Electric Vehicle Charging Coordination - Economics of Renewable Energy Integration

This work investigates the potential application of EV load flexibility with respect to different coordination objectives. They include individual cost or conventional generator use minimization, given volatile supply from renewable sources. The evaluation shows that charging coordination can generate savings and increase the direct utilization of renewable energy sources. Allowing for resale of stored energy to the grid can further increase savings but is limited by battery wear conditions

Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Road condition estimation based on Extended Floating Car Data (XFCD) from smart devices allows for determining given quality indicators like the international roughness index (IRI). Such approaches currently face the challenge to utilize measurements from heterogeneous sources. This paper investigates how a statistical learning based self-calibration overcomes individual sensor characteristics. We investigate how well the approach handles variations in the sensing frequency. Since the self-calibration approach requires the training of individual models for each participant, it is examined how a reduction of the amount of data sent to the backend system for training purposes affects the model performance. We show that reducing the amount of data by approximately 50 % does not reduce the models’ performance. Likewise, we observe that the approach can handle sensing frequencies up to 25 Hz without a performance reduction compared to the baseline scenario with 50 Hz

Recent Decisions

INCOME TAX--LIQUIDATION OF FOREIGN CORPORATIONS--SHAREHOLDERS IN A LIQUIDATING FOREIGN CORPORATION MUST INCLUDE IN THE CORPORATION\u27S EARNINGS AND PROFITS ACCOUNT THE AMOUNT OF RECAPTURED EXCESS DEPRECIATION REALIZED UPON THE SALE OF ITS ASSETS William W. Allen CUSTOMS DUTIES--ANTIDUMPING ACT OF 1921--THE SECRETARY OF THE TREASURY HAS No AUTHORITY TO TERMINATE A WITH-HOLDING OF APPRAISEMENT PRIOR TO THE PUBLICATION OF A DUMPING FINDING BASED ON A LIKELIHOOD OF INJURY DETERMINATION BY THE INTERNATIONAL TRADE COMMISSION Alexander A. Hassani INTERNATIONAL BANKING--BANKRUPTCY-FOREIGN BANKS NEITHER REGULATED BY NOR LICENSED To Do BUSINESS IN THE UNITED STATES MAY FILE FOR VOLUNTARY BANKRUPTCY UNDER THE NATIONAL BANKRUPTCY ACT Peter Appleton Schulle

Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results

Humour is a substantial element of human affect and cognition. Its automatic understanding can facilitate a more naturalistic human-device interaction and the humanisation of artificial intelligence. Current methods of humour detection are solely based on staged data making them inadequate for 'real-world' applications. We address this deficiency by introducing the novel Passau-Spontaneous Football Coach Humour (Passau-SFCH) dataset, comprising of about 11 hours of recordings. The Passau-SFCH dataset is annotated for the presence of humour and its dimensions (sentiment and direction) as proposed in Martin's Humor Style Questionnaire. We conduct a series of experiments, employing pretrained Transformers, convolutional neural networks, and expert-designed features. The performance of each modality (text, audio, video) for spontaneous humour recognition is analysed and their complementarity is investigated. Our findings suggest that for the automatic analysis of humour and its sentiment, facial expressions are most promising, while humour direction can be best modelled via text-based features. The results reveal considerable differences among various subjects, highlighting the individuality of humour usage and style. Further, we observe that a decision-level fusion yields the best recognition result. Finally, we make our code publicly available at https://www.github.com/EIHW/passau-sfch. The Passau-SFCH dataset is available upon request.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible (Major Revision

Zero-shot personalization of speech foundation models for depressed mood monitoring

The monitoring of depressed mood plays an important role as a diagnostic tool in psychotherapy. An automated analysis of speech can provide a non-invasive measurement of a patient’s affective state. While speech has been shown to be a useful biomarker for depression, existing approaches mostly build population-level models that aim to predict each individual’s diagnosis as a (mostly) static property. Because of inter-individual differences in symptomatology and mood regulation behaviors, these approaches are ill-suited to detect smaller temporal variations in depressed mood. We address this issue by introducing a zero-shot personalization of large speech foundation models. Compared with other personalization strategies, our work does not require labeled speech samples for enrollment. Instead, the approach makes use of adapters conditioned on subject-specific metadata. On a longitudinal dataset, we show that the method improves performance compared with a set of suitable baselines. Finally, applying our personalization strategy improves individual-level fairness

Ecology & computer audition: applications of audio technology to monitor organisms and environment

Among the 17 Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13th SDG is a call for action to combat climate change. Moreover, SDGs 14 and 15 claim the protection and conservation of life below water and life on land, respectively. In this work, we provide a literature-founded overview of application areas, in which computer audition – a powerful but in this context so far hardly considered technology, combining audio signal processing and machine intelligence – is employed to monitor our ecosystem with the potential to identify ecologically critical processes or states. We distinguish between applications related to organisms, such as species richness analysis and plant health monitoring, and applications related to the environment, such as melting ice monitoring or wildfire detection. This work positions computer audition in relation to alternative approaches by discussing methodological strengths and limitations, as well as ethical aspects. We conclude with an urgent call to action to the research community for a greater involvement of audio intelligence methodology in future ecosystem monitoring approaches