EOFs and cross-EOF feedbacks to fully understand the natural and forced variability of the zonal-mean large-scale circulation. </p><p><strong>Reference: </strong>Lubis, S. W. , & Hassanzadeh, P. (2021). <strong>An Eddy&#8211;Zonal Flow Feedback Model for Propagating Annular Modes</strong>,&#160;<em>Journal of the Atmospheric Sciences</em>,&#160;<em>78</em>(1), 249-267. </p>