Miracle-dz/UniFAR-FTUs huggingface dataset:
Tunable UV generation with a frequency-mixed type II OPO

Remote measurements of atmospheric gases such as ozone from airborne and ground-based platforms are currently made with a wide variety of UV laser sources. The capabilities of these existing systems could be significantly enhanced if compact, efficient, reliable, high peak power, tunable solid state laser sources could be developed. Such developments would also enable these systems to be deployed in small aircraft or on orbiting Space platforms. We have produced 38 mJ pulses of UV laser radiation with a frequency-mixed type II KTA OPO in the 290-300 nm region. The OPO exhibits 42% slope efficiency. The entire transmitter fits into a lidar transceiver package consistent with the size constraints of an unmanned aeronautical vehicle (UAV). The conversion efficiency from 1064-nm pump to 300 nm for this laser system (7%) is among the highest reported.
Indoor localisation using white LEDs

Energy efficient white LEDs are increasingly being used for indoor lighting but unlike conventional lighting, the intensity of the light emitted by white LEDs can be modulated at high frequencies. Described is a novel positioning technique based on transmitting signals with known sinusoidal components from a number of LEDs. The receiver uses the resultant optical signal at the receiver photodiode to calculate the receiver position. Proof of concept experiments show that white LEDs designed for indoor lighting can be used as the transmitters. The modulation bandwidth of white LEDs is large enough to give a range of localisation that is compatible with typical indoor applications.
Modelling ventilation and cooling of the rotors of salient pole machines

Computational fluid dynamics (CFD) offers the potential to model ventilation and cooling in electrical machines. This can provide the opportunity to optimize the thermal design of machines at an early stage without the need for extensive and costly experimentation. However, designers need to have confidence in the use of CFD and this paper summarizes the results of research work to validate the CFD modelling of large salient pole machines. It is shown that the commercial CFD code Fluent demonstrates a good ability to predict air flow and heat transfer on the rotor of a salient pole machine.
Discovery of I-WP minimal-surface-based photonic crystal in the scale of a longhorn beetle

The structural colours of certain insects are produced by three-dimensional periodic cuticle networks. The topology of the cuticle network is known to be based on the mathematically well-defined triply periodic minimal surface. In this paper, we report the discovery of an I-WP minimal-surface-based photonic crystal on the scale of a longhorn beetle. In contrast to gyroid or diamond surfaces, which are found in butterfly and weevil scales, respectively, the I-WP surface is an unbalanced minimal surface, wherein two subspaces separated by the surface are different in terms of shape and volume fraction. Furthermore, adjacent photonic crystal domains were observed to share a particular crystal plane as their domain boundary, indicating that they were developed as twin crystals. These structural features pose certain new questions regarding the development of biological photonic crystals. We also performed an optical analysis of the structural colour of the longhorn beetle and successfully explained the wavelength of reflection by the photonic bandgap of the I-WP photonic crystal.
Development of a mesoscopic framework spanning nanoscale protofibril dynamics to macro-scale fibrin clot formation

Thrombi form a micro-scale fibrin network consisting of an interlinked structure of nanoscale protofibrils, resulting in haemostasis. It is theorized that the mechanical effect of the fibrin clot is caused by the polymeric protofibrils between crosslinks, or to their dynamics on a nanoscale order. Despite a number of studies, however, it is still unknown, how the nanoscale protofibril dynamics affect the formation of the macro-scale fibrin clot and thus its mechanical properties. A mesoscopic framework would be useful to tackle this multi-scale problem, but it has not yet been established. We thus propose a minimal mesoscopic model for protofibrils based on Brownian dynamics, and performed numerical simulations of protofibril aggregation. We also performed stretch tests of polymeric protofibrils to quantify the elasticity of fibrin clots. Our model results successfully captured the conformational properties of aggregated protofibrils, e.g., strain-hardening response. Furthermore, the results suggest that the bending stiffness of individual protofibrils increases to resist extension.
Automatic Classification of Attributes in German Adjective-Noun Phrases

Adjectives such as heavy (as in heavy rain) and windy (as in windy day) provide possible values for the attributes intensity and climate, respectively. The attributes themselves are not overtly realized and are in this sense implicit. While these attributes can be easily inferred by humans, their automatic classification poses a challenging task for computational models. We present the following contributions: (1) We gain new insights into the attribute selection task for German. More specifically, we develop computational models for this task that are able to generalize to unseen data. Moreover, we show that classification accuracy depends, inter alia, on the degree of polysemy of the lexemes involved, on the generalization potential of the training data and on the degree of semantic transparency of the adjective-noun pairs in question. (2) We provide the first resource for computational and linguistic experiments with German adjective-noun pairs that can be used for attribute selection and related tasks. In order to safeguard against unwelcome memorization effects, we present an automatic data augmentation method based on a lexical resource that can increase the size of the training data to a large extent.
Requesting clarifications with speech and gestures

In multimodal natural language interaction both speech and non-speech gestures are involved in the basic mechanism of grounding and repair. We discuss a couple of multimodal clarifica- tion requests and argue that gestures, as well as speech expressions, underlie comparable paral- lelism constraints. In order to make this precise, we slightly extend the formal dialogue frame- work KoS to cover also gestural counterparts of verbal locutionary propositions.
Predicate Representations and Polysemy in VerbNet Semantic Parsing

Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate how role labeling performance on low-frequency predicates and out-of-domain data can be further improved by using VerbNet, a verb lexicon that groups verbs into hierarchical classes based on shared syntactic and semantic behavior and defines semantic representations describing relations between arguments. We find that VerbNet classes provide an effective level of abstraction, improving generalization on low-frequency predicates by allowing them to learn from the training examples of other predicates belonging to the same class. We also find that joint training of VerbNet role labeling and predicate disambiguation of VerbNet classes for polysemous verbs leads to improvements in both tasks, naturally supporting the extraction of VerbNet’s semantic representations.
Large-scale text pre-training helps with dialogue act recognition, but not without fine-tuning

We use dialogue act recognition (DAR) to investigate how well BERT represents utterances in dialogue, and how fine-tuning and large-scale pre-training contribute to its performance. We find that while both the standard BERT pre-training and pretraining on dialogue-like data are useful, task-specific fine-tuning is essential for good performance.
Book Review: Resisting the News: Engaged Audiences, Alternative Media, and Popular Critique of Journalism by Jennifer Rauch

While the current hurricane of global disinformation/misinformation promulgated by far-Right populist-authoritarian politics shows little sign yet of abating, Resisting the News lends itself valuably to understanding it. In expanding audience research into non-legacy news media, the title Resisting the News indicates a focus not on audiences wholly abstaining from news, but how audiences resist certain kinds of news while embracing others—with a particular interest in what these audiences regard as hazily defined non-legacy alternative media, both leftand right-wing. The title also emphasizes that news is not an inert element simply conveyed unchanged from makers to audiences. The intentions of news purveyors cannot be assumed to automatically replicate in readers’ heads. Instead, news of whatever ideological stripe consists of dynamic propositions about the world, which audiences actively interpret. For media scholars familiar with decades of research presuming an “active audience,” this is hardly a novel claim. However, given how today’s commentators have such a hard time understanding much less explaining how large swaths of readers ignore accounts by legacy news organizations in favor of screeds by baldfaced partisan rumor-mongering outfits, it is an insight well worth recovering, and this book does an admirable job doing so. The book also does one better than typical reception studies by clearly establishing that audiences’ interpretations of news are not simply individual preferences either random or infinite. Instead, its important theoretical contribution is the recovery of the social, ritual basis of audience interpretation. The nature and variety of interpretations are generated through pre-existing, interpretive frameworks consisting in significant part of lay theories of media and society. This focus valuably steers the book’s argument away from simply cataloging audience interpretations Book Review
The spiral antenna

A simplified description of the radiation mechanism of the spiral antenna is given, and the effects of the various parameters on the characteristics of the cavity-backed Archimedian spiral are discussed. Experimental data illustrating the effect of cavity diameter, cavity depth, conductor loading, and spiral rate of growth are offered as design guides. Finally, a spiral antenna and balun feed are described. Over an octave frequency range, ellipticity ratios of less than 3 db have been maintained over azimuth angles greater than \pm60 degrees, boresight errors have been virtually eliminated, and standing wave ratios of less than 2:1 have been realized.
Application of magnetorheological fluids for a miniature haptic button: Experimental evaluation

This study investigates a miniature haptic button actuated by magnetorheological fluids with the aim of conveying kinesthetic information or realistic button sensations to users for small electronic devices. To this end, a prototype haptic button that creates varying kinesthetic sensations was designed and constructed. The design focus was to maximize the resistive force generated by magnetorheological fluids in a given size by using multiple operating modes of the fluids. In order to evaluate the performance of the prototype, a test setup consisting of a microstage and a precision load cell was constructed. Using the setup, the resistive force of the prototype button was measured by changing the indented depth and the input current. The results show that the force rate (defined as the ratio of the difference between the maximum force and the minimum force to the maximum force) is over 72% for all indented depths (up to 1.5 mm). This change is sufficient to create various button sensations, indicating that the proposed haptic button can offer a range of resistive force change that can be conveyed to human operators.
Exposure to Robotic Virtual Agent Affects Adoption of Intentional Stance

Understanding how and when humans attribute intentionality to artificial agents is a key issue in contemporary human and technological sciences. This paper addresses the question of whether adopting intentional stance can be modulated by exposure to a 3D animated robot character, and whether this depends on the human-likeness of the character's behavior. We report three experiments investigating how appearance and behavioral features of a virtual character affect humans’ attribution of intentionality toward artificial social agents. The results show that adoption of intentional stance can be modulated depending on participants' expectations about the agent. This study brings attention to specific features of virtual agents and insights for further work in the field of virtual interaction.
The Effect of Robot Decision Making on Human Perception of a Robot in a Collaborative Task - A Remote Study

The use of collaborative robots is becoming more widespread across industries. This makes it essential to study robot planning in order to work effectively and smoothly with human teammates while maintaining a positive human perception of the robots. This paper evaluates the influence of a robot’s strategy and decision making on the participants’ perception of the robot. We designed an online experiment where a robot and participants need to collaborate and organize a set of objects. We studied three different strategies where the robot either prioritizes the human’s objective, its own objective, or uses a balanced strategy. We then analyze and report the results based on participants’ answers to questionnaires before and after the experiment, their comments, and their actions during the experiment. The results show that strategies prioritizing the human’s objective, or balancing between the robot’s and the human’s objectives can effectively improve participants’ perception of the robot and create a collaborative environment.
From market logics to esthetic logics: interaction between de novos and de alios in the comics publishing industry

Abstract The article contributes to a better understanding of the impact of the interaction between diverse organizations in the same creative industry on their logics and practices, by investigating the role of the origin of organizations – as a dimension of organizational diversity – in this process. The main distinction is that between two organizational categories: de novos, who are founded to function in a specific field, and de alios, who have their origin in another, earlier developed field. The interaction between both types is analyzed within a specific creative industry: Dutch language comics publishers. Using data on industry level that cover the period 1945–1994, and combining this with case studies of four individual publishers, I argue that when this industry gradually became dominated by de novos, de alio publishers who aspired to stay in the field, adapted their practices to those of de novos. If in a creative industry smaller, specialized companies (de novos) prevail, this may force much larger companies (de alios) in the same field to copy their practices. Thus, even in an originally mainly market-oriented industry such as that of comics publishing, within-field interaction can lead to a shift towards more esthetic logics.
Psychophysiology, cognition, and political differences Guest editors' introduction to the special issue

Abstract. We introduce the Politics and the Life Sciences special issue on Psychophysiology, Cognition, and Political Differences. This issue represents the second special issue funded by the Association for Politics and the Life Sciences that adheres to the Open Science Framework for registered reports (RR). Here pre-analysis plans (PAPs) are peer-reviewed and given in-principle acceptance (IPA) prior to data being collected and/or analyzed, and are published contingent upon the preregistration of the study being followed as proposed. Bound by a common theme of the importance of incorporating psychophysiological perspectives into the study of politics, broadly defined, the articles in this special issue feature a unique set of research questions and methodologies. In the following, we summarize the findings, discuss the innovations produced by this research, and highlight the importance of open science for the future of political science research.
Emergence and maintenance of variable-length actin filaments in a limiting pool of building blocks

Actin is one of the key structural components of the eukaryotic cytoskeleton that regulates cellular architecture and mechanical properties. Dynamic regulation of actin filament length and organization is essential for the control of many physiological processes including cell adhesion, motility and division. While previous studies have mostly focused on the mechanisms controlling the mean length of individual actin filaments, it remains poorly understood how distinct actin filament populations in cells maintain different lengths using the same set of molecular building blocks. Here we develop a theoretical model for the length regulation of multiple actin filaments by nucleation and growth rate modulation by actin binding proteins in a limiting pool of monomers. We first show that spontaneous nucleation of actin filaments naturally leads to heterogeneities in filament length distribution. We then investigate the effects of filament growth inhibition by capping proteins and growth promotion by formin proteins on filament length distribution. We find that filament length heterogeneity can be increased by growth inhibition, whereas growth promoters do not significantly affect length heterogeneity. Interestingly, a competition between filament growth inhibitors and growth promoters can give rise to bimodal filament length distribution as well as a highly heterogeneous length distribution with large statistical dispersion. We quantitatively predict how heterogeneity in actin filament length can be modulated by tuning F-actin nucleation and growth rates in order to create distinct filament subpopulations with different lengths. SIGNIFICANCE Actin filaments organize into different functional network architectures within eukaryotic cells. To maintain distinct actin network architectures, it is essential to regulate the lengths of actin filaments. While the mechanisms controlling the lengths of individual actin filaments have been extensively studied, the regulation of length heterogeneity in actin filament populations is not well understood. Here we show that the modulation of actin filament growth and nucleation rates by actin binding proteins can regulate actin length distribution and create distinct sub-populations with different lengths. In particular, by tuning concentrations of formin, profilin and capping proteins, various aspects of actin filament length distribution can be controlled. Insights gained from our results may have significant implications for the regulation of actin filament length heterogeneity and architecture within a cell.
Sequential Randomized Smoothing for Adversarially Robust Speech Recognition

While Automatic Speech Recognition has been shown to be vulnerable to adversarial attacks, defenses against these attacks are still lagging. Existing, naive defenses can be partially broken with an adaptive attack. In classification tasks, the Randomized Smoothing paradigm has been shown to be effective at defending models. However, it is difficult to apply this paradigm to ASR tasks, due to their complexity and the sequential nature of their outputs. Our paper overcomes some of these challenges by leveraging speech-specific tools like enhancement and ROVER voting to design an ASR model that is robust to perturbations. We apply adaptive versions of state-of-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion.
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection

Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental and fine-grained sub-tasks in aspect-level sentiment analysis (ALSA). In the textual analysis, joint extracting both aspect terms and sentiment polarities has been drawn much attention due to the better applications than individual sub-task. However, in the multi-modal scenario, the existing studies are limited to handle each sub-task independently, which fails to model the innate connection between the above two objectives and ignores the better applications. Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA). Specifically, we first build an auxiliary text-image relation detection module to control the proper exploitation of visual information. Second, we adopt the hierarchical framework to bridge the multi-modal connection between MATE and MASC, as well as separately visual guiding for each sub module. Finally, we can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. Extensive experiments show the effectiveness of our approach against the joint textual approaches, pipeline and collapsed multi-modal approaches.
Integrating Personalized PageRank into Neural Word Sense Disambiguation

Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph. However, state-of-the-art approaches have been successful in exploiting only the local structure of the graph, with only close neighbors of a given synset influencing the prediction. In this work, we improve a classification model by recomputing logits as a function of both the vanilla independently produced logits and the global WordNet graph. We achieve this by incorporating an online neural approximated PageRank, which enables us to refine edge weights as well. This method exploits the global graph structure while keeping space requirements linear in the number of edges. We obtain strong improvements, matching the current state of the art. Code is available at https://github.com/SapienzaNLP/neural-pagerank-wsd
Effective Convolutional Attention Network for Multi-label Clinical Document Classification

Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documents. Our innovations are three-fold: (1) we utilize a deep convolution-based encoder with the squeeze-and-excitation networks and residual networks to aggregate the information across the document and learn meaningful document representations that cover different ranges of texts; (2) we explore multi-layer and sum-pooling attention to extract the most informative features from these multi-scale representations; (3) we combine binary cross entropy loss and focal loss to improve performance for rare labels. We focus our evaluation study on MIMIC-III, a widely used dataset in the medical domain. Our models outperform prior work on medical coding and achieve new state-of-the-art results on multiple metrics. We also demonstrate the language independent nature of our approach by applying it to two non-English datasets. Our model outperforms prior best model and a multilingual Transformer model by a substantial margin.