Miracle-dz/UniFAR-FTUs huggingface dataset:
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector

—Unsupervised visual anomaly detection conveys practical signiﬁcance in many scenarios and is a challenging task due to the unbounded deﬁnition of anomalies. Moreover, most previous methods are application-speciﬁc, and establishing a uniﬁed model for anomalies across application scenarios remains unsolved. This paper proposes a novel hybrid framework termed Siamese Transition Masked Autoencoders(ST-MAE) to handle various visual anomaly detection tasks uniformly via deep feature transition. Concretely, the proposed method ﬁrst extracts hierar- chical semantics features from a pre-trained deep convolutional neural network and then develops a feature decoupling strategy to split the deep features into two disjoint feature patch subsets. Leveraging the decoupled features, the ST-MAE is developed with the Siamese encoders that operate on each subset of feature patches and perform the latent representations transition of two subsets, along with a lightweight decoder that reconstructs the original feature from the transitioned latent representation. Finally, the anomalous attributes can be detected using the semantic deep feature residual. Our deep feature transition scheme yields a nontrivial and semantic self-supervisory task to extract prototypical normal patterns, which allows for learning uniform models that generalize well for different visual anomaly detection tasks. The extensive experiments conducted demon- strate that the proposed ST-MAE method can advance state-of-the-art performance on multiple benchmarks across application scenarios with a superior inference efﬁciency, which exhibits great potential to be the uniform model for unsupervised visual anomaly detection.
Algebra of N-event synchronization

We have previously defined synchronization (Gomez, E. and K. Schubert 2011) as a relation between the times at which a pair of events can happen, and introduced an algebra that covers all possible relations for such pairs. In this work we introduce the synchronization matrix, to make it easier to calculate the properties and results of $N$ event synchronizations, such as are commonly encountered in parallel execution of multiple processes. The synchronization matrix leads to the definition of N-event synchronization algebras as specific extensions to the original algebra. We derive general properties of such synchronization, and we are able to analyze effects of synchronization on the phase space of parallel execution introduced in (Gomez E Kai R, Schubert KE 2017)
Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks

Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to Ethereum network being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in ﬁnancial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning (QML) algorithms such as Quantum Support Vector Machine (QSVM) and Variational Quantum Classiﬁer (VQC). With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score as well as phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classiﬁcation task. All codes are available as tutorial notebooks: https://github.com/anupamaray/EnsembleQML_application along with the data required to reproduce the results reported.
Hydrostatic pressure effects on the structural and electronic properties of carbon nanotubes

We study the structural and electronic properties of isolated single‐wall carbon nanotubes (SWNTs) under hydrostatic pressure using a combination of theoretical techniques: Continuum elasticity models, classical molecular dynamics simulations, tight‐binding electronic structure methods, and first‐principles total energy calculations within the density‐functional and pseudopotential frameworks. For pressures below a certain critical pressure Pc, the SWNTs' structure remains cylindrical and the Kohn–Sham energy gaps of semiconducting SWNTs have either positive or negative pressure coefficients depending on the value of (n, m), with a distinct “family” (of the same n–m) behavior. The diameter and chirality dependence of the pressure coefficients can be described by a simple analytical expression. At Pc, molecular‐dynamics simulations predict that isolated SWNTs undergo a pressure‐induced symmetry‐breaking transformation from a cylindrical shape to a collapsed geometry. This transition is described by a simple elastic model as arising from the competition between the bond‐bending and PV terms in the enthalpy. The good agreement between calculated and experimental values of Pc provides a strong support to the “collapse” interpretation of the experimental transitions in bundles. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Vector fields on non-compact manifolds

Let $M$ be a non-compact connected manifold with a cocompact and properly discontinuous action of a discrete group $G$. We establish a Poincar\'{e}-Hopf theorem for a bounded vector field on $M$ satisfying a mild condition on zeros. As an application, we show that such a vector field must have infinitely many zeros whenever $G$ is amenable and the Euler characteristic of $M/G$ is non-zero.
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach

Bayesian optimization (BO) has demonstrated potential for optimizing control performance in data-limited settings, especially for systems with unknown dynamics or unmodeled performance objectives. The BO algorithm efficiently trades-off exploration and exploitation by leveraging uncertainty estimates using surrogate models. These surrogates are usually learned using data collected from the target dynamical system to be optimized. Intuitively, the convergence rate of BO is better for surrogate models that can accurately predict the target system performance. In classical BO, initial surrogate models are constructed using very limited data points, and therefore rarely yield accurate predictions of system performance. In this paper, we propose the use of meta-learning to generate an initial surrogate model based on data collected from performance optimization tasks performed on a variety of systems that are different to the target system. To this end, we employ deep kernel networks (DKNs) which are simple to train and which comprise encoded Gaussian process models that integrate seamlessly with classical BO. The effectiveness of our proposed DKN-BO approach for speeding up control system performance optimization is demonstrated using a well-studied nonlinear system with unknown dynamics and an unmodeled performance function.
A robust estimator of mutual information for deep learning interpretability

We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.
Recurrent Neural Networks and Universal Approximation of Bayesian Filters

We consider the Bayesian optimal filtering problem: i.e. estimating some conditional statistics of a latent time-series signal from an observation sequence. Classical approaches often rely on the use of assumed or estimated transition and observation models. Instead, we formulate a generic recurrent neural network framework and seek to learn directly a recursive mapping from observational inputs to the desired estimator statistics. The main focus of this article is the approximation capabilities of this framework. We provide approximation error bounds for filtering in general non-compact domains. We also consider strong time-uniform approximation error bounds that guarantee good long-time performance. We discuss and illustrate a number of practical concerns and implications of these results.
CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion

In this letter, we present a method for integrating central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework to produce robust and omnidirectional quadruped locomotion. The agent learns to directly modulate the intrinsic oscillator setpoints (amplitude and frequency) and coordinate rhythmic behavior among different oscillators. This approach also allows the use of DRL to explore questions related to neuroscience, namely the role of descending pathways, interoscillator couplings, and sensory feedback in gait generation. We train our policies in simulation and perform a sim-to-real transfer to the Unitree A1 quadruped, where we observe robust behavior to disturbances unseen during training, most notably to a dynamically added 13.75 kg load representing 115% of the nominal quadruped mass. We test several different observation spaces based on proprioceptive sensing and show that our framework is deployable with no domain randomization and very little feedback, where along with the oscillator states, it is possible to provide only contact booleans in the observation space.
An Analysis of Degenerating Speech Due to Progressive Dysarthria on ASR Performance

Although personalized automatic speech recognition (ASR) models have recently been improved to recognize even severely impaired speech, model performance may degrade over time for persons with degenerating speech. The aims of this study were to (1) analyze the change of performance of ASR over time in individuals with degrading speech, and (2) explore mitigation strategies to optimize recognition throughout disease progression. Speech was recorded by four individuals with degrading speech due to amyotrophic lateral sclerosis (ALS). Word error rates (WER) across recording sessions were computed for three ASR models: Unadapted Speaker Independent (U-SI), Adapted Speaker Independent (A-SI), and Adapted Speaker Dependent (A-SD or personalized). The performance of all models degraded significantly over time as speech became more impaired, but the A-SD model improved markedly when updated with recordings from the severe stages of speech progression. Recording additional utterances early in the disease before significant speech degradation did not improve the performance of A-SD models. This emphasizes the importance of continuous recording (and model retraining) when providing personalized models for individuals with progressive speech impairments.
Text-Only Training for Image Captioning using Noise-Injected CLIP

We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images. Our approach relies on the fact that CLIP is trained to make visual and textual embeddings similar. Therefore, we only need to learn how to translate CLIP textual embeddings back into text, and we can learn how to do this by learning a decoder for the frozen CLIP text encoder using only text. We argue that this intuition is"almost correct"because of a gap between the embedding spaces, and propose to rectify this via noise injection during training. We demonstrate the effectiveness of our approach by showing SOTA zero-shot image captioning across four benchmarks, including style transfer. Code, data, and models are available on GitHub.
Exact order, gap and counting statistics of a Brownian gas correlated by resetting

We study a one-dimensional gas of N Brownian particles that diﬀuse independently, but are simultaneously reset to the origin at a constant rate r . The system approaches a non-equilibrium stationary state (NESS) with long-range interactions induced by the simultaneous resetting. Despite the presence of strong correlations, we show that several observables can be computed exactly, which include the global average density, the distribution of the position of the rightmost particle, the spacing distribution between two successive particles and the full counting statistics, i.e., the distribution of the number of particles in a given interval. Our analytical results are conﬁrmed by numerical simulations. We also discuss a possible experimental realisation of this resetting gas using optical traps.
Analysis Without Data: Teaching Students to Tackle the VAST Challenge

The VAST Challenges have been shown to be an effective tool in visual analytics education, encouraging student learning while enforcing good visualization design and development practices. However, research has observed that students often struggle at identifying a good"starting point"when tackling the VAST Challenge. Consequently, students who could not identify a good starting point failed at finding the correct solution to the challenge. In this paper, we propose a preliminary guideline for helping students approach the VAST Challenge and identify initial analysis directions. We recruited two students to analyze the VAST 2017 Challenge using a hypothesis-driven approach, where they were required to pre-register their hypotheses prior to inspecting and analyzing the full dataset. From their experience, we developed a prescriptive guideline for other students to tackle VAST Challenges. In a preliminary study, we found that the students were able to use the guideline to generate well-formed hypotheses that could lead them towards solving the challenge. Additionally, the students reported that with the guideline, they felt like they had concrete steps that they could follow, thereby alleviating the burden of identifying a good starting point in their analysis process.
Contextual Mixture of Experts: Integrating Knowledge into Predictive Modeling

This article proposes a new data-driven model devised to integrate process knowledge into its structure to increase the human–machine synergy in the process industry. The proposed contextual mixture of experts (cMoE) explicitly uses process knowledge along the model learning stage to mold the historical data to represent operators' context related to the process through possibility distributions. This model was evaluated in two real case studies for quality prediction, including a sulfur recovery unit and a polymerization process. The cMoEs was employed to represent different contexts in both experiments. The results indicate that integrating process knowledge has increased the predictive performance while improving interpretability by providing insights into the variables affecting the process's different regimes.
Phase Space Models of the Dwarf Spheroidal Galaxies

This paper introduces new phase-space models of dwarf spheroidal galaxies (dSphs). The stellar component has an isotropic, lowered isothermal (or King) distribution function. A physical basis for the isotropization of stellar velocities is given by tidal stirring, whilst the isothermality of the distribution function guarantees the observed flatness of the velocity dispersion profile in the inner parts. Our models reproduce the data on the half-light radius and line of sight central velocity dispersion of the dSphs. We show that different dark halo profiles -- whether cored or cusped -- lead to very similar mass estimates within one particular radius, namely 1.7 half-light radii. Deviations between mass measures due to different density profiles are substantially smaller than the uncertainties propagated by the observational errors. We produce a mass measure for each of the Milky Way dSphs and find that the two most massive are the most luminous, namely Sagittarius (~ 2.8 x 10^8 solar masses) and Fornax (~ 1.3 x 10^8 solar masses). The least massive of the Milky Way satellites are Willman 1 (~ 4 x 10^5 solar masses) and Segue 1 (~ 6 x 10^5 solar masses).
Iterative solution of the electromagnetic inverse scattering problem from the transient scattered field

This paper deals with an iterative approach to solve the electromagnetic inverse scattering problem from single view transient data. The measurement configuration consists in illuminating a two‐dimensional scattering system with an electromagnetic transient source and in measuring the time domain response all around the investigated region. The aim is then to determine the electromagnetic properties of the target from the measurements. The problem is formulated in the frequency domain for a large number of frequencies, such that the incident pulse is accurately sampled, rather than in the time domain. The parameters of interest, namely, the relative permittivity and the conductivity profiles, are built up iteratively by minimizing a cost functional involving the discrepancy between the measured scattered fields and those that would be obtained via a forward model. Numerical examples are presented to prove the efficiency of the suggested method and its robustness against noise. The influence of the central frequency and of the bandwidth of the incident field on the achievable performances is also illustrated.
Batch Active Learning from the Perspective of Sparse Approximation

Active learning enables efﬁcient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation’s perspective. Our active learning method aims to ﬁnd an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart. We realize the framework as sparsity-constrained discontinuous optimization problems, which explicitly balance uncertainty and representation for large-scale applications and could be solved by greedy or proximal iterative hard thresholding algorithms. The proposed method can adapt to various settings, including both Bayesian and non-Bayesian neural networks. Numerical experiments show that our work achieves competitive performance across different settings with lower computational complexity.
SCA: Streaming Cross-Attention Alignment For Echo Cancellation

End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art methods for echo cancellation are either classical DSP-based or hybrid DSP-ML algorithms. Components such as the delay estimator and adaptive linear filter are based on traditional signal processing concepts, and deep learning algorithms typically only serve to replace the non-linear residual echo suppressor. This paper introduces an end-to-end echo cancellation network with a streaming cross-attention alignment (SCA). Our proposed method can handle unaligned inputs without requiring external alignment and generate high-quality speech without echoes. At the same time, the end-to-end algorithm simplifies the current echo cancellation pipeline for time-variant echo path cases. We test our proposed method on the ICASSP2022 and Inter-speech2021 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the other hybrid and end-to-end methods.
Detection of (Hidden) Emotions from Videos using Muscles Movements and Face Manifold Embedding

We provide a new non-invasive, easy-to-scale for large amounts of subjects and a remotely accessible method for (hidden) emotion detection from videos of human faces. Our approach combines face manifold detection for accurate location of the face in the video with local face manifold embedding to create a common domain for the measurements of muscle micro-movements that is invariant to the movement of the subject in the video. In the next step, we employ the Digital Image Speckle Correlation (DISC) and the optical ﬂow algorithm to compute the pattern of micro-movements in the face. The corresponding vector ﬁeld is mapped back to the original space and superimposed on the original frames of the videos. Hence, the resulting videos include additional information about the direction of the movement of the muscles in the face. We take the publicly available CK++ dataset of visible emotions and add to it videos of the same format but with hidden emotions. We process all the videos using our micro-movement detection and use the results to train a state-of-the-art network for emotions classiﬁcation from videos—Frame Attention Network (FAN) from [1]. Although the original FAN model achieves very high out-of-sample performance on the original CK++ videos, it does not perform so well on hidden emotions videos. The performance improves signiﬁcantly when the model is trained and tested on videos with the vector ﬁelds of muscle movements. Intuitively, the corresponding arrows serve as edges in the image that are easily captured by the convolutions ﬁlters in the FAN network.
Finding the ground state of a lattice gauge theory with fermionic tensor networks: a $2+1d$ $\mathbb{Z}_2$ demonstration

Tensor network states, and in particular Projected Entangled Pair States (PEPS) have been a strong ansatz for the variational study of complicated quantum many-body systems, thanks to their built-in entanglement entropy area law. In this work, we use a special kind of PEPS - Gauged Gaussian Fermionic PEPS (GGFPEPS) to find the ground state of $2+1d$ dimensional pure $\mathbb{Z}_2$ lattice gauge theories for a wide range of coupling constants. We do so by combining PEPS methods with Monte-Carlo computations, allowing for efficient contraction of the PEPS and computation of correlation functions. Previously, such numerical computations involved the calculation of the Pfaffian of a matrix scaling with the system size, forming a severe bottleneck; in this work we show how to overcome this problem. This paves the way for applying the method we propose and benchmark here to other gauge groups, higher dimensions, and models with fermionic matter, in an efficient, sign-problem-free way.