Miracle-dz/UniFAR-FTUs huggingface dataset:
Decremental APSP in Directed Graphs Versus an Adaptive Adversary

Given a directed graph $G = (V,E)$, undergoing an online sequence of edge deletions with $m$ edges in the initial version of $G$ and $n = |V|$, we consider the problem of maintaining all-pairs shortest paths (APSP) in $G$. Whilst this problem has been studied in a long line of research [ACM'81, FOCS'99, FOCS'01, STOC'02, STOC'03, SWAT'04, STOC'13] and the problem of $(1+\epsilon)$-approximate, weighted APSP was solved to near-optimal update time $\tilde{O}(mn)$ by Bernstein [STOC'13], the problem has mainly been studied in the context of oblivious adversaries, which assumes that the adversary fixes the update sequence before the algorithm is started. In this paper, we make significant progress on the problem in the setting where the adversary is adaptive, i.e. can base the update sequence on the output of the data structure queries. We present three new data structures that fit different settings: We first present a deterministic data structure that maintains exact distances with total update time $\tilde{O}(n^3)$. We also present a deterministic data structure that maintains $(1+\epsilon)$-approximate distance estimates with total update time $\tilde O(\sqrt{m} n^2/\epsilon)$ which for sparse graphs is $\tilde O(n^{2+1/2}/\epsilon)$. Finally, we present a randomized $(1+\epsilon)$-approximate data structure which works against an adaptive adversary; its total update time is $\tilde O(m^{2/3}n^{5/3} + n^{8/3}/(m^{1/3}\epsilon^2))$ which for sparse graphs is $\tilde O(n^{2+1/3})$. Our exact data structure matches the total update time of the best randomized data structure by Baswana et al. [STOC'02] and maintains the distance matrix in near-optimal time. Our approximate data structures improve upon the best data structures against an adaptive adversary which have $\tilde{O}(mn^2)$ total update time [JACM'81, STOC'03].
Understanding the Predictability of Gesture Parameters from Speech and their Perceptual Importance

Gesture behavior is a natural part of human conversation. Much work has focused on removing the need for tedious hand-animation to create embodied conversational agents by designing speech-driven gesture generators. However, these generators often work in a black-box manner, assuming a general relationship between input speech and output motion. As their success remains limited, we investigate in more detail how speech may relate to different aspects of gesture motion. We determine a number of parameters characterizing gesture, such as speed and gesture size, and explore their relationship to the speech signal in a two-fold manner. First, we train multiple recurrent networks to predict the gesture parameters from speech to understand how well gesture attributes can be modeled from speech alone. We find that gesture parameters can be partially predicted from speech, and some parameters, such as path length, being predicted more accurately than others, like velocity. Second, we design a perceptual study to assess the importance of each gesture parameter for producing motion that people perceive as appropriate for the speech. Results show that a degradation in any parameter was viewed negatively, but some changes, such as hand shape, are more impactful than others. A video summarization can be found at https://youtu.be/aw6-_5kmLjY.
Self-Play Reinforcement Learning for Fast Image Retargeting

In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes. In one of the best-performing methods called MULTIOP, multiple retargeting operators were combined and retargeted images at each stage were generated to find the optimal sequence of operators that minimized the distance between original and retargeted images. The limitation of this method is in its tremendous processing time, which severely prohibits its practical use. Therefore, the purpose of this study is to find the optimal combination of operators within a reasonable processing time; we propose a method of predicting the optimal operator for each step using a reinforcement learning agent. The technical contributions of this study are as follows. Firstly, we propose a reward based on self-play, which will be insensitive to the large variance in the content-dependent distance measured in MULTIOP. Secondly, we propose to dynamically change the loss weight for each action to prevent the algorithm from falling into a local optimum and from choosing only the most frequently used operator in its training. Our experiments showed that we achieved multi-operator image retargeting with less processing time by three orders of magnitude and the same quality as the original multi-operator-based method, which was the best-performing algorithm in retargeting tasks.
Deep4Air: A Novel Deep Learning Framework for Airport Airside Surveillance

Airport runway and taxiway (airside) areas are highly complex environments, where safe-separation procedures must be maintained by Air Traffic Controllers (ATCs) under varying visibility and traffic conditions. In this paper, we propose a novel computer-vision based framework, namely Deep4Air, for automated visual monitoring of airside operations, providing real-time data including aircraft location, speed, and distance analytics. This framework includes an adaptive deep neural network that exploits a depth-wise convolutional operator for efficient detection and tracking of aircraft. The experimental results show an average precision of detection and tracking of up to 98.2% on simulated data with validation on surveillance videos from the digital tower at George Bush Intercontinental Airport. The results also demonstrate that Deep4Air can locate aircraft positions relative to the airport runway and taxiway infrastructure with high accuracy. Furthermore, aircraft speed and separation distance are monitored in real-time, providing enhanced safety management.
Quantifying Privacy Leakage in Graph Embedding

Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collection of personal data, graph embeddings can be trained on private and sensitive data. For the first time, we quantify the privacy leakage in graph embeddings through three inference attacks targeting Graph Neural Networks. Our membership inference attack aims to infer whether a graph node corresponding to an individual user’s data was a member of the model’s private training data or not. We consider a blackbox setting where the adversary exploits the output prediction scores and a whitebox setting where the adversary has also access to the released node embeddings. Our attack provides accuracy up to 28% (blackbox) and 36% (whitebox) beyond the random guess by exploiting the distinguishable footprint between train and test data records left by the graph embedding. In our graph reconstruction attack, the adversary aims to reconstruct the target graph given the corresponding graph embeddings. Here, the adversary can reconstruct the graph with more than 80% of accuracy and infer the link between two nodes with ∼ 30% more accuracy than the random guess. Finally, we propose an attribute inference attack where the adversary aims to infer the sensitive node attributes corresponding to an individual user. We show that the strong correlation between the graph embeddings and node attributes allows the adversary to infer sensitive information (e.g., gender or location).
A hybrid simulation of gravitational wave production in first-order phase transitions

The LISA telescope will provide the first opportunity to probe the scenario of a first-order phase transition happening close to the electroweak scale. In thermal transitions, the main contribution to the GW spectrum comes from the sound waves propagating through the plasma. Current estimates of the GW spectrum are based on numerical simulations of a scalar field interacting with the plasma or on analytical approximations — the so-called sound shell model. In this work we present a novel setup to calculate the GW spectra from sound waves. We use a hybrid method that uses a 1d simulation (with spherical symmetry) to evolve the velocity and enthalpy profiles of a single bubble after collision and embed it in a 3d realization of multiple bubble collisions, assuming linear superposition of the velocity and enthalpy. The main advantage of our method compared to 3d hydrodynamic simulations is that it does not require to resolve the scale of bubble wall thickness. This makes our simulations more economical and the only two relevant physical length scales that enter are the bubble size and the fluid shell thickness (that are in turn enclosed by the box size and the grid spacing). The reduced costs allow for extensive parameter studies and we provide a parametrization of the final GW spectrum as a function of the wall velocity and the fluid kinetic energy.
Investigating the magnetospheric accretion process in the young pre-transitional disk system DoAr 44 (V2062 Oph)

Context. Young stars interact with their accretion disk through their strong magnetosphere. Aims. We aim to investigate the magnetospheric accretion/ejection process in the young stellar system DoAr 44 (V2062 Oph). Methods. We monitored the system over several rotational cycles, combining high-resolution spectropolarimetry at both optical and near-IR wavelengths with long-baseline near-IR inteferometry and multicolor photometry. Results. We derive a rotational period of 2.96 d from the system’s light curve, which is dominated by stellar spots. We fully characterize the central star’s properties from the high signal-to-noise, high-resolution optical spectra we obtained during the campaign. DoAr 44 is a young 1.2 M⊙ star, moderately accreting from its disk (Ṁacc = 6.5 10−9 M⊙ yr−1), and seen at a low inclination (i ≃ 30°). Several optical and near-IR line profiles probing the accretion funnel flows (Hα, Hβ, HeI 1083 nm, Paβ) and the accretion shock (HeI 587.6 nm) are modulated at the stellar rotation period. The most variable line profile is HeI 1083 nm, which exhibits modulated redshifted wings that are a signature of accretion funnel flows, as well as deep blueshifted absorptions indicative of transient outflows. The Zeeman-Doppler analysis suggests the star hosts a mainly dipolar magnetic field, inclined by about 20° onto the spin axis, with an intensity reaching about 800 G at the photosphere, and up to 2 ± 0.8 kG close to the accretion shock. The magnetic field appears strong enough to disrupt the inner disk close to the corotation radius, at a distance of about 4.6 R⋆ (0.043 au), which is consistent with the 5 R⋆ (0.047 au) upper limit we derived for the size of the magnetosphere in our Paper I from long baseline interferometry. Conclusions. DoAr 44 is a pre-transitional disk system, exhibiting a 25–30 au gap in its circumstellar disk, with the inner and outer disks being misaligned. On a scale of 0.1 au or less, our results indicate that the system is steadily accreting from its inner disk through its tilted dipolar magnetosphere. We conclude that in spite of a highly structured disk on the large scale, perhaps the signature of ongoing planetary formation, the magnetospheric accretion process proceeds unimpeded at the star-disk interaction level.
Evaluating Real-Time Probabilistic Forecasts With Application to National Basketball Association Outcome Prediction

Abstract Motivated by the goal of evaluating real-time forecasts of home team win probabilities in the National Basketball Association, we develop new tools for measuring the quality of continuously updated probabilistic forecasts. This includes introducing calibration surface plots, and simple graphical summaries of them, to evaluate at a glance whether a given continuously updated probability forecasting method is well-calibrated, as well as developing statistical tests and graphical tools to evaluate the skill, or relative performance, of two competing continuously updated forecasting methods. These tools are demonstrated in an application to evaluate the continuously updated forecasts published by the United States-based multinational sports network ESPN on its principle webpage espn.com. This application lends statistical evidence that the forecasts published there are well-calibrated, and exhibit improved skill over several naïve models, but do not demonstrate significantly improved skill over simple logistic regression models based solely on a measurement of each teams’ relative strength, and the evolving score difference throughout the game.
Polyphonic Piano Transcription Using Autoregressive Multi-State Note Model

Recent advances in polyphonic piano transcription have been made primarily by a deliberate design of neural network architectures that detect different note states such as onset or sustain and model the temporal evolution of the states. The majority of them, however, use separate neural networks for each note state, thereby optimizing multiple loss functions, and also they handle the temporal evolution of note states by abstract connections between the state-wise neural networks or using a post-processing module. In this paper, we propose a unified neural network architecture where multiple note states are predicted as a softmax output with a single loss function and the temporal order is learned by an auto-regressive connection within the single neural network. This compact model allows to increase note states without architectural complexity. Using the MAESTRO dataset, we examine various combinations of multiple note states including on, onset, sustain, re-onset, offset, and off. We also show that the autoregressive module effectively learns inter-state dependency of notes. Finally, we show that our proposed model achieves performance comparable to state-of-the-arts with fewer parameters.
Experimental Evidence that Zn Impurities Pin Pair-Density-Wave Order in La$_{2-x}$Ba$_x$CuO$_4$

Both Zn-doping and $c$-axis magnetic fields have been observed to increase the spin stripe order in La$_{2-x}$Ba$_x$CuO$_4$ with $x$ close to 1/8. For $x=0.095$, the applied magnetic field also causes superconducting layers to decouple, presumably by favoring pair-density-wave order that consequently frustrates interlayer Josephson coupling. Here we show that introducing 1\%\ Zn also leads to an initial onset of two-dimensional (2D) superconductivity, followed by 3D superconductivity at lower temperatures, even in zero field. We infer that the Zn pins pair-density-wave order locally, establishing the generality of such behavior.
On "Efficiency versus Instability in plasma accelerators".

Recently in a paper by Lebedev et al it was demonstrated that the efficiency of the energy transfer from the drive bunch to the witness bunch in the plasma wakefield accelerator has a limit due to the BBU instability of the witness bunch. It was stated that the efficiency-instability relation is universal and thus should be considered as a fundamental limit. In this note, we show that recent results on the short-range wakefields indicate that this relation should be modified and conclusions of the paper by Lebedev et al should be reconsidered. In particular, we argue that the efficiency-instability relation produces only the lower bound for efficiency and thus does not produce a fundamental limit.
Unified Treatment of Dynamical Processes on Generalized Networks: Higher-Order, Multilayer, and Temporal Interactions

When describing complex interconnected systems, one often has to go beyond the traditional network description to account for generalized interactions. Here, we establish a unified framework to optimally simplify the analysis of cluster synchronization patterns for a wide range of generalized networks, including hypergraphs, multilayer networks, and temporal networks. The framework is based on finding the finest simultaneous block diagonalization (SBD) of the matrices encoding the synchronization pattern and the interaction pattern. As an application, we use the SBD framework to characterize chimera states induced by nonpairwise interactions and by time-varying interactions. The unified framework established here can be extended to other dynamical processes and can facilitate the discovery of novel emergent phenomena in complex systems with generalized interactions.
AMR: autonomous coin mixer with privacy preserving reward distribution

It is well known that users on open blockchains are tracked by an industry providing services to governments, law enforcement, secret services, and alike. While most blockchains do not protect their users' privacy and allow external observers to link transactions and addresses, a growing research interest attempts to design add-on privacy solutions to help users regain their privacy on non-private blockchains. In this work, we propose to our knowledge the first censorship resilient mixer, which can reward its users in a privacy-preserving manner for participating in the system. Increasing the anonymity set size, and diversity of users, is, as we believe, an important endeavor to raise a mixer's contributed privacy in practice. The paid-out rewards can take the form of governance tokens to decentralize the voting on system parameters, similar to how popular "Decentralized Finance (Defi) farming" protocols operate. Moreover, by leveraging existing Defi lending platforms, AMR is the first mixer design that allows participating clients to earn financial interest on their deposited funds. Our system AMR is autonomous as it does not rely on any external server or a third party. The evaluation of our AMR implementation shows that the system supports today on Ethereum anonymity set sizes beyond thousands of users, and a capacity of over 66,000 deposits per day, at constant system costs. We provide a formal specification of our zk-SNARK-based AMR system, a privacy and security analysis, implementation, and evaluation with both the MiMC and Poseidon hash functions.
The ratio of two general continuous-state branching processes with immigration, and its relation to coalescent theory

We study the ratio of two different continuous-state branching processes with immigration whose total mass is forced to be constant at a dense set of times. These lead to the definition of the $\Lambda$- asymmetric frequency process ($\Lambda$-AFP) as a solution of to an SDE. We prove that this SDE has a unique strong solution which is a Feller process. We also calculate a large population limit when the mass tends to infinity and study the fluctuations of the process around its deterministic limit. Furthermore, we find conditions for the $\Lambda$-AFP to have a moment dual. The dual can be interpreted in terms of selection, (coordinated) mutation, pairwise branching (efficiency), coalescence, and a novel component that comes from the asymmetry between the reproduction mechanisms. A pair of equally distributed continuous-state branching processes has an associated $\Lambda$-AFP whose dual is a $\Lambda$-coalescent. The map that sends each continuous-state branching process to its associated $\Lambda$-coalescent (according to the former procedure) is a homeomorphism between metric spaces.
Mass–Velocity Dispersion Relation in HIFLUGCS Galaxy Clusters

We investigate the mass–velocity dispersion relation (MVDR) in 29 galaxy clusters in the HIghest X-ray FLUx Galaxy Cluster Sample (HIFLUGCS). We measure the spatially resolved, line-of-sight velocity dispersion profiles of these clusters, which we find to be mostly flat at large radii, reminiscent of the rotation curves of galaxies. We discover a tight empirical relation between the baryonic mass M bar and the flat velocity dispersion σ of the member galaxies, i.e., MVDR, log(Mbar/M⊙)=4.1−0.4+0.4log(σ/kms−1)+1.6−1.3+1.0 , with the lognormal intrinsic scatter of 12−3+3% . The residuals of the MVDR are uncorrelated with other cluster properties like temperature, cluster radius, baryonic mass surface density, and redshift. These characteristics are reminiscent of the MVDR for individual galaxies, albeit at about a ten times larger characteristic acceleration scale. The cluster baryon fraction falls short of the cosmic value, exposing a problem: the discrepancy increases systematically for clusters of lower mass and lower baryonic acceleration.
Augmenting Machine Learning with Information Retrieval to Recommend Real Cloned Code Methods for Code Completion

Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones accumulated in these repositories hence represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. In previous work, we introduced DeepClone, a deep neural network model trained by fine tuning GPT-2 model over the BigCloneBench dataset to predict code clone methods. The probabilistic nature of DeepClone output generation can lead to syntax and logic errors that requires manual editing of the output for final reuse. In this paper, we propose a novel approach of applying an information retrieval (IR) technique on top of DeepClone output to recommend real clone methods closely matching the predicted output. We have quantitatively evaluated our strategy, showing that the proposed approach significantly improves the quality of recommendation.
Distributed Proximal Splitting Algorithms with Rates and Acceleration

We analyze several generic proximal splitting algorithms well suited for large-scale convex nonsmooth optimization. We derive sublinear and linear convergence results with new rates on the function value suboptimality or distance to the solution, as well as new accelerated versions, using varying stepsizes. In addition, we propose distributed variants of these algorithms, which can be accelerated as well. While most existing results are ergodic, our nonergodic results significantly broaden our understanding of primal–dual optimization algorithms.
Evaluating a Generative Adversarial Framework for Information Retrieval

Recent advances in Generative Adversarial Networks (GANs) have resulted in its widespread applications to multiple domains. A recent model, IRGAN, applies this framework to Information Retrieval (IR) and has gained significant attention over the last few years. In this focused work, we critically analyze multiple components of IRGAN, while providing experimental and theoretical evidence of some of its shortcomings. Specifically, we identify issues with the constant baseline term in the policy gradients optimization and show that the generator harms IRGAN's performance. Motivated by our findings, we propose two models influenced by self-contrastive estimation and co-training which outperform IRGAN on two out of the three tasks considered.
Stochastic Analysis of Satellite Broadband by Mega-Constellations with Inclined LEOs

As emerging massive constellations are intended to provide seamless connectivity for remote areas using hundreds of small low Earth orbit (LEO) satellites, new methodologies have great importance to study the performance of these networks. In this paper, we derive both downlink and uplink analytical expressions for coverage probability and data rate of an inclined LEO constellation under general fading, regardless of exact satellites’ positions. Our solution involves two phases as we, first, abstract the network into a uniformly distributed network. Secondly, we obtain a new parameter, effective number of satellites, for every user’s latitude which compensates for the performance mismatch between the actual and uniform constellations. In addition to exact derivation of the network performance metrics, this study provides insight into selecting the constellation parameters, e.g., the total number of satellites, altitude, and inclination angle.