611 research outputs found
Thyroid hormones correlate with field metabolic rate in ponies, Equus ferus caballus
Acknowledgments The authors thank Jürgen Dörl for technical help and for taking care of the animals and Peter Thompson for technical assistance with the doubly labelled water analysis. Funding The study was supported by a grant from the German Research Foundation (DFG;GE 704/13-1).Peer reviewedPublisher PD
On the authenticity of individual dynamic binaural synthesis
A simulation that is perceptually indistinguishable from the corresponding real sound field could be termed authentic. Using binaural technology, such a simulation would theoretically be achieved by reconstructing the sound pressure at a listener's ears. However, inevitable errors in the measurement, rendering, and reproduction introduce audible degradations, as it has been demonstrated in previous studies for anechoic environments and static binaural simulations (fixed head orientation). The current study investigated the authenticity of individual dynamic binaural simulations for three different acoustic environments (anechoic, dry, wet) using a highly sensitive listening test design. The results show that about half of the participants failed to reliably detect any differences for a speech stimulus, whereas all participants were able to do so for pulsed pink noise. Higher detection rates were observed in the anechoic condition, compared to the reverberant spaces, while the source position had no significant effect. It is concluded that the authenticity mainly depends on how comprehensive the spectral cues are provided by the audio content, and the amount of reverberation, whereas the source position plays a minor role. This is confirmed by a broad qualitative evaluation, suggesting that remaining differences mainly affect the tone color rather than the spatial, temporal or dynamical qualities.DFG, 174776315, FOR 1557: Simulation and Evaluation of Acoustical Environments (SEACEN
Product Attribute Value Extraction using Large Language Models
E-commerce applications such as faceted product search or product comparison
are based on structured product descriptions like attribute/value pairs. The
vendors on e-commerce platforms do not provide structured product descriptions
but describe offers using titles or descriptions. To process such offers, it is
necessary to extract attribute/value pairs from textual product attributes.
State-of-the-art attribute/value extraction techniques rely on pre-trained
language models (PLMs), such as BERT. Two major drawbacks of these models for
attribute/value extraction are that (i) the models require significant amounts
of task-specific training data and (ii) the fine-tuned models face challenges
in generalizing to attribute values not included in the training data. This
paper explores the potential of large language models (LLMs) as a training
data-efficient and robust alternative to PLM-based attribute/value extraction
methods. We consider hosted LLMs, such as GPT-3.5 and GPT-4, as well as
open-source LLMs based on Llama2. We evaluate the models in a zero-shot
scenario and in a scenario where task-specific training data is available. In
the zero-shot scenario, we compare various prompt designs for representing
information about the target attributes of the extraction. In the scenario with
training data, we investigate (i) the provision of example attribute values,
(ii) the selection of in-context demonstrations, and (iii) the fine-tuning of
GPT-3.5. Our experiments show that GPT-4 achieves an average F1-score of 85% on
the two evaluation datasets while the best PLM-based techniques perform on
average 5% worse using the same amount of training data. GPT-4 achieves a 10%
higher F1-score than the best open-source LLM. The fine-tuned GPT-3.5 model
reaches a similar performance as GPT-4 while being significantly more
cost-efficient
SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines
The goal of entity resolution is to identify records in multiple datasets
that represent the same real-world entity. However, comparing all records
across datasets can be computationally intensive, leading to long runtimes. To
reduce these runtimes, entity resolution pipelines are constructed of two
parts: a blocker that applies a computationally cheap method to select
candidate record pairs, and a matcher that afterwards identifies matching pairs
from this set using more expensive methods. This paper presents SC-Block, a
blocking method that utilizes supervised contrastive learning for positioning
records in the embedding space, and nearest neighbour search for candidate set
building. We benchmark SC-Block against eight state-of-the-art blocking
methods. In order to relate the training time of SC-Block to the reduction of
the overall runtime of the entity resolution pipeline, we combine SC-Block with
four matching methods into complete pipelines. For measuring the overall
runtime, we determine candidate sets with 99.5% pair completeness and pass them
to the matcher. The results show that SC-Block is able to create smaller
candidate sets and pipelines with SC-Block execute 1.5 to 2 times faster
compared to pipelines with other blockers, without sacrificing F1 score.
Blockers are often evaluated using relatively small datasets which might lead
to runtime effects resulting from a large vocabulary size being overlooked. In
order to measure runtimes in a more challenging setting, we introduce a new
benchmark dataset that requires large numbers of product offers to be blocked.
On this large-scale benchmark dataset, pipelines utilizing SC-Block and the
best-performing matcher execute 8 times faster than pipelines utilizing another
blocker with the same matcher reducing the runtime from 2.5 hours to 18
minutes, clearly compensating for the 5 minutes required for training SC-Block
Neural data search for table augmentation
Tabular data is widely available on the web and in private data lakes run by commercial companies or research institutes. However, data that is essential for a specific task at hand is often scattered throughout numerous tables in these data lakes. Accessing this data requires retrieving the relevant information for the task. One approach to retrieve this data is through table augmentation. Table augmentation adds an additional attribute to a query table and populates the values of that attribute with data from the data lake. My research focuses on evaluating methods for augmenting a table with an additional attribute. Table augmentation presents a variety of challenges due to the heterogeneity of data sources and the multitude of possible combinations of methods. To successfully augment a query table based on tabular data from a data lake, several tasks such as data normalization, data search, schema matching, information extraction and data fusion must be performed. In my work, I empirically compare methods for data search, information extraction and data fusion as well as complete table augmentation pipelines using different datasets containing tabular data found in real-world data lakes. Methodologically, I plan to introduce new neural techniques for data search, information extraction and data fusion in the context of table augmentation. These new methods, as well as existing symbolic data search methods for table augmentation, will be empirically evaluated on two sets of benchmark query tables. The aim is to identify task- and dataset-specific challenges for data search, information extraction and data fusion methods. By profiling the datasets and analysing the errors made by the evaluated methods on the test query tables, the strengths and weaknesses of the methods can be systematically identified. Data search and information extraction methods should maximize recall while data fusion methods should achieve high accuracy. Pipelines built on the basis of the new methods should deliver their results quickly without compromising the highest possible accuracy of the augmented attribute values
Vorkommen und Kontrolle lebensmittelrelevanter Mikroorganismen und Verbreitung Shiga Toxin- bildender Escherichia Coli in verschiedenen Stadien der Rohwurstherstellung aus konventioneller und ökologischer Produktion
Einige Stämme des Darmbakteriums Escherichia coli sind in der Lage Shiga- Toxine zu produzieren, die gastrointestinale Erkrankungen auslösen können. Wiederkäuer, vor allem Rinder, Schafe und Ziegen gelten als Hauptreservoir für STEC. STEC-Infektionen treten weltweit vor allem in Ländern mit hoch entwickelter Landwirtschaft auf. Als wichtigste Infektionsquelle gelten vor allem rohe oder nicht ausreichend erhitzte Lebensmittel tierischen Ursprungs wie unzureichend gegartes Rinderhackfleisch, Rohmilch und Rohmilchprodukte. Aber auch Rohwürste wurden bereits mit humanen Infektionen in Verbindung gebracht.
Vorrangiges Ziel dieser Studie war es, mögliche STEC- Kontaminationsquellen in zwei Rohwurst- produzierenden Betrieben abzuklären. Begleitend wurden mikrobiologische Untersuchungen durchgeführt sowie die Wasseraktivität und der pH- Wert gemessen. Ein Betrieb umfasst Schlachtung, Zerlegung und Produktion sowohl in der konventionellen Herstellungsschiene wie auch in der Bio- Produktion. Der zweite untersuchte Betrieb ist ein reiner Biobetrieb.
Insgesamt wurden 323 Proben aus Betrieb 1 untersucht. 206 Proben stammen aus der Bio-, 117 aus der konventionellen Produktion. Dabei wurden Rinder- Schlachttierkörper, Proben aus der Zerlegung sowie kurz- und langgereifte Rohwürste in unterschiedlichen Reifungsstadien ausgewählt. Für den STEC- Nachweis wurden sowohl die Lebensmittel und die nach der Schlachtung und Zerlegung entnommenen Tupferproben nach Anreicherung in modifizierter Tryptose- Soja- Bouillon (mTSB) auf das Vorhandensein des Shiga Toxin- Gens mittels PCR und anschließender Gelektrophorese gemäß der Amtlichen Sammlung nach §64 LFBG (L.07.18) untersucht. Der Nachweis von Enterobacteriaceae, E.coli, Milchsäurebakterien und Laktobazillen erfolgte in Anlehnung an die Amtliche Methode nach §64 LFBG. Der pH- Wert und die Wasseraktivität wurde gemäß der amtlichen Sammlung nach §64 LFBG ermittelt.
Der Warnwert bezüglich Enterobacteriaceae nach DGHM wurde von 25,5% der untersuchten fertigen Würste überschritten. Konventionell hergestellte Produkte waren dabei mit 42,9% der fertigen Produkte über dem DGHM- Warnwert erheblich mehr belastet als Bio- Produkte (11,5%). Der DGHM- Warnwert bezüglich E. coli wurde von 6,4% der fertig gereiften Produkte überschritten. Säuernde Mikroorganismen waren in den untersuchten Produkten zu wenig vorhanden.
In 8 der 323 untersuchten Proben wurde STEC nachgewiesen. Insgesamt 5 der 96 beprobten Schlachttierkörper (Rind) waren STEC- positiv: 4 Tiere aus der Bio-Produktion und 1 Tier aus der konventionellen Produktion. 3 von 62 untersuchten kurzgereiften Rohwürsten waren STEC- positiv: 1 aus der Bio- Produktion und zwei aus der konventionellen Produktion. Alle untersuchten Proben aus der Zerlegung und alle langgereiften Rohwürste reagierten STEC- negativ.
Aus Betrieb 2 wurden 108 Proben untersucht. Hier wurde nur bei einem Endprodukt Enterobacteriaceae über dem DGHM- Warnwert festgestellt. In keinem Endprodukt fanden sich E. coli. In keiner Probe wurde STEC nachgewiesen
Audibility and Interpolation of Head-Above-Torso Orientation in Binaural Technology
Head-related transfer functions (HRTFs) incorporate fundamental cues required for human spatial hearing and are often applied to auralize results obtained from room acoustic simulations. HRTFs are typically available for various directions of sound incidence and a fixed head-above-torso orientation (HATO). If-in interactive auralizations-HRTFs are exchanged according to the head rotations of a listener, the auralization result most often corresponds to a listener turning head and torso simultaneously, while-in reality-listeners usually turn their head independently above a fixed torso. In the present study, we show that accounting for HATO produces clearly audible differences, thereby suggesting the relevance of correct HATO when aiming at perceptually transparent binaural synthesis. Furthermore, we addressed the efficient representation of variable HATO in interactive acoustic simulations using spatial interpolation. Hereby, we evaluated two different approaches: interpolating between HRTFs with identical torso-to-source but different head-to-source orientations (head interpolation) and interpolating between HRTFs with the same head-to-source but different torso-to-source orientations (torso interpolation). Torso interpolation turned out to be more robust against increasing interpolation step width. In this case the median threshold of audibility for the head-above-torso resolution was about 25 degrees, whereas with head interpolation the threshold was about 10 degrees. Additionally, we tested a non-interpolation approach (nearest neighbor) as a suitable means for mobile applications with limited computational capacities
Assessing the Authenticity of Individual Dynamic Binaural Synthesis
Binaural technology allows to capture sound fields by recording the sound pressure arriving at the listener’s ear canal entrances. If these signals are reconstructed for the same listener the simulation should be indistinguishable from the corresponding real sound field. A simulation fulfilling this premise could be termed as perceptually authentic. Authenticity has been assessed previously for static binaural resynthesis of sound sources in anechoic environments, i.e. for HRTF-based simulations not accounting for head movements of the listeners. Results indicated that simulations were still discernable from real sound fields, at least, if critical audio material was used. However, for dynamic binaural synthesis to our knowledge – and probably because this technology is even more demanding – no such study has been conducted so far. Thus, having developed a state-of-the-art system for individual dynamic auralization of anechoic and reverberant acoustical environments, we assessed its perceptual authenticity by letting subjects directly compare binaural simulations and real sound fields. To this end, individual binaural room impulses were acquired for two different source positions in a medium-sized recording studio, as well as individual headphone transfer functions. Listening tests were conducted for two different audio contents applying a most sensitive ABX test paradigm. Results showed that for speech signals many of the subjects failed to reliably detect the simulation. For pink noise pulses, however, all subjects could distinguish the simulation from reality. Results further provided evidence for future improvements.DFG, WE 4057/3-1, Simulation and Evaluation of
Acoustical Environments (SEACEN
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