Miracle-dz/UniFAR-FTUs huggingface dataset:
A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection

Overloading in DC servo motors is a major concern in industries, as many companies face the problem of finding expert operators, and also human monitoring may not be an effective solution. Therefore, this paper proposed an embedded Artificial intelligence (AI) approach using a Convolutional Neural Network (CNN) using a new transformation to extract faults from real-time input signals without human interference. Our main purpose is to extract as many as possible features from the input signal to achieve a relaxed dataset that results in an effective but compact network to provide real-time fault detection even in a low-memory microcontroller. Besides, fault detection method a synchronous dual-motor system is also proposed to take action in faulty events. To fulfill this intention, a one-dimensional input signal from the output current of each DC servo motor is monitored and transformed into a 3d stack of data and then the CNN is implemented into the processor to detect any fault corresponding to overloading, finally experimental setup results in 99.9997% accuracy during testing for a model with nearly 8000 parameters. In addition, the proposed dual-motor system could achieve overload reduction and provide a fault-tolerant system and it is shown that this system also takes advantage of less energy consumption.
Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction

Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data. However, current Deep Neural Network (DNN)-based supervised online learning models require a large amount of training data and cannot quickly adapt when the underlying system changes. Moreover, these models require continuous retraining with incoming data making them highly inefficient. We present a novel Continuous Learning-based Unsupervised Recurrent Spiking Neural Network Model (CLURSNN), trained with spike timing dependent plasticity (STDP) to solve these issues. CLURSNN makes online predictions by reconstructing the underlying dynamical system using Random Delay Embedding by measuring the membrane potential of neurons in the recurrent layer of the recurrent spiking neural network (RSNN) with the highest betweenness centrality. We also use topological data analysis to propose a novel methodology using the Wasserstein Distance between the persistent homologies of the predicted and observed time series as a loss function. We show that the proposed online time series prediction methodology outperforms state-of-the-art DNN models when predicting an evolving Lorenz63 dynamical system.
Visual anemometry: physics-informed inference of wind for renewable energy, urban sustainability, and environmental science

Accurate measurements of atmospheric flows at meter-scale resolution are essential for a broad range of sustainability applications, including optimal design of wind and solar farms, safe and efficient urban air mobility, monitoring of environmental phenomena such as wildfires and air pollution dispersal, and data assimilation into weather and climate models. Measurement of the relevant microscale wind flows is inherently challenged by the optical transparency of the wind. This review explores new ways in which physics can be leveraged to"see"environmental flows non-intrusively, that is, without the need to place measurement instruments directly in the flows of interest. Specifically, while the wind itself is transparent, its effect can be visually observed in the motion of objects embedded in the environment and subjected to wind -- swaying trees and flapping flags are commonly encountered examples. We describe emerging efforts to accomplish visual anemometry, the task of quantitatively inferring local wind conditions based on the physics of observed flow-structure interactions. Approaches based on first-principles physics as well as data-driven, machine learning methods will be described, and remaining obstacles to fully generalizable visual anemometry will be discussed.
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval

The vulnerability in the algorithm supply chain of deep learning has imposed new challenges to image retrieval systems in the downstream. Among a variety of techniques, deep hashing is gaining popularity. As it inherits the algorithmic backend from deep learning, a handful of attacks are recently proposed to disrupt normal image retrieval. Unfortunately, the defense strategies in softmax classification are not readily available to be applied in the image retrieval domain. In this paper, we propose an efficient and unsupervised scheme to identify unique adversarial behaviors in the hamming space. In particular, we design three criteria from the perspectives of hamming distance, quantization loss and denoising to defend against both untargeted and targeted attacks, which collectively limit the adversarial space. The extensive experiments on four datasets demonstrate 2-23% improvements of detection rates with minimum computational overhead for real-time image queries.
Josephson Parametric Amplifier in Axion Experiments

The axion is a hypothetical particle, a promising candidate for dark matter, and a solution to the strong CP problem. Axion haloscope search experiments deal with a signal power comparable to noise uncertainty at millikelvin temperature. We use a flux-driven Josephson parametric amplifier (JPA) with the aim of approaching a noise level near the theoretically allowed limit of half quanta. In our measurements to characterize the JPA we have found the added noise to the system with a JPA as the first-stage amplifier to be lower than 110 mK at the frequencies from 0.938 GHz to 0.963 GHz.
ICDAR 2023 Video Text Reading Competition for Dense and Small Text

Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenarios, while ignoring extreme video text challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., Game, sports, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task 2)). During the competition period (opened on 15th February 2023 and closed on 20th March 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise video text research in the community.
On forbidden moves and the delta move

We consider the quotient of welded knotted objects under several equivalence relations, generated respectively by self-crossing changes, ∆ moves, self-virtualizations and forbidden moves. We prove that for welded objects up to forbidden moves or classical objects up to ∆ moves, the notions of links and string links coincide, and that they are classified by the (virtual) linking numbers; we also prove that the ∆ move is an unknotting operation for welded (long) knots. For welded knotted objects, we prove that forbidden moves imply the ∆ move, the self-crossing change and the self-virtualization, and that these four local moves yield pairwise different quotients, while they collapse to only two distinct quotients in the classical case.
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities.
Using Logs Data to Identify When Software Engineers Experience Flow or Focused Work

Beyond self-report data, we lack reliable and non-intrusive methods for identifying flow. However, taking a step back and acknowledging that flow occurs during periods of focus gives us the opportunity to make progress towards measuring flow by isolating focused work. Here, we take a mixed-methods approach to design a logs-based metric that leverages machine learning and a comprehensive collection of logs data to identify periods of related actions (indicating focus), and validate this metric against self-reported time in focus or flow using diary data and quarterly survey data. Our results indicate that we can determine when software engineers at a large technology company experience focused work which includes instances of flow. This metric speaks to engineering work, but can be leveraged in other domains to non-disruptively measure when people experience focus. Future research can build upon this work to identify signals associated with other facets of flow.
Estimation of Ground NO2 Measurements from Sentinel-5P Tropospheric Data through Categorical Boosting

This study aims to analyse the Nitrogen Dioxide (NO2) pollution in the Emilia Romagna Region (Northern Italy) during 2019, with the help of satellite retrievals from the Sentinel-5P mission of the European Copernicus Programme and ground-based measurements, obtained from the ARPA site (Regional Agency for the Protection of the Environment). The final goal is the estimation of ground NO2measurements when only satellite data are available. For this task, we used a Machine Learning (ML) model, Categorical Boosting, which was demonstrated to work quite well and allowed us to achieve a Root-Mean-Square Error (RMSE) of 0.0242 over the 43 stations utilised to get the Ground Truth values. This procedure represents the starting point to understand which other actions must be taken to improve the final performance of the model and extend its validity.
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution

Since the number of incident energies is limited, it is difficult to directly acquire hyperspectral images (HSI) with high spatial resolution. Considering the high dimensionality and correlation of HSI, super-resolution (SR) of HSI remains a challenge in the absence of auxiliary high-resolution images. Furthermore, it is very important to extract the spatial features effectively and make full use of the spectral information. This paper proposes a novel HSI super-resolution algorithm, termed dual-domain network based on hybrid convolution (SRDNet). Specifically, a dual-domain network is designed to fully exploit the spatial-spectral and frequency information among the hyper-spectral data. To capture inter-spectral self-similarity, a self-attention learning mechanism (HSL) is devised in the spatial domain. Meanwhile the pyramid structure is applied to increase the acceptance field of attention, which further reinforces the feature representation ability of the network. Moreover, to further improve the perceptual quality of HSI, a frequency loss(HFL) is introduced to optimize the model in the frequency domain. The dynamic weighting mechanism drives the network to gradually refine the generated frequency and excessive smoothing caused by spatial loss. Finally, In order to better fully obtain the mapping relationship between high-resolution space and low-resolution space, a hybrid module of 2D and 3D units with progressive upsampling strategy is utilized in our method. Experiments on a widely used benchmark dataset illustrate that the proposed SRDNet method enhances the texture information of HSI and is superior to state-of-the-art methods.
Linking a predictive model to causal effect estimation

A predictive model makes outcome predictions based on some given features, i.e., it estimates the conditional probability of the outcome given a feature vector. In general, a predictive model cannot estimate the causal effect of a feature on the outcome, i.e., how the outcome will change if the feature is changed while keeping the values of other features unchanged. This is because causal effect estimation requires interventional probabilities. However, many real world problems such as personalised decision making, recommendation, and fairness computing, need to know the causal effect of any feature on the outcome for a given instance. This is different from the traditional causal effect estimation problem with a fixed treatment variable. This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w.r. t. a given instance. The theoretical results naturally link a predictive model to causal effect estimations and imply that a predictive model is causally interpretable when the conditions identified in the paper are satisfied. The paper also reveals the robust property of a causally interpretable model. We use experiments to demonstrate that various types of predictive models, when satisfying the conditions identified in this paper, can estimate the causal effects of features as accurately as state-of-the-art causal effect estimation methods. We also show the potential of such causally interpretable predictive models for robust predictions and personalised decision making.
SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results.
Catching a nova X-ray/UV flash in the visible? Early spectroscopy of the very slow Nova Velorum 2022 (Gaia22alz)

We present early spectral observations of the very slow Galactic nova Gaia22alz, over its gradual rise to peak brightness that lasted 180 days. During the first 50 days, when the nova was only 3–4 magnitudes above its normal brightness, the spectra showed narrow (FWHM ≈ 400 km s−1) emission lines of H Balmer, He i, He ii, and C iv, but no P Cygni absorption. A few weeks later, the high-excitation He ii and C iv lines disappeared, and P Cygni profiles of Balmer, He i, and eventually Fe ii lines emerged, yielding a spectrum typical of classical novae before peak. We propose that the early (first 50 days) spectra of Gaia22alz, particularly the emission lines with no P Cygni profiles, are produced in the white dwarf’s optically thin envelope or accretion disc, reprocessing ultraviolet and potentially X-ray emission from the white dwarf after a dramatic increase in the rate of thermonuclear reactions, during a phase known as the ‘early X-ray/UV flash’. If true, this would be one of the rare times that the optical signature of the early X-ray/UV flash has been detected. While this phase might last only a few hours in other novae and thus be easily missed, it was possible to detect in Gaia22alz due to its very slow and gradual rise and thanks to the efficiency of new all-sky surveys in detecting transients on their rise. We also consider alternative scenarios that could explain the early spectral features of Gaia22alz and its gradual rise.
High Speed Neuromorphic Vision-Based Inspection of Countersinks in Automated Manufacturing Processes

Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection due to high latency and measurement uncertainties with motion, leading to prolonged execution times of the inspection task. The neuromorphic vision sensor, on the other hand, has the potential to expedite the countersink inspection process, but the unorthodox output of the neuromorphic technology prohibits utilizing traditional image processing techniques. Therefore, novel event-based perception algorithms need to be introduced. We propose a countersink detection approach on the basis of event-based motion compensation and the mean-shift clustering principle. In addition, our framework presents a robust event-based circle detection algorithm to precisely estimate the depth of the countersink specimens. The proposed approach expedites the inspection process by a factor of 10$$\times $$ × compared to conventional countersink inspection methods. The work in this paper was validated for over 50 trials on three countersink workpiece variants. The experimental results show that our method provides a standard deviation of 0.025 mm and an accuracy of 0.026 mm for countersink depth inspection despite the low resolution of commercially available neuromorphic cameras. Video Link: https://www.dropbox. com/s/pateqqwh4d605t3/final_video_new. mp4? dl=0.
Waiting for Fake News

This paper studies a dynamic model of information acquisition, in which information might be secretly manipulated. A principal must choose between a safe action with known payoff and a risky action with uncertain payoff, favoring the safe action under the prior belief. She may delay her decision to acquire additional news that reveals the risky action's payoff, without knowing exactly when such news will arrive. An uninformed agent with a misaligned preference may have the capability to generate a false arrival of news, which is indistinguishable from a real one, distorting the information content of news and the principal's search. The analysis characterizes the positive and normative distortions in the search for news arising from such manipulation, and it considers three remedies that increase the principal's payoff: a commitment to naive search, transfer of authority to the agent, and delegation to an intermediary who is biased in the agent's favor.
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning

Spurious correlations that degrade model generalization or lead the model to be right for the wrong reasons are one of the main robustness concerns for real-world deployments. However, mitigating these correlations during pre-training for large-scale models can be costly and impractical, particularly for those without access to high-performance computing resources. This paper proposes a novel approach to address spurious correlations during fine-tuning for a given domain of interest. With a focus on multi-modal models (e.g., CLIP), the proposed method leverages different modalities in these models to detect and explicitly set apart spurious attributes from the affected class, achieved through a multi-modal contrastive loss function that expresses spurious relationships through language. Our experimental results and in-depth visualizations on CLIP show that such an intervention can effectively i) improve the model's accuracy when spurious attributes are not present, and ii) directs the model's activation maps towards the actual class rather than the spurious attribute when present. In particular, on the Waterbirds dataset, our algorithm achieved a worst-group accuracy 23% higher than ERM on CLIP with a ResNet-50 backbone, and 32% higher on CLIP with a ViT backbone, while maintaining the same average accuracy as ERM.
A stochastic model and kinetic Monte Carlo simulation of solute interactions with stationary and moving grain boundaries. I. Model formulation and application to 1D systems

A simple stochastic model of solute drag by moving grain boundaries (GBs) is presented. Using a small number of parameters, the model describes solute interactions with GBs and captures nonlinear GB dynamics, solute saturation in the segregation atmosphere, and the breakaway from the atmosphere. The model is solved by kinetic Monte-Carlo (KMC) simulations with time-dependent transition barriers. The non-Markovian nature of the KMC process is discussed. In Part I of this work, the model is applied to planar GBs driven by an external force. The model reproduces all basic features of the solute drag eﬀect, including the maximum of the drag force at a critical GB velocity. The force-velocity functions obtained depart from the scaling predicted by the classical models by Cahn and L¨ucke-St¨uwe, which are based on more restrictive assumptions. The paper sets the stage for Part II, in which the GB will be treated as a 2D solid-on-solid interface.