Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization

This paper proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algorithm generalizes different algorithms by defining the robust criterion and sparsity-aware penalty. Furthermore, by alternating optimization of the parameters (AOP) of the algorithm, including the step-size and the sparsity penalty weight, we develop the AOP-SA-RNSAF algorithm, which not only exhibits fast convergence but also obtains low steady-state misadjustment for sparse systems. Simulations in various noise scenarios have verified that the proposed AOP-SA-RNSAF algorithm outperforms existing techniques.
Nearly optimal resolution estimate for the two-dimensional super-resolution and a new algorithm for direction of arrival estimation with uniform rectangular array

In this paper, we develop a new technique to obtain nearly optimal estimates of the computational resolution limits introduced in Appl. Comput. Harmon. Anal. 56 (2022) 402-446; IEEE Trans. Inf. Theory 67(7) (2021) 4812-4827; Inverse Probl. 37(10) (2021) 104001 for two-dimensional super-resolution problems. Our main contributions are fivefold: (i) Our work improves the resolution estimate for number detection and location recovery in two-dimensional super-resolution problems to nearly optimal; (ii) As a consequence, we derive a stability result for a sparsity-promoting algorithm in two-dimensional super-resolution problems (or Direction of Arrival problems (DOA)). The stability result exhibits the optimal performance of sparsity promoting in solving such problems; (iii) Our techniques pave the way for improving the estimate for resolution limits in higher-dimensional super-resolutions to nearly optimal; (iv) Inspired by these new techniques, we propose a new coordinate-combination-based model order detection algorithm for two-dimensional DOA estimation and theoretically demonstrate its optimal performance, and (v) we also propose a new coordinate-combination-based MUSIC algorithm for super-resolving sources in two-dimensional DOA estimation. It has excellent performance and enjoys many advantages compared to the conventional DOA algorithms. The coordinate-combination idea seems to be a promising way for multi-dimensional DOA estimation.
PathologyBERT - Pre-trained Vs. A New Transformer Language Model for Pathology Domain

Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pathology database can play a critical role to advance 'big data' cancer research like similarity-based treatment selection, case identification, prognostication, surveillance, clinical trial screening, risk stratification, and many others. While there is a growing interest in developing language models for more specific clinical domains, no pathology-specific language space exist to support the rapid data-mining development in pathology space. In literature, a few approaches fine-tuned general transformer models on specialized corpora while maintaining the original tokenizer, but in fields requiring specialized terminology, these models often fail to perform adequately. We propose PathologyBERT - a pre-trained masked language model which was trained on 347,173 histopathology specimen reports and publicly released in the Huggingface1 repository2. Our comprehensive experiments demonstrate that pre-training of transformer model on pathology corpora yields performance improvements on Natural Language Understanding (NLU) and Breast Cancer Diagnose Classification when compared to nonspecific language models.
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech Synthesis

Style transfer for out-of-domain (OOD) speech synthesis aims to generate speech samples with unseen style (e.g., speaker identity, emotion, and prosody) derived from an acoustic reference, while facing the following challenges: 1) The highly dynamic style features in expressive voice are difficult to model and transfer; and 2) the TTS models should be robust enough to handle diverse OOD conditions that differ from the source data. This paper proposes GenerSpeech, a text-to-speech model towards high-fidelity zero-shot style transfer of OOD custom voice. GenerSpeech decomposes the speech variation into the style-agnostic and style-specific parts by introducing two components: 1) a multi-level style adaptor to efficiently model a large range of style conditions, including global speaker and emotion characteristics, and the local (utterance, phoneme, and word-level) fine-grained prosodic representations; and 2) a generalizable content adaptor with Mix-Style Layer Normalization to eliminate style information in the linguistic content representation and thus improve model generalization. Our evaluations on zero-shot style transfer demonstrate that GenerSpeech surpasses the state-of-the-art models in terms of audio quality and style similarity. The extension studies to adaptive style transfer further show that GenerSpeech performs robustly in the few-shot data setting. Audio samples are available at https://GenerSpeech.github. io/
Evaluation of Femtosecond Time-bin Qubits Using Frequency Up-conversion Technique

Time-bin qubits, in which quantum information is encoded in a single photon at diﬀerent times ∆ t , are suitable for long-distance transmission via optical ﬁbers. However, detection of time-bin qubits has been limited to the nanosecond range owing to the limited temporal resolution of single-photon detectors. In this study, we developed an up-conversion single-photon detector (UCSPD), using commercial nonlinear crystals of diﬀerent lengths. By changing the crystal length and pump power, we quantitatively evaluated the eﬃciency and temporal resolution of the UCSPD and determined the optimal conditions for measuring femtosecond time-bin qubits. This detector achieved a temporal resolution of 415 fs and up-conversion eﬃciency of 10.1 %. Consequently, we successfully evaluated single-photon level pseudo femtosecond time-bin qubits with a pulse interval of only 800 fs.
GraphHD: Efficient graph classification using hyperdimensional computing

Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic representation of information to achieve a good balance between accuracy, efficiency and robustness. HDC models have already been proven to be useful in different learning applications, especially in resource-limited settings such as the increasingly popular Internet of Things (IoT). One class of learning tasks that is missing from the current body of work on HDC is graph classification. Graphs are among the most important forms of information representation, yet, to this day, HDC algorithms have not been applied to the graph learning problem in a general sense. Moreover, graph learning in IoT and sensor networks, with limited compute capabilities, introduce challenges to the overall design methodology. In this paper, we present GraphHD - a baseline approach for graph classification with HDC. We evaluate GraphHD on real-world graph classification problems. Our results show that when compared to the state-of-the-art Graph Neural Networks (GNNs) the proposed model achieves comparable accuracy, while training and inference times are on average $14.6\times$ and $2.0 \times$ faster, respectively.
PSS/E——an Advanced Software Package for Power System Analysis and Simulation

PSS/E—an software package for power system an alysis and simulation, released by PTI, is introduced in this paper. Its fundame ntal functions and some advanced applications are briefly reviewed. At last, two cases are simulated by PSS/E. The simulation results show that the models provi ded by PSS/E are reliable.
Design optimisation of piezoelectric energy harvesters for bridge infrastructure

Vibrational energy harvested from the bridge excitation due to the traffic flow or the wind load can be supplied to sensors in a Structural Health Monitoring (SHM) system and help prolong its service life, reduce chemical battery waste and enable its use in remote locations. A common approach to designing a Piezoelectric Energy Harvester (PEH) consists in tuning its fundamental frequency to some target value (e.g., the fundamental frequency of a bridge). However, such approach does not answer the question if the chosen target frequency is optimal, and does not take into account the possibility to have multiple design configurations with the same fundamental frequency. In this work, we approach the problem of a PEH design optimisation in a rigorous way, using a PEH model based on the Kirchhoff-Love plate theory and Isogeometric Analysis (IGA), coupled with the Modal Order Reduction (MOR) approach and Runge-Kutta time integration method. The model is further equipped with the Particle Swarm Optimisation (PSO) algorithm that allows finding geometry which maximises energy output from a given base acceleration signal. A comprehensive study is conducted to infer the impact of a PEH geometry on its energy harvesting performance in a real-world setting by considering field health monitoring data of a large-scale cable-stayed bridge in NSW, Australia. A shape optimisation framework is developed based on acceleration events, i.e., where the level of response exceeds a certain threshold. Designs obtained in the event-based optimisation are then clustered to propose several best candidates for continuous energy generation. This work represents the first study on PEH design optimisation for real operational conditions.
Weighted approximation in higher-dimensional missing digit sets

In this note, we use the mass transference principle for rectangles, recently obtained by Wang and Wu (Math. Ann., 2021), to study the Hausdorﬀ dimension of sets of “weighted Ψ-well-approximable” points in certain self-similar sets in R d . Speciﬁcally, we investigate weighted Ψ-well-approximable points in “missing digit” sets in R d . The sets we consider are natural generalisations of Cantor-type sets in R to higher dimensions and include, for example, four corner Cantor sets (or Cantor dust ) in the plane with contraction ratio 1 n with n ∈ N .
Stochastic entropy production for continuous measurements of an open quantum system

We investigate the total stochastic entropy production of a two-level bosonic open quantum system under protocols of time dependent coupling to a harmonic environment. These processes are intended to represent the measurement of a system observable, and consequent selection of an eigenstate, whilst the system is also subjected to thermalising environmental noise. The entropy production depends on the evolution of the system variables and their probability density function, and is expressed through system and environmental contributions. The continuous stochastic dynamics of the open system is based on the Markovian approximation to the exact, noise-averaged stochastic Liouville-von Neumann equation, unravelled through the addition of stochastic environmental disturbance mimicking a measuring device. Under the thermalising influence of time independent coupling to the environment, the mean rate of entropy production vanishes asymptotically, indicating equilibrium. In contrast, a positive mean production of entropy as the system responds to time dependent coupling characterises the irreversibility of quantum measurement, and a comparison of its production for two coupling protocols, representing connection to and disconnection from the external measuring device, satisfies a detailed fluctuation theorem.
A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation

Several recent studies attempt to address the biological implausibility of the well-known backpropagation (BP) method. While promising methods such as feedback alignment, direct feedback alignment, and their variants like sign-concordant feedback alignment tackle BP's weight transport problem, their validity remains controversial owing to a set of other unsolved issues. In this work, we answer the question of whether it is possible to realize random backpropagation solely based on mechanisms observed in neuroscience. We propose a hypothetical framework consisting of a new microcircuit architecture and its supporting Hebbian learning rules. Comprising three types of cells and two types of synaptic connectivity, the proposed microcircuit architecture computes and propagates error signals through local feedback connections and supports the training of multi-layered spiking neural networks with a globally defined spiking error function. We employ the Hebbian rule operating in local compartments to update synaptic weights and achieve supervised learning in a biologically plausible manner. Finally, we interpret the proposed framework from an optimization point of view and show its equivalence to sign-concordant feedback alignment. The proposed framework is benchmarked on several datasets including MNIST and CIFAR10, demonstrating promising BP-comparable accuracy.
Multiple-Photon Resonance Enabled Quantum Interference in Emission Spectroscopy of N_2^+

Quantum interference occurs frequently in the interaction of laser radiation with materials, leading to a series of fascinating effects such as lasing without inversion, electromagnetically induced transparency, Fano resonance, etc. Such quantum interference effects are mostly enabled by single-photon resonance with transitions in the matter, regardless of how many optical frequencies are involved. Here, we demonstrate quantum interference driven by multiple photons in the emission spectroscopy of nitrogen ions that are resonantly pumped by ultrafast infrared laser pulses. In the spectral domain, Fano resonance is observed in the emission spectrum, where a laser-assisted dynamic Stark effect creates the continuum. In the time domain, the fast-evolving emission is measured, revealing the nature of free-induction decay (FID) arising from quantum radiation and molecular cooperativity. These findings clarify the mechanism of coherent emission of nitrogen ions pumped with MIR pump laser and are likely to be universal. The present work opens a route to explore the important role of quantum interference during the interaction of intense laser pulses with materials near multiple photon resonance.
The Visual Survey Group: A Decade of Hunting Exoplanets and Unusual Stellar Events with Space-based Telescopes

This article presents the history of the Visual Survey Group (VSG)—a Professional-Amateur (Pro-Am) collaboration within the field of astronomy working on data from several space missions (Kepler, K2 and Transiting Exoplanet Survey Satellite). This paper covers the formation of the VSG, its survey-methods including the most common tools used and its discoveries made over the past decade. So far, the group has visually surveyed nearly 10 million light curves and authored 69 peer-reviewed papers which mainly focus on exoplanets and discoveries involving multistellar systems. The preferred manual search-method carried out by the VSG has revealed its strength by detecting numerous objects which were overlooked or discarded by automated search programs, uncovering some of the most rare stars in our galaxy, and leading to several serendipitous discoveries of unprecedented astrophysical phenomena. The main purpose of the VSG is to assist in the exploration of our local universe, and we therefore advocate continued crowd-sourced examination of time-domain data sets, and invite other research teams to reach out in order to establish collaborating projects.
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model

This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a normalizing flow that transforms an initial simple density to a target density by applying a sequence of invertible transformations. The second flow model is a Langevin flow that runs finite steps of gradient-based MCMC toward an energy-based model. We start from proposing a generative framework that trains an energy-based model with a normalizing flow as an amortized sampler to initialize the MCMC chains of the energy-based model. In each learning iteration, we generate synthesized examples by using a normalizing flow initialization followed by a short-run Langevin flow revision toward the current energy-based model. Then we treat the synthesized examples as fair samples from the energy-based model and update the model parameters with the maximum likelihood learning gradient, while the normalizing flow directly learns from the synthesized examples by maximizing the tractable likelihood. Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow. We provide an understating of the CoopFlow algorithm by information geometry and show that it is a valid generator as it converges to a moment matching estimator. We demonstrate that the trained CoopFlow is capable of synthesizing realistic images, reconstructing images, and interpolating between images.
Efficient Deep Learning Methods for Identification of Defective Casting Products

Quality inspection has become crucial in any large-scale manufacturing industry recently. In order to reduce human error, it has become imperative to use efficient and low computational AI algorithms to identify such defective products. In this paper, we have compared and contrasted various pre-trained and custom-built architectures using model size, performance and CPU latency in the detection of defective casting products. Our results show that custom architectures are efficient than pre-trained mobile architectures. Moreover, custom models perform 6 to 9 times faster than lightweight models such as MobileNetV2 and NasNet. The number of training parameters and the model size of the custom architectures is significantly lower (~386 times & ~119 times respectively) than the best performing models such as MobileNetV2 and NasNet. Augmentation experimentations have also been carried out on the custom architectures to make the models more robust and generalizable. Our work sheds light on the efficiency of these custom-built architectures for deployment on Edge and IoT devices and that transfer learning models may not always be ideal. Instead, they should be specific to the kind of dataset and the classification problem at hand.
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional neural networks (CNNs) are frequently used in remote sensing, which is subject to strong seasonal effects. We propose to blend the strengths of deep learning and the clear modeling capabilities of GPs by using a composite kernel that combines a kernel implicitly defined by a neural network with a second kernel function chosen to model known properties (e.g., seasonality). We implement this idea by combining a deep network and an efficient mapping based on the Nystrom approximation, which we call Implicit Composite Kernel (ICK). We then adopt a sample-then-optimize approach to approximate the full GP posterior distribution. We demonstrate that ICK has superior performance and flexibility on both synthetic and real-world data sets. We believe that ICK framework can be used to include prior information into neural networks in many applications.
Chromospheric swirls. I. Automated detection in Halpha observations and their statistical properties.

Context. Chromospheric swirls are related to convectively driven vortex ﬂows and considered to play a signiﬁcant role in the dynamics and heating of the upper solar atmosphere. It is important to automatically detect and track them in chromospheric observations and determine their properties. Aims. We aim to detect and track chromospheric swirls both in space and time by applying a newly developed novel automated method on high quality time series of H α observations and to conduct a statistical analysis to determine their properties. Methods. We applied a recently developed automated chromospheric swirl detection method to time-series observations of a quiet region of the solar chromosphere obtained in the H α -0. 2Å wavelength of the H α spectral line by the CRISP instrument at the Swedish 1-m Solar Telescope. The algorithm exploits the morphological characteristics of swirling events in high contrast chromospheric observations and results in the detection of these structures in each frame of the time series and their tracking over time. We conducted a statistical analysis to determine their various properties, including a survival analysis for deriving the mean lifetime. Results. A mean number of 146 ± 9 swirls was detected within the H α -0. 2Å ﬁeld of view at any given time. The mean surface density is found equal to ∼ 0.08swirlsMm − 2 and the occurrence rate is ∼ 10 − 2 swirlsMm − 2 min − 1 . These values are much higher than those previously reported from chromospheric observations. The radii of the detected swirls range between 0.5 and 2.5Mm, with a mean value equal to 1.3 ± 0.3Mm, which is slightly higher than previous reports. The lifetimes range between 1.5min and 33.7min (equal to the duration of the observations) with an arithmetic mean value of ∼ 8.5min. A survival analysis of the lifetimes, however, using the Kaplan-Meier estimator in combination with a parametric model results in a mean lifetime of 10.3 ± 0.6min. Conclusions. Swirls are ubiquitous in the solar chromosphere. An automated method sheds more light on their abundance than visual inspection, while higher cadence, higher resolution observations will most probably result in the detection of a higher number of such features on smaller scales and with shorter lifetimes.
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues

How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports on a first attempt at an AI teacher test. We built a solution around the insight that you can run conversational agents in parallel to human teachers in real-world dialogues, simulate how different agents would respond to a student, and compare these counterpart responses in terms of three abilities: speak like a teacher, understand a student, help a student. Our method builds on the reliability of comparative judgments in education and uses a probabilistic model and Bayesian sampling to infer estimates of pedagogical ability. We find that, even though conversational agents (Blender in particular) perform well on conversational uptake, they are quantifiably worse than real teachers on several pedagogical dimensions, especially with regard to helpfulness (Blender: {\Delta} ability = -0.75; GPT-3: {\Delta} ability = -0.93).