Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stable Weighted Residual Finite Element Formulation for the Simulation of Linear Moving Conductor Problems

The finite element method is one of the widely employed numerical techniques in electrical engineering for the study of electric and magnetic fields. When applied to the moving conductor problems, the finite element method is known to have numerical oscillations in the solution. To resolve this, the upwinding techniques, which are developed for the transport equation are borrowed and directly employed for the magnetic induction equation. In this work, an alternative weighted residual formulation is explored for the simulation of the linear moving conductor problems. The formulation is parameter-free and the stability of the formulation is analytically studied for the 1D version of the moving conductor problem. Then the rate of convergence and the accuracy are illustrated with the help of several test cases in 1D as well as 2D. Subsequently, the stability of the formulation is demonstrated with a 3D moving conductor simulation.
Constraining Annihilating Dark Matter by the Radio Continuum Spectrum of the Large Magellanic Cloud

Recent radio observations have obtained stringent constraints for annihilating dark matter. In this article, we use the radio continuum spectral data of the Large Magellanic Cloud to analyze the dark matter annihilation signals. We have discovered a slightly positive signal of dark matter annihilation with a 1. 5σ statistical significance. The overall best-fit dark matter mass is m DM ≈ 90 GeV, annihilating via the bb¯ channel. We have also constrained the 3σ lower limits of dark matter mass with the standard thermal dark matter annihilation cross section for the e + e −, μ + μ −, τ + τ −, and bb¯ channels.
2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition

Current advances in technology have highlighted the importance of video analysis in the domain of computer vision. However, video analysis has considerably high computational costs with traditional artificial neural networks (ANNs). Spiking neural networks (SNNs) are third generation biologically plausible models that process the information in the form of spikes. Unsupervised learning with SNNs using the spike timing dependent plasticity (STDP) rule has the potential to overcome some bottlenecks of regular artificial neural networks, but STDP-based SNNs are still immature and their performance is far behind that of ANNs. In this work, we study the performance of SNNs when challenged with the task of human action recognition, because this task has many real-time applications in computer vision, such as video surveillance. In this paper we introduce a multi-layered 3D convolutional SNN model trained with unsupervised STDP. We compare the performance of this model to those of a 2D STDP-based SNN when challenged with the KTH and Weizmann datasets. We also compare single-layer and multi-layer versions of these models in order to get an accurate assessment of their performance. We show that STDP-based convolutional SNNs can learn motion patterns using 3D kernels, thus enabling motion-based recognition from videos. Finally, we give evidence that 3D convolution is superior to 2D convolution with STDP-based SNNs, especially when dealing with long video sequences.
Flying Hydraulically Amplified Electrostatic Gripper System for Aerial Object Manipulation

Rapid and versatile object manipulation in air is an open challenge. An energy-efficient and adaptive soft gripper combined with an agile aerial vehicle could revolutionize aerial robotic manipulation in areas such as warehousing. This paper presents a bio-inspired gripper powered by hydraulically amplified electrostatic actuators mounted to a quadcopter that can interact safely and naturally with its environment. Our gripping concept is motivated by an eagle's foot. Our custom multi-actuator concept is inspired by a scorpion tail design (consisting of a base electrode with pouches stacked adjacently) and spider-inspired joints (classic pouch motors with a flexible hinge layer). A hybrid of these two designs realizes a higher force output under moderate deflections of up to 25{\deg} compared to single-hinge concepts. In addition, sandwiching the hinge layer improves the robustness of the gripper. For the first time, we show that soft manipulation in air is possible using electrostatic actuation. This study demonstrates the potential of untethered hydraulically amplified actuators in aerial robotic manipulation. Our proof of concept opens up the use of hydraulic electrostatic actuators in mobile aerial systems.
Is the optical conductivity of heavy fermion strange metals Planckian?

Strange metal behavior appears across a variety of condensed matter settings and beyond, and achieving a universal understanding is an exciting prospect. The beyond-Landau quantum criticality of Kondo destruction has had considerable success in describing the behavior of strange metal heavy fermion compounds, and there is some evidence that the associated partial localization-delocalization nature can be generalized to diverse materials classes. Other potential overarching principles at play are also being explored. An intriguing proposal is that Planckian scattering, with a rate of k B T/ℏ, leads to the linear temperature dependence of the (dc) electrical resistivity, which is a hallmark of strange metal behavior. Here we extend a previously introduced analysis scheme based on the Drude description of the dc resistivity to optical conductivity data. When they are well described by a simple (ac) Drude model, the scattering rate can be directly extracted. This avoids the need to determine the ratio of charge carrier concentration to effective mass, which has complicated previous analyses based on the dc resistivity. However, we point out that strange metals typically exhibit strong deviations from Drude behavior, as exemplified by the “extreme” strange metal YbRh2Si2. This calls for alternative approaches, and we point to the power of strange metal dynamical (energy-over-temperature) scaling analyses for the inelastic part of the optical conductivity. If such scaling extends to the low-frequency limit, a strange metal relaxation rate can be estimated, and may ultimately be used to test whether strange metals relax in a Planckian manner.
Effects of CO-dark Gas on Measurements of Molecular Cloud Stability and the Size–Linewidth Relationship

Stars form within molecular clouds, so characterizing the physical states of molecular clouds is key to understanding the process of star formation. Cloud structure and stability are frequently assessed using metrics including the virial parameter and Larson scaling relationships between cloud radius, velocity dispersion, and surface density. Departures from the typical Galactic relationships between these quantities have been observed in low-metallicity environments. The amount of H2 gas in cloud envelopes without corresponding CO emission is expected to be high under these conditions; therefore, this CO-dark gas could plausibly be responsible for the observed variations in cloud properties. We derive simple corrections that can be applied to empirical clump properties (mass, radius, velocity dispersion, surface density, and virial parameter) to account for CO-dark gas in clumps following power-law and Plummer mass density profiles. We find that CO-dark gas is not likely to be the cause of departures from Larson’s relationships in low-metallicity regions, but that virial parameters may be systematically overestimated. We demonstrate that correcting for CO-dark gas is critical for accurately comparing the dynamical state and evolution of molecular clouds across diverse environments.
Urban Rhapsody: Large‐scale exploration of urban soundscapes

Noise is one of the primary quality‐of‐life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low‐cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes. In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state‐of‐the‐art audio representation, machine learning and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high‐precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five‐year deployment of a one‐of‐a‐kind sensor network in New York City.
Signal processing in massive MIMO systems realized with low complexity hardware

The global mobile data traffic, as well as the energy consumption, for networks is constantly increasing. For this reason, techniques which can provide higher spectral efficiency while being more energy efficient are needed. Massive Multiple-Input Multiple-Output (MIMO) is a technique which can bring these two together and will play an important role in future wireless networks. The most power consuming part is the base stations and for the complexity in the digital signal processing, the per antenna functions are dominating. More specifically, in an Orthogonal Frequency-Division Multiplexing (OFDM) based system, this includes the filter and the inverse fast Fourier transform (IFFT). In this thesis the possibilities of implementing the per antenna functions in low complexity hardware is investigated. Both low accuracy and scaling down the supply voltage to the integrated circuits, exploring the error resilience of the system, are considered. Due to this, a remarkable amount of energy can be saved. Results in this thesis show that for a certain communication system implemented with low accuracy and allowing errors to occur, 97% of the signal processing power can be saved at a Signal-to-Noise Ratio (SNR) degradation of only 2 dB. Concluding this work, massive MIMO can provide high spectral efficiency and implemented with low accuracy hardware it can still be error resilient and lead to higher energy efficiency.
Innovation Begets Innovation and Concentration: The Case of Upstream Oil & Gas in the North Sea

We investigate the effect of technology adoption on competition by leveraging a unique dataset on production, costs, and asset characteristics for North Sea upstream oil&gas companies. Relying on heterogeneity in the geological suitability of fields and a landmark decision of the Norwegian Supreme Court that increased the returns of capital investment in Norway relative to the UK, we show that technology adoption increases market concentration. Firms with prior technology-specific know-how specialize more in fields suitable for the same technology but also invest more in high-risk-high-return fields (e.g., ultra-deep recovery), diversifying their technology portfolio and ultimately gaining larger shares of the North Sea market. Our analyses illustrate how technology adoption can lead to market concentration both directly through specialization and indirectly via experimentation.
Tight Memory-Independent Parallel Matrix Multiplication Communication Lower Bounds

Communication lower bounds have long been established for matrix multiplication algorithms. However, most methods of asymptotic analysis have either ignored the constant factors or not obtained the tightest possible values. Recent work has demonstrated that more careful analysis improves the best known constants for some classical matrix multiplication lower bounds and helps to identify more efficient algorithms that match the leading-order terms in the lower bounds exactly and improve practical performance. The main result of this work is the establishment of memory-independent communication lower bounds with tight constants for parallel matrix multiplication. Our constants improve on previous work in each of three cases that depend on the relative sizes of the aspect ratios of the matrices.
Variance-Aware Sparse Linear Bandits

It is well-known that for sparse linear bandits, when ignoring the dependency on sparsity which is much smaller than the ambient dimension, the worst-case minimax regret is $\widetilde{\Theta}\left(\sqrt{dT}\right)$ where $d$ is the ambient dimension and $T$ is the number of rounds. On the other hand, in the benign setting where there is no noise and the action set is the unit sphere, one can use divide-and-conquer to achieve $\widetilde{\mathcal O}(1)$ regret, which is (nearly) independent of $d$ and $T$. In this paper, we present the first variance-aware regret guarantee for sparse linear bandits: $\widetilde{\mathcal O}\left(\sqrt{d\sum_{t=1}^T \sigma_t^2} + 1\right)$, where $\sigma_t^2$ is the variance of the noise at the $t$-th round. This bound naturally interpolates the regret bounds for the worst-case constant-variance regime (i.e., $\sigma_t \equiv \Omega(1)$) and the benign deterministic regimes (i.e., $\sigma_t \equiv 0$). To achieve this variance-aware regret guarantee, we develop a general framework that converts any variance-aware linear bandit algorithm to a variance-aware algorithm for sparse linear bandits in a"black-box"manner. Specifically, we take two recent algorithms as black boxes to illustrate that the claimed bounds indeed hold, where the first algorithm can handle unknown-variance cases and the second one is more efficient.
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequences of graphs can be processed by autoregressive graph neural networks to recursively learn representations at each discrete point in time and space. Spatiotemporal graphs are often highly sparse, with time series characterized by multiple, concurrent, and long sequences of missing data, e.g., due to the unreliable underlying sensor network. In this context, autoregressive models can be brittle and exhibit unstable learning dynamics. The objective of this paper is, then, to tackle the problem of learning effective models to reconstruct, i.e., impute, missing data points by conditioning the reconstruction only on the available observations. In particular, we propose a novel class of attention-based architectures that, given a set of highly sparse discrete observations, learn a representation for points in time and space by exploiting a spatiotemporal propagation architecture aligned with the imputation task. Representations are trained end-to-end to reconstruct observations w.r. t. the corresponding sensor and its neighboring nodes. Compared to the state of the art, our model handles sparse data without propagating prediction errors or requiring a bidirectional model to encode forward and backward time dependencies. Empirical results on representative benchmarks show the effectiveness of the proposed method.
Learning Deterministic Finite Automata Decompositions from Examples and Demonstrations

The identification of a deterministic finite automaton (DFA) from labeled examples is a well-studied problem in the literature; however, prior work focuses on the identification of monolithic DFAs. Although monolithic DFAs provide accurate descriptions of systems' behavior, they lack simplicity and inter-pretability; moreover, they fail to capture sub-tasks realized by the system and introduce inductive biases away from the inherent decomposition of the overall task. In this paper, we present an algorithm for learning conjunctions of DFAs from labeled examples. Our approach extends an existing SAT-based method to systematically enumerate Pareto-optimal candidate solutions. We highlight the utility of our approach by integrating it with a state-of-the-art algorithm for learning DFAs from demonstrations. Our experiments show that the algorithm learns sub-tasks realized by the labeled examples, and it is scalable in the domains of interest.
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification

Class imbalance naturally exists when label distributions are not aligned across source and target domains. However, existing state-of-the-art UDA models learn domain-invariant representations across domains and evaluate primarily on class-balanced data. In this work, we propose an unsupervised domain adaptation approach via reinforcement learning that jointly leverages feature variants and imbalanced labels across domains. We experiment with the text classification task for its easily accessible datasets and compare the proposed method with five baselines. Experiments on three datasets prove that our proposed method can effectively learn robust domain-invariant representations and successfully adapt text classifiers on imbalanced classes over domains.
VizInspect Pro - Automated Optical Inspection (AOI) solution

Traditional vision based Automated Optical Inspection (referred to as AOI in paper) systems present multiple challenges in factory settings including inability to scale across multiple product lines, requirement of vendor programming expertise, little tolerance to variations and lack of cloud connectivity for aggregated insights. The lack of flexibility in these systems presents a unique opportunity for a deep learning based AOI system specifically for factory automation. The proposed solution, VizInspect pro is a generic computer vision based AOI solution built on top of Leo - An edge AI platform. Innovative features that overcome challenges of traditional vision systems include deep learning based image analysis which combines the power of self-learning with high speed and accuracy, an intuitive user interface to configure inspection profiles in minutes without ML or vision expertise and the ability to solve complex inspection challenges while being tolerant to deviations and unpredictable defects. This solution has been validated by multiple external enterprise customers with confirmed value propositions. In this paper we show you how this solution and platform solved problems around model development, deployment, scaling multiple inferences and visualizations.
The Promise of Behavioral Tracking Systems for Advancing Primate Animal Welfare

Simple Summary Computerized tracking systems for primates and other animals are one of the great inventions of the 21st century. These systems have already revolutionized the study of primatology, psychology, neuroscience, and biomedicine. Less discussed is that they also promise to greatly enhance animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare, and applying our understanding of animal behavior to increase the “natural” behaviors in captive and wild populations, especially those under threat. We are optimistic that these changes will greatly increase the welfare of primates, including those in laboratories, zoos, primate centers, and in the wild. Abstract Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and biomedicine. Here, we discuss the promise of these technologies for animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare within the context of the three Rs of animal research (reduction, refinement, and replacement), and applying our understanding of animal behavior to increase the “natural” behaviors in captive and wild populations facing human impact challenges. We note that these benefits are often incidental to the designed purpose of these tracking systems, a reflection of the fact that animal welfare is not inimical to research progress, but instead, that the aligned interests between basic research and welfare hold great promise for improvements to animal well-being.
SoK: Decentralized Randomness Beacon Protocols

The scientiﬁc interest in the area of Decentralized Randomness Beacon (DRB) protocols has been thriving recently. Partially that interest is due to the success of the disruptive technologies introduced by modern cryptography, such as cryptocurrencies, blockchain technologies, and decentralized ﬁnances, where there is an enormous need for a public, reliable, trusted, veriﬁable, and distributed source of randomness. On the other hand, recent advancements in the development of new cryptographic primitives brought a huge interest in constructing a plethora of DRB protocols diﬀering in design and underlying primitives. To the best of our knowledge, no systematic and comprehensive work systematizes and analyzes the existing DRB protocols. Therefore, we present a Systematization of Knowledge (SoK) intending to structure the multi-faced body of research on DRB protocols. In this SoK, we delineate the DRB protocols along the following axes: their underlying primitive, properties, and security. This SoK tries to ﬁll that gap by providing basic standard deﬁnitions and requirements for DRB protocols, such as Unpredictability, Bias-resistance, Availability (or Liveness), and Public Veriﬁability. We classify DRB protocols according to the nature of interactivity among protocol participants. We also highlight the most signiﬁcant features of DRB protocols such as scalability, complexity, and performance along with a brief discussion on its improvement. We present future research directions along with a few interesting research problems.
Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI

The availability of large unlabeled datasets compared to labeled ones motivate the use of self-supervised pretraining to initialize deep learning models for subsequent segmentation tasks. We consider two pre-training approaches for driving a CNN to learn different representations using: a) a reconstruction loss that exploits spatial dependencies and b) a contrastive loss that exploits semantic similarity. The techniques are evaluated in two MR segmentation applications: a) liver and b) prostate segmentation in T2-weighted images. We observed that CNNs pretrained using self-supervision can be finetuned for comparable performance with fewer labeled datasets.