University of Mannheim

About: University of Mannheim is a education organization based out in Mannheim, Germany. It is known for research contribution in the topics: Context (language use) & Politics. The organization has 4448 authors who have published 12918 publications receiving 446557 citations. The organization is also known as: Uni Mannheim & UMA.

Transient linear price impact and fredholm integral equations

Abstract: We consider the linear-impact case in the continuous-time market impact model with transient price impact proposed by Gatheral. In this model, the absence of price manipulation in the sense of Huberman and Stanzl can easily be characterized by means of Bochner’s theorem. This allows us to study the problem of minimizing the expected liquidation costs of an asset position under constraints on the trading times. We prove that optimal strategies can be characterized as measure-valued solutions of a generalized Fredholm integral equation of the first kind and analyze several explicit examples. We also prove theorems on the existence and nonexistence of optimal strategies. We show in particular that optimal strategies always exist and are nonalternating between buy and sell trades when price impact decays as a convex function of time. This is based on and extends a recent result by Alfonsi, Schied, and Slynko on the nonexistence of transaction-triggered price manipulation. We also prove some qualitative properties of optimal strategies and provide explicit expressions for the optimal strategy in several special cases of interest.

Using Whole-Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster

Abstract: Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP–based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.

Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions

Abstract: Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.

Effect of Neoadjuvant Chemotherapy Plus Regional Hyperthermia on Long-term Outcomes Among Patients With Localized High-Risk Soft Tissue Sarcoma: The EORTC 62961-ESHO 95 Randomized Clinical Trial

Abstract: Importance Patients with soft tissue sarcoma are at risk for local recurrence and distant metastases despite optimal local treatment. Preoperative anthracycline plus ifosfamide chemotherapy improves outcome in common histological subtypes. Objective To analyze whether the previously reported improvement in local progression-free survival by adding regional hyperthermia to neoadjuvant chemotherapy translates into improved survival. Design, Setting, and Participants Open-label, phase 3 randomized clinical trial to evaluate the efficacy and toxic effects of neoadjuvant chemotherapy plus regional hyperthermia. Adult patients (age ≥18 years) with localized soft tissue sarcoma (tumor ≥5 cm, French Federation Nationale des Centers de Lutte Contre le Cancer [FNCLCC] grade 2 or 3, deep) were accrued across 9 centers (6, Germany; 1, Norway; 1, Austria; 1, United States) from July 1997 to November 2006. Follow-up ended December 2014. Interventions After stratification for tumor presentation and site, patients were randomly assigned to either neoadjuvant chemotherapy consisting of doxorubicin, ifosfamide, and etoposide alone, or combined with regional hyperthermia. Main Outcomes and Measures The primary end point was local progression-free survival. Secondary end points included treatment safety and survival, with survival defined from date of randomization to death due to disease or treatment. Patients lost to follow-up were censored at the date of their last follow-up. Results A total of 341 patients were randomized, and 329 (median [range] age, 51 [18-70] years; 147 women, 182 men) were eligible for the intention-to-treat analysis. By December 2014, 220 patients (67%; 95% CI, 62%-72%) had experienced disease relapse, and 188 (57%; 95% CI, 52%-62%) had died. Median follow-up was 11.3 years. Compared with neoadjuvant chemotherapy alone, adding regional hyperthermia improved local progression-free survival (hazard ratio [HR], 0.65; 95% CI, 0.49-0.86;P = .002). Patients randomized to chemotherapy plus hyperthermia had prolonged survival rates compared with those randomized to neoadjuvant chemotherapy alone (HR, 0.73; 95% CI, 0.54-0.98;P = .04) with 5-year survival of 62.7% (95% CI, 55.2%-70.1%) vs 51.3% (95% CI, 43.7%-59.0%), respectively, and 10-year survival of 52.6% (95% CI, 44.7%-60.6%) vs 42.7% (95% CI, 35.0%-50.4%). Conclusions and Relevance Among patients with localized high-risk soft tissue sarcoma the addition of regional hyperthermia to neoadjuvant chemotherapy resulted in increased survival, as well as local progression-free survival. For patients who are candidates for neoadjuvant treatment, adding regional hyperthermia may be warranted. Trial Registration clinicaltrials.gov Identifier:NCT00003052