Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships in the system. Different models may entail the same distributions, offering competing causal explanations for the given observations. We fully characterize this equivalence class and offer identifiability results, which we use to derive a greedy algorithm called GnIES to recover the equivalence class of the data-generating model without knowledge of the intervention targets. In addition, we develop a novel procedure to generate semi-synthetic data sets with known causal ground truth but distributions closely resembling those of a real data set of choice. We leverage this procedure and evaluate the performance of GnIES on synthetic, real, and semi-synthetic data sets. Despite the strong Gaussian distributional assumption, GnIES is robust to an array of model violations and competitive in recovering the causal structure in small- to large-sample settings. We provide, in the Python packages"gnies"and"sempler", implementations of GnIES and our semi-synthetic data generation procedure.
Dynamics of interaction of topological localized structures in reversed time

: The results of numerical simulation of the interaction of topological solitons (2+1)-dimensional O(3) non-linear sigma model in reversed time are presented. At the first stage, models of interactions of topological vortices are developed, where, depending on the dynamic parameters, processes of their decay into localized perturbations and phased annihilation are observed. Also, models for the phased annihilation of topological vortices during their interaction with 180-degree domain walls are considered. On the basis of the models obtained, initial conditions for numerical simulation of interaction processes in reversed time are developed. The models describing the complete restoration of the initial topological field of interacting solitons at the combination of localized perturbations and radiation waves are obtained. Also, models are obtained that describe the formation of topological vortices in the plane of the domain wall and their subsequent emission. Thus, the T-invariance property of the field-theoretic model under study is confirmed. Numerical calculations were carried out in a stratified space on the basis of methods of the theory of finite difference schemes, using the properties of a stereo-graphic projection. The experiments were carried out for different values of the Kronecker-Hopf index of topological vortices. A complex program module is proposed that implements a special algorithm for the numerical calculation of the evolution of the interaction of space-time topological structures in reversed time.
Multi-Transmitter Coded Caching with Secure Delivery over Linear Networks - Extended Version

In this paper, we consider multiple cache-enabled end-users connected to multiple transmitters through a linear network. We also prevent a totally passive eavesdropper, who sniffs the packets in the delivery phase, from obtaining any information about the original files in cache-aided networks. Three different secure centralized multi-transmitter coded caching scenarios namely, secure multi-transmitter coded caching, secure multi-transmitter coded caching with reduced subpacketization, and secure multi-transmitter coded caching with reduced feedback, are considered and closed-form coding delay and secret shared key storage expressions are provided. As our security guarantee, we show that the delivery phase does not reveal any information to the eavesdropper using the mutual information metric. Moreover, we investigate the secure decentralized multi-transmitter coded caching scenario, in which there is no cooperation between the clients and transmitters during the cache content placement phase and study its performance compared to the centralized scheme. We analyze the system's performance in terms of Coding Delay and guarantee the security of our presented schemes using the Mutual Information metric. Numerical evaluations verify that security incurs a negligible cost in terms of memory usage when the number of files and users are scaled up, in both centralized and decentralized scenarios. Also, we numerically show that by increasing the number of files and users, the secure coding delay of centralized and decentralized schemes became asymptotically equal.
Hide and Seek: Scaling Machine Learning for Combinatorial Optimization via the Probabilistic Method

Applying deep learning to solve real-life instances of hard combinatorial problems has tremendous potential. Research in this direction has focused on the Boolean satis-ﬁability (SAT) problem, both because of its theoretical centrality and practical importance. A major roadblock faced, though, is that training sets are restricted to random formulas of size several orders of magnitude smaller than formulas of practical interest, raising serious concerns about generalization. This is because labeling random formulas of increasing size rapidly becomes intractable. By exploiting the probabilistic method in a fundamental way, we remove this roadblock entirely: we show how to generate correctly labeled random formulas of any desired size, without having to solve the underlying decision problem. Moreover, the diﬃculty of the classiﬁcation task for the formulas produced by our generator is tunable by varying a simple scalar parameter. This opens up an entirely new level of sophistication for the machine learning methods that can be brought to bear on Satisﬁability. Using our generator, we train existing state-of-the-art models for the task of predicting satisﬁability on formulas with 10,000 variables. We ﬁnd that they do no better than random guessing. As a ﬁrst indication of what can be achieved with the new generator, we present a novel classiﬁer that performs signiﬁcantly better than random guessing (99%) on the same datasets, for most diﬃculty levels. Crucially, unlike past approaches that learn based on syntactic features of a formula, our classiﬁer performs its learning on a short preﬁx of a solver’s computation, an approach that we expect to be of independent interest.
ETF construction on CRIX

Investments in cryptocurrencies (CCs) remain risky due to high volatility. Exchange Traded Funds (ETFs) are a suitable tool to diversify risk and to benefit from the growth of the whole CC sector. We construct an ETF on the CRIX, the CRyptocurrency IndeX that maps the non-stationary CC dynamics closely by adapting its constituents weights dynamically. The scenario analysis considers the fee schedules of regulated CC exchanges, spreads obtained from high-frequency order book data, and models capital deposits to the ETF stochastically. The analysis yields valuable insights into the mechanisms, costs and risks of this new financial product: i) although the composition of the CRIX ETF changes frequently (from 5 to 30 constituents), it remains robust in its core, as the weights of Bitcoin (BTC) and Ethereum (ETH) are robust over time, ii) on average, a portion of 5.2% needed to be rebalanced at the rebalancing dates, iii) trading costs are low compared to traditional assets, iv) the liquidity of the CC sector has increased significantly during the analysis period, spreads occur especially for altcoins and increase by the size of the transactions. But since BTC and ETH are most affected by rebalancing, the cost of spreads remains limited.
The Information Ecosystem of Conspiracy Theory: Examining the QAnon Narrative on Facebook

There has been concern about the proliferation of the "QAnon" conspiracy theory on Facebook, but little is known about how its misleading narrative propagated on the world's largest social media platform. Thus, the present research analyzed content generated by 2,813 Facebook pages and groups that contributed to promoting the conspiracy narrative between 2017 and 2020. The result demonstrated that activities of QAnon pages and groups started a significant surge months before the 2020 U.S. Presidential Election. We found that these pages and groups increasingly relied on internal sources, i.e., Facebook accounts or their content on the platform, while their dependence on external information sources decreased continuously since 2017. It was also found that QAnon posts based on the Facebook internal sources attracted significantly more shares and comments compared with other QAnon posts. These findings suggest that QAnon pages and groups increasingly isolated themselves from sources outside Facebook while having more internal interactions within the platform, and the endogenous creation and circulation of disinformation might play a significant role in boosting the influence of the misleading narrative within Facebook. The findings imply that the efforts to tackle disinformation on social media should target not only the cross-platform infiltration of falsehood but also the intra-platform production and propagation of disinformation.
Continual Learning Beyond a Single Model

A growing body of research in continual learning focuses on the catastrophic forgetting problem. While many attempts have been made to alleviate this problem, the majority of the methods assume a single model in the continual learning setup. In this work, we question this assumption and show that employing ensemble models can be a simple yet effective method to improve continual performance. However, ensembles' training and inference costs can increase significantly as the number of models grows. Motivated by this limitation, we study different ensemble models to understand their benefits and drawbacks in continual learning scenarios. Finally, to overcome the high compute cost of ensembles, we leverage recent advances in neural network subspace to propose a computationally cheap algorithm with similar runtime to a single model yet enjoying the performance benefits of ensembles.
CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising

A mainstream type of the state of the arts (SOTAs) based on convolutional neural network (CNN) for real image denoising contains two sub-problems, i.e., noise estimation and non-blind denoising. This paper considers real noise approximated by heteroscedastic Gaussian/Poisson Gaussian distributions with in-camera signal processing pipelines. The related works always exploit the estimated noise prior via channel-wise concatenation followed by a convolutional layer with spatially sharing kernels. Due to the variable modes of noise strength and frequency details of all feature positions, this design cannot adaptively tune the corresponding denoising patterns. To address this problem, we propose a novel conditional filter in which the optimal kernels for different feature positions can be adaptively inferred by local features from the image and the noise map. Also, we bring the thought that alternatively performs noise estimation and non-blind denoising into CNN structure, which continuously updates noise prior to guide the iterative feature denoising. In addition, according to the property of heteroscedastic Gaussian distribution, a novel affine transform block is designed to predict the stationary noise component and the signal-dependent noise component. Compared with SOTAs, extensive experiments are conducted on five synthetic datasets and three real datasets, which shows the improvement of the proposed CFNet.
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning

Humans have perfected the art of learning from multiple modalities through sensory organs. Despite their impressive predictive performance on a single modality, neural networks cannot reach human level accuracy with respect to multiple modalities. This is a particularly challenging task due to variations in the structure of respective modalities. Conditional Batch Normalization (CBN) is a popular method that was proposed to learn contextual features to aid deep learning tasks. This technique uses auxiliary data to improve representational power by learning affine transformations for convolutional neural networks. Despite the boost in performance observed by using CBN layers, our work reveals that the visual features learned by introducing auxiliary data via CBN deteriorates. We perform comprehensive experiments to evaluate the brittleness of CBN networks to various datasets, suggesting that learning from visual features alone could often be superior for generalization. We evaluate CBN models on natural images for bird classification and histology images for cancer type classification. We observe that the CBN network learns close to no visual features on the bird classification dataset and partial visual features on the histology dataset. Our extensive experiments reveal that CBN may encourage shortcut learning between the auxiliary data and labels.
Measures of string similarities based on the Hamming distance

In this paper we consider measures of similarity between two sets of strings built up using the Hamming distance and tools of persistence homology as a basis. First we describe the construction of the \v Cech filtration adjoined to the set of strings, the persistence module corresponding to this filtration and its barcode structure. Using these means, we introduce a novel similarity measure for two sets of strings, based on a comparison of bars within their barcodes of the same dimension. Our idea is to look for a comparison that will take under consideration not only the overlap of bars, but also ensure that observed bars are qualitatively matched, in the sense that they represent similar homological features. To make this idea happen, we developed a method called the separation of simplex radii technique.
Nonexistence of Vortices for Rotating Two-Component Focusing Bose Gases

This paper is concerned with ground states of two-component Bose gases confined in a harmonic trap V (x) = x21 + Λ 2x22 rotating at the velocity Ω > 0, where Λ ≥ 1 and (x1, x2) ∈ R . We focus on the case where the intraspecies interaction (−a1,−a2) and the interspecies interaction −β are both attractive, i.e, a1, a2 and β are all positive. It is shown that for any 0 < Ω < Ω∗ := 2, ground states exist if and only if 0 < a1, a2 < a ∗ := ‖w‖22 and 0 < β < β∗ := a∗ + √ (a − a1)(a − a2), where w > 0 is the unique positive solution of −∆w+w −w = 0 in R. By developing the argument of refined expansions, we further prove the nonexistence of vortices for ground states as β ր β∗, where 0 < Ω < Ω∗ and 0 < a1, a2 < a ∗ are fixed.
Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats

Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many downstream tasks. However, current works on topic segmentation often focus on segmentation of structured texts. In this paper, we comprehensively analyze the generalization capabilities of state-of-the-art topic segmentation models on unstructured texts. We find that: (a) Current strategies of pre-training on a large corpus of structured text such as Wiki-727K do not help in transferability to unstructured texts. (b) Training from scratch with only a relatively small-sized dataset of the target unstructured domain improves the segmentation results by a significant margin.
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and build the classifier automatically for decision making, which have been widely used in the SEI research. Considering the insufficiently labeled training samples and large-unlabeled training samples, the semi-supervised learning-based SEI (SS-SEI) methods have been proposed. However, there are few SS-SEI methods focusing on extracting the discriminative and generalized semantic features of radio signals. In this article, we propose an SS-SEI method using metric-adversarial training (MAT). Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open-source large-scale real-world automatic-dependent surveillance–broadcast (ADS-B) data set and Wi-Fi data set and is compared with the state-of-the-art methods. The simulation results show that the proposed method achieves better identification performance than existing state-of-the-art methods. Specifically, when the ratio of the number of labeled training samples to the number of all training samples is 10%, the identification accuracy is 84.80% under the ADS-B data set and 80.70% under the Wi-Fi data set. Our code can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI.
Waveflow: Enforcing boundary conditions in smooth normalizing flows with application to fermionic wave functions

In this paper, we introduce four main novelties: First, we present a new way of handling the topology problem of normalizing flows. Second, we describe a technique to enforce certain classes of boundary conditions onto normalizing flows. Third, we introduce the I-Spline bijection, which, similar to previous work, leverages splines but, in contrast to those works, can be made arbitrarily often differentiable. And finally, we use these techniques to create Waveflow, an Ansatz for the one-space-dimensional multi-particle fermionic wave functions in real space based on normalizing flows, that can be efficiently trained with Variational Quantum Monte Carlo without the need for MCMC nor estimation of a normalization constant. To enforce the necessary anti-symmetry of fermionic wave functions, we train the normalizing flow only on the fundamental domain of the permutation group, which effectively reduces it to a boundary value problem.
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching

In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields. Different from existing works that represent deformation fields by training a general-purpose neural network, we advocate for an approximation based on mesh-free methods. By letting the network learn deformation parameters at a sparse set of positions in space (nodes), we reconstruct the continuous deformation field in a closed-form with guaranteed smoothness. With this reduction in degrees of freedom, we show significant improvement in terms of data-efficiency thus enabling limited supervision. Furthermore, our approximation provides direct access to first-order derivatives of deformation fields, which facilitates enforcing desirable regularization effectively. Our resulting model has high expressive power and is able to capture complex deformations. We illustrate its effectiveness through state-of-the-art results across multiple deformable shape matching benchmarks. Our code and data are publicly available at: https://github.com/Sentient07/DeformationBasis.
Tube-based Distributionally Robust Model Predictive Control for Nonlinear Process Systems via Linearization

Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic models are however affected by plant-model mismatch and process uncertainties, which can lead to closed-loop performance deterioration and constraint violations. Methods such as stochastic MPC (SMPC) have been proposed to alleviate these problems; however, the resulting closed-loop state trajectory might still significantly violate the prescribed constraints if the real system deviates from the assumed disturbance distributions made during the controller design. In this work we propose a novel data-driven distributionally robust MPC scheme for nonlinear systems. Unlike SMPC, which requires the exact knowledge of the disturbance distribution, our scheme decides the control action with respect to the worst distribution from a distribution ambiguity set. This ambiguity set is defined as a Wasserstein ball centered at the empirical distribution. Due to the potential model errors that cause off-sets, the scheme is also extended by leveraging an offset-free method. The favorable results of this control scheme are demonstrated and empirically verified with a nonlinear mass spring system and a nonlinear CSTR case study.
Combining quantum noise reduction resources: a practical approach

Optomechanical sensors are capable of transducing external perturbations to resolvable optical signals. A particular regime of interest is that of high-bandwidth force detection, where an impulse is delivered to the system over a short period of time. Exceedingly sensitive impulse detection has been proposed to observe very weak signals like those for long range interactions with dark matter requiring much higher sensitivities than current sensors can provide. Quantum resources to go beyond the standard quantum limit of noise in these sensors include squeezing of the light used to transduce the signal, backaction evasion by measuring the optimum quadrature, and quantum nondemolition (QND) measurements which reduce backaction directly. However, it has been extremely difficult to determine a scheme where all these quantum resources contribute to noise reduction thereby exceeding the benefit of using only one quantum resource alone. We provide the theoretical limits to noise reduction while combining quantum enhanced readout techniques such as squeezing and QND measurements for these optomechanical sensors. We demonstrate that backaction evasion through QND techniques dramatically reduces the technical challenges presented when using squeezed light for broadband force detection, paving the way for combining multiple quantum noise reduction techniques for enhanced sensitivity in the context of impulse metrology.
Causal Inference with Confounders MNAR under Treatment-independent Missingness Assumption

Causal inference in observational studies can be challenging when confounders are subject to missingness. Generally, the identification of causal effects is not guaranteed even under restrictive parametric model assumptions when confounders are missing not at random. To address this, We propose a general framework to establish the identification of causal effects when confounders are subject to treatment-independent missingness, which means that the missing data mechanism is independent of the treatment, given the outcome and possibly missing confounders. We give special consideration to commonly-used models for continuous and binary outcomes and provide counterexamples when identification fails. For estimation, we provide a weighted estimation equation estimating method for model parameters and purpose three estimators for the average causal effect based on the estimated models. We evaluate the finite-sample performance of the estimators via simulations. We further illustrate the proposed method with real data sets from the National Health and Nutrition Examination Survey.