When the Music Stops: Tip-of-the-Tongue Retrieval for Music

We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately recall important identifying information. ToT information needs are characterized by complexity, verbosity, uncertainty, and possible false memories. We make four contributions. (1) We collect a dataset - TOTMUSIC--of 2,278 information needs and ground truth answers. (2) We introduce a schema for these information needs and show that they often involve multiple modalities encompassing several Music IR sub-tasks such as lyric search, audio-based search, audio fingerprinting, and text search. (3) We underscore the difficulty of this task by benchmarking a standard text retrieval approach on this dataset. (4) We investigate the efficacy of query reformulations generated by a Large Language Model (LLM), and show that they are not as effective as simply employing the entire information need as a query--leaving several open questions for future research

Sub-arc mantle enrichment in the Sunda rear-arc inferred from HFSE systematics in high-K lavas from Java

Many terrestrial silicate reservoirs display a characteristic depletion in Nb, which has been explained in some studies by the presence of reservoirs on Earth with superchondritic Nb/Ta. As one classical example, K-rich lavas from the Sunda rear-arc, Indonesia, have been invoked to tap such a high-Nb/Ta reservoir. To elucidate the petrogenetic processes active beneath the Java rear-arc and the causes for the superchondritic Nb/Ta in some of these lavas, we studied samples from the somewhat enigmatic Javanese rear-arc volcano Muria, which allow conclusions regarding the across-arc variations in volcanic output, source mineralogy and subduction components. We additionally report some data for an along-arc sequence of lavas from the Indonesian part of the Sunda arc, extending from Krakatoa in the west to the islands of Bali and Lombok in the east. We present major and trace element concentrations, Sr–Nd–Hf–Pb isotope compositions, and high-field-strength element (HFSE: Nb, Ta, Zr, Hf, W) concentrations obtained via isotope dilution and MC-ICP-MS analyses. The geochemical data are complemented by melting models covering different source compositions with slab melts formed at variable P–T conditions. The radiogenic isotope compositions of the frontal arc lavas in combination with their trace element systematics confirm previously established regional variations of subduction components along the arc. Melting models show a clear contribution of a sediment-derived component to the HFSE budget of the frontal arc lavas, particularly affecting Zr–Hf and W. In contrast, the K-rich rear-arc lavas tap more hybrid and enriched mantle sources. The HFSE budget of the rear-arc lavas is in particular characterized by superchondritic Nb/Ta (up to 25) that are attributed to deep melting involving overprint by slab melts formed from an enriched garnet–rutile-bearing eclogitic residue. Sub-arc slab melting was potentially triggered along a slab tear beneath the Sunda arc, which is the result of the forced subduction of an oceanic basement relief ~ 8 Myr ago as confirmed by geophysical studies. The purported age of the slab tear coincides with a paucity in arc volcanism, widespread thrusting of the Javanese basement crust as well as the short-lived nature of the K-rich rear-arc volcanism at that time. © 2021, The Author(s)

Mineralogical Controls on the Ti Isotope Composition of Subduction Zone Magmas

The positive Ti isotope versus SiO2-content correlation in igneous rocks reflects the fractional crystallization of Ti-bearing oxide minerals. However, Ti isotope variations of subduction-related igneous rocks indicate that the Ti isotope compositions of their mantle sources are heterogeneous and additional mineral phases may promote Ti isotope fractionation. We have determined the Ti isotope composition of well-characterized subduction-related basalts, andesites and boninites. Samples from the Solomon Islands, the Troodos ophiolite in Cyprus, and Cape Vogel in Papua New Guinea show small but resolvable variations that may be related to differences in their mantle sources. Specifically, the δ49Ti of boninites (+0.109‰ to +0.168‰) is slightly higher than that of tholeiites (−0.027‰ to +0.111‰) from the same localities (Troodos in Cyprus and Cape Vogel in Papua New Guinea). Modeling suggests the partial melting of progressively depleted mantle sources where residual Cr-spinel plays a greater role in controlling the Ti budget during partial melting. More pronounced variations in δ49Ti are clearly linked to the fractional crystallization of Ti-oxides: Samples from Rabaul Volcanic Complex (New Britain, Papua New Guinea) show increasing δ49Ti (up to +0.373‰) with increasing Ti/V and decreasing Dy/Yb. Fractional crystallization models suggest that oxide minerals and amphibole are needed to sufficiently increase the δ49Ti of these magmas. Our study highlights that the combination of diagnostic trace element patterns and Ti isotope compositions in subduction-related igneous rocks can be a powerful tool to constrain petrogenetic processes and to discriminate between different crystallizing mineral phases

Reusing historical interaction data for faster online learning to rank for IR

Online learning to rank for information retrieval (IR) holds promise for allowing the development of "self-learning" search engines that can automatically adjust to their users. With the large amount of e.g., click data that can be collected in web search settings, such techniques could enable highly scalable ranking optimization. However, feedback obtained from user interactions is noisy, and developing approaches that can learn from this feedback quickly and reliably is a major challenge. In this paper we investigate whether and how previously collected (historical) interaction data can be used to speed up learning in online learning to rank for IR. We devise the first two methods that can utilize historical data (1) to make feedback available during learning more reliable and (2) to preselect candidate ranking functions to be evaluated in interactions with users of the retrieval system. We evaluate both approaches on 9 learning to rank data sets and find that historical data can speed up learning, leading to substantially and significantly higher online performance. In particular, our pre-selection method proves highly effective at compensating for noise in user feedback. Our results show that historical data can be used to make online learning to rank for IR much more effective than previously possible, especially when feedback is noisy

Tier-specific evolution of match performance characteristics in the English Premier League: it's getting tougher at the top.

This study investigated the evolution of physical and technical performances in the English Premier League (EPL), with special reference to league ranking. Match performance observations (n = 14,700) were collected using a multiple-camera computerised tracking system across seven consecutive EPL seasons (2006-07 to 2012-13). Final league rankings were classified into Tiers: (A) 1st-4th ranking (n = 2519), (B) 5th-8th ranking (n = 2965), (C) 9th-14th ranking (n = 4448) and (D) 15th-20th ranking (n = 4768). Teams in Tier B demonstrated moderate increases in high-intensity running distance while in ball possession from the 2006-07 to 2012-13 season (P < 0.001; effect size [ES]: 0.68), with Tiers A, C and D producing less pronounced increases across the same period (P < 0.005; ES: 0.26, 0.41 and 0.33, respectively). Large increases in sprint distance were observed from the 2006-07 to 2012-13 season for Tier B (P < 0.001; ES: 1.21), while only moderate increases were evident for Tiers A, C and D (P < 0.001; ES: 0.75, 0.97 and 0.84, respectively). Tier B demonstrated large increases in the number of passes performed and received in 2012-13 compared to 2006-07 (P < 0.001; ES: 1.32-1.53) with small-to-moderate increases in Tier A (P < 0.001; ES: 0.30-0.38), Tier C (P < 0.001; ES: 0.46-0.54) and Tier D (P < 0.001; ES: 0.69-0.87). The demarcation line between 4th (bottom of Tier A) and 5th ranking (top of Tier B) in the 2006-07 season was 8 points, but this decreased to just a single point in the 2012-13 season. The data demonstrate that physical and technical performances have evolved more in Tier B than any other Tier in the EPL and could indicate a narrowing of the performance gap between the top two Tiers

Heterogeneously catalyzed hydrothermal processing of C5-C6 sugars

Biomass has been long exploited as an anthropogenic energy source; however, the 21st century challenges of energy security and climate change are driving resurgence in its utilization both as a renewable alternative to fossil fuels and as a sustainable carbon feedstock for chemicals production. Deconstruction of cellulose and hemicellulose carbohydrate polymers into their constituent C5 and C6 sugars, and subsequent heterogeneously catalyzed transformations, offer the promise of unlocking diverse oxygenates such as furfural, 5-hydroxymethylfurfural, xylitol, sorbitol, mannitol, and gluconic acid as biorefinery platform chemicals. Here, we review recent advances in the design and development of catalysts and processes for C5-C6 sugar reforming into chemical intermediates and products, and highlight the challenges of aqueous phase operation and catalyst evaluation, in addition to process considerations such as solvent and reactor selection

Stabilization of mesoporous nanocrystalline zirconia with Laponite

The mesoporous nanocrystalline zircoina was synthesized via solid state reaction-structure directing method in the presence of Laponite. The introduction of Laponite renders the higher thermal stability and lamellar track to the zirconia. Laponite acts as inhibitor for crystal growth and also hard template for the mesostructure. The role of Laponite is attributed to the interaction between the zirconia precursors and the nano-platelets of Laponite via the bridge of hydrophilic segments of surfactant. It results in the formation of Zr-O-Mg-O-Si frameworks in the direction of Laponite layer with the condensation of frameworks during the calcination process, which contributes the higher stability and lamellar structure to the nano-sized zirconia samples