Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Socialbot Learning via Multi-Agent Deep Hierarchical Reinforcement Learning

Socialbots are software-driven user accounts on social platforms, acting autonomously (mimicking human behavior), with the aims to influence the opinions of other users or spread targeted misinfor-mation for particular goals. As socialbots undermine the ecosystem of social platforms, they are often considered harmful. As such, there have been several computational efforts to auto-detect the socialbots. However, to our best knowledge, the adversarial nature of these socialbots has not yet been studied. This begs a question “can adversaries, controlling socialbots, exploit AI techniques to their advantage? " To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected. We first formulate the adversarial socialbot learning as a cooperative game between two functional hierarchical RL agents. While one agent curates a sequence of activities that can avoid the detection, the other agent aims to maximize network influence by selectively connecting with right users. Our proposed policy networks train with a vast amount of synthetic graphs and generalize better than baselines on unseen real-life graphs both in terms of maximizing network influence (up to +18%) and sustainable stealthiness (up to +40% undetectability) under a strong bot detector (with 90% detection accuracy). During inference, the complexity of our approach scales linearly, independent of a network’s structure and the virality of news. This makes our approach a practical adversarial attack when deployed in a real-life setting.
Stability and Performance Analysis of NMPC: Detectable Stage Costs and General Terminal Costs

We provide a stability and performance analysis for nonlinear model predictive control (NMPC) schemes subject to input constraints. Given an exponential stabilizability and detectability condition w.r. t. the employed state cost, we provide a sufficiently long prediction horizon to ensure asymptotic stability and a desired performance bound w.r. t. the infinite-horizon optimal controller. Compared to existing results, the provided analysis is applicable to positive semidefinite (detectable) cost functions, provides tight bounds using a linear programming analysis, and allows for a seamless integration of general positive-definite terminal cost functions in the analysis. The practical applicability of the derived theoretical results is demonstrated with numerical examples.
Contrastive Document Representation Learning with Graph Attention Networks

Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention complexity, most of the pretrained Transformers models can only handle relatively short text. It is still a challenge when it comes to modeling very long documents. In this work, we propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. This graph attention network allows us to leverage the high-level semantic structure of the document. In addition, based on our graph document model, we design a simple contrastive learning strategy to pretrain our models on a large amount of unlabeled corpus. Empirically, we demonstrate the effectiveness of our approaches in document classification and document retrieval tasks.
Independent Natural Policy Gradient Always Converges in Markov Potential Games

Multi-agent reinforcement learning has been successfully applied to fully-cooperative and fully-competitive environments, but little is currently known about mixed cooperative/competitive environments. In this paper, we focus on a particular class of multi-agent mixed cooperative/competitive stochastic games called Markov Potential Games (MPGs), which include cooperative games as a special case. Recent results have shown that independent policy gradient converges in MPGs but it was not known whether Independent Natural Policy Gradient converges in MPGs as well. We prove that Independent Natural Policy Gradient always converges in the last iterate using constant learning rates. The proof deviates from the existing approaches and the main challenge lies in the fact that Markov Potential Games do not have unique optimal values (as single-agent settings exhibit) so different initializations can lead to different limit point values. We complement our theoretical results with experiments that indicate that Natural Policy Gradient outperforms Policy Gradient in routing games and congestion games.
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization

While much research has been done in text-to-image synthesis, little work has been done to explore the usage of linguistic structure of the input text. Such information is even more important for story visualization since its inputs have an explicit narrative structure that needs to be translated into an image sequence (or visual story). Prior work in this domain has shown that there is ample room for improvement in the generated image sequence in terms of visual quality, consistency and relevance. In this paper, we first explore the use of constituency parse trees using a Transformer-based recurrent architecture for encoding structured input. Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story. Third, we also incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images within a dual learning setup. We show that off-the-shelf dense-captioning models trained on Visual Genome can improve the spatial structure of images from a different target domain without needing fine-tuning. We train the model end-to-end using intra-story contrastive loss (between words and image sub-regions) and show significant improvements in visual quality. Finally, we provide an analysis of the linguistic and visuo-spatial information.
Emergence of Gapless Quantum Spin Liquid from Deconfined Quantum Critical Point

A quantum spin liquid (QSL) is a novel phase of matter with long-range entanglement where localized spins are highly correlated with the vanishing of magnetic order. Such exotic quantum states provide the opportunities to develop new theoretical frameworks for many-body physics and have the potential application in realizing robust quantum computations. Here we show that a gapless QSL can naturally emerge from a deconﬁned quantum critical point (DQCP), which is originally proposed to describe Landau forbidden continuous phase transition between antiferromagnetic (AFM) and valence-bond solid (VBS) phases. Via large-scale tensor network simulations of a square-lattice spin-1 / 2 frustrated Heisenberg model, both QSL state and DQCP-type AFM-VBS transition are observed. With tuning coupling constants, the AFM-VBS transition vanishes and instead, a gapless QSL phase gradually develops in between. Remarkably, along the phase boundaries of AFM-QSL and QSL-VBS transitions, we always observe the same correlation length exponents ν ≈ 1 . 0, which is intrinsically di ﬀ erent from the one of the DQCP-type transition, indicating new types of universality classes. Our results explicitly demonstrate a new scenario for understanding the emergence of gapless QSL from an underlying DQCP. The discovered QSL phase survives in a large region of tuning parameters and we expect its experimental realizations in solid state materials or quantum simulators.
An analysis of core-mantle boundary related seismic waves using full-waveform modelling and adjoint methods

The core-mantle boundary (CMB) is the most abrupt internal discontinuity in the Earth, marking the solid-fluid boundary between mantle and outer core that strongly affects the dynamics of the Earth’s interior. However, good agreement between models of CMB topographic variations is still lacking. This is probably due to difficulties relating to observations on seismograms and to the lack of good models of lowermost mantle velocity structure. Using spectral-element synthetic seismograms and adjoint methods, we perform traveltime analyses of seismic waves interacting with the core-mantle boundary. We focus on reflected and refracted P- and S- waves. We select some of the most important and routinely used seismic phases: ScS, SKS, SKKS, PcP, PKP, PKKP and PcS, given their path through mantle and core and their interaction with the CMB. These seismic waves have been widely deployed by seismologists trying to image CMB topography and lowermost mantle structure. To analyse the reliability of measuring their traveltimes to infer CMB topography, we perform experiments in two ways. First, we compute synthetic seismograms with a dominant period of T ≈11s, for computational efficiency, using existing models of CMB topography. We compare traveltime perturbations measured by cross-correlation on the synthetics to those predicted using ray theory. We find deviations from a perfect agreement between ray theoretical predictions of time shifts and those measured on synthetics with and without CMB topography. Second, we calculate Fréchet sensitivity kernels of traveltimes with respect to shear and compressional wavespeeds. We also explicitly compute boundary sensitivities with respect to the CMB interface. We observe that the overall sensitivity of the traveltimes is mostly due to volumetric velocity structure and that imprints of CMB on traveltimes are less pronounced. Our study explains the observed difficulties relating to inferring CMB topography using traveltimes and provides a suite of finite frequency sensitivity kernels computed with the adjoint method. The kernels allow us to qualitatively explain the behaviour of measured traveltimes and understand the trade-off between velocity and CMB topography. They can also serve as reference of finite frequency effects on traveltimes of observed seismic phases. From our analyses we conclude that: i) Traveltime anomalies measured on S-waves are more in accord with ray theoretical predictions, ii) PcP, PKP, ScS and SKS phases have more pronounced sensitivity to the boundary and iii) Separating the greater effects of velocity from those due to the boundary structure is difficult, as they intricately affect the traveltime. We propose that jointly inverting for CMB topography and lowermost mantle velocity structure using full-waveform synthetics and adjoint sensitivity kernels can progress our understanding of deep Earth structure and finite frequency effects on observed waveforms.
A High-velocity Scatterer Revealed in the Thinning Ejecta of a Type II Supernova

We present deep, nebular-phase spectropolarimetry of the Type II-P/L SN 2013ej, obtained 167 days after explosion with the European Southern Observatory’s Very Large Telescope. The polarized flux spectrum appears as a nearly perfect (92% correlation), redshifted (by ∼4000 km s−1) replica of the total flux spectrum. Such a striking correspondence has never been observed before in nebular-phase supernova spectropolarimetry, although data capable of revealing it have heretofore been only rarely obtained. Through comparison with 2D polarized radiative transfer simulations of stellar explosions, we demonstrate that localized ionization produced by the decay of a high-velocity, spatially confined clump of radioactive 56Ni—synthesized by and launched as part of the explosion with final radial velocity exceeding 4500 km s−1—can reproduce the observations through enhanced electron scattering. Additional data taken earlier in the nebular phase (day 134) yield a similarly strong correlation (84%) and redshift, whereas photospheric-phase epochs that sample days 8 through 97 do not. This suggests that the primary polarization signatures of the high-velocity scattering source only come to dominate once the thick, initially opaque hydrogen envelope has turned sufficiently transparent. This detection in an otherwise fairly typical core-collapse supernova adds to the growing body of evidence supporting strong asymmetries across nature’s most common types of stellar explosions, and establishes the power of polarized flux—and the specific information encoded by it in line photons at nebular epochs—as a vital tool in such investigations going forward.
One Model to Enhance Them All: Array Geometry Agnostic Multi-Channel Personalized Speech Enhancement

With the recent surge of video conferencing tools usage, providing high-quality speech signals and accurate captions have become essential to conduct day-to-day business or connect with friends and families. Single-channel personalized speech enhancement (PSE) methods show promising results compared with the unconditional speech enhancement (SE) methods in these scenarios due to their ability to remove interfering speech in addition to the environmental noise. In this work, we leverage spatial information afforded by microphone arrays to improve such systems’ performance further. We investigate the relative importance of speaker embeddings and spatial features. Moreover, we propose a new causal array-geometry-agnostic multi-channel PSE model, which can generate a high-quality enhanced signal from arbitrary microphone geometry. Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy. We also demonstrate the effectiveness of the proposed approach for unseen array geometries.
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling

Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretation without training on fully annotated image datasets. CAM methods are typically integrated within off-the-shelf CNN backbones, such as ResNet50. Due to convolution and pooling operations, these backbones yield low resolution CAMs with a down-scaling factor of up to 32, contributing to inaccurate localizations. Interpolation is required to restore full size CAMs, yet it does not consider the statistical properties of objects, such as color and texture, leading to activations with inconsistent boundaries, and inaccurate localizations. As an alternative, we introduce a generic method for parametric upscaling of CAMs that allows constructing accurate full resolution CAMs (FCAMs). In particular, we propose a trainable decoding architecture that can be connected to any CNN classifier to produce highly accurate CAM localizations. Given an original low resolution CAM, foreground and background pixels are randomly sampled to fine-tune the decoder. Additional priors such as image statistics and size constraints are also considered to expand and refine object boundaries. Extensive experiments1, over three CNN backbones and six WSOL baselines on the CUB-200-2011 and OpenImages datasets, indicate that our F-CAM method yields a significant improvement in CAM localization accuracy. F-CAM performance is competitive with state-of-art WSOL methods, yet it requires fewer computations during inference.
Diagonalization of Polynomial-Time Turing Machines Via Nondeterministic Turing Machine

The diagonalization technique was invented by Cantor to show that there are more real numbers than algebraic numbers, and is very important in computer science. In this work, we enumerate all polynomial-time deterministic Turing machines and diagonalize over all of them by an universal nondeterministic Turing machine. As a result, we obtain that there is a language L d not accepted by any polynomial-time deterministic Turing machines but accepted by a nondeterministic Turing machine working within O ( n k ) for any k ∈ N 1 , i
An Empirical Study: Extensive Deep Temporal Point Process

Temporal point process as the stochastic process on continuous domain of time is commonly used to model the asynchronous event sequence featuring with occurrence timestamps. Thanks to the strong expressivity of deep neural networks, they are emerging as a promising choice for capturing the patterns in asynchronous sequences, in the context of temporal point process. In this paper, we first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process, which can be concluded into four fields: encoding of history sequence, formulation of conditional intensity function, relational discovery of events and learning approaches for optimization. We introduce most of recently proposed models by dismantling them into the four parts, and conduct experiments by remodularizing the first three parts with the same learning strategy for a fair empirical evaluation. Besides, we extend the history encoders and conditional intensity function family, and propose a Granger causality discovery framework for exploiting the relations among multi-types of events. Because the Granger causality can be represented by the Granger causality graph, discrete graph structure learning in the framework of Variational Inference is employed to reveal latent structures of the graph. Further experiments show that the proposed framework with latent graph discovery can both capture the relations and achieve an improved fitting and predicting performance.
Policy Choice and Best Arm Identification: Asymptotic Analysis of Exploration Sampling

We consider the"policy choice"problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental design. Theorem 1 of Kasy and Sautmann (2021) provides three asymptotic results that give theoretical guarantees for exploration sampling developed for this setting. We first show that the proof of Theorem 1 (1) has technical issues, and the proof and statement of Theorem 1 (2) are incorrect. We then show, through a counterexample, that Theorem 1 (3) is false. For the former two, we correct the statements and provide rigorous proofs. For Theorem 1 (3), we propose an alternative objective function, which we call posterior weighted policy regret, and derive the asymptotic optimality of exploration sampling.
Merging Multigrid Optimization with SESOP

A merger of two optimization frameworks is introduced: SEquential Subspace OPtimization (SESOP) with MultiGrid (MG) optimization. At each iteration of the algorithm, the search direction implied by the coarse-grid correction process of MG is added to the low dimensional search-space of SESOP, which includes the preconditioned gradient and search directions involving the previous iterates, called {\em history}. Numerical experiments demonstrate the effectiveness of this approach. We then study the asymptotic convergence factor of the two-level version of SESOP-MG (dubbed SESOP-TG) for optimization of quadratic functions, and derive approximately optimal fixed parameters, which may reduce the computational overhead for such problems significantly.
Newtonian Mechanics Based Transient Stability PART II: Individual Machine

The paper analyzes the mechanisms of the individual-machine and also its advantages in TSA. Based on the critical-machine monitoring of the original system trajectory, it is clarified that the individual-machine strictly follows the machine paradigms. These strict followings of the paradigms bring the two advantages of the individual-machine method in TSA: (i) the individual-machine trajectory stability is characterized precisely, and (ii) the individual-machine trajectory variance is depicted clearly at IMPP. The two advantages are fully reflected in the precise definitions of individual-machine based transient stability concepts. In particular, the critical machine swing is clearly depicted through the IDSP or IDLP of the machine, the critical stability of the system is strictly defined as the critical stability of the most-severely disturbed machine, and the individual-machine potential energy surface is also precisely modeled through the IMPE of the machine. Simulation results show the effectiveness of the individual-machine in TSA.
Fuzzy-Depth Objects Grasping Based on FSG Algorithm and a Soft Robotic Hand

Autonomous grasping is an important factor for robots physically interacting with the environment and executing versatile tasks. However, a universally applicable, cost-effective, and rapidly deployable autonomous grasping approach is still limited by those target objects with fuzzy-depth information. Examples are transparent, specular, flat, and small objects whose depth is difficult to be accurately sensed. In this work, we present a solution to those fuzzy-depth objects. The framework of our approach includes two major components: one is a soft robotic hand and the other one is a Fuzzy-depth Soft Grasping (FSG) algorithm. The soft hand is replaceable for most existing soft hands/grippers with body compliance. FSG algorithm exploits both RGB and depth images to predict grasps while not trying to reconstruct the whole scene. Two grasping primitives are designed to further increase robustness. The proposed method outperforms reference baselines in unseen fuzzy-depth objects grasping experiments (84% success rate).
AniFormer: Data-driven 3D Animation with Transformer

We present a novel task, i.e., animating a target 3D object through the motion of a raw driving sequence. In previous works, extra auxiliary correlations between source and target meshes or intermedia factors are inevitable to capture the motions in the driving sequences. Instead, we introduce AniFormer, a novel Transformer-based architecture, that generates animated 3D sequences by directly taking the raw driving sequences and arbitrary same-type target meshes as inputs. Specifically, we customize the Transformer architecture for 3D animation that generates mesh sequences by integrating styles from target meshes and motions from the driving meshes. Besides, instead of the conventional single regression head in the vanilla Transformer, AniFormer generates multiple frames as outputs to preserve the sequential consistency of the generated meshes. To achieve this, we carefully design a pair of regression constraints, i.e., motion and appearance constraints, that can provide strong regularization on the generated mesh sequences. Our AniFormer achieves high-fidelity, realistic, temporally coherent animated results and outperforms compared start-of-the-art methods on benchmarks of diverse categories. Code is available: https://github.com/mikecheninoulu/AniFormer.
Boole's probability bounding problem, linear programming aggregations, and nonnegative quadratic pseudo-Boolean functions

George Boole first proposed the union bounding problem, which is a class of probabilistic satisfiability problems. Based on Hailperin’s linear programming formulation of the problem, a very large body of research was conducted to provide good bounds by considering a relaxation that is either polynomial dimension in n or is at least polynomially computable. We study monotone linear mappings L to a lower N dimensional space such that L(R n −1 + ) ⊆ R N + and provide a complete characterization of the cone LE(R Ω +) for three aggregation models from the literature. This characterization leads to strictly improved bounds in two of these cases as a result. We also obtain an O(n) dimensional linear program that is equivalent with Hailperin’s model. An interesting side result is that there exists a connection between Hailperin’s model and the cut-polytope.