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The "Vision Zero" policy, introduced by the Swedish Parliament in 1997, aims to eliminate fatalities and serious injuries resulting from traffic accidents. To achieve this goal, the use of self-driving vehicles in traffic is envisioned and a roadmap for the certification of self-driving vehicles is aimed to be determined. However, it is still unclear how the basic safety requirements that autonomous vehicles must meet will be verified and certified, and which methods will be used. This paper focuses on the comparative evaluation of the prominent methods planned to be used in the certification process of autonomous vehicles. It examines the prominent methods used in the certification process, develops a pipeline for the certification process of autonomous vehicles, and determines the stages, actors, and areas where the addressed methods can be applied.
Developing effective multimodal fusion approaches has become increasingly essential in many real-world scenarios, such as health care and finance. The key challenge is how to preserve the feature expressiveness in each modality while learning cross-modal interactions. Previous approaches primarily focus on the cross-modal alignment, while over-emphasis on the alignment of marginal distributions of modalities may impose excess regularization and obstruct meaningful representations within each modality. The Dirichlet process (DP) mixture model is a powerful Bayesian non-parametric method that can amplify the most prominent features by its richer-gets-richer property, which allocates increasing weights to them. Inspired by this unique characteristic of DP, we propose a new DP-driven multimodal learning framework that automatically achieves an optimal balance between prominent intra-modal representation learning and cross-modal alignment. Specifically, we assume that each modality follows a mixture of multivariate Gaussian distributions and further adopt DP to calculate the mixture weights for all the components. This paradigm allows DP to dynamically allocate the contributions of feat
Scientists collaborate through intricate networks, which impact the quality and scope of their research. At the same time, funding and institutional arrangements, as well as scientific and political cultures, affect the structure of collaboration networks. Since such arrangements and cultures differ across regions in the world in systematic ways, we surmise that collaboration networks and impact should also differ systematically across regions. To test this, we compare the structure of collaboration networks among prominent researchers in North America and Europe. We find that prominent researchers in Europe establish denser collaboration networks, whereas those in North-America establish more decentralized networks. We also find that the impact of the publications of prominent researchers in North America is significantly higher than for those in Europe, both when they collaborate with other prominent researchers and when they do not. Although Europeans collaborate with other prominent researchers more often, which increases their impact, we also find that repeated collaboration among prominent researchers decreases the synergistic effect of collaborating.
Context: Several spiral galaxies, as beautifully exhibited by the case of NGC 6946, display a prominent large-scale spiral structure in their gaseous outer disk. Such structure is often thought to pose a dynamical puzzle, because grand-design spiral structure is traditionally interpreted as the result of density waves carried mostly in the stellar disk. Aims. Here we argue that the outer spiral arms in the cold gas outside the bright optical disk actually have a natural interpretation as the manifestation of the mechanism that excites grand-design spiral structure in the main, star-dominated body of the disk: the excitation is driven by angular momentum transport to the outer regions, through trailing density waves outside the corotation circle that can penetrate beyond the Outer Lindblad Resonance in the gaseous component of the disk. Methods: Because of conservation of the density wave action, these outgoing waves are likely to become more prominent in the outer disk and eventually reach non-linear amplitudes. To calculate the desired amplitude profiles, we make use of the theory of dispersive waves. Results: If the conditions beyond the optical radius allow for an approximate tr
The h index was introduced by Hirsch to quantify an individual's scientific research output. It has been widely used in different fields to show the relevance of the research work of prominent scientists. I have worked out 26 practical cases of physicists which are not so prominent. Therefore this case study should be more relevant to discuss various features of the Hirsch index which are interesting or disturbing or both for the more average situation. In particular, I investigate quantitatively some pitfalls in the evaluation and the influence of self-citations.
Long-lasting activity complexes (ACs), characterised as a series of closely located, continuously emerging solar active regions (ARs), are considered generating prominent poleward surges from observations. The surges lead to significant variations of the polar field, which are important for the modulation of solar cycles. We aim to study a prominent poleward surge during solar cycle 24 on the southern hemisphere, and analyse its originating ACs and the effect on the polar field evolution. We automatically identify and characterize ARs based on synoptic magnetograms from the Solar Dynamic Observatory. We assimilate these ARs with realistic magnetic configuration into a surface flux transport model, and simulate the creation and migration of the surge. Our simulations well reproduce the characteristics of the surge and show that the prominent surge is mainly caused by the ARs belonging to two ACs during Carrington Rotations 2145-2159 (December 2013-January 2015). The surge has a strong influence on the polar field evolution of the southern hemisphere during the latter half of cycle 24. Without the about one-year-long flux emergence in the form of ACs, the polar field around the cycle
Relative attribute models can compare images in terms of all detected properties or attributes, exhaustively predicting which image is fancier, more natural, and so on without any regard to ordering. However, when humans compare images, certain differences will naturally stick out and come to mind first. These most noticeable differences, or prominent differences, are likely to be described first. In addition, many differences, although present, may not be mentioned at all. In this work, we introduce and model prominent differences, a rich new functionality for comparing images. We collect instance-level annotations of most noticeable differences, and build a model trained on relative attribute features that predicts prominent differences for unseen pairs. We test our model on the challenging UT-Zap50K shoes and LFW10 faces datasets, and outperform an array of baseline methods. We then demonstrate how our prominence model improves two vision tasks, image search and description generation, enabling more natural communication between people and vision systems.
We present a sample of 54 disk galaxies which have well developed extraplanar structures. We selected them using visual inspections from the color images of the Sloan Digital Sky Survey. Since the sizes of the extraplanar structures are comparable to the disks, they are considered as prominent stellar halos rather than large bulges. A single Sérsic profile fitted to the surface brightness along the minor-axis of the disk shows a luminosity excess in the central regions for the majority of sample galaxies. This central excess is considered to be caused by the central bulge component. The mean Sérsic index of the single component model is $1.1\pm0.9$. A double Sérsic profile model that employs $n=1$ for the inner region, and varying $n$ for the outer region, provides a better fit than the single Sérsic profile model. For a small fraction of galaxies, a Sérsic profile fitted with $n=4$ for the inner region gives similar results. There is a weak tendency of increasing $n$ with increasing luminosity and central velocity dispersion, but there is no dependence on the local background density.
We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy---e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. Technically, we consider an ever-growing table of objects with dimension and measure attributes. A situational fact is a "contextual" skyline tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes, when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual skyline tuple, and discover them quickly before the event becomes yesterday's news. A brute-force approach requires exhaustive comparison with every tuple, under every constraint, and in every measure subspace. We design algorithms in response to these challenges using th
The decay psi(2S) --> eta J/psi is used to measure, for the first time, all prominent eta-meson branching fractions with the same experiment in the same dataset, thereby providing a consistent treatment of systematics across branching fractions. We present results for eta decays to gamma gamma, pi+pi-pi0, 3 pi0, pi+ pi- gamma, and e+ e- gamma, accounting for 99.9% of all eta decays. The precisions for several of the branching fractions and their ratios are improved. Two channels, pi+ pi- gamma and e+ e- gamma, show results that differ at the level of three standard deviations from those previously determined.
The Fraunhofer G-band around 4304 Å is widely used as a photospheric diagnostic and is generally not expected to show signatures of chromospheric or coronal structures. However, recent amateur observations have suggested the presence of off-limb prominence emission in this spectral region. We investigate the origin of the prominence emission in the G-band to determine if this is caused by methylene (CH) or other lines in this band. We also aim to test these lines for the presence of ionization memory effects in neutral lines. We present a case study of two prominences, one obtained with a Solar Explorer (Sol'Ex) spectroheliograph and another with the high-resolution Fast Multi-Line Universal Spectrograph (FaMuLUS) camera system at the echelle spectrograph of the German Vacuum Tower Telescope (VTT). Line widths are measured for simultaneously observed neutral and ionized metal lines, allowing a comparison of thermal and non-thermal broadening components to see if these lines exhibit any ionization memory effects. We report clear prominence emission in several metal lines within the G-band, primarily from Ti II and Ca I lines, while contributions from CH molecular lines are not obser
Search prominence may have a detrimental impact on a firm's profits in the presence of costly product returns. We analyze the impact of search prominence on firm profitability in a duopoly search model, considering the presence of costly product returns. Consumer match values are assumed to be independently and identically distributed across the two products. Our results show that the non-prominent firm benefits from facing consumers with relatively low match values for the prominent firm's products, thus avoiding costly returns. When return costs are sufficiently high, the prominent firm may earn lower profits than its non-prominent competitor. This outcome holds under both price exogeneity and price competition. Furthermore, the profitability advantage of prominence diminishes as return costs increase. Platforms that maximize ad revenue should consider retaining positive return cost for consumers rather than fully passing it on to firms. For e-commerce platforms, it is crucial to align product return policies with broader management objectives to optimize firm profitability.
Solar prominences are the most prominent large-scale structures observed above the solar limb in emission in chromospheric lines but in absorption in coronal lines. At the bottom of prominences often appears a bubble, with plumes occasionally rising from the prominence-bubble interface. The plumes may potentially play an important role in the mass supply and thermodynamic evolution of prominences, but their nature and generation mechanism are elusive. Here we use the high-resolution H-alpha observations obtained by the New Vacuum Solar Telescope (NVST) to investigate a quiescent prominence with bubbles and plumes on 8 November 2022. Within an interval of about two hours, enhanced spicular activity disturb the prominence-bubble interface, producing bursts of small-scale plumes rising through the prominence. Characterized by clustered spicules jetting at higher speeds (sometimes exceeding the typical chromopsheric Alfven speed) and longer life-time (over 15 minutes), the enhanced spicular activity differs markedly from regular spicules. We hence conjecture that the enhanced spicular activity may drive shock waves, which trigger the magnetic Richtmyer-Meshkov instability at the promin
This paper investigates prominence-aware automatic speech recognition (ASR) by combining prominence detection and speech recognition for conversational Austrian German. First, prominence detectors were developed by fine-tuning wav2vec2 models to classify word-level prominence. The detector was then used to automatically annotate prosodic prominence in a large corpus. Based on those annotations, we trained novel prominence-aware ASR systems that simultaneously transcribe words and their prominence levels. The integration of prominence information did not change performance compared to our baseline ASR system, while reaching a prominence detection accuracy of 85.53% for utterances where the recognized word sequence was correct. This paper shows that transformer-based models can effectively encode prosodic information and represents a novel contribution to prosody-enhanced ASR, with potential applications for linguistic research and prosody-informed dialogue systems.
In this paper we focus on the analysis of the multiwavelength spectroscopic observations of a quiescent prominence. The spectral and geometrical parameters in the prominence were derived and used to constrain the NLTE radiative transfer models. Applying this method with multiwavelength observations provides a good opportunity to reduce the large range of thermodynamic parameters in solar prominences. We used time-slice and optical flow methods in order to derive the plane-of-sky (POS) velocities, and used gravity center and peak position methods on Mg II h&k and H I Ly-alpha profiles to compute the line-of-sight (LOS) velocities. We used the integrated intensities and FWHM values of the H-alpha, Ca II H, and Mg II h&k lines to compare with the NLTE radiative transfer computations. Ionization degree and thickness of the prominence plasma could be further derived. Opposite flows are observed along two strands between prominence barbs. The POS velocity can reach 20 km/s and the largest LOS velocity is > 90 km/s. The derived electron densities range from 6.5e9 cm-3 to 2.7e10 cm-3, and the derived total hydrogen densities range from 7.4e9 cm-3 to 6.6e10 cm-3. The temperature
This is an obituary of Rod Burstall, written in his honour. Rod was a prominent computer scientist whose contributions span over forty years. Most of his career was spent at Edinburgh University. He lead the team programming Freddy, the first hand-eye assembly robot, with much of his effort being devoted to the development of the POP-2 programming language. He became interested in a mathematical approach to software development: he recognised the central role of structural induction; his work on reasoning about mutable data structures was an influential precursor of separation logic; he was the first to point out the connection between program proof and modal logic; and he was responsible for the idea that stores are mappings from locations to their contents. As part of his quest for correctness of programs, Rod, with John Darlington, undertook the first major work on program transformation. His interest in novel programming languages continued with the experimental language HOPE, developed with Don Sannella and David MacQueen. Robin Milner's Standard ML and its relatives integrated ideas from Hope, and Rod was an active member of the Standard ML design team. Rod pioneered the use
We present an observational study of the formation and disappearance of a funnel prominence. Before the funnel prominence formed, cool materials from the top of a preexisting polar crown prominence flowed along saddle-shaped coronal loops to their base, forming a smaller prominence. Meanwhile, the saddle-shaped coronal loops gradually rose, and U-shaped coronal loops, termed prominence horns, began to appear along with a coronal cavity. Afterwards, a cool column emerged from the chromosphere, rose vertically into the corona, and then moved laterally to be transported into the U-shaped coronal loops. The formed prominence slid into the chromosphere, while the U-shaped coronal loops and the coronal cavity became more pronounced. As cool materials accumulated at the base of the U-shaped coronal loops, these loops underwent a significant descent and a V-shaped structure appeared at the base of the cool materials, indicating that the U-shaped coronal loops may be dragged down to sag. Subsequently, cool materials from the V-shaped structure continued to flow almost vertically toward the chromosphere, forming the funnel prominence. The vertical downflows might be produced by magnetic reco
Sympathetic eruptions of solar prominences have been studied for decades, however, it is usually difficult to identify their causal links. Here we present two failed prominence eruptions on 26 October 2022 and explore their connections. Using stereoscopic observations, the south prominence (PRO-S) erupts with untwisting motions, flare ribbons occur underneath, and new connections are formed during the eruption. The north prominence (PRO-N) rises up along with PRO-S, and its upper part disappears due to catastrophic mass draining along an elongated structure after PRO-S failed eruption. We suggest that the eruption of PRO-S initiates due to a kink instability, further rises up, and fails to erupt due to reconnection with surrounding fields. The elongated structure connecting PRO-N overlies PRO-S, which causes the rising up of PRO-N along with PRO-S and mass drainage after PRO-S eruption. This study suggests that a prominence may end its life through mass drainage forced by an eruption underneath.
The prominence of a spoken word is the degree to which an average native listener perceives the word as salient or emphasized relative to its context. Speech prominence estimation is the process of assigning a numeric value to the prominence of each word in an utterance. These prominence labels are useful for linguistic analysis, as well as training automated systems to perform emphasis-controlled text-to-speech or emotion recognition. Manually annotating prominence is time-consuming and expensive, which motivates the development of automated methods for speech prominence estimation. However, developing such an automated system using machine-learning methods requires human-annotated training data. Using our system for acquiring such human annotations, we collect and open-source crowdsourced annotations of a portion of the LibriTTS dataset. We use these annotations as ground truth to train a neural speech prominence estimator that generalizes to unseen speakers, datasets, and speaking styles. We investigate design decisions for neural prominence estimation as well as how neural prominence estimation improves as a function of two key factors of annotation cost: dataset size and the n
Expressive reading, considered the defining attribute of oral reading fluency, comprises the prosodic realization of phrasing and prominence. In the context of evaluating oral reading, it helps to establish the speaker's comprehension of the text. We consider a labeled dataset of children's reading recordings for the speaker-independent detection of prominent words using acoustic-prosodic and lexico-syntactic features. A previous well-tuned random forest ensemble predictor is replaced by an RNN sequence classifier to exploit potential context dependency across the longer utterance. Further, deep learning is applied to obtain word-level features from low-level acoustic contours of fundamental frequency, intensity and spectral shape in an end-to-end fashion. Performance comparisons are presented across the different feature types and across different feature learning architectures for prominent word prediction to draw insights wherever possible.