Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Communications for Smart Manufacturing and Industrial IoT: Existing Technologies, 5G and Beyond

Smart manufacturing is a vision and major driver for change in today’s industry. The goal of smart manufacturing is to optimize manufacturing processes through constantly monitoring, controlling, and adapting processes towards more efficient and personalised manufacturing. This requires and relies on technologies for connected machines incorporating a variety of computation, sensing, actuation, and machine to machine communications modalities. As such, understanding the change towards smart manufacturing requires knowledge of the enabling technologies, their applications in real world scenarios and the communication protocols and their performance to meet application requirements. Particularly, wireless communication is becoming an integral part of modern smart manufacturing and is expected to play an important role in achieving the goals of smart manufacturing. This paper presents an extensive review of wireless communication protocols currently applied in manufacturing environments and provides a comprehensive review of the associated use cases whilst defining their expected impact on the future of smart manufacturing. Based on the review, we point out a number of open challenges and directions for future research in wireless communication technologies for smart manufacturing.
Exponential Concentration in Stochastic Approximation

We analyze the behavior of stochastic approximation algorithms where iterates, in expectation, progress towards an objective at each step. When progress is proportional to the step size of the algorithm, we prove exponential concentration bounds. These tail-bounds contrast asymptotic normality results, which are more frequently associated with stochastic approximation. The methods that we develop rely on a geometric ergodicity proof. This extends a result on Markov chains due to Hajek (1982) to the area of stochastic approximation algorithms. We apply our results to several different Stochastic Approximation algorithms, specifically Projected Stochastic Gradient Descent, Kiefer-Wolfowitz and Stochastic Frank-Wolfe algorithms. When applicable, our results prove faster $O(1/t)$ and linear convergence rates for Projected Stochastic Gradient Descent with a non-vanishing gradient.
Pose Forecasting in Industrial Human-Robot Collaboration

Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the interaction of the spatial, temporal and channel-wise dimensions in GCNs, and it learns sparse adjacency matrices by a teacher-student framework. Compared to the state-of-the-art, it only uses 1.72% of the parameters and it is ~4 times faster, while still performing comparably in forecasting accuracy on Human3.6M at 1 second in the future, which enables cobots to be aware of human operators. As a second contribution, we present a new benchmark of Cobots and Humans in Industrial COllaboration (CHICO). CHICO includes multi-view videos, 3D poses and trajectories of 20 human operators and cobots, engaging in 7 realistic industrial actions. Additionally, it reports 226 genuine collisions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run time of 2.3 msec, and collision detection, by comparing the forecasted human motion with the known cobot motion, obtaining an F1-score of 0.64.
Cloud-Based Real-Time Molecular Screening Platform with MolFormer

With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed. Here, we present a cloud-based real-time platform that allows users to virtually screen molecules of interest. For this purpose, molecular embeddings inferred from a recently proposed large chemical language model, named MolFormer, are leveraged. The platform currently supports three tasks: nearest neighbor retrieval, chemical space visualization, and property prediction. Based on the functionalities of this platform and results obtained, we believe that such a platform can play a pivotal role in automating chemistry and chemical engineering research, as well as assist in drug discovery and material design tasks. A demo of our platform is provided at \url{www.ibm. biz/molecular_demo}.
Estimating Personal Model Parameters from Utterances in Model-based Reminiscence

Reminiscence therapy is mental health care based on the recollection of memories. However, the effectiveness of this method varies amongst individuals. To solve this problem, it is necessary to provide more personalized support; therefore, this study utilized a computational model of personal memory recollection based on a cognitive architecture adaptive control of thought-rational (ACT-R). An ACT-R memory model reflecting the state of users is expected to facilitate personal recollection. In this study, we proposed a method for estimating the internal states of users through repeated interactions with the memory model. The model, which contains the lifelog of the user, presents a memory item (stimulus) to the user, and receives the response of the user to the stimulus, based on which it adjusts the internal parameters of the model. Through the repetition of these processes, the parameters of the model will reflect the internal states of the user. To confirm the feasibility of the proposed method, we analyzed utterances of users when using a system that incorporates this model. The results confirmed the ability of the method to estimate the memory retrieval parameters of the model from the utterances of the user. In addition, the ability of the method to estimate changes in the mood of the user caused by using the system was confirmed. These results support the feasibility of the interactive method for estimating human internal states, which will eventually contribute to the ability to induce memory recall and emotions for our well-being.
A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals

Rolling bearings are the most crucial components of rotating machinery. Identifying defective bearings in a timely manner may prevent the malfunction of an entire machinery system. The mechanical condition monitoring field has entered the big data phase as a result of the fast advancement of machine parts. When working with large amounts of data, the manual feature extraction approach has the drawback of being inefficient and inaccurate. Data-driven methods like Deep Learning have been successfully used in recent years for mechanical intelligent fault detection. Convolutional neural networks (CNNs) were mostly used in earlier research to detect and identify bearing faults. The CNN model, however, suffers from the drawback of having trouble managing fault-time information, which results in a lack of classification results. In this study, bearing defects have been classified using a state-of-the-art Vision Transformer (ViT). Bearing defects were classified using Case Western Reserve University (CWRU) bearing failure laboratory experimental data. The research took into account 13 distinct kinds of defects under 0-load situations in addition to normal bearing conditions. Using the Short Time Fourier Transform (STFT), the vibration signals were converted into 2D time-frequency images. The 2D time-frequency images are then used as input parameters for the ViT. The model achieved an overall accuracy of 98.8%.
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness

Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often an overlooked preprocessing step. When explicitly considered, attention is placed on overall performance, ignoring how this preprocessing can reinforce groupspecific inequities. Our work questions this choice by studying how imputation affects downstream algorithmic fairness. First, we provide a structured view of the relationship between clinical presence mechanisms and groupspecific missingness patterns. Then, through simulations and real-world experiments, we demonstrate that the imputation choice influences marginalised group performance and that no imputation strategy consistently reduces disparities. Importantly, our results show that current practices may endanger health equity as similarly performing imputation strategies at the population level can affect marginalised groups differently. Finally, we propose recommendations for mitigating inequities that may stem from a neglected step of the machine learning pipeline.
A Blockchain-based Decentralised and Dynamic Authorisation Scheme for the Internet of Things

An authorisation has been recognised as an important security measure for preventing unauthorised access to critical resources, such as devices and data, within the Internet of Things (IoT) networks. To achieve authorisation, access control mechanisms are extensively utilised, restricting the user’s actions within the network or system based on predetermined access control policies with speciﬁc control actions. Existing authorisation methods for the IoT network is based on traditional access control models, which have several drawbacks, including architecture centralisation, policy tampering, access rights validation, malicious third party policy assignment and control, and network-related overheads. The increasing trend of integrating Blockchain technology with IoT networks demonstrates its importance and potential to address the shortcomings of traditional IoT network authorisation mechanisms. However, existing Blockchain-based authorisation solutions for IoT networks overlook the importance of utilising the full potential of Blockchain technology and under-perform to handle the dynamicity of the underlying network in terms of malicious user behaviour, static policies, and auditability of user requests and resources. This paper proposes a decentralised secure, dynamic, and ﬂexible authorisation scheme for IoT networks based on attribute-based access control (ABAC) ﬁne-grained policies stored on a distributed immutable ledger. We design a Blockchain-based ABAC policy management framework divided into Attribute Management Authority (AMA) and Policy Management Authority (PMA) frameworks that use smart contract features to initialise, store, and manage attributes and policies on the Blockchain. To achieve ﬂexibility and dynamicity in the authorisation process, we capture and utilise the environmental-related attributes in conjunction with the subject and object attributes of the ABAC model to deﬁne the policies. Furthermore, we designed the Blockchain-based Access Management Framework (AMF) to manage user requests to access IoT devices while maintaining the privacy and auditability of user requests and assigned policies. We implemented a prototype of our proposed scheme and executed it on the local Ethereum Blockchain. Finally, we demonstrated the applicability and ﬂexibility of our proposed scheme for an IoT-based smart home scenario, taking into account deployment, execution and ﬁnancial costs.
ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool

We present Elevant, a tool for the fully automatic fine-grained evaluation of a set of entity linkers on a set of benchmarks. Elevant provides an automatic breakdown of the performance by various error categories and by entity type. Elevant also provides a rich and compact, yet very intuitive and self-explanatory visualization of the results of a linker on a benchmark in comparison to the ground truth. A live demo, the link to the complete code base on GitHub and a link to a demo video are provided under https://elevant.cs. uni-freiburg.de .
Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions

Online extremism has severe societal consequences, including normalizing hate speech, user radicalization, and increased social divisions. Various mitigation strategies have been explored to address these consequences. One such strategy uses positive interventions: controlled signals that add attention to the opinion ecosystem to boost certain opinions. To evaluate the effectiveness of positive interventions, we introduce the Opinion Market Model (OMM), a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions. The size of the opinion attention market is modeled in the first tier using the multivariate discrete-time Hawkes process; in the second tier, opinions cooperate and compete for market share, given limited attention using the market share attraction model. We demonstrate the convergence of our proposed estimation scheme on a synthetic dataset. Next, we test OMM on two learning tasks, applying to two real-world datasets to predict attention market shares and uncover latent relationships between online items. The first dataset comprises Facebook and Twitter discussions containing moderate and far-right opinions about bushfires and climate change. The second dataset captures popular VEVO artists' YouTube and Twitter attention volumes. OMM outperforms the state-of-the-art predictive models on both datasets and captures latent cooperation-competition relations. We uncover (1) self- and cross-reinforcement between far-right and moderate opinions on the bushfires and (2) pairwise artist relations that correlate with real-world interactions such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed for positive interventions and show how media coverage modulates the spread of far-right opinions.
Distributed Robust Principal Component Analysis

We study the robust principal component analysis (RPCA) problem in a distributed setting. The goal of RPCA is to find an underlying low-rank estimation for a raw data matrix when the data matrix is subject to the corruption of gross sparse errors. Previous studies have developed RPCA algorithms that provide stable solutions with fast convergence. However, these algorithms are typically hard to scale and cannot be implemented distributedly, due to the use of either SVD or large matrix multiplication. In this paper, we propose the first distributed robust principal analysis algorithm based on consensus factorization, dubbed DCF-PCA. We prove the convergence of DCF-PCA and evaluate DCF-PCA on various problem setting
Renormalization group analysis of multi-band many-electron systems at half-filling

Renormalization group analysis for multi-band many-electron systems at half-filling at positive temperature is presented. The analysis includes the Matsubara ultra-violet integration and the infrared integration around the zero set of the dispersion relation. The multi-scale integration schemes are implemented in a finite-dimensional Grassmann algebra indexed by discrete position-time variables. In order that the multi-scale integrations are justified inductively, various scale-dependent estimates on Grassmann polynomials are established. We apply these theories in practice to prove that for the half-filled Hubbard model with nearest-neighbor hopping on a square lattice the infinite-volume, zero-temperature limit of the free energy density exists as an analytic function of the coupling constant in a neighborhood of the origin if the system contains the magnetic flux pi (mod 2 pi) per plaquette and 0 (mod 2 pi) through the large circles around the periodic lattice. Combined with Lieb's result on the flux phase problem ([Lieb, E. H., Phys. Rev. Lett. 73 (1994), 2158]), this theorem implies that the minimum free energy density of the flux phase problem converges to an analytic function of the coupling constant in the infinite-volume, zero-temperature limit. The proof of the theorem is based on a four-band formulation of the model Hamiltonian and an extension of Giuliani-Mastropietro's renormalization designed for the half-filled Hubbard model on the honeycomb lattice ([Giuliani, A. and V. Mastropietro, Commun. Math. Phys. 293 (2010), 301-346]).
Controversies and Progress in the Problem of Dynamical Fluctuation-Electromagnetic Interactions

A review is given of the key works relevant to the problem of dynamical fluctuation-electromagnetic interaction, a problem of topical interest over a period of several decades. We discuss controversies and present day progress in this problem considering two basic geometrical configurations: a small particle moving above a plate, and two parallel plates in relative motion.
HA-ResNet: Residual Neural Network With Hidden Attention for ECG Arrhythmia Detection Using Two-Dimensional Signal

Arrhythmia is an abnormal heart rhythm, a common clinical problem in cardiology. Long-term or severe arrhythmia may lead to stroke and sudden cardiac death. The electrocardiogram (ECG) is the most commonly used tool to diagnose arrhythmia. However, the traditional diagnosis relies on experts for manual interpretation, which is time-consuming and laborious. In recent years, many automatic arrhythmia detection methods have emerged due to advancements in deep learning. These methods can reduce manual intervention and improve diagnostic efficiency. However, extracting useful features from raw ECG signals for arrhythmia detection is still challenging due to the low frequency of ECG signals and noise distribution. In this paper, we propose a novel hidden attention residual network (HA-ResNet) for automated arrhythmia classification. In this model, the one-dimensional ECG signals are first converted into two-dimensional images and fed into an embedding layer to obtain the relevant shallow features in ECG. Then, a hidden attention layer combining Squeeze-and-Excitation (SE) block and Bidirectional Convolutional LSTM (BConvLSTM) is used to further capture the deep Spatio-temporal features. We evaluate our HA-ResNet on two public datasets and achieve F1 scores of 96.0%, 96.7%, and 87.6% on 2s segments, 5s segments, and 10s segments, respectively, which significantly outperform the existing state-of-the-art approaches. The experimental results demonstrate the effectiveness and generalization of our method.
WFPC2 Long-Term Photometric Stability

We examine the WFPC2 stellar photometric monitoring data obtained over the last four years. The new analysis shows that, for bright targets, (1) the long-term photometric throughput continues to be quite stable: fluctuations are ~2% or less peak-to-peak over 4 years in filters longwards of and including F336W, (2) the UV throughput has gradually evolved, that is, the clean count rates (immediately after the decons) have increased over the 4 years in some filters, e.g., F160BW by ~12% in the PC, and F170W by ~9%, while decreasing slightly in others, e.g., F255W by ~3% and, (3) the contamination growth rates have slowed slightly for the UV filters, e.g., ~1%/day to 0.5%/day in F160BW on the PC. The small zeropoint discontinuity around Feb. 1995 is discussed. These results are being used to update the WFPC2 time-dependent photometric calibration within SYNPHOT.
Invasive Group A Streptococcal Penicillin Binding Protein 2× Variants Associated with Reduced Susceptibility to β-Lactam Antibiotics in the United States, 2015–2021

All known group A streptococci [GAS] are susceptible to β-lactam antibiotics. We recently identified an invasive GAS (iGAS) variant (emm43.4/PBP2x-T553K) with unusually high minimum inhibitory concentrations (MICs) for ampicillin and amoxicillin, although clinically susceptible to β-lactams. We aimed to quantitate PBP2x variants, small changes in β-lactam MICs, and lineages within contemporary population-based iGAS. PBP2x substitutions were comprehensively identified among 13,727 iGAS recovered during 2015-2021, in the USA. Isolates were subjected to antimicrobial susceptibility testing employing low range agar diffusion and PBP2x variants were subjected to phylogenetic analyses. Fifty-five variants were defined based upon substitutions within an assigned PBP2x transpeptidase domain. Twenty-nine of these variants, representing 338/13,727 (2.5%) isolates and 16 emm types, exhibited slightly elevated β-lactam MICs, none of which were above clinical breakpoints. ABSTRACT All known group A streptococci [GAS] are susceptible to β-lactam antibiotics. We recently identified an invasive GAS (iGAS) variant (emm43.4/PBP2x-T553K) with unusually high minimum inhibitory concentrations (MICs) for ampicillin and amoxicillin, although clinically susceptible to β-lactams. We aimed to quantitate PBP2x variants, small changes in β-lactam MICs, and lineages within contemporary population-based iGAS. PBP2x substitutions were comprehensively identified among 13,727 iGAS recovered during 2015-2021, in the USA. Isolates were subjected to antimicrobial susceptibility testing employing low range agar diffusion and PBP2x variants were subjected to phylogenetic analyses. Fifty-five variants were defined based upon substitutions within an assigned PBP2x transpeptidase domain. Twenty-nine of these variants, representing 338/13,727 (2.5%) isolates and 16 emm types, exhibited slightly elevated β-lactam MICs, none of which were above clinical breakpoints. The emm43.4/PBP2x-T553K variant, comprised of two isolates, displayed the most significant phenotype (ampicillin MIC 0.25 μg/ml) and harbored missense mutations within 3 non-PBP genes with known involvement in antibiotic efflux, membrane insertion of PBP2x, and peptidoglycan remodeling. The proportion of all PBP2x variants with elevated MICs remained stable throughout 2015-2021 (<3.0%). The predominant lineage (emm4/PBP2x-M593T/ermT) was resistant to macrolides/lincosamides and comprised 129/340 (37.9%) of isolates with elevated β-lactam MICs. Continuing β-lactam selective pressure is likely to have selected PBP2x variants that had escaped scrutiny due to MICs that remain below clinical cutoffs. Higher MICs exhibited by emm43.4/PBP2x-T553K are probably rare due to the requirement of additional mutations. Although elevated β-lactam MICs remain uncommon, emm43.4/PBP2x-T553K and emm4/PBP2x-M593T/ermT lineages indicate that antibiotic stewardship and strain monitoring is necessary.