Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnitude and Uncertainty Pruning Criterion for Neural Networks

Neural networks have achieved dramatic improvements in recent years and depict the state-of-the-art methods for many real-world tasks nowadays. One drawback is, however, that many of these models are overparameterized, which makes them both computationally and memory intensive. Furthermore, overparameterization can also lead to undesired overfitting side-effects. Inspired by recently proposed magnitude-based pruning schemes and the Wald test from the field of statistics, we introduce a novel magnitude and uncertainty (M&U) pruning criterion that helps to lessen such shortcomings. One important advantage of our M&U pruning criterion is that it is scale-invariant, a phenomenon that the magnitude-based pruning criterion suffers from. In addition, we present a “pseudo bootstrap” scheme, which can efficiently estimate the uncertainty of the weights by using their update information during training. Our experimental evaluation, which is based on various neural network architectures and datasets, shows that our new criterion leads to more compressed models compared to models that are solely based on magnitude-based pruning criteria, with, at the same time, less loss in predictive power.
Reversible Gates in Emerging Quantum-dot Cellular Automata Technology: An Innovative Approach to Design and Simulation

Power dissipation is known as the most notable limiting factor in all nano-electronic design techniques including Quantum-dot Cellular Automata (QCA). The familiar reversible computing approach is used as a reasonably reliable solution, mitigating power dissipation. This study presents, a comprehensive multi-objective method for designing R-Fs in emerging QCA technology. The results are investigated in both logical and layout levels, in detail. The results verify that the approach offered in this study has advantage over the most efficient approaches available in the literature by far. This comparison can be made on various parameters ranging from area, complexity (cell amount), delay (clocking zones), and to even logical levels including levels, Control inputs, the number of majority and NOT gates.
Accurate trajectory alignment in cold-atom interferometers with separated laser beams

Cold-atom interferometers commonly face systematic effects originating from the coupling between the trajectory of the atomic wave packet and the wave front of the laser beams driving the interferometer. Detrimental for the accuracy and the stability of such inertial sensors, these systematics are particularly enhanced in architectures based on spatially separated laser beams. Here we analyze the effect of a coupling between the relative alignment of two separated laser beams and the trajectory of the atomic wave packet in a four-light-pulse cold-atom gyroscope operated in fountain configuration. We present a method to align the two laser beams at the $0.2 \ \mu$rad level and to determine the optimal mean velocity of the atomic wave packet with an accuracy of $0.2\ \textrm{mm}\cdot\textrm{s}^{-1}$. Such fine tuning constrains the associated gyroscope bias to a level of $1\times 10^{-10}~\textrm{rad}\cdot\textrm{s}^{-1}$. In addition, we reveal this coupling using the point-source interferometry technique by analyzing single-shot time-of-flight fluorescence traces, which allows us to measure large angular misalignments between the interrogation beams. The alignment method which we present here can be employed in other sensor configurations and is particularly relevant to emerging gravitational wave detector concepts based on cold-atom interferometry.
Predicting dark matter halo formation in N-body simulations with deep regression networks

Dark matter haloes play a fundamental role in cosmological structure formation. The most common approach to model their assembly mechanisms is through N-body simulations. In this work, we present an innovative pathway to predict dark matter halo formation from the initial density field using a Deep Learning algorithm. We implement and train a Deep Convolutional Neural Network to solve the task of retrieving Lagrangian patches from which dark matter haloes will condense. The volumetric multilabel classification task is turned into a regression problem by means of the Euclidean distance transformation. The network is complemented by an adaptive version of the watershed algorithm to form the entire protohalo identification pipeline. We show that splitting the segmentation problem into two distinct subtasks allows for training smaller and faster networks, while the predictive power of the pipeline remains the same. The model is trained on synthetic data derived from a single full N-body simulation and achieves deviations of ∼10 per cent when reconstructing the dark matter halo mass function at z = 0. This approach represents a promising framework for learning highly non-linear relations in the primordial density field. As a practical application, our method can be used to produce mock dark matter halo catalogues directly from the initial conditions of N-body simulations.
Interfacial load monitoring and failure detection in total joint replacements via piezoresistive bone cement and electrical impedance tomography

Aseptic loosening, or loss of implant fixation, is a common complication following total joint replacement. Revision surgeries cost the healthcare system over $8 billion annually in the United States. Despite the prevalence of aseptic loosening, timely and accurate detection remains a challenge because traditional imaging modalities, such as plain radiographs, struggle to reliably detect the early stages of implant loosening. Motivated by this challenge, we present a novel approach for in vivo monitoring and failure detection of cemented joint replacements. Poly(methyl methacrylate) (PMMA) bone cement is modified with low volume fractions of chopped carbon fiber (CF) to impart piezoresistive-based self-sensing. Electrical impedance tomography (EIT) is then used to detect and monitor load-induced deformation and fracture of CF/PMMA in a phantom tank. We therefore show that EIT indeed is able to detect loading force on a prosthetic surrogate, distinguish between increasing load magnitudes, detect failure of implant fixation, and even distinguish between cement cracking and cement de-bonding without direct contact to the surrogate. Because EIT is a low-cost, physiologically benign, and potentially real-time imaging modality, the feasibility study herein presented could positively impact orthopedic researchers by providing, via in vivo monitoring, insight into the factors that initiate aseptic loosening.
Learning to Discriminate Information for Online Action Detection

From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies.
Evolution of 3D Printing Methods and Materials for Electrochemical Energy Storage

Additive manufacturing has revolutionized the building of materials, and 3D‐printing has become a useful tool for complex electrode assembly for batteries and supercapacitors. The field initially grew from extrusion‐based methods and quickly evolved to photopolymerization printing, while supercapacitor technologies less sensitive to solvents more often involved material jetting processes. The need to develop higher‐resolution multimaterial printers is borne out in the performance data of recent 3D printed electrochemical energy storage devices. Underpinning every part of a 3D‐printable battery are the printing method and the feed material. These influence material purity, printing fidelity, accuracy, complexity, and the ability to form conductive, ceramic, or solvent‐stable materials. The future of 3D‐printable batteries and electrochemical energy storage devices is reliant on materials and printing methods that are co‐operatively informed by device design. Herein, the material and method requirements in 3D‐printable batteries and supercapacitors are addressed and requirements for the future of the field are outlined by linking existing performance limitations to requirements for printable energy‐storage materials, casings, and direct printing of electrodes and electrolytes. A guide to materials and printing method choice best suited for alternative‐form‐factor energy‐storage devices to be designed and integrated into the devices they power is thus provided.
Learning to generate new indoor scenes

Deep generative models have been used in recent years to learn coherent latent representations in order to synthesize high quality images. In this work we propose a neural network to learn a generative model for sampling consistent indoor scene layouts. Our method learns the co-occurrences, and appearance parameters such as shape and pose, for different objects categories through a grammar-based auto-encoder, resulting in a compact and accurate representation for scene layouts. In contrast to existing grammar-based methods with a user-specified grammar, we construct the grammar automatically by extracting a set of production rules on reasoning about object co-occurrences in training data. The extracted grammar is able to represent a scene by an augmented parse tree. The proposed auto-encoder encodes these parse trees to a latent code, and decodes the latent code to a parse-tree, thereby ensuring the generated scene is always valid. We experimentally demonstrate that the proposed auto-encoder learns not only to generate valid scenes (i.e. the arrangements and appearances of objects), but it also learns coherent latent representations where nearby latent samples decode to similar scene outputs. The obtained generative model is applicable to several computer vision tasks such as 3D pose and layout estimation from RGB-D data.
Fraud detection in telephone conversations for financial services using linguistic features

Detecting the elements of deception in a conversation is one of the most challenging problems for the AI community. It becomes even more difficult to design a transparent system, which is fully explainable and satisfies the need for financial and legal services to be deployed. This paper presents an approach for fraud detection in transcribed telephone conversations using linguistic features. The proposed approach exploits the syntactic and semantic information of the transcription to extract both the linguistic markers and the sentiment of the customer's response. We demonstrate the results on real-world financial services data using simple, robust and explainable classifiers such as Naive Bayes, Decision Tree, Nearest Neighbours, and Support Vector Machines.
ChainifyDB: How to Blockchainify any Data Management System

Today's permissioned blockchain systems come in a stand-alone fashion and require the users to integrate yet another full-fledged transaction processing system into their already complex data management landscape. This seems odd as blockchains and traditional DBMSs share large parts of their processing stack. Thus, rather than replacing the established data systems altogether, we advocate to simply 'chainify' them with a blockchain layer on top. Unfortunately, this task is far more challenging than it sounds: As we want to build upon heterogeneous transaction processing systems, which potentially behave differently, we cannot rely on every organization to execute every transaction deterministically in the same way. Further, as these systems are already filled with data and being used by top-level applications, we also cannot rely on every organization being resilient against tampering with its local data. Therefore, in this work, we will drop these assumptions and introduce a powerful processing model that avoids them in the first place: The so-called Whatever-LedgerConsensus (WLC) model allows us to create a highly flexible permissioned blockchain layer coined ChainifyDB that (a) is centered around bullet-proof database technology, (b) makes even stronger guarantees than existing permissioned systems, (c) provides a sophisticated recovery mechanism, (d) has an up to 6x higher throughput than the permissioned blockchain system Fabric, and (e) can easily be integrated into an existing heterogeneous database landscape.
The Young Suns Exoplanet Survey: Detection of a wide-orbit planetary-mass companion to a solar-type Sco-Cen member

The Young Suns Exoplanet Survey consists of a homogeneous sample of 70 young, solar-mass stars located in the Lower Centaurus-Crux subgroup of the Scorpius-Centaurus association with an average age of 15 ± 3 Myr. We report the detection of a co-moving companion around the K3IV star TYC 8998-760-1 (2MASSJ13251211–6456207) that is located at a distance of 94.6 ± 0.3 pc using SPHERE/IRDIS on the VLT. Spectroscopic observations with VLT/X-SHOOTER constrain the mass of the star to $1.00\pm 0.02\, \mathrm{M}_{\odot }$ and an age of $16.7\pm 1.4\,$ Myr. The companion TYC 8998-760-1 b is detected at a projected separation of 1.71″, which implies a projected physical separation of 162 au. Photometric measurements ranging from Y to M band provide a mass estimate of $14\pm 3\, M_\mathrm{jup}$ by comparison to BT-Settl and AMES-dusty isochrones, corresponding to a mass ratio of q = 0.013 ± 0.003 with respect to the primary. We rule out additional companions to TYC 8998-760-1 that are more massive than $12\, M_\mathrm{jup}$ and farther than 12 au away from the host. Future polarimetric and spectroscopic observations of this system with ground and space based observatories will facilitate testing of formation and evolution scenarios shaping the architecture of the circumstellar environment around this ‘young Sun’.
Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization

Sound event detection (SED) is a task to detect sound events in an audio recording. One challenge of the SED task is that many datasets such as the Detection and Classification of Acoustic Scenes and Events (DCASE) datasets are weakly labelled. That is, there are only audio tags for each audio clip without the onset and offset times of sound events. We compare segment-wise and clip-wise training for SED that is lacking in previous works. We propose a convolutional neural network transformer (CNN-Transfomer) for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN). Another challenge of SED is that thresholds are required for detecting sound events. Previous works set thresholds empirically, and are not an optimal approaches. To solve this problem, we propose an automatic threshold optimization method. The first stage is to optimize the system with respect to metrics that do not depend on thresholds, such as mean average precision (mAP). The second stage is to optimize the thresholds with respect to metrics that depends on those thresholds. Our proposed automatic threshold optimization system achieves a state-of-the-art audio tagging F1 of 0.646, outperforming that without threshold optimization of 0.629, and a sound event detection F1 of 0.584, outperforming that without threshold optimization of 0.564.
From computation to comparison of tensor decompositions

Decompositions of higher-order tensors into sums of simple terms are ubiquitous. We show that in order to verify that two tensors are generated by the same (possibly scaled) terms it is not necessary to compute the individual decompositions. In general the explicit computation of such a decomposition may have high complexity and can be ill-conditioned. We now show that under some assumptions the verification can be reduced to a comparison of both the column and row spaces of the corresponding matrix representations of the tensors. We consider rank-1 terms as well as low multilinear rank terms (also known as block terms) and show that the number of the terms and their multilinear rank can be inferred as well. The comparison relies only on numerical linear algebra and can be done in a numerically reliable way. We also illustrate how our results can be applied to solve a multi-label classification problem that appears in the context of blind source separation.
Electronic Circuit Inspired Optimization of Nanogap Electrochemical Biosensors

Electrochemical biosensors and the related concept of redox detection at nanogap electrodes are increasingly explored for ultra-sensitive detection of biomolecules. While experimental demonstrations have been encouraging, the associated design and optimization of electrode geometry, beyond the simple one-dimensional architectures, is inherently challenging from multiple aspects related to numerical complexity. Here we develop a facile simulation scheme to address this challenge using well established electronic circuit analysis techniques that are available as open source-ware. Based on this approach, we show that electrode geometry, especially nanostructured redox electrodes on a planar surface, has interesting implications on the detection limits and settling time of electrochemical biosensors. The methodology we developed and the insights obtained could be useful for electrode optimization for a wide variety of problems ranging from biosensors to electrochemical storage.
A thick disc as massive as it gets: the vertical structure of the enormous edge-on lenticular galaxy NGC7572

Galactic discs are known to have a complex multi-layer structure. An in-depth study of the stellar population properties of the thin and thick components can elucidate the formation and evolution of disc galaxies. Even though thick discs are ubiquitous, their origin is still debated. Here we probe the thick disc formation scenarios by investigating NGC7572, an enormous edge-on galaxy having $R_{25}\approx 25$ kpc and $V_{rot} \approx 370$ km/s, which substantially exceeds the Milky Way size and mass. We analysed DECaLS archival imaging and found that the disc of NGC7572 contains two flaring stellar discs (a thin and a thick disc) with similar radial scales. We collected deep long-slit spectroscopic data using the 6-m Russian BTA telescope and analysed them with a novel technique. We first reconstructed a non-parametric stellar line-of-sight velocity distribution along the radius of the galaxy and then fitted it with two kinematic components accounting for the orbital distribution of stars in thin and thick discs. The old thick disc turned out to be 2.4 times as massive as the intermediate-age thin component, $1.9\cdot 10^{11}$ Msun vs. $7.8\cdot10^{10}$ Msun, which is very unusual. The different duration of the formation epochs evidenced by the [Mg/Fe] values of +0.3 dex and +0.15 dex for the thick and thin discs respectively, their kinematics and the mass ratio suggest that in NGC7572 we observe a rapidly formed very massive thick disc and an under-developed thin disc, whose formation ended prematurely due to the exhaustion of the cold gas likely because of environmental effects.
Driving The Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks

In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to stay connected, however, the use of such applications while driving could have serious repercussions - often leading the user to be distracted from the road and ending up in an accident. In the context of impression management, it has been discovered that individuals often take a risk (such as teens smoking cigarettes, indulging in narcotics, and participating in unsafe sex) to improve their social standing. Therefore, viewing the phenomena of posting distracted driving posts under the lens of self-presentation, it can be hypothesized that users often indulge in risk-taking behavior on social media to improve their impression among their peers. In this paper, we first try to understand the severity of such social-media-based distractions by analyzing the content posted on a popular social media site where the user is driving and is also simultaneously creating content. To this end, we build a deep learning classifier to identify publicly posted content on social media that involves the user driving. Furthermore, a framework proposed to understand factors behind voluntary risk-taking activity observes that younger individuals are more willing to perform such activities, and men (as opposed to women) are more inclined to take risks. Grounding our observations in this framework, we test these hypotheses on 173 cities across the world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content posting behavior changes due to varied demographics. We discover that the factors put forth by the framework are significant in estimating the extent of such behavior.
Expansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques

Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks. However, it is difficult to construct a theoretical model due to the imbalances in the dataset for discriminating attacks from the overall dataset. Multilayer Perceptron (MLP) technique will provide improvement in accuracy and increase the performance of detecting the attack and benign data from a balanced dataset. We have worked on the UGR'16 dataset publicly available for this work. Data wrangling has been done due to prepare test set from in the original set. We fed the neural network classifier larger input to the neural network in an increasing manner (i.e. 10000, 50000, 1 million) to see the distribution of features over the accuracy. We have implemented a GAN model that can produce samples of different attack labels (e.g. blacklist, anomaly spam, ssh scan). We have been able to generate as many samples as necessary based on the data sample we have taken from the UGR'16. We have tested the accuracy of our model with the imbalance dataset initially and then with the increasing the attack samples and found improvement of classification performance for the latter.