Miracle-dz/UniFAR-FTUs huggingface dataset:
H OW “T ROLL ” ARE YOU ? M EASURING AND D ETECTING T ROLL B EHAVIOR IN O NLINE S OCIAL N ETWORKS

The detection of state-sponsored trolls acting in misinformation operations is an unsolved and critical challenge for the research community, with repercussions that go beyond the online realm. In this paper, we propose a novel approach for the detection of troll accounts, which consists of two steps. The ﬁrst step aims at classifying trajectories of accounts’ online activities as belonging to either a troll account or to an organic user account. In the second step, we exploit the classiﬁed trajectories to compute a metric, namely “troll score", which allows us to quantify the extent to which an account behaves like a troll. Experimental results show that our approach identiﬁes accounts’ trajectories with an AUC close to 99% and, accordingly, classify trolls and organic users with an AUC of 97%. Finally, we evaluate whether the proposed solution can be generalized to different contexts (e.g., discussions about Covid-19) and generic misbehaving users, showing promising results that will be further expanded in our future endeavors.
Quantum Simulation of the Majorana Equation and Unphysical Operations

A quantum simulator is a device engineered to reproduce the properties of an ideal quantum model. It allows the study of quantum systems that cannot be efficiently simulated on classical computers. While a universal quantum computer is also a quantum simulator, only particular systems have been simulated up to now. Still, there is a wealth of successful cases, such as spin models, quantum chemistry, relativistic quantum physics and quantum phase transitions. Here, we show how to design a quantum simulator for the Majorana equation, a non-Hamiltonian relativistic wave equation that might describe neutrinos and other exotic particles beyond the standard model. The simulation demands the implementation of charge conjugation, an unphysical operation that opens a new front in quantum simulations, including the discrete symmetries associated with complex conjugation and time reversal. Finally, we show how to implement this general method in trapped ions.
Learning Dual Memory Dictionaries for Blind Face Restoration

Blind face restoration is a challenging task due to the unknown, unsynthesizable and complex degradation, yet is valuable in many practical applications. To improve the performance of blind face restoration, recent works mainly treat the two aspects, i.e., generic and specific restoration, separately. In particular, generic restoration attempts to restore the results through general facial structure prior, while on the one hand, cannot generalize to real-world degraded observations due to the limited capability of direct CNNs’ mappings in learning blind restoration, and on the other hand, fails to exploit the identity-specific details. On the contrary, specific restoration aims to incorporate the identity features from the reference of the same identity, in which the requirement of proper reference severely limits the application scenarios. Generally, it is a challenging and intractable task to improve the photo-realistic performance of blind restoration and adaptively handle the generic and specific restoration scenarios with a single unified model. Instead of implicitly learning the mapping from a low-quality image to its high-quality counterpart, this paper suggests a DMDNet by explicitly memorizing the generic and specific features through dual dictionaries. First, the generic dictionary learns the general facial priors from high-quality images of any identity, while the specific dictionary stores the identity-belonging features for each person individually. Second, to handle the degraded input with or without specific reference, dictionary transform module is suggested to read the relevant details from the dual dictionaries which are subsequently fused into the input features. Finally, multi-scale dictionaries are leveraged to benefit the coarse-to-fine restoration. The whole framework including the generic and specific dictionaries is optimized in an end-to-end manner and can be flexibly plugged into different application scenarios. Moreover, a new high-quality dataset, termed CelebRef-HQ, is constructed to promote the exploration of specific face restoration in the high-resolution space. Experimental results demonstrate that the proposed DMDNet performs favorably against the state of the arts in both quantitative and qualitative evaluation, and generates more photo-realistic results on the real-world low-quality images. The codes, models and the CelebRef-HQ dataset will be publicly available at https://github.com/csxmli2016/DMDNet.
Packed-Ensembles for Efficient Uncertainty Estimation

Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world systems constrain to smaller ensembles and lower-capacity networks, significantly deteriorating their performance and properties. We introduce Packed-Ensembles (PE), a strategy to design and train lightweight structured ensembles by carefully modulating the dimension of their encoding space. We leverage grouped convolutions to parallelize the ensemble into a single shared backbone and forward pass to improve training and inference speeds. PE is designed to operate within the memory limits of a standard neural network. Our extensive research indicates that PE accurately preserves the properties of DE, such as diversity, and performs equally well in terms of accuracy, calibration, out-of-distribution detection, and robustness to distribution shift. We make our code available at https://github.com/ENSTA-U2IS/torch-uncertainty.
oViT: An Accurate Second-Order Pruning Framework for Vision Transformers

Models from the Vision Transformer (ViT) family have recently provided break-through results across image classiﬁcation tasks such as ImageNet. Yet, they still face barriers to deployment, notably the fact that their accuracy can be severely impacted by compression techniques such as pruning. In this paper, we take a step towards addressing this issue by introducing Optimal ViT Surgeon (oViT) , a new state-of-the-art method for the weight sparsiﬁcation of Vision Transformers (ViT) models. At the technical level, oViT introduces a new weight pruning algo-rithm which leverages second-order information, speciﬁcally adapted to be both highly-accurate and efﬁcient in the context of ViTs. We complement this accurate one-shot pruner with an in-depth investigation of gradual pruning, augmentation, and recovery schedules for ViTs, which we show to be critical for successful ViT compression. We validate our method via extensive experiments on classical ViT and DeiT models, as well as on newer variants, such as XCiT, EfﬁcientFormer and Swin. Moreover, our results are even relevant to recently-proposed highly-accurate ResNets. Our results show for the ﬁrst time that ViT-family models can in fact be pruned to high sparsity levels (e.g. ≥ 75% ) with low impact on accuracy ( ≤ 1% relative drop), and that our approach outperforms prior methods by signiﬁcant margins at high sparsities. In addition, we show that our method is compatible with structured pruning methods and quantization, and that it can lead to signiﬁcant speedups on a sparsity-aware inference engine.
Discovery of a very weak magnetic field on the Am star Alhena

Alhena (γ Gem) was observed in the frame of the BRIght Target Explorer spectropolarimetric survey, which gathers high resolution, high signal-to-noise, high sensitivity, spectropolarimetric observations of all stars brighter than V = 4 to combine seismic and spectropolarimetric studies of bright stars. We present here the discovery of a very weak magnetic field on the Am star Alhena, thanks to very high signal-to-noise spectropolarimetric data obtained with Narval at Telescope Bernard Lyot. All previously studied Am stars show the presence of ultraweak (sub-Gauss) fields with Zeeman signatures with an unexpected prominent positive lobe. However, Alhena presents a slightly stronger (but still very weak, only a few Gauss) field with normal Zeeman signatures with a positive and negative lobe, as found in stronger field (hundreds or thousands of Gauss) stars. It is the first detection of a normal magnetic signature in an Am star. Alhena is thus a very interesting object, which might provide the clue to understanding the peculiar shapes of the magnetic signatures of the other Am stars.
Inductive Logical Query Answering in Knowledge Graphs

Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural methods achieved impressive results in link prediction and complex query answering tasks by learning representations of entities, relations, and queries. Still, most existing query answering methods rely on transductive entity embeddings and cannot generalize to KGs containing new entities without retraining the entity embeddings. In this work, we study the inductive query answering task where inference is performed on a graph containing new entities with queries over both seen and unseen entities. To this end, we devise two mechanisms leveraging inductive node and relational structure representations powered by graph neural networks (GNNs). Experimentally, we show that inductive models are able to perform logical reasoning at inference time over unseen nodes generalizing to graphs up to 500% larger than training ones. Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes. Code is available at https://github.com/DeepGraphLearning/InductiveQE.
The equivalence principle and inertial-gravitational decoherence

This work is divided into two parts. The first examines recent proposals for"witnessing"quantum gravity via entanglement from the point of view of Bronstein's original objection to a quantization of gravity. Using techniques from open quantum systems we sketch how unavoidable decoherence from both inertial and gravitational backreaction between probe and detector could spoil the experimental detection of the quantization of gravity. We argue that this"failure"is actually an inherent feature of any quantum description that attempts to incorporate the equivalence principle exactly within quantum dynamics. In the second part, we speculate on how an exact realization of the equivalence principle might be implemented in an effective quantum field theory via the general covariance of correlators. While we are far from giving an explicit construction of such a theory we point out some features and consequences of such a program.
Call Graph Evolution Analytics over a Version Series of an Evolving Software System

Software evolution analytics can be supported by generating and comparing call graph evolution information over versions of a software system. Call Graph evolution analytics can assist a software engineer when maintaining or evolving a software system. This paper proposes Call Graph Evolution Analytics to extract information from a set of Evolving Call Graphs ECG = {CG1, CG2,. CGN} representing a Version Series VS = {V1, V2,. VN} of an evolving software system. This is done using Call Graph Evolution Rules (CGERs) and Call Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the CGERs are used to capture co-occurrences of dependencies in the system. Like subgraph patterns in a call graph, the CGESs are used to capture evolution of dependency patterns in evolving call graphs. Call graph analytics on the evolution in these patterns can identify potentially affected dependencies (or procedure calls) that need attention. The experiments are done on the evolving call graphs of 10 large evolving systems to support dependency evolution management. This is demonstrated with detailed results for evolving call graphs of Maven-Core’s version series.
Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving

Learning-based perception and prediction modules in modern autonomous driving systems typically rely on expensive human annotation and are designed to perceive only a handful of predefined object categories. This closed-set paradigm is insufficient for the safety-critical autonomous driving task, where the autonomous vehicle needs to process arbitrarily many types of traffic participants and their motion behaviors in a highly dynamic world. To address this difficulty, this paper pioneers a novel and challenging direction, i.e., training perception and prediction models to understand open-set moving objects, with no human supervision. Our proposed framework uses self-learned flow to trigger an automated meta labeling pipeline to achieve automatic supervision. 3D detection experiments on the Waymo Open Dataset show that our method significantly outperforms classical unsupervised approaches and is even competitive to the counterpart with supervised scene flow. We further show that our approach generates highly promising results in open-set 3D detection and trajectory prediction, confirming its potential in closing the safety gap of fully supervised systems.
Intruder structures observed in 122Te through inelastic neutron scattering

The excited levels of 122Te to 3.3-MeV excitation have been studied using γ -ray spectroscopy following inelastic neutron scattering. The decay characteristics of these levels have been determined from γ -ray excitation functions, angular distributions at En = 1.72, 2.80, and 3.35 MeV, Doppler shifts, and γ γ coincidences. Electromagnetic transition rates were deduced for many levels, as were multipole-mixing and branching ratios. Level energies and electromagnetic transition rates were compared to interacting boson model (IBM) calculations, both with and without intruder-state mixing, and to particle-core coupling model calculations. The energies of low-lying levels of 122Te are well described by the IBM with intruder-state mixing calculations, and observed transition rates support emerging intruder bands built on 0+ levels. The other models considered do not produce enough low-lying positive parity states; however, U(5) energies to the four quadrupole-phonon level agree very well with observations when states with large intruder configurations are ignored. Mixed-symmetry and quadrupole-octupole excitations have been investigated, but mixing with other configurations and fragmentation of strength prohibit a clear identification of these states.
Security and Privacy in Big Data Sharing: State-of-the-Art and Research Directions

Big Data Sharing (BDS) refers to the act of the data owners to share data so that users can find, access and use data according to the agreement. In recent years, BDS has been an emerging topic due to its wide applications, such as big data trading and cross-domain data analytics. However, as the multiple parties are involved in a BDS platform, the issue of security and privacy violation arises. There have been a number of solutions for enhancing security and preserving privacy at different big data operations (e.g., data operation, data searching, data sharing and data outsourcing). To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these security and privacy solutions. In this study, we conduct a comprehensive survey of the state-of-the-art solutions introduced to tackle security and privacy issues in BDS. For a better understanding, we first introduce a general model for BDS and identify the security and privacy requirements. We discuss and classify the state-of-the-art security and privacy solutions for BDS according to the identified requirements. Finally, based on the insights gained, we present and discuss new promising research directions.
Temporal Word Meaning Disambiguation using TimeLMs

Meaning of words constantly change given the events in modern civilization. Large Language Models use word embeddings, which are often static and thus cannot cope with this semantic change. Thus, it is important to resolve ambiguity in word meanings. This paper is an effort in this direction, where we explore methods for word sense disambiguation for the EvoNLP shared task. We conduct rigorous ablations for two solutions to this problem. We see that an approach using time-aware language models helps this task. Furthermore, we explore possible future directions to this problem.
Locally Typical Sampling

Today’s probabilistic language generators fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics (e.g., perplexity). This discrepancy has puzzled the language generation community for the last few years. In this work, we posit that the abstraction of natural language generation as a discrete stochastic process—which allows for an information-theoretic analysis—can provide new insights into the behavior of probabilistic language generators, for example, why high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, aiming to do so in a simultaneously efficient and error-minimizing manner; in fact, psycholinguistics research suggests humans choose each word in a string with this subconscious goal in mind. We formally define the set of strings that meet this criterion: Those for which each word has an information content close to the expected information content, namely, the conditional entropy of our model. We then propose a simple and efficient procedure for enforcing this criterion when generating from probabilistic models, which we call locally typical sampling. Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of quality while consistently reducing degenerate repetitions.
WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments

Recent advances in deep reinforcement learning (RL) have demonstrated complex decision-making capabilities in simulation environments such as Arcade Learning Environment, MuJoCo, and ViZDoom. However, they are hardly extensible to more complicated problems, mainly due to the lack of complexity and variations in the environments they are trained and tested on. Furthermore, they are not extensible to an open-world environment to facilitate long-term exploration research. To learn realistic task-solving capabilities, we need to develop an environment with greater diversity and complexity. We developed WILD-SCAV, a powerful and extensible environment based on a 3D open-world FPS (First-Person Shooter) game to bridge the gap. It provides realistic 3D environments of variable complexity, various tasks, and multiple modes of interaction, where agents can learn to perceive 3D environments, navigate and plan, compete and cooperate in a human-like manner. WILD-SCAV also supports different complexities, such as configurable maps with different terrains, building structures and distributions, and multi-agent settings with cooperative and competitive tasks. The experimental results on configurable complexity, multi-tasking, and multi-agent scenarios demonstrate the effectiveness of WILD-SCAV in benchmarking various RL algorithms, as well as it is potential to give rise to intelligent agents with generalized task-solving abilities. The link to our open-sourced code can be found here https://github.com/inspirai/wilderness-scavenger.
Fast interrogation wavelength tuning for all-optical photoacoustic imaging

One of the most widespread all-optical photoacoustic imaging techniques is based on Fabry-Pérot cavities with a thin polymer spacer. However, the deposition process can lead to inhomogeneities in the thickness of this sensing layer. They can be compensated by precisely controlling the interrogation beam, to provide optimal sensitivity. This is commonly achieved with slowly tunable narrowband lasers, eventually limiting the acquisition speed. We propose instead to use a broadband amplified spontaneous emission source and a fast tunable acousto-optic filter to adjust the interrogation wavelength at each pixel within a few microseconds. This enables us to maximize the sensitivity of the optical interferometric ultrasound detection at each point of the Fabry-Perot cavity. We experimentally show that this greatly enhances the detection bandwidth of the ultrasound sensors. We demonstrate the validity of this approach by performing photoacoustic imaging with a highly inhomogeneous sensor.
Stability and instability of solitary waves in fractional generalized KdV equation in all dimensions

We study stability properties of solitary wave solutions for the fractional generalized Korteweg-de Vries equation $$ \partial_t u- \partial_{x_1} D^{\alpha}u+ \tfrac{1}{m}\partial_{x_1}(u^m)=0, ~ (x_1,\dots,x_d)\in \mathbb{R}^d, \, \, t\in \mathbb{R}, \, \, 0<\alpha<2, $$ in any spatial dimension $d\geq 1$ and nonlinearity $m>1$. In the $L^2$-subcritical case, $1<m<\frac{2d}{\alpha}+1$, we prove the orbital stability of solitary waves using the concentration-compactness argument, the commutator estimates and expansions of nonlocal operator $D^\alpha$ in several variables. In the $L^2$-supercritical case, $\frac{2d}{\alpha}+1<m<m_{\ast}$, we show that solitary waves are unstable. The instability is obtained by making use of an explicit sequence of initial conditions in conjunction with modulation and truncation arguments. This work maybe be of interest for studying solitary waves and other coherent structures in dispersive equations that involve nonlocal operators. The arguments developed here are independent of the spatial dimension and rely on a careful analysis of spatial decay of ground states and their regularity. In particular, in the one-dimensional setting, we show the instability of solitary waves of the supercritical generalized Benjamin-Ono equation and the dispersion-generalized Benjamin-Ono equation, furthermore, new results on the instability are obtained in the weaker dispersion regime $\frac{1}{2}<\alpha<1$.