Unitary cocycles and processes on the full Fock space

We consider a unitary cocycle or Sch\"urmann triple on the non-commutative unitary group fixed by a complex matrix which induces an additive free white noise or an additive free L\'evy process on the tensor algebra over the full Fock space. A L\'evy process on a Voiculescu dual semi-group is given by a generator or Sch\"urmann triple. We will show how a free L\'evy process on the non-commutative unitary group fixed by a complex matrix can be obtained by infinitesimally convolving the additive free white noise.Comment: 18 page

Assessing the word recognition skills of german elementary students in silent reading - Psychometric properties of an item pool to generate curriculum-based measurements

Given the high proportion of struggling readers in school and the long-term negative consequences of underachievement for those affected, the question of prevention options arises. The early identification of central indicators for reading literacy is a noteworthy starting point. In this context, curriculum-based measurements have established themselves as reliable and valid instruments for monitoring the progress of learning processes. This article is dedicated to the assessment of word recognition in silent reading as an indicator of adequate reading fluency. The process of developing an item pool is described, from which instruments for learning process diagnostics can be derived. A sample of 4268 students from grades 1–4 processed a subset of items. Each student template included anchor items, which all students processed. Using Item Response Theory, item statistics were estimated for the entire sample and all items. After eliminating unsuitable items (N = 206), a one-dimensional, homogeneous pool of items remained. In addition, there are high correlations with another established reading test. This provides the first evidence that the recording of word recognition skills for silent reading can be seen as an economic indicator for reading skills. Although the item pool forms an important basis for the extraction of curriculum-based measurements, further investigations to assess the diagnostic suitability (e.g., the measurement invariance over different test times) are still pending

DECISION SUPPORT IN CAR LEASING: A FORECASTING MODEL FOR RESIDUAL VALUE ESTIMATION

The paper proposes a methodology to support pricing decisions in the car leasing industry. In particular, the price is given by the monthly fee to be paid by the lessee as compensation for using a car over some contract horizon. After contract expiration, lessors are obliged to take back the vehicle, which will then be sold in the used car market. Therefore, lessors require an accurate estimate of cars’ residual values to manage the risk inherent to their business and determine profitable prices. We explore the organizational and technical requirements associated with this forecasting task and develop a prediction model that complies with identified application constraints. The model is rigorously tested within an empirical study and compared to established benchmarks. The results obtained in several experiments provide strong evidence for the proposed model being effective in generating accurate predictions of cars’ residual values and efficient in requiring little user intervention

AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis

The generation of realistic and contextually relevant co-speech gestures is a challenging yet increasingly important task in the creation of multimodal artificial agents. Prior methods focused on learning a direct correspondence between co-speech gesture representations and produced motions, which created seemingly natural but often unconvincing gestures during human assessment. We present an approach to pre-train partial gesture sequences using a generative adversarial network with a quantization pipeline. The resulting codebook vectors serve as both input and output in our framework, forming the basis for the generation and reconstruction of gestures. By learning the mapping of a latent space representation as opposed to directly mapping it to a vector representation, this framework facilitates the generation of highly realistic and expressive gestures that closely replicate human movement and behavior, while simultaneously avoiding artifacts in the generation process. We evaluate our approach by comparing it with established methods for generating co-speech gestures as well as with existing datasets of human behavior. We also perform an ablation study to assess our findings. The results show that our approach outperforms the current state of the art by a clear margin and is partially indistinguishable from human gesturing. We make our data pipeline and the generation framework publicly available

Towards Just-In-Time Arrival for Container Ships by the Integration of Prediction Models

Within the context of green shipping, the concept of Just-In-Time (JIT) arrival has attracted much attention. Research achieves the JIT arrival for container ships by combining the berth allocation and quay crane assignment problem (BACAP) and the vessel speed optimization (VSO), both subject to the data exchange. Many prediction models of the research to date generally aim to reduce the uncertainty of the communicated estimated time of arrivals. There is a lack of research that simultaneously assesses the application effect of prediction models on both plans of the BACAP and the VSO. Therefore, this paper proposes a two-stage model that integrates the prediction of the vessel arrival time with the optimization of the BACAP-VSO. The application in our specific case study shows that the random forest performs best in the first stage. The results are forwarded to the second stage and lead to a reduction of the service delay, fuel consumption cost, and vessel emissions