Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Realism Assessment for Face-swap Videos

Deep-learning based face-swap videos, also known as deep fakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also important as a quality assessment metric to compare different face-swap methods. In this paper, we make a small step towards this new VRA direction by building a benchmark for evaluating the effectiveness of different automatic VRA models, which range from using traditional hand-crafted features to different kinds of deep-learning features. The evaluations are based on a recent competition dataset named DFGC 2022, which contains 1400 diverse face-swap videos that are annotated with Mean Opinion Scores (MOS) on visual realism. Comprehensive experiment results using 11 models and 3 protocols are shown and discussed. We demonstrate the feasibility of devising effective VRA models for assessing face-swap videos and methods. The particular usefulness of existing deepfake detection features for VRA is also noted. The code can be found at https://github.com/XianyunSun/VRA.git.
High resolution and four-dimensional analysis as a prospect for ocean modelling

Abstract The results of research directed to the construction of a model for four-dimensional analysis of the ocean with a high spatial resolution are presented in the paper. These are, first, the development of the dynamic core of a hydrostatic ocean model; second, the study of some important physical phenomena in the Arctic and approaches to their simulation; third, the results of application of the dynamic-stochastic data assimilation method in the model of the tropical region of the Atlantic Ocean.
Goal Alignment: A Human-Aware Account of Value Alignment Problem

Value alignment problems arise in scenarios where the specified objectives of an AI agent don't match the true underlying objective of its users. The problem has been widely argued to be one of the central safety problems in AI. Unfortunately, most existing works in value alignment tend to focus on issues that are primarily related to the fact that reward functions are an unintuitive mechanism to specify objectives. However, the complexity of the objective specification mechanism is just one of many reasons why the user may have misspecified their objective. A foundational cause for misalignment that is being overlooked by these works is the inherent asymmetry in human expectations about the agent's behavior and the behavior generated by the agent for the specified objective. To address this lacuna, we propose a novel formulation for the value alignment problem, named goal alignment that focuses on a few central challenges related to value alignment. In doing so, we bridge the currently disparate research areas of value alignment and human-aware planning. Additionally, we propose a first-of-its-kind interactive algorithm that is capable of using information generated under incorrect beliefs about the agent, to determine the true underlying goal of the user.
Signaling Games with Costly Monitoring

If in a signaling game the receiver expects to gain no information by monitoring the signal of the sender, then when a cost to monitor is implemented he will never pay that cost regardless of his off-path beliefs. This is the argument of a recent paper by T. Denti (2021). However, which pooling equilibrium does a receiver anticipate to gain no information through monitoring? This paper seeks to prove that given a sufficiently small cost to monitor any pooling equilibrium with a non-zero index will survive close to the original equilibrium.
CREPES: Cooperative RElative Pose EStimation towards Real-World Multi-Robot Systems

Mutual localization plays a crucial role in multirobot systems. In this work, we propose a novel system to estimate the 3D relative pose targeting real-world applications. We design and implement a compact hardware module using active infrared (IR) LEDs, an IR fish-eye camera, an ultrawideband (UWB) module and an inertial measurement unit (IMU). By leveraging IR light communication, the system solves data association between visual detection and UWB ranging. Ranging measurements from the UWB and directional information from the camera offer relative 3D position estimation. Combining the mutual relative position with neighbors and the gravity constraints provided by IMUs, we can estimate the 3D relative pose from every single frame of sensor fusion. In addition, we design an estimator based on the errorstate Kalman filter (ESKF) to enhance system accuracy and robustness. When multiple neighbors are available, a Pose Graph Optimization (PGO) algorithm is applied to further improve system accuracy. We conduct experiments in various environments, and the results show that our system outperforms state-of-the-art accuracy and robustness, especially in challenging environments.
Kondo QED: The Kondo effect and photon trapping in a two-impurity Anderson model ultrastrongly coupled to light

The Kondo effect is one of the most studied examples of strongly correlated quantum many-body physics. Another type of strongly correlated physics that has only recently been explored in detail (and become experimentally accessible) is that of ultrastrong coupling between light and matter. Here, we study a system which we denote as"Kondo QED") that combines both phenomena, consisting of a two-impurity Anderson model ultra-strongly coupled to a single-mode cavity. While presented as an abstract model, it is relevant for a range of future hybrid cavity-QED systems. Using the hierarchical equations of motion approach we show that the ultrastrong coupling of cavity photons to the electronic states (impurity) noticeably suppresses the electronic Kondo resonance due to the destruction of many-body correlations of the Kondo cloud. We observe this transfer of correlations from the Kondo cloud to the cavity by computing the entropy and mutual information of the impurity-cavity subsystems. In addition, in the weak lead-coupling limit and at zero-bias, the model exhibits a ground-state photon accumulation effect originating entirely from counter-rotating terms in the impurity-cavity interaction. Interestingly, in the strong lead-coupling limit, this accumulation is ``Kondo-enhanced'' by new transition paths opening when increasing the hybridization to the leads. This suggests a new mechanism for the generation of real photons from virtual states. We further show that the suppression of the Kondo effect is stable under broadening of the cavity resonance as a consequence of the interaction to an external bosonic continuum. Our findings pave the way for the simultaneous control of both the Kondo QED effect and a photon accumulation effect using the ultrastrong coupling of light and matter.
DPCIPI: A pre-trained deep learning model for predicting cross-immunity between drifted strains of Influenza A/H3N2

Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development. Traditional neural network methods, such as BiLSTM, could be ineffective due to the lack of lab data for model training and the overshadowing of crucial features within sequence concatenation. The current work proposes a less data-consuming model incorporating a pre-trained gene sequence model and a mutual information inference operator. Our methodology utilizes gene alignment and deduplication algorithms to preprocess gene sequences, enhancing the model's capacity to discern and focus on distinctions among input gene pairs. The model, i.e., DNA Pretrained Cross-Immunity Protection Inference model (DPCIPI), outperforms state-of-the-art (SOTA) models in predicting hemagglutination inhibition titer from influenza viral gene sequences only. Improvement in binary cross-immunity prediction is 1.58% in F1, 2.34% in precision, 1.57% in recall, and 1.57% in Accuracy. For multilevel cross-immunity improvements, the improvement is 2.12% in F1, 3.50% in precision, 2.19% in recall, and 2.19% in Accuracy. Our study highlights the potential of pre-trained gene models in revolutionizing gene sequence-related prediction tasks. With more gene sequence data being harnessed and larger models trained, we foresee a significant impact of pre-trained models on clinical and public health applications.
Predicting Molecule-Target Interaction by Learning Biomedical Network and Molecule Representations

The study of molecule-target interaction is quite important for drug discovery in terms of target identification, hit identification, pathway study, drug-drug interaction, etc. Most existing methodologies utilize either biomedical network information or molecule structural features to predict potential interaction link. However, the biomedical network information based methods usually suffer from cold start problem, while structure based methods often give limited performance due to the structure/interaction assumption and data quality. To address these issues, we propose a pseudo-siamese Graph Neural Network method, namely MTINet+, which learns both biomedical network topological and molecule structural/chemical information as representations to predict potential interaction of given molecule and target pair. In MTINet+, 1-hop subgraphs of given molecule and target pair are extracted from known interaction of biomedical network as topological information, meanwhile the molecule structural and chemical attributes are processed as molecule information. MTINet+ learns these two types of information as embedding features for predicting the pair link. In the experiments of different molecule-target interaction tasks, MTINet+ significantly outperforms over the state-of-the-art baselines. In addition, in our designed network sparsity experiments , MTINet+ shows strong robustness against different sparse biomedical networks.
Bootstrapping two-loop QCD amplitudes

Form factors of self-dual gauge theory are equal to correlators of an (extended) celestial chiral algebra. This suggests that these form factors can be computed using the"bootstrap"method familiar from 2d CFTs. The method can also be applied to certain QCD amplitudes, which are built from form-factors of self-dual gauge theory. In this paper this bootstrap method is applied to compute two-loop all-plus QCD amplitudes, for $SU(N)$ gauge theory with certain special matter conent. A closed formula is presented for all single-trace amplitudes.
Convolutional Neural Operators

Although very successfully used in machine learning, convolution based neural network architectures – believed to be inconsistent in function space – have been largely ignored in the context of learning solution operators of PDEs. Here, we adapt convolutional neural networks to demonstrate that they are indeed able to process functions as inputs and outputs. The resulting architecture, termed as convolutional neural operators (CNOs), is shown to significantly outperform competing models on benchmark experiments, paving the way for the design of an alternative robust and accurate framework for learning operators.
Interparticle correlations and chemical bonding from physical side: Covalency $\textit{versus}$ atomicity and ionicity

In this Chapter we reexamine the concept of $\textit{covalency}$ and $\textit{ionicity}$ on example of the simplest molecules. First, starting from the exact expression for the two-particle wave function in the case of $H_2$ molecule within the Heitler-London model, we demonstrate an unphysical behavior of the covalency at large interatomic distance which, within standard definition, reaches the maximal value in the limit of separated atoms. Second, we correct this deficiency by introducing the concept of $\textit{atomicity}$, with the help of which, we define the $\textit{true}$ (intrinsic) $\textit{covalency}$, as well as retain the precise concept of $\textit{ionicity}$. We connect the introduced atomicity to the onset of Mott-Hubbard localization, adopted here from the well established notion in the condensed matter. The evolution from the molecular to atomic states develops rapidly with interatomic distance beyond the localization threshold. This brief overview is intented as pedagogical in nature, nonetheless is analyzed quantitatively for the case of $H_2$ molecule. At the end, we outline a similar-type of model for the related case of the hydrogen bond on example of adenine-tymine pair. Methodologically, the approach is based on combining the first- (wave mechanics) and second-quantization into a single scheme of formal description.
Human Not in the Loop: Objective Sample Difficulty Measures for Curriculum Learning

Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures of samples. In the medical domain, previous work applied domain knowledge from human experts to qualitatively assess classification difficulty of medical images to guide curriculum learning, which requires extra annotation efforts, relies on subjective human experience, and may introduce bias. In this work, we propose a new automated curriculum learning technique using the variance of gradients (VoG) to compute an objective difficulty measure of samples and evaluated its effects on elbow fracture classification from X-ray images. Specifically, we used VoG as a metric to rank each sample in terms of the classification difficulty, where high VoG scores indicate more difficult cases for classification, to guide the curriculum training process We compared the proposed technique to a baseline (without curriculum learning), a previous method that used human annotations on classification difficulty, and anti-curriculum learning. Our experiment results showed comparable and higher performance for the binary and multi-class bone fracture classification tasks.
Bayesian Optimization of Multiple Objectives with Different Latencies

Multi-objective Bayesian optimization aims to find the Pareto front of optimal trade-offs between a set of expensive objectives while collecting as few samples as possible. In some cases, it is possible to evaluate the objectives separately, and a different latency or evaluation cost can be associated with each objective. This presents an opportunity to learn the Pareto front faster by evaluating the cheaper objectives more frequently. We propose a scalarization based knowledge gradient acquisition function which accounts for the different evaluation costs of the objectives. We prove consistency of the algorithm and show empirically that it significantly outperforms a benchmark algorithm which always evaluates both objectives.
A Bayesian tune of the Herwig Monte Carlo event generator

The optimisation (tuning) of the free parameters of Monte Carlo event generators by comparing their predictions with data is important since the simulations are used to calculate experimental efficiency and acceptance corrections, or provide predictions for signatures of hypothetical new processes in experiments. We present a tuning procedure that is based on Bayesian reasoning and that allows for a proper statistical interpretation of the results. The parameter space is fully explored using Markov Chain Monte Carlo. We apply the tuning procedure to the Herwig7 event generator with both the cluster and the string hadronization models and a large set of measurements from hadronic Z-boson decays produced at LEP in e + e - collisions. Furthermore, we introduce a coherent propagation of uncertainties from the realm of parameters to the realm of observables and we show the effects of including experimental correlations of the measurements. To allow comparison with the approaches of other groups, we repeat the tuning considering weights for individual measurements.
New Linear-time Algorithm for SubTree Kernel Computation based on Root-Weighted Tree Automata

Tree kernels have been proposed to be used in many areas as the automatic learning of natural language applications. In this paper, we propose a new linear time algorithm based on the concept of weighted tree automata for SubTree kernel computation. First, we introduce a new class of weighted tree automata, called Root-Weighted Tree Automata, and their associated formal tree series. Then we define, from this class, the SubTree automata that represent compact computational models for finite tree languages. This allows us to design a theoretically guaranteed linear-time algorithm for computing the SubTree Kernel based on weighted tree automata intersection. The key idea behind the proposed algorithm is to replace DAG reduction and nodes sorting steps used in previous approaches by states equivalence classes computation allowed in the weighted tree automata approach. Our approach has three major advantages: it is output-sensitive, it is free sensitive from the tree types (ordered trees versus unordered trees), and it is well adapted to any incremental tree kernel based learning methods. Finally, we conduct a variety of comparative experiments on a wide range of synthetic tree languages datasets adapted for a deep algorithm analysis. The obtained results show that the proposed algorithm outperforms state-of-the-art methods.
Imitating careful experts to avoid catastrophic events

RL is increasingly being used to control robotic systems that interact closely with humans. This interaction raises the problem of safe RL: how to ensure that a RL-controlled robotic system never, for instance, injures a human. This problem is especially challenging in rich, realistic settings where it is not even possible to clearly write down a reward function which incorporates these outcomes. In these circumstances, perhaps the only viable approach is based on IRL, which infers rewards from human demonstrations. However, IRL is massively underdetermined as many different rewards can lead to the same optimal policies; we show that this makes it difficult to distinguish catastrophic outcomes (such as injuring a human) from merely undesirable outcomes. Our key insight is that humans do display different behaviour when catastrophic outcomes are possible: they become much more careful. We incorporate carefulness signals into IRL, and find that they do indeed allow IRL to disambiguate undesirable from catastrophic outcomes, which is critical to ensuring safety in future real-world human-robot interactions.
A room-temperature moir\'e interlayer exciton laser

Moir\'e superlattices in van der Waals heterostructures offer highly tunable quantum systems with emergent electronic and excitonic properties such as superconductivity, topological edge states, and moir\'e-trapped excitons. Theoretical calculations predicted the existence of the moir\'e potential at elevated temperatures; however, its impact on the optical properties of interlayer excitons (IXs) at room temperature is lacking, and the benefits of the moir\'e effects for lasing applications remain unexplored. We report that the moir\'e potential in a molybdenum disulfide/tungsten diselenide (MoS2/WSe2) heterobilayer system can significantly enhance light emission, elongate the IX lifetime, and modulate the IX emission energy at room temperature. By integrating a moir\'e superlattice with a silicon topological nanocavity, we achieve ultra-low-threshold lasing at the technologically important telecommunication O-band thanks to the significant moir\'e modulation. Moreover, the high-quality topological nanocavities facilitate the highest spectral coherence of<0.1 nm linewidth among all reported two-dimensional material-based laser systems. Our findings not only open a new avenue for studying correlated states at elevated temperatures, but also enable novel architectures for integrated on-chip photonics and optoelectronics.
Effective Random Test Generation for Deep Learning Compilers

Deep learning compilers help address difficulties of deploying deep learning models on diverse types of hardware. Testing deep learning compilers is highly crucial, because they are impacting countless AI applications that use them for model optimization and deployment. To test deep learning compilers, random testing, being popularly used for compiler testing practices, faces the challenge of generating semantically valid test inputs, i.e., deep learning models that satisfy the semantic model specifications (in short as semantic specifications). To tackle this challenge, in this paper, we propose a novel approach named Isra, including a domain-specific constraint solver that resolves the constraints from the semantic specifications without backtracking. We implement and apply our approach on three popular real-world deep learning compilers including TVM, Glow, and a commercial compiler. The evaluation results show that Isra is more effective than the state-of-the-art approaches and the baseline approaches on constructing valid test inputs for compiler-bug detection, and Isra successfully finds 24 previously unknown bugs in released versions of the three compilers. These results indicate effectiveness and practical value of Isra.
The All-Pairs Vitality-Maximization (VIMAX) Problem

Traditional network interdiction problems focus on removing vertices or edges from a network so as to disconnect or lengthen paths in the network; network diversion problems seek to remove vertices or edges to reroute flow through a designated critical vertex or edge. We introduce the all-pairs vitality maximization problem (VIMAX), in which vertex deletion attempts to maximize the amount of flow passing through a critical vertex, measured as the all-pairs vitality of the vertex. The assumption in this problem is that in a network for which the structure is known but the physical locations of vertices may not be known (e.g. a social network), locating a person or asset of interest might require the ability to detect a sufficient amount of flow (e.g., communications or financial transactions) passing through the corresponding vertex in the network. We formulate VIMAX as a mixed integer program, and show that it is NP-Hard. We compare the performance of the MIP and a simulated annealing heuristic on both real and simulated data sets and highlight the potential increase in vitality of key vertices that can be attained by subset removal. We also present graph theoretic results that can be used to narrow the set of vertices to consider for removal.
All Projective Measurements Can be Self-tested

We show that every real-valued projective measurement can be self-tested from correlations. To achieve this, we develop the theory of post-hoc self-testing, which extends existing self-tested strategies to incorporate more measurements. A sufﬁcient and computationally feasible condition for a projective measurement to be post-hoc self-tested by a given strategy is proven. Recent work by Manˇcinska et al . [arxiv:2103.01729] showed that a strategy containing d + 1 two-output projective measurements and the maximally entangled state with the local dimension d is self-tested. Applying the post-hoc self-testing technique to this work results in an extended strategy that can incorporate any real-valued projective measurement. We further study the general theory of iterative post-hoc self-testing whenever the state in the initial strategy is maximally entangled, and characterize the iteratively post-hoc self-tested measurements in terms of a Jordan algebra generated by the initial strategy.