Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Adaptation As a Problem of Inference on Graphical Models

This paper is concerned with data-driven unsupervised domain adaptation, where it is unknown in advance how the joint distribution changes across domains, i.e., what factors or modules of the data distribution remain invariant or change across domains. To develop an automated way of domain adaptation with multiple source domains, we propose to use a graphical model as a compact way to encode the change property of the joint distribution, which can be learned from data, and then view domain adaptation as a problem of Bayesian inference on the graphical models. Such a graphical model distinguishes between constant and varied modules of the distribution and specifies the properties of the changes across domains, which serves as prior knowledge of the changing modules for the purpose of deriving the posterior of the target variable $Y$ in the target domain. This provides an end-to-end framework of domain adaptation, in which additional knowledge about how the joint distribution changes, if available, can be directly incorporated to improve the graphical representation. We discuss how causality-based domain adaptation can be put under this umbrella. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed framework for domain adaptation. The code is available at this https URL .
DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. The resulting symbol detector is based on integrating dedicated machine-learning methods into the iterative SIC algorithm. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed learning-based MIMO receivers. Furthermore, in the presence of CSI uncertainty, DeepSIC significantly outperforms model-based approaches. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.
On the Insecurities of Mobile D2D File Sharing Applications

With more than 1.3 Billion in the cumulative number of downloads reported, the top six applications compete in the niche of Wi-Fi Direct D2D file sharing on Android. With the highest userbase in India and Indonesia, ShareIT itself reports the number of active users of their application on desktop and mobile devices exceeding 1.8 billion, ranked top 7 globally by the number of downloads on Google Play and Apple App Store in 2018. Wi-Fi Direct, also known as Wi-Fi P2P, is commonly used for peer-to-peer, high-speed file transfer between mobile devices, as well as a close proximity connection mode for wireless cameras, network printers, TVs and other IoT and mobile devices. For its end users, such type of direct file transfer does not result in cellular data charges and allows to keep their primary Wi-Fi interface up concurrently with a dedicated Wi-Fi P2P interface, which is commonly provided by the default wireless module of the mobile phone. However, despite the popularity of these solutions demonstrated by Google Play download statistics, we observe that the software vendors tend to prioritize the ease of user flow over the security of the implementation, introducing serious security flaws. We perform a comprehensive security analysis in the context of security and usability behind the identified flaws and report our findings in the form of 16 Common Vulnerabilities and Exposures (CVE), disclosed to the corresponding vendors. To address the similar flaws at the early stage of the application design, we propose a joint consideration of Security and Usability for such applications and their protocols that can be visualized in form of a User Journey Map (UJM).
Exploring the Memorization-Generalization Continuum in Deep Learning

Human learners appreciate that some facts demand memorization whereas other facts support generalization. For example, English verbs have irregular cases that must be memorized (e.g., go → went) and regular cases that generalize well (e.g., kiss → kissed, miss → missed). Likewise, deep neural networks have the capacity to memorize rare or irregular forms but nonetheless generalize across instances that share common patterns or structures. We analyze how individual instances are treated by a model on the memorization-generalization continuum via a consistency score . The score is the expected accuracy of a particular architecture for a held-out instance on a training set of a ﬁxed size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple data sets, and we show that the score identiﬁes out-of-distribution and mislabeled examples at one end of the continuum and regular examples at the other end. We explore three proxies to the consistency score: kernel density estimation on input and hidden representations; and the time course of training, i.e., learning speed. In addition to helping to understand the memorization versus generalization dynamics during training, the C-score proxies have potential application for out-of-distribution detection, curriculum learning, and active data collection.
Edge colouring Game on Trees with maximum degree Δ=4

Consider the following game. We are given a tree $T$ and two players (say) Alice and Bob who alternately colour an edge of a tree (using one of $k$ colours). If all edges of the tree get coloured, then Alice wins else Bob wins. Game chromatic index of trees of is the smallest index $k$ for which there is a winning strategy for Alice. If the maximum degree of a node in tree is $\Delta$, Erdos this http URL. [6], show that the game chromatic index is at least $\Delta+1$. The bound is known to be tight for all values of $\Delta\neq 4$. In this paper we show that for $\Delta=4$, even if Bob is allowed to skip a move, Alice can always choose an edge to colour and win the game for $k=\Delta+1$. Thus the game chromatic index of trees of maximum degree $4$ is also $5$. Hence, game chromatic index of trees of maximum degree $\Delta$ is $\Delta+1$ for all $\Delta\geq 2$. Moreover,the tree can be preprocessed to allow Alice to pick the next edge to colour in $O(1)$ time. A result of independent interest is a linear time algorithm for on-line edge-deletion problem on trees.
Higher-order exceptional points in ferromagnetic trilayers

Magnetometers with exceptional sensitivity are highly demanded in solving a variety of physical and engineering problems, such as measuring Earth's weak magnetic fields and prospecting mineral deposits and geological structures. It has been shown that the non-Hermitian degeneracy at exceptional points (EPs) can provide a new route for that purpose, because of the nonlinear response to external perturbations. One recent work [H. Yang et al. Phys. Rev. Lett. 121, 197201 (2018)] has made the first step to realize the second-order magnonic EP in ferromagnetic bilayers respecting the parity-time symmetry. In this paper, we generalize the idea to higher-order cases by considering ferromagnetic trilayers consisting of a gain, a neutral, and a (balanced-)loss layer. We observe both second- and third-order magnonic EPs by tuning the interlayer coupling strength, the external magnetic field, and the gain-loss parameter. We show that the magnetic sensitivity can be enhanced by three orders of magnitude comparing to the conventional magnetic tunneling junction-based sensors. Our results pave the way for studying high-order EPs in purely magnetic system and for designing magnetic sensors with ultrahigh sensitivity.
On Contrastive Learning for Likelihood-free Inference

Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sampled from some reference distribution, which implicitly learns a density ratio proportional to the likelihood. Another popular class of methods fits a conditional distribution to the parameter posterior directly, and a particular recent variant allows for the use of flexible neural density estimators for this task. In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be run and compared.
Efficient ECM factorization in parallel with the lyness map

The Lyness map is a birational map in the plane which provides one of the simplest discrete analogues of a Hamiltonian system with one degree of freedom, having a conserved quantity and an invariant symplectic form. As an example of a symmetric Quispel-Roberts-Thompson (QRT) map, each generic orbit of the Lyness map lies on a curve of genus one, and corresponds to a sequence of points on an elliptic curve which is one of the fibres in a pencil of biquadratic curves in the plane. Here we present a version of the elliptic curve method (ECM) for integer factorization, which is based on iteration of the Lyness map with a particular choice of initial data. More precisely, we give an algorithm for scalar multiplication of a point on an arbitrary elliptic curve over Q, which is represented by one of the curves in the Lyness pencil. In order to avoid field inversion (I), and require only field multiplication (M), squaring (S) and addition, projective coordinates in P1 ×P1 are used. Neglecting multiplication by curve constants (assumed small), each addition of the chosen point uses 2M, while each doubling step requires 15M. We further show that the doubling step can be implemented efficiently in parallel with four processors, dropping the effective cost to 4M. In contrast, the fastest algorithms in the literature use twisted Edwards curves (equivalent to Montgomery curves), which correspond to a subset of all elliptic curves. Scalar muliplication on twisted Edwards curves with suitable small curve constants uses 8M for point addition and 4M+4S for point doubling, both of which can be run in parallel with four processors to yield effective costs of 2M and 1M + 1S, respectively. Thus our scalar multiplication algorithm should require, on average, roughly twice as many multiplications per bit as state of the art methods using twisted Edwards curves. In our conclusions, we discuss applications where the use of Lyness curves may provide potential advantages.
ABBA: Saliency-Regularized Motion-Based Adversarial Blur Attack

Deep neural networks are vulnerable to noise-based adversarial examples, which can mislead the networks by adding random-like noise. However, such examples are hardly found in the real world and easily perceived when thumping noises are used to keep their high transferability across different models. In this paper, we identify a new attacking method termed motion-based adversarial blur attack (ABBA) that can generate visually natural motion-blurred adversarial examples even with relatively high perturbation, allowing much better transferability than noise-based methods. To this end, we first formulate the kernel-prediction-based attack where an input image is convolved with kernels in a pixel-wise way, and the misclassification capability is achieved by tuning the kernel weights. To generate visually more natural and plausible examples, we further propose the saliency-regularized adversarial kernel prediction where the salient region serves as a moving object, and the predicted kernel is regularized to achieve naturally visual effects. Besides, the attack can be further enhanced by adaptively tuning the translations of object and background. Extensive experimental results on the NeurIPS'17 adversarial competition dataset validate the effectiveness of ABBA by considering various kernel sizes, translations, and regions. Furthermore, we study the effects of state-of-the-art GAN-based deblurring mechanisms to our methods.
Tuning of multivariable model predictive controllers through expert bandit feedback

For certain industrial control applications an explicit function capturing the non-trivial trade-off between competing objectives in closed loop performance is not available. In such scenarios it is common practice to use the human innate ability to implicitly learn such a relationship and manually tune the corresponding controller to achieve the desirable closed loop performance. This approach has its deficiencies because of individual variations due to experience levels and preferences in the absence of an explicit calibration metric. Moreover, as the complexity of the underlying system and/or the controller increase, in the effort to achieve better performance, so does the tuning time and the associated tuning cost. To reduce the overall tuning cost, a tuning framework is proposed herein, whereby a supervised machine learning is used to extract the human-learned cost function and an optimisation algorithm that can efficiently deal with a large number of variables, is used for optimising the extracted cost function. Given the interest in the implementation across many industrial domains and the associated high degree of freedom present in the corresponding tuning process, a Model Predictive Controller applied to air path control in a diesel engine is tuned for the purpose of demonstrating the potential of the framework.
Projective Preferential Bayesian Optimization

Bayesian optimization is an effective method for finding extrema of a black-box function. We propose a new type of Bayesian optimization for learning user preferences in high-dimensional spaces. The central assumption is that the underlying objective function cannot be evaluated directly, but instead a minimizer along a projection can be queried, which we call a projective preferential query. The form of the query allows for feedback that is natural for a human to give, and which enables interaction. This is demonstrated in a user experiment in which the user feedback comes in the form of optimal position and orientation of a molecule adsorbing to a surface. We demonstrate that our framework is able to find a global minimum of a high-dimensional black-box function, which is an infeasible task for existing preferential Bayesian optimization frameworks that are based on pairwise comparisons.
Corrected score methods for estimating Bayesian networks with error‐prone nodes

Motivated by inferring cellular signaling networks using noisy flow cytometry data, we develop procedures to draw inference for Bayesian networks based on error‐prone data. Two methods for inferring causal relationships between nodes in a network are proposed based on penalized estimation methods that account for measurement error and encourage sparsity. We discuss consistency of the proposed network estimators and develop an approach for selecting the tuning parameter in the penalized estimation methods. Empirical studies are carried out to compare the proposed methods with a naive method that ignores measurement error. Finally, we apply these methods to infer signaling networks using single cell flow cytometry data.
A Calder\'on type inverse problem for quantum trees

We solve the inverse problem of recovering a metric tree from the knowledge of the Dirichlet-to-Neumann matrix on the tree's boundary corresponding to the Laplacian with standard vertex conditions. This result can be viewed as a counterpart of the Calderon problem in the analysis of PDEs; in contrast to earlier results for quantum graphs, we only assume knowledge of the Dirichlet-to-Neumann matrix for a fixed energy, not of a whole matrix-valued function. The proof is based on tracing back the problem to an inverse problem for the Schur complement of the discrete Laplacian on an associated weighted tree. In addition, we provide examples which show that several possible generalizations of this result, e.g. to graphs with cycles, fail.
Localization transition on the Random Regular Graph as an unstable tricritical point in a log-normal Rosenzweig-Porter random matrix ensemble

Gaussian Rosenzweig-Porter (GRP) random matrix ensemble is the only one in which the robust multifractal phase and ergodic transition have a status of a mathematical theorem. Yet, this phase in GRP model is oversimplified: the spectrum of fractal dimensions is degenerate and the mini-band in the local spectrum is not multifractal. In this paper we suggest an extension of the GRP model by adopting a logarithmically-normal (LN) distribution of off-diagonal matrix elements. A family of such LN-RP models is parametrized by a symmetry parameter $p$ and it interpolates between the GRP at $p\rightarrow 0$ and Levy ensembles at $p\rightarrow\infty$. A special point $p=1$ is shown to be the simplest approximation to the Anderson localization model on a random regular graph.We study in detail the phase diagram of LN-RP model and show that $p=1$ is a tricritical point where the multifractal phase first collapses. This collapse is shown to be unstable with respect to the truncation of the log-normal distribution. We suggest a new criteria of stability of the non-ergodic phases and prove that the Anderson transition in LN-RP model is discontinuous at all $p>0$.
End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement

In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency. In particular, the extracted features are compactly coded in an end-to-end manner by optimizing the rate-distortion cost to achieve feature-in-feature representation. In order to further improve the compression performance, we present a latent code level teacher-student enhancement model, which could efficiently transfer the low bit-rate representation into a high bit rate one. Such a strategy further allows us to adaptively shift the representation cost to decoding computations, leading to more flexible feature compression with enhanced decoding capability. We verify the effectiveness of the proposed model with the facial feature, and experimental results reveal better compression performance in terms of rate-accuracy compared with existing models.
Comparative intranight optical variability of X-ray and γ-ray-detected narrow-line Seyfert 1 galaxies

In a systematic program to characterize the intranight optical variability (INOV) of different classes of narrow-line Seyfert 1 (NLSy1s) galaxies, we report here the first comparative INOV study of NLSy1 sets detected in the X-ray and γ-ray bands. Our sample consists of 18 sources detected in X-rays but not in γ-rays (hereafter x_NLSy1s) and seven sources detected in γ-rays (hereafter g_NLSy1s), out of which five are detected also in X-rays. We have monitored these two sets of NLSy1s, respectively, in 24 and 21 sessions of a minimum of 3-h duration each. The INOV duty cycles for these two sets are found to be 12 per cent and 53 per cent, respectively (at a 99 per cent confidence level). In the set of 18 x_NLSy1s, INOV duty cycle is found to be zero for the 13 radio-quiet members (monitored in 14 sessions) and 43 per cent for the five radio-loud members (10 sessions). The latter is very similar to the aforementioned duty cycle of 53 per cent found here for the set of g_NLSy1s (all of which are radio-loud). Thus, it appears that the radio-loudness level is the prime factor behind the INOV detection and the pattern of the high-energy radiation plays only a minor role.
ML-misfit: Learning a robust misfit function for full-waveform inversion using machine learning

Most of the available advanced misfit functions for full waveform inversion (FWI) are hand-crafted, and the performance of those misfit functions is data-dependent. Thus, we propose to learn a misfit function for FWI, entitled ML-misfit, based on machine learning. Inspired by the optimal transport of the matching filter misfit, we design a neural network (NN) architecture for the misfit function in a form similar to comparing the mean and variance for two distributions. To guarantee the resulting learned misfit is a metric, we accommodate the symmetry of the misfit with respect to its input and a Hinge loss regularization term in a meta-loss function to satisfy the "triangle inequality" rule. In the framework of meta-learning, we train the network by running FWI to invert for randomly generated velocity models and update the parameters of the NN by minimizing the meta-loss, which is defined as accumulated difference between the true and inverted models. We first illustrate the basic principle of the ML-misfit for learning a convex misfit function for travel-time shifted signals. Further, we train the NN on 2D horizontally layered models, and we demonstrate the effectiveness and robustness of the learned ML-misfit by applying it to the well-known Marmousi model.
Modeling Musical Onset Probabilities via Neural Distribution Learning

Musical onset detection can be formulated as a time-to-event (TTE) or time-since-event (TSE) prediction task by defining music as a sequence of onset events. Here we propose a novel method to model the probability of onsets by introducing a sequential density prediction model. The proposed model estimates TTE & TSE distributions from mel-spectrograms using convolutional neural networks (CNNs) as a density predictor. We evaluate our model on the Bock dataset show-ing comparable results to previous deep-learning models.
First and second order optimality conditions for the control of Fokker-Planck equations

In this article we study an optimal control problem subject to the Fokker-Planck equation ∂tρ − ν∆ρ − div(ρB[u]) = 0 The control variable u is time-dependent and possibly multidimensional, and the function B depends on the space variable and the control. The cost functional is of tracking type and includes a quadratic regularization term on the control. For this problem, we prove existence of optimal controls and first order necessary conditions. Main emphasis is placed on second order necessary and sufficient conditions.