Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysing Ultra-Wide Band Positioning for Geofencing in a Safety Assurance Context

There is a desire to move towards more flexible and automated factories. To enable this, we need to assure the safety of these dynamic factories. This safety assurance must be achieved in a manner that does not unnecessarily constrain the systems and thus negate the benefits of flexibility and automation. We previously developed a modular safety assurance approach, using safety contracts, as a way to achieve this. In this case study we show how this approach can be applied to Autonomous Guided Vehicles (AGV) operating as part of a dynamic factory and why it is necessary. We empirically evaluate commercial, indoor fog/edge localisation technology to provide geofencing for hazardous areas in a laboratory. The experiments determine how factors such as AGV speeds, tag transmission timings, control software and AGV capabilities affect the ability of the AGV to stop outside the hazardous areas. We describe how this approach could be used to create a safety case for the AGV operation.
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection

News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users’ selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items’ stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users’ opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework

Purpose: Long scan time in phase encoding for forming complete K-space matrices is a critical drawback of MRI, making patients uncomfortable and wasting important time for diagnosing emergent diseases. This paper aims to reducing the scan time by actively and sequentially selecting partial phases in a short time so that a slice can be accurately reconstructed from the resultant slice-specific incomplete K-space matrix.
Towards Open Intent Detection

—The open intent detection problem is presented in this paper, which aims to identify known intents and detect open intent in natural language understanding. Current methods have two core challenges. On the one hand, the existing methods have limitations in learning robust representations to detect the open intent without any prior knowledge. On the other hand, there lacks an effective approach to learning the speciﬁc and compact decision boundary to distinguish the known intents and the open intent. This paper introduces an original pipeline framework, DA-ADB, to address these issues, which successively learns discriminative intent features with distance-aware strategy and appropriate decision boundaries adaptive to the feature space for open intent detection. The proposed method ﬁrst leverages distance information to enhance the distinguishing capability of the intent representations. Then, it obtains discriminative decision boundaries adaptive to the known intent feature space by balancing both the empirical and open space risks. Extensive experiments show the effectiveness of distance-aware and boundary learning strategies. Compared with the state-of-the-art methods, our method achieves substantial improvements on three benchmark intent datasets. It also yields robust performance with different proportions of labeled data and known categories.
Abstract Excision and $\ell^1$-Homology

Excision and `-Homology Johannes Witzig∗ Version of March 14, 2022 Abstract We use the abstract setting of excisive functors in the language of∞-categories to show that the best approximation to the `-homology functor by an excisive functor is trivial. Then we make an effort to explain the used language on a conceptual level for those who do not feel at home with ∞-categories, prove that the singular chain complex functor is indeed excisive in the abstract sense, and show how the latter leads to classical excision statements in the form of Mayer-Vietoris sequences.We use the abstract setting of excisive functors in the language of∞-categories to show that the best approximation to the `-homology functor by an excisive functor is trivial. Then we make an effort to explain the used language on a conceptual level for those who do not feel at home with ∞-categories, prove that the singular chain complex functor is indeed excisive in the abstract sense, and show how the latter leads to classical excision statements in the form of Mayer-Vietoris sequences.
Quantifying Collective Memories

(150 words) Collective memory is a common representation of the past shared by a group of people that modulates its identity. Recent literature on computational social science quantifies collective memories using expressions of those memories operationalized as the amount of collective attention focused on specific cultural icons, either artifacts or people. We model the temporal dynamics of collective memory using a two-step decay process, characterized by a short-lived and intense initial stage followed by a long-lived and milder decline of collective attention. Different collective memory mechanisms sustain the two-step process. The first is communicative memory, which corresponds to all memories supported by socializing acts. The second is cultural memory, which corresponds to all memories sustained by accessing records. Thus, this model predicts a transition time when cultural memory overcomes communicative memory. It has practical consequences related to shrinking or extending the time a specific topic is part of a community’s conversation, i.e., communicative memory, empowering policymakers with valuable time to generate solutions and compromises for contingent and public problems.
The origin of MOND acceleration and deep-MOND behavior from mass and energy cascade in dark matter flow

The MOND paradigm is an empirically motivated theory with modified gravity to reproduce many astronomical observations without invoking the dark matter hypothesis. Instead of falsifying the existence of dark matter, we propose that MOND is an effective theory naturally emerging from the long-range interaction and collisionless nature of dark matter flow. It essentially describes the dynamics of baryonic mass suspended in fluctuating dark matter fluid. We first review the unique properties of self-gravitating collisionless dark matter flow (SG-CFD), followed by their implications in the origin of MOND theory. To maximize system entropy, the long-range interaction requires a broad size of halos to be formed. These halos facilitate an inverse mass and energy cascade from small to large mass scales that involves a constant rate of energy transfer 7 2 3 4.6 10 u m s      across different scales. In addition to the velocity fluctuation with a typical scale u , the long-range interaction leads to a fluctuation in acceleration with a typical scale 0 a that matches the value of critical MOND acceleration. The velocity and acceleration fluctuations in dark matter flow satisfy the equality   0 3 u a u     that determines the critical MOND acceleration. A notable (unexplained) coincidence of cosmological constant   0 a c   might point to a dark energy density proportional to the variance of acceleration fluctuation, i.e. 2 0 vac a G   . With   0 0 354.61 u u z km s    from N-body simulation, the value of   10 2 0 0 1.2 10 a z m s     can be obtained. The mass and energy cascade represents an intermediate statistically steady state of dark matter flow. For a given particle velocity p v , the maximum entropy distributions developed from mass/energy cascade lead to a particle kinetic energy k p v   at small acceleration 0 a a  and 2 k p v   for 0 a a  . Combining this with the constant rate of energy transfer u  , both regular Newtonian dynamics and “deep-MOND” behavior can be fully recovered. One-Sentence Summary: The nature of self-gravitating collisionless dark matter flow might reveal the origin of MOND theory.
Tree-level Interference in VBF production of $Vh$

We study the production of the Higgs in a association with a vector ($V=W,Z$) via the VBF process, VBF-VH. In the Standard Model (SM), this process exhibits tree-level destructive interference between between $W$ and $Z$ mediated processes and is thus very sensitive to deviations in Higgs couplings to vector bosons. We study this process at both the HL-LHC as well as future high energy lepton colliders. We show in particular that the scenario where Higgs couplings have the same magnitude but opposite relative sign as in the SM, a scenario that is very difficult to distinguish without interference, can be probed with this process at either collider.
Particle Swarm Optimization based on Novelty Search

In this paper we propose a Particle Swarm Optimization algorithm combined with Novelty Search. Novelty Search finds novel place to search in the search domain and then Particle Swarm Optimization rigorously searches that area for global optimum solution. This method is never blocked in local optima because it is controlled by Novelty Search which is objective free. For those functions where there are many more local optima and second global optimum is far from true optimum, the present method works successfully. The present algorithm never stops until it searches entire search area. A series of experimental trials prove the robustness and effectiveness of the present algorithm on complex optimization test functions.
Surface acoustic wave lasing in a silicon optomechanical cavity

Integrated optomechanical cavities stand as a promising means to interface mechanical motion and guided optical modes. State-of-the-art demonstrations rely on optical and mechanical modes tightly confined of in micron-scale areas to achieve strong optomechanical coupling. However, the need for tight optomechanical confinement and the general use of suspended devices hinders interaction with external devices, limiting the potential for the implementation of complex circuits. Here, we propose and demonstrate a new approach for optomechanical cavities coupling free-propagating surface acoustic waves (SAWs) and guided optical modes. The cavity is formed by a periodic array of silicon nanopillars with subwavelength separation, implemented in silicon-on-insulator substrate. Optical pumping yields a strong radiation pressure that drives the harmonic vibration of the pillars, periodically deforming the silica under-cladding and exciting the SAW. The propagation of the SAW deforms the cavity period, modulating the resonance wavelength to close the optomechanical coupling loop. Based on this concept, we experimentally demonstrate a phonon laser at room temperature and ambient conditions with optical pump power as low as 1 mW. We also show the possibility to cascade this process, achieving a frequency comb generation with more than 30 harmonic lines. These results open a new path to achieve strong bidirectional coupling between integrated waveguides and SAW, with a great potential for a wide range of applications in quantum and classical domains.
Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU

Physiological monitoring in intensive care units (ICU) generates data that can be used in clinical research. However, the recording conditions in clinical settings limit the automated extraction of relevant information from physiological signals due to noise and artifacts. Therefore, removing artifacts before clinical research is essential. Manual annotation by experienced researchers, which is the gold standard for removing artifacts, is time-consuming and costly due to the volume of the data generated in the ICU. In this study, we propose a hybrid artifact detection system that combines a Variational Autoencoder with a statistical detection component for the labeling of artifactual samples to automate the costly process of cleaning physiological recordings. The system is applied to minute-by-minute mean blood pressure signals from an intensive care unit dataset. Its performance is verified by manual annotations made by an expert. We benchmark the performance of our system with two other systems that combine an ARIMA or an autoencoder-based model with our statistical detection component. Our results indicate that the system consistently achieves sensitivity and specificity levels of over 90%. Thus, it provides an initial foundation to automate data cleaning in recordings from ICU.
Automatic Identification and Classification of Bragging in Social Media

Bragging is a speech act employed with the goal of constructing a favorable self-image through positive statements about oneself. It is widespread in daily communication and especially popular in social media, where users aim to build a positive image of their persona directly or indirectly. In this paper, we present the first large scale study of bragging in computational linguistics, building on previous research in linguistics and pragmatics. To facilitate this, we introduce a new publicly available data set of tweets annotated for bragging and their types. We empirically evaluate different transformer-based models injected with linguistic information in (a) binary bragging classification, i.e., if tweets contain bragging statements or not; and (b) multi-class bragging type prediction including not bragging. Our results show that our models can predict bragging with macro F1 up to 72.42 and 35.95 in the binary and multi-class classification tasks respectively. Finally, we present an extensive linguistic and error analysis of bragging prediction to guide future research on this topic.
Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation

In this work, we study the deception of a Linear-Quadratic-Gaussian (LQG) agent by manipulating the cost signals. We show that a small falsification of the cost parameters will only lead to a bounded change in the optimal policy. The bound is linear on the amount of falsification the attacker can apply to the cost parameters. We propose an attack model where the attacker aims to mislead the agent into learning a `nefarious' policy by intentionally falsifying the cost parameters. We formulate the attack's problem as a convex optimization problem and develop necessary and sufficient conditions to check the achievability of the attacker's goal. We showcase the adversarial manipulation on two types of LQG learners: the batch RL learner and the other is the adaptive dynamic programming (ADP) learner. Our results demonstrate that with only 2.296% of falsification on the cost data, the attacker misleads the batch RL into learning the 'nefarious' policy that leads the vehicle to a dangerous position. The attacker can also gradually trick the ADP learner into learning the same `nefarious' policy by consistently feeding the learner a falsified cost signal that stays close to the actual cost signal. The paper aims to raise people's awareness of the security threats faced by RL-enabled control systems.
Single-index models for extreme value index regression

Since the extreme value index (EVI) controls the tail behaviour of the distribution function, the estimation of EVI is a very important topic in extreme value theory. Recent developments in the estimation of EVI along with covariates have been in the context of nonparametric regression. However, for the large dimension of covariates, the fully nonparametric estimator faces the problem of the curse of dimensionality. To avoid this, we apply the single index model to EVI regression under Pareto-type tailed distribution. We study the penalized maximum likelihood estimation of the single index model. The asymptotic properties of the estimator are also developed. Numerical studies are presented to show the efficiency of the proposed model.
Contextualized Sensorimotor Norms: multi-dimensional measures of sensorimotor strength for ambiguous English words, in context

Most large language models are trained on linguistic input alone, yet humans appear to ground their understanding of words in sensorimotor experience. A natural solution is to augment LM representations with human judgments of a word's sensorimotor associations (e.g., the Lancaster Sensorimotor Norms), but this raises another challenge: most words are ambiguous, and judgments of words in isolation fail to account for this multiplicity of meaning (e.g.,"wooden table"vs."data table"). We attempted to address this problem by building a new lexical resource of contextualized sensorimotor judgments for 112 English words, each rated in four different contexts (448 sentences total). We show that these ratings encode overlapping but distinct information from the Lancaster Sensorimotor Norms, and that they also predict other measures of interest (e.g., relatedness), above and beyond measures derived from BERT. Beyond shedding light on theoretical questions, we suggest that these ratings could be of use as a"challenge set"for researchers building grounded language models.
High Definition, Inexpensive, Underwater Mapping

In this paper we present a complete framework for Underwater SLAM utilizing a single inexpensive sensor. Over the recent years, imaging technology of action cameras is producing stunning results even under the challenging conditions of the underwater domain. The GoPro 9 camera provides high definition video in synchronization with an Inertial Measurement Unit (IMU) data stream encoded in a single mp4 file. The visual inertial SLAM framework is augmented to adjust the map after each loop closure. Data collected at an artificial wreck of the coast of South Carolina and in caverns and caves in Florida demonstrate the robustness of the proposed approach in a variety of conditions.
Random Ensemble Reinforcement Learning for Traffic Signal Control

Traffic signal control is a significant part of the construction of intelligent transportation. An efficient traffic signal control strategy can reduce traffic congestion, improve urban road traffic efficiency and facilitate people's lives. Existing reinforcement learning approaches for traffic signal control mainly focus on learning through a separate neural network. Such an independent neural network may fall into the local optimum of the training results. Worse more, the collected data can only be sampled once, so the data utilization rate is low. Therefore, we propose the Random Ensemble Double DQN Light (RELight) model. It can dynamically learn traffic signal control strategies through reinforcement learning and combine random ensemble learning to avoid falling into the local optimum to reach the optimal strategy. Moreover, we introduce the Update-To-Data (UTD) ratio to control the number of data reuses to improve the problem of low data utilization. In addition, we have conducted sufficient experiments on synthetic data and real-world data to prove that our proposed method can achieve better traffic signal control effects than the existing optimal methods.
CVaR-based Safety Analysis in the Infinite Time Horizon Setting

We develop a risk-averse safety analysis method for stochastic systems on discrete infinite time horizons. Our method quantifies the notion of risk for a control system in terms of the severity of a harmful random outcome in a fraction of the worst cases. In contrast, classical methods quantify risk in terms of the probability of a harmful event. Our theoretical arguments are based on the analysis of a value iteration algorithm on an augmented state space. We provide conditions to guarantee the existence of an optimal policy on this space. We illustrate the method numerically using an example from the domain of stormwater management.
Mechatronics: Principles, Technologies and Applications

Mastering the finest art of ‘mechatronics' currently looks one of the most attractive task of modern engineering technology and science. Many are the applications which resort to the interdisciplinary approach of mechatronics to enhance the performance, quality and safety of either product or process. Some are very traditional, like hard disk drives, biomedical, automotive and aerospace systems, other are fairly new like micro and nano electromechanical systems, unmanned air vehicles, intelligent machining and manufacturing systems or bioinspired devices. A first generation of mechatronic products was conceived to embed a suitable ‘smartness' to improve the skill of self-adapting to any abrupt variation of operating conditions, by resorting to the ‘synergistic integration of mechanical engineering with electronics and control in the design and manufacturing of product process' as mechatronics was brightly defined. Nowadays, a mechatronic design is surely based on its interdisciplinary nature, but its real meaning was harmonized with an effective contamination among different application domains, methodologies and technologies, being smartly applied to reach the highest result in any product, system and process development. A recent experience within the frame of the EMEA District of the American Society of Mechanical Engineers (ASME) was a chance to get an impression of the scientific and industrial research activity performed in some fields of mechatronics. Some exciting examples describing how different competences, disciplines, technologies met in an innovative mechatronic system are herein exposed by some researchers of the EMEA area of the world. They deal with several domains, like the hard disk drive technology, biomedical prostheses, fluidic automation, UAV Vision System, vibration monitoring and suppression in steelmaking plants, materials machining and smart composites. These examples will narrate to the reader who is still looking for the meaning of mechatronics how some approaches, as neural network positioning control, chaos prevention, myoelectric stimulation of prosthesis, human detection by vision system, multi-physics modeling and control of dynamics are currently implemented in a sort of artificial intelligence in small scale device, as in a finger of a biotronic hand or in a large equipment like an electric arc furnace. Moreover, the reader will realize how intensively this goal is achieved by exploiting the available technologies as additive manufacturing or fiber optics embedded into composite structures to reduce the cost, weight or volume of the product or to improve the quality and accuracy of a material processing like in rolling or in turning against the risk of self-excited chatter vibration. This scenario is covering a wide range of mechatronic applications, although many others are currently developed in several fileds of engineering