Miracle-dz/UniFAR-FTUs huggingface dataset:
TaxoNN: A Light-Weight Accelerator for Deep Neural Network Training

Emerging intelligent embedded devices rely on Deep Neural Networks (DNNs) to be able to interact with the real-world environment. This interaction comes with the ability to retrain DNNs, since environmental conditions change continuously in time. Stochastic Gradient Descent (SGD) is a widely used algorithm to train DNNs by optimizing the parameters over the training data iteratively. In this work, first we present a novel approach to add the training ability to a baseline DNN accelerator (inference only) by splitting the SGD algorithm into simple computational elements. Then, based on this heuristic approach we propose TaxoNN, a light-weight accelerator for DNN training. TaxoNN can easily tune the DNN weights by reusing the hardware resources used in the inference process using a time-multiplexing approach and low-bitwidth units. Our experimental results show that TaxoNN delivers, on average, 0.97% higher misclassification rate compared to a full-precision implementation. Moreover, TaxoNN provides 2.1× power saving and 1.65× area reduction over the state-of-the-art DNN training accelerator.
All the PAHs: An AKARI–Spitzer Cross-archival Spectroscopic Survey of Aromatic Emission in Galaxies

We present a large sample of 2.5–38 μm galaxy spectra drawn from a cross-archival comparison in the AKARI–Spitzer Extragalactic Spectral Survey, and investigate a subset of 113 star-forming galaxies with prominent polycyclic aromatic hydrocarbon (PAH) emission spanning a wide range of star formation properties. With AKARI’s extended 2.5–5 μm wavelength coverage, we self-consistently model for the first time all PAH emission bands using a modified version of Pahfit. We find L PAH 3.3/L IR ∼ 0.1%, and the 3.3 μm PAH feature contributes ∼1.5%–3% to the total PAH power—somewhat less than earlier dust models have assumed. We establish a calibration between 3.3 μm PAH emission and star formation rate, but also find regimes where it loses reliability, including at high luminosity and low metallicity. The 3.4 μm aliphatic emission and a broad plateau feature centered at 3.47 μm are also modeled. As the PAH feature with the shortest wavelength, the one at 3.3 μm is susceptible to attenuation, leading to differences of a factor of ∼3 in the inferred star formation rate at high obscuration with different assumed attenuation geometries. Surprisingly, L PAH 3.3/L Σ PAH shows no sign of decline at high luminosities, and the low-metallicity dwarf galaxy II Zw 40 exhibits an unusually strong 3.3 μm band; both results suggest either that the smallest PAHs are better able to survive under intense radiation fields than presumed, or that PAH emission is shifted to shorter wavelengths in intense and high-energy radiation environments. A photometric surrogate for 3.3 μm PAH luminosity using JWST/NIRCam is provided and found to be highly reliable at low redshift.
Cooling of accretion disc coronae by Type I X-ray bursts

Understanding the persistent emission is crucial for studying type I X-ray bursts, which provide insight into neutron star properties. Although accretion disc coronae appear to be common in many accreting systems, their fundamental properties remain insufficiently understood. Recent work suggests that Type I X-ray bursts from accreting neutron stars provide an opportunity to probe the characteristics of coronae. Several studies have observed hard X-ray shortages from the accretion disk during an X-ray burst implying strong coronal cooling by burst photons. Here, we use the plasma emission code EQPAIR to study the impact of X-ray bursts on coronae, and how the coronal and burst properties affect the coronal electron temperatures and emitted spectra. Assuming a constant accretion rate during the burst, our simulations show that soft photons can cool coronal electrons by a factor of $\gtrsim 10$ and cause a reduction of emission in the $30$-$50$ keV band to $\lesssim 1\%$ of the pre-burst emission. This hard X-ray drop is intensified when the coronal optical depth and aspect ratio is increased. In contrast, depending on the properties of the burst and corona, the emission in the $8$-$24$ keV band can either increase, by a factor of $\gtrsim20$, or decrease, down to $\lesssim 1\%$ of the pre-burst emission. An increasing accretion rate during the X-ray burst reduces the coronal cooling effects and the electron temperature drop can be mitigated by $\gtrsim60\%$. These results indicate that changes of the hard X-ray flux during an X-ray burst probe the geometrical properties of the corona.
RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm

We propose a novel and efficient training method for RNNs by iteratively seeking a local minima on the loss surface within a small region, and leverage this directional vector for the update, in an outer-loop. We propose to utilize the Frank-Wolfe (FW) algorithm in this context. Although, FW implicitly involves normalized gradients, which can lead to a slow convergence rate, we develop a novel RNN training method that, surprisingly, even with the additional cost, the overall training cost is empirically observed to be lower than backpropagation. Our method leads to a new Frank-Wolfe method, that is in essence an SGD algorithm with a restart scheme. We prove that under certain conditions our algorithm has a sublinear convergence rate of $O$(1/∊) for ∊ error. We then conduct empirical experiments on several benchmark datasets including those that exhibit long-term dependencies, and show significant performance improvement. We also experiment with deep RNN architectures and show efficient training performance. Finally, we demonstrate that our training method is robust to noisy data.
Rare-event Simulation for Neural Network and Random Forest Predictors

We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests. This problem is motivated from fast emerging studies on the safety evaluation of intelligent systems, robustness quantification of learning models, and other potential applications to large-scale simulation in which machine learning tools can be used to approximate complex rare-event set boundaries. We investigate an importance sampling scheme that integrates the dominating point machinery in large deviations and sequential mixed integer programming to locate the underlying dominating points. Our approach works for a range of neural network architectures including fully connected layers, rectified linear units, normalization, pooling and convolutional layers, and random forests built from standard decision trees. We provide efficiency guarantees and numerical demonstration of our approach using a classification model in the UCI Machine Learning Repository.
Neural-Symbolic Reasoning on Knowledge Graphs

Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval and recommendation. Since knowledge graph can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic reasoning, neural reasoning and the neural-symbolic reasoning on knowledge graphs. We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.
We Can Detect Your Bias: Predicting the Political Ideology of News Articles

We explore the task of predicting the leading political ideology or bias of news articles. First, we collect and release a large dataset of 34,737 articles that were manually annotated for political ideology -left, center, or right-, which is well-balanced across both topics and media. We further use a challenging experimental setup where the test examples come from media that were not seen during training, which prevents the model from learning to detect the source of the target news article instead of predicting its political ideology. From a modeling perspective, we propose an adversarial media adaptation, as well as a specially adapted triplet loss. We further add background information about the source, and we show that it is quite helpful for improving article-level prediction. Our experimental results show very sizable improvements over using state-of-the-art pre-trained Transformers in this challenging setup.
How to Hijack Twitter: Online Polarisation Strategies of Germany's Political Far-Right

With a network approach, we examine the case of the German far-right party Alternative fur Deutschland (AfD) and their potential use of a "hashjacking" strategy. Our findings suggest that right-wing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by "hashjacking" the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (e.g. migration), but as a direct consequence of their digital political communication strategy.
Measurement of Higgs-boson self-coupling with single-Higgs and double-Higgs production channels.

The trilinear self-coupling can be measured directly using the Higgs-boson-pair production cross section, or indirectly through the measurement of single-Higgs-boson production and decay modes. In fact, at next-to-leading order in electroweak interaction, the Higgs-decay partial widths and the cross sections of the main single-Higgs production processes depend on the Higgs-boson self-coupling via weak loops. Measurements of $\kappa_\lambda$, i.e. the rescaling of the trilinear Higgs self-coupling, are presented in this dissertation. Results are obtained exploiting proton-proton collision data from the Large Hadron Collider at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector in 2015, 2016 and 2017, corresponding to a luminosity of up to 79.8 fb$^{-1}$. Constraints on the Higgs self-coupling are presented considering the most sensitive double-Higgs channels (HH), $b\bar{b}\tau^+\tau^-$, $b\bar{b}\gamma\gamma$ and $b\bar{b}b\bar{b}$, considering single-Higgs (H) production modes, $ggF$, $VBF$, $ZH$, $WH$ and $t\bar{t}H$, together with $WW^*$, $ZZ^*$, $\tau^+\tau^-$, $\gamma \gamma$ and $b\bar{b}$ decay channels, and combining the aforementioned analyses (H+HH) to improve the sensitivity on $\kappa_\lambda$. Under the assumption that new physics affects only the Higgs-boson self-coupling, the combined H+HH best-fit value of the coupling modifier is: $\kappa_\lambda = 4.6^{+3.2}_{-3.8}$, excluding values outside the interval $-2.3<\kappa_\lambda<10.3$ at 95% confidence level. Results with less stringent assumptions are also provided, decoupling the Higgs-boson self-coupling and the other Standard Model couplings. The final results of this thesis provide the most stringent constraint on $\kappa_\lambda$ from experimental measurements to date.
A Model Compression Method With Matrix Product Operators for Speech Enhancement

The deep neural network (DNN) based speech enhancement approaches have achieved promising performance. However, the number of parameters involved in these methods is usually enormous for the real applications of speech enhancement on the device with the limited resources. This seriously restricts the applications. To deal with this issue, model compression techniques are being widely studied. In this paper, we propose a model compression method based on matrix product operators (MPO) to substantially reduce the number of parameters in DNN models for speech enhancement. In this method, the weight matrices in the linear transformations of neural network model are replaced by the MPO decomposition format before training. In experiment, this process is applied to the causal neural network models, such as the feedforward multilayer perceptron (MLP) and long short-term memory (LSTM) models. Both MLP and LSTM models with/without compression are then utilized to estimate the ideal ratio mask for monaural speech enhancement. The experimental results show that our proposed MPO-based method outperforms the widely-used pruning method for speech enhancement under various compression rates, and further improvement can be achieved with respect to low compression rates. Our proposal provides an effective model compression method for speech enhancement, especially in cloud-free application.
Focusing the latent heat release in 3D phase field simulations of dendritic crystal growth

We investigate a family of phase field models for simulating dendritic growth of a pure supercooled substance. The central object of interest is the reaction term in the Allen–Cahn equation, which is responsible for the spatial distribution of latent heat release during solidification. In this context, several existing forms of the reaction term are analyzed. Inspired by the known conclusions of matched asymptotic analysis, we propose a new variant (the ‘ΣP1-P’ model) that is simple enough to allow mathematical and numerical analysis and robust enough to be applicable to solidification under very large supercooling. The important component of the model (the Σ limiter) can also be incorporated into the original models to extend the range of their applicability. The individual models are tested in a number of numerical simulations focusing on mesh-dependence and model parameter settings. When the phase interface thickness is kept large with respect to the microscopic capillary length to make numerical computations feasible, the parameters of the Σ limiter can be tuned to improve agreement with previous models. The results obtained using the ΣP1-P model exhibit a good quantitative agreement with experimental data from rapid solidification of nickel melts.
Quantitative Argument Summarization and beyond: Cross-Domain Key Point Analysis

When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments. The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data. Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews. An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.
Pinched Hysteresis Loops In Nonlinear Resonators

This paper shows that pinched hysteresis can be observed in simple nonlinear resonance circuits containing a single diode that behaves as a voltage-controlled switch. Mathematical models are derived and numerically validated for both series and parallel resonator circuits. The lobe area of the pinched loop is found to increase with increased frequency and multiple pinch-points are possible with an odd symmetrical nonlinearity such as a cubic nonlinearity. Experiments have been performed to prove the existence of pinched hysteresis with a single diode and with two anti-parallel diodes. The formation of a pinched loop in these circuits confirms that: 1) pinched hysteresis is not a finger-print of memristors and that 2) the existence of a nonlinearity is essential for generating this behavior. Finally, an application in a digital logic circuit is validated.
Larger wavelengths suit hydrodynamics of carangiform swimmers

The wavelength of undulatory kinematics of fish is an important parameter to determine their hydrodynamic performance. This study focuses on numerical examination of this feature by reconstructing the real physiological model and kinematics of steadily swimmning Jack Fish. We perform three-dimensional numerical simulations for flows over these models composed of the trunk, and dorsal, anal, and caudal fins. Moreover, we prescribe the carangiform-like motion for its undulation for a range of wavelengths. Undulation with larger wavelengths improves the hydrodynamic performance of the carangiform swimmer in terms of better thrust production by the caudal fin, lower drag production on the trunk, and reduced power consumption by the trunk. This coincides with the formation of stronger posterior body vortices and leading-edge vortices with more circulation on the caudal fin. The real kinematics of Jack Fish surpasses the performance of those with prescribed motion owing to the flexibility of the caudal fin.
Exploiting Knowledge Graphs for Facilitating Product/Service Discovery

Most of the existing techniques to product discovery rely on syntactic approaches, thus ignoring valuable and specific semantic information of the underlying standards during the process. The product data comes from different heterogeneous sources and formats giving rise to the problem of interoperability. Above all, due to the continuously increasing influx of data, the manual labeling is getting costlier. Integrating the descriptions of different products into a single representation requires organizing all the products across vendors in a single taxonomy. Practically relevant and quality product categorization standards are still limited in number; and that too in academic research projects where we can majorly see only prototypes as compared to industry. This work presents a cost-effective solution for e-commerce on the Data Web by employing an unsupervised approach for data classification and exploiting the knowledge graphs for matching. The proposed architecture describes available products in web ontology language OWL and stores them in a triple store. User input specifications for certain products are matched against the available product categories to generate a knowledge graph. This mullti-phased top-down approach to develop and improve existing, if any, tailored product recommendations will be able to connect users with the exact product/service of their choice.
Human-in-the-loop Debugging Deep Text Classifiers

Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets. These classifiers are thus likely to have undesirable properties. For instance, they may have biases against some sub-populations or may not work effectively in the wild due to overfitting. In this paper, we propose FIND – a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features. Experiments show that by using FIND, humans can improve CNN text classifiers which were trained under different types of imperfect datasets (including datasets with biases and datasets with dissimilar train-test distributions).
Interference in Atomic Magnetometry

Atomic magnetometers are highly sensitive detectors of magnetic fields that monitor the evolution of the macroscopic magnetic moment of atomic vapors, and opening new applications in biological, physical, and chemical science. However, the performance of atomic magnetometers is often limited by hidden systematic effects that may cause misdiagnosis for a variety of applications, for example, in nuclear magnetic resonance (NMR) and in biomagnetism. In this work, a hitherto unexplained interference effect in atomic magnetometers is uncovered, which causes an important systematic effect to greatly deteriorate the accuracy of measuring magnetic fields. A standard approach to detecting and characterizing the interference effect in, but not limited to, atomic magnetometers is presented. As applications of the work, the effect of the interference in NMR structural determination and locating the brain electrophysiological symptom is considered, and it is shown that it will help to improve the measurement accuracy by taking interference effects into account. Through the experiments, good agreement is indeed found between the prediction and the asymmetric amplitudes of resonant lines in ultralow‐field NMR spectra—an effect that has not been understood so far. It is anticipated that the work will stimulate interesting new researches for magnetic interference phenomena in a wide range of magnetometers and their applications.
An Empirical Study on Detecting COVID-19 in Chest X-ray Images Using Deep Learning Based Methods

COVID-19 is a widespread infectious disease that affects millions of people worldwide. On account of the alarming rate of the spread of COVID-19, scientists are looking for new strategies for the diagnosis of this disease. X-rays are much more affordable and widely available compared to CT screening. The PCR testing process is time-consuming and experiences false negative rates, these traditional medical imaging modalities play a vital role in the control of the pandemic. In this paper, we have developed and examined different CNN models to identify the best method for diaognosing this disease. The efforts of providing testing kits have increased due to the transmission of COVID 19. The preparation of these kits are complicated, rare, and expensive moreover, the difficulty of using them is another issue. The results have shown that the testing kits take crucial time to diagnose the virus, in addition to the fact that they have a 30 % loss rate. In this article, we have studied the usage of ubiquitous X-ray imaging, for the classification of COVID-19 chest images, using existing convolutional neural networks (CNNs). Different CNN architectures, including VGG19, Densnet-121, and Xception are applied to train the network by chest X-rays of infected patients but not the infected ones. After applying these methods the results showed different accuracies but were more precise than the state-of-the-art models. The DenseNet-121 network obtained 97% accuracy, 98% precision, and 96% F1 score. COVID-19 is a widespread infectious disease that affects millions of people worldwide. On account of the alarming rate of the spread of COVID-19 scientists are looking for new strategies for the diagnosis of this disease. In this article, we have examined the performance of different CNN models to identify the best method for the classification of this disease. The VGG 19 method showed 93 % accuracy.
Revising FUNSD dataset for key-value detection in document images

FUNSD is one of the limited publicly available datasets for information extraction from document im-ages. The information in the FUNSD dataset is defined by text areas of four categories ("key","value","header","other", and"background") and connectivity between areas as key-value relations. In-specting FUNSD, we found several inconsistency in labeling, which impeded its applicability to thekey-value extraction problem. In this report, we described some labeling issues in FUNSD and therevision we made to the dataset. We also reported our implementation of for key-value detection onFUNSD using a UNet model as baseline results and an improved UNet model with Channel-InvariantDeformable Convolution.