Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust H ∞  control via a stable decentralized nonlinear output feedback controller

This paper presents a new method to construct a decentralized nonlinear robust H ∞ controller for a class of large‐scale nonlinear uncertain systems. The admissible uncertainties and nonlinearities in the system satisfy integral quadratic constraints and global Lipschitz conditions, respectively. The decentralized controller, which is required to be stable, is capable of exploiting known nonlinearities and interconnections between subsystems without treating them as uncertainties. Instead, additional uncertainties are introduced because of the discrepancies between nondecentralized and decentralized nonlinear output feedback controllers. The H ∞ control objective is to achieve an absolutely stable closed‐loop system with a specified disturbance attenuation level. A solution to this control problem involves stabilizing solutions to algebraic Riccati equations parametrized by scaling constants corresponding to the uncertainties and nonlinearities. This formulation is nonconvex; hence, an evolutionary optimization method is applied to solve the control problem considered. Copyright © 2012 John Wiley & Sons, Ltd.
Ctp1 CtIP and Rad32 Mre11 Nuclease Activity Are Required for Rec12 Spo11 Removal, but Rec12 Spo11 Removal Is Dispensable for Other MRN-Dependent Meiotic Functions (cid:1)

The evolutionarily conserved Mre11/Rad50/Nbs1 (MRN) complex is involved in various aspects of meiosis. Whereas available evidence suggests that the Mre11 nuclease activity might be responsible for Spo11 removal in Saccharomyces cerevisiae , this has not been conﬁrmed experimentally. This study demonstrates for the ﬁrst time that Mre11 ( Schizosaccharomyces pombe Rad32 Mre11 ) nuclease activity is required for the removal of Rec12 Spo11 . Furthermore, we show that the CtIP homologue Ctp1 is required for Rec12 Spo11 removal, conﬁrming functional conservation between Ctp1 CtIP and the more distantly related Sae2 protein from Saccharomyces cerevisiae . Finally, we show that the MRN complex is required for meiotic recombination, chromatin remodeling at the ade6-M26 recombination hot spot, and formation of linear elements (which are the equivalent of However, a small fraction of cells are able to remove Rec12 Spo11 (through an unknown mechanism), allowing repair of the DSBs and viable spore formation. For the rad50 (cid:3) mutant, these survivors show a strong reduction in recombination rates, suggesting that they survive through a nonrecombinogenic survival mechanism. The rad50S cells are proﬁcient for meiotic recombination (once Rec12 Spo11 has been removed), and the surviving spores therefore show normal meiotic recombination levels.
Health impact of the Volendam fire disaster

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BATSE 5B Sky Exposure and Trigger Efficiency

Sky Exposure and Trigger Efficiency are presented for the BATSE 5B burst catalog. Previously published BATSE sky exposure has been improved by an updated CGRO orbital model. Trigger efficiency is presented for the long soft class of bursts as well as for the short hard class of bursts. This demonstrates that BATSE is significantly more sensitive to hard bursts, implying that (1) short bursts are being oversampled relative to long bursts based on spectral characteristics, and (2) there is little evidence for a class of undetected hard bursts.
Impact of the electron density and temperature gradient on drift-wave turbulence in the Large Plasma Device

In this paper we present an experimental study of edge turbulence in the Large Plasma Device at UCLA. We utilize a scan of discharge power and prefill pressure (neutral density) to show experimentally that turbulent density fluctuations decrease with decreasing density gradient, as predicted for resistive drift-wave turbulence (RDWT). As expected for RDWT, we observe that the cross-phase between the density and potential fluctuations is close to 0. Moreover, the addition of an electron temperature gradient leads to a reduction in the amplitude of the density fluctuations, as expected for RDWT. However, counter to theoretical expectations, we find that the potential fluctuations do not follow the same trends as the density fluctuations for changes either in density gradients or the addition of a temperature gradient. The disconnect between the density and potential fluctuations is connected to changes in the parallel flows as a result of differences in the prefill pressure, i.e. neutral density. Further analysis of the density and potential fluctuation spectra show that the electron temperature gradient reduces the low frequency fluctuations up to $10 \,{\rm kHz}$ and the introduction of a temperature gradient leads to an unexpected ${\sim }{\rm \pi}$ shift of the density–potential cross-phase at ${\sim }10\,{\rm kHz}$, while maintaining the typical resistive drift-wave cross-phase at lower frequencies. These experiments partly confirm existing knowledge on resistive drift-wave turbulence, but also introduce new observations that indicate a need for dedicated nonlinear three-dimensional turbulence simulations that include neutrals.
Nicotinamide Adenine Dinucleotide Supplementation in Parkinson's Disease: A Potential Disease‐Modifying Agent Targeting Multiple Pathways

modi ﬁ cation and neuroprotection are still unmet needs in Parkinson ’ s disease (PD), due to several challenges, including multiple pathogenic the upregulation of genes involved in proteasomal function, mitochondrial homeostasis, and antioxidant function. Particu-larly, given the lack of effective neuroprotective therapies and compounds targeting single pathways of neurodegeneration that have failed to maintain their ef ﬁ cacy when tested in phase II and III trials. 1 NAD + supplementation with NR appears to impact multiple pathways involved in the pathophysiology of PD, supporting further investigation of NR as a promising adjunct to the current treatments of PD.
Using CHERCHE to Empower Newcomers into Neural Information Retrieval - Extended Abstract

In this paper, we briefly 1 present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models and show their capabilities in small collections. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. CHERCHE is oriented to newcomers into that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche
Possibilities Emerging on the Trajectory from IoT to IoMusT: Enabling Ubiquitous Musical Interactions for Wellbeing

The Internet of Musical Things (IoMusT) and ubiquitous music (ubimus) are interrelated research fields concerned with the design and development of advanced technology to support novel musical and artistic experiences. IoMusT primarily motivates the deployment of embedded computing devices and platforms for musically oriented individual or collaborative activities. Ubimus schemes are musically driven while keeping the participating agents and entities (such as tablets or computers), interfaces (hardware, or software), interaction metaphors (visual, tactile, or gestural), and instruments (traditional or novel) not necessarily complex. It also encourages the design of new musical interactions for participants with little know-how of the musical domain. Within the context of initiatives embracing the internet of things (IoT) for healthcare, alongside remodeling IoT devices for IoMusT interactions, this paper considers the area of ubiquitous musical interaction design for music therapies, offering an example application. This convergence, with the appropriate technologies, could enable new opportunities for the promotion of wellbeing, both individually and collectively, in-person and remotely.
Transformer-based Model for Text Classification in Ukrainian

The purpose of this paper is to find a solution for printed text classification in Ukrainian, as well as to choose the means for its implementation. The paper considers the problem of identification of short texts by their scientific topic. A model, built for classification, is described. The current state of NLP and transfer learning is studied too. In practice, the effectiveness of the implemented methods is proven, which allows to obtain good results of text classification. These methods and approaches include concepts such as transfer learning, NLP, BERT. The model is built using Python programming language and some of its machine learning libraries. A multilanguage BERT model by Google is additionally trained on Ukrainian texts from school subjects. After that, the questions from the external independent evaluation are submitted to the input, and the model classifies them.
Towards an Information Retrieval Evaluation Library

This manuscript discusses our ongoing work on ranx , a Python evaluation library for Information Retrieval. First, we introduce our work, summarize the already available functionalities, show the user-friendly nature of our tool through code snippets, and briefly discuss the technologies we relied on for the implementation and their advantages. Then, we present the upcoming features, such as several Metasearch algorithms, and introduce the long-term goals of our project.
Signal Processing in Space and Time - A Multidimensional Fourier Approach

Sound waves propagate through space and time by transference of energy between the particles in the medium, which vibrate according to the oscillation patterns of the waves. These vibrations can be captured by a microphone and translated into a digital signal, representing the amplitude of the sound pressure as a function of time. The signal obtained by the microphone characterizes the time-domain behavior of the acoustic wave field, but has no information related to the spatial domain. The spatial information can be obtained by measuring the vibrations with an array of microphones distributed at multiple locations in space. This allows the amplitude of the sound pressure to be represented not only as a function of time but also as a function of space. The use of microphone arrays creates a new class of signals that is somewhat unfamiliar to Fourier analysis. Current paradigms try to circumvent the problem by treating the microphone signals as multiple “cooperating” signals, and applying the Fourier analysis to each signal individually. Conceptually, however, this is not faithful to the mathematics of the wave equation, which expresses the acoustic wave field as a single function of space and time, and not as multiple functions of time. The goal of this thesis is to provide a formulation of Fourier theory that treats the wave field as a single function of space and time, and allows it to be processed as a multidimensional signal using the theory of digital signal processing (DSP). We base this on a physical principle known as the Huygens principle, which essentially says that the wave field can be sampled at the surface of a given region in space and subsequently reconstructed in the same region, using only the samples obtained at the surface. To translate this into DSP language, we show that the Huygens principle can be expressed as a linear system that is both spaceand time-invariant, and can be formulated as a convolution operation. If the input signal is transformed into the spatio-temporal Fourier domain, the system can also be analyzed according to its frequency response. In the first half of the thesis, we derive theoretical results that express the 4-D Fourier transform of the wave field as a function of the parameters of the scene, such as the number of sources and their locations, the source signals, and the geometry of the microphone array. We also show that the wave field can be effectively analyzed on a small scale using what we call the space/time–frequency representation space, consisting of a Gabor representation across the spatio-temporal manifold defined by the microphone array. These results are obtained by treating the signals as continuous functions of space and time. The second half of the thesis is dedicated to processing the wave field in discrete space and time, using Nyquist sampling theory and multidimensional filter banks theory. In particular, we show examples of orthogonal filter banks that effectively represent the wave field in terms of its elementary components while satisfying the requirements of critical sampling
Deep traps in unpassivated and Sc2O3-passivated AlGaN/GaN high electron mobility transistors

Sc2O3 thin-film layers deposited by rf plasma-assisted molecular-beam epitaxy were found to significantly reduce the concentration of prominent surface traps with activation energies of 1 and 0.9 eV on AlGaN/GaN high electron mobility transistors (HEMTs). The surface passivation is accompanied by effective mitigation of the current collapse observed under rf conditions in HEMTs without Sc2O3. The passivation is stable to post-deposition annealing temperatures of 400 °C and device degradation at higher temperatures is due to reaction of the gate metal with the AlGaN.
Multiagent Reinforcement Learning for Traffic Signal Control: a k-Nearest Neighbors Based Approach

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence in particular. As it is often the case, it is not possible to increase the capacity of road networks, therefore a more efficient use of the available transportation infrastructure is required. This relates closely to multiagent systems and to multiagent reinforcement learning, as many problems in traffic management and control are inherently distributed. However, one of the main challenges of this domain is that the state space is large and continuous, which makes it difficult to properly discretize states and also causes many RL algorithms to converge more slowly. To address these issues, a multiagent system with agents learning independently via a temporal difference learning algorithm based on k-nearest neighbors is presented as an option to control traffic signals in real-time. Our results show that the proposed method is both effective (reduces the average waiting time of vehicles in a traffic network) and efficient (the learning task is significantly accelerated), when compared to a baseline reported in the literature.
Evaluating Recommendations in a User Interface With Multiple Carousels

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels , each built with a specific criteria (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. The traditional offline evaluation protocol however does not take this into account. To address this limitation, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. Our results indicate that under a carousel setting the ranking of the algorithms changes sometimes significantly. This work is an extended abstract of [1].
An Analysis of User Click Behaviour in Online Hotel Search

Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors in their decision making. Hence, we aim at exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on (i) ranking position, (ii) price, (iii) rating, and (iv) number of reviews when analyzing users’ click behavior. The results showed that there were “two elephants in the room”: position and price, that heavily influenced the user decision-making and need to be taken into account when, for instance, trying to learn user preferences from offline data in order to bootstrap a recommender system.
Why Users of the Internet of Things need Error-Avoiding Modeling Languages and Queries

The Internet of Things (IoT) is used by non-technical users. In the future, they will be able to arrange their IoT devices and, therefore, create processes. Since the devices can send their information simultaneously, these processes must enable parallelism. This paper argues why modeling languages for non-technical users should only create structurally correct processes that avoid deadlocks and abundances during runtime. Furthermore, since many users will create their own processes, it should be possible to reuse and share process models. This makes a query language for process models necessary. Using loop decomposition, an approach to make cyclic processes acyclic, this paper explains why modeling languages can be simplified for users and why only acyclic processes should be stored in process collections. For all of these topics, IoT can learn from business process management and compiler theory.
Routing for Wireless Sensor Networks: From Collection to Event-Triggered Applications

Wireless Sensor Networks (WSNs) are collections of sensing devices using wireless communication to exchange data. In the past decades, steep advancements in the areas of microelectronics and communication systems have driven an explosive growth in the deployment of WSNs. Novel WSN applications have penetrated multiple areas, from monitoring the structural stability of historic buildings, to tracking animals in order to understand their behavior, or monitoring humans’ health. The need to convey data from increasingly complex applications in a reliable and cost-effective manner translates into stringent performance requirements for the underlying WSNs. In the frame of this thesis, we have focused on developing routing protocols for multi-hop WSNs, that significantly improve their reliability, energy consumption and latency. Acknowledging the need for application-specific trade-offs, we have split our contribution into two parts. Part 1 focuses on collection protocols, catering to applications with high reliability and energy efficiency constraints, while the protocols developed in part 2 are subject to an additional bounded latency constraint. The two mechanisms introduced in the first part, WiseNE and Rep, enable the use of composite metrics, and thus significantly improve the link estimation accuracy and transmission reliability, at an energy expense far lower than the one achieved in previous proposals. The novel beaconing scheme WiseNE enables the energy-efficient addition of the RSSI (Received Signal Strength Indication) and LQI (Link Quality Indication) metrics to the link quality estimate by decoupling the sampling and exploration periods of each mote. This decoupling allows the use of the Trickle Algorithm, a key driver of protocols’ energy efficiency, in conjunction with composite metrics. WiseNE has been applied to the Triangle Metric and validated in an online deployment. The section continues by introducing Rep, a novel sampling mechanism that leverages the packet repetitions already present in low-power preamble-sampling MAC protocols in order to improve the WSN energy consumption by one order of magnitude. WiseNE, Rep and the novel PRSSI (Penalized RSSI, a combination of PRR and RSSI) composite metric have been validated in a real smart city deployment. Part 2 introduces two mechanisms that were developed in the frame of the WiseSkin project (an initiative aimed at designing highly sensitive artificial skin for human limb prostheses), and are generally applicable to the domain of cyber-physical systems. It starts with Glossy-W, a protocol that leverages the superior energy-latency trade-off of flooding schemes based on concurrent transmissions. Glossy-W ensures the stringent synchronization requirements
TIPS: Search and Analytics for Social Science Research

The vast amount of digital data available online, which include digitized traditional media, offers new opportunities for social science researchers. This is, for instance, the case of social processes where the temporal dimension is crucial and longitudinal data is necessary. This paper presents a system called TIPS, designed in order to support social science researchers in their investigations. We will present the main modules of the system and how it can support diverse research tasks on longitudinal data such as archives of digitized newspapers
An IoT-based Immersive Smart Home System for Seniors with Neurocognitive Disorders

Commercial Internet of Things (IoT) devices allow for creating affordable and customized smart home systems for seniors with neurocognitive disorders (SwNCDs), such as dementia and Alzheimer’s. An IoT-based system can support SwNCDs in completing daily living activities by sending reminders and notifications via smartphone applications. However, this approach comes with two main challenges that negatively affect the overall system usability. Firstly, customizing and interacting with IoT devices requires a certain level of technology literacy, which many SwNCDs and caregivers don’t have. Secondly, relying solely on smartphone notifications is not suitable for homecare purposes. In response to these challenges, we present the design, development and architecture of our immersive IoT-based smart home system prototype. Our proposed system allows senior users to interact with the smart home system effortlessly and hands-free via a head-mounted mixed reality device. In addition, the current prototype supports two use cases: medication reminders and cooking safety.
TemporalWord Embeddings for Early Detection of Signs of Depression

Depression is one of the most debilitating mental health diseases. Detecting the presence of depressive symptoms in the early stages of the disease is essential to reduce further consequences. As the study of language and behaviour is a pivotal component in mental research, social network content positions itself as a helpful tool. This paper introduces a general framework to analyze variations in the individual’s use of language over time on social media. We present a novel approach using temporal word representations to quantify the magnitude of words movements. This framework allows us to evaluate if words evolution can reveal the presence of depressive tendencies. We adapted different temporal word embedding representations to our framework and assessed them in Reddit benchmark datasets. Our results achieve high competitiveness compared with state-of-the-art methods, showing the potential that time-aware word representation models can bring to early detection scenarios.