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This vision article outlines the main building blocks of what we term AI Compliance, an effort to bridge two complementary research areas: computer science and the law.
Such research has the goal to model, measure, and affect the quality of AI artifacts, such as data, models, and applications, to then facilitate adherence to legal standards.
DrDimont: explainable drug response prediction from differential analysis of multi-omics networks
(2022)
Motivation:
While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging.
Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning.
However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem.
Results:
We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks.
It allows for comparative conclusions between two conditions and translates them into differential drug response predictions.
DrDimont focuses on molecular interactions.
It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks.
DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response.
Residential segregation is a wide-spread phenomenon that can be observed in almost every major city.
In these urban areas residents with different racial or socioeconomic background tend to form homogeneous clusters.
Schelling's famous agent-based model for residential segregation explains how such clusters can form even if all agents are tolerant, i.e., if they agree to live in mixed neighborhoods.
For segregation to occur, all it needs is a slight bias towards agents preferring similar neighbors.
Very recently, Schelling's model has been investigated from a game-theoretic point of view with selfish agents that strategically select their residential location.
In these games, agents can improve on their current location by performing a location swap with another agent who is willing to swap.
We significantly deepen these investigations by studying the influence of the underlying topology modeling the residential area on the existence of equilibria, the Price of Anarchy and on the dynamic properties of the resulting strategic multi-agent system. Moreover, as a new conceptual contribution, we also consider the influence of locality, i.e., if the location swaps are restricted to swaps of neighboring agents.
We give improved almost tight bounds on the Price of Anarchy for arbitrary underlying graphs and we present (almost) tight bounds for regular graphs, paths and cycles. Moreover, we give almost tight bounds for grids, which are commonly used in empirical studies.
For grids we also show that locality has a severe impact on the game dynamics.
"Bad" data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it.
Duplicates - multiple but different representations of the same real-world entities are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential.
Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results.
To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform.
It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results.
Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results, as shown in Figure 1.
HPI Future SOC Lab
(2024)
The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners.
The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies.
This technical report presents results of research projects executed in 2020. Selected projects have presented their results on April 21st and November 10th 2020 at the Future SOC Lab Day events.
Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection
(2022)
Background:
Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied.
Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens.
No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.
Results:
We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes.
We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning.
We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity.
Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.
Conclusions:
The neural networks trained using our data collection enable accurate detection of novel fungal pathogens.
A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.
Um in der Schule bereits frühzeitig ein Verständnis für informatische Prozesse zu vermitteln wurde das neue Informatikfach Digitale Welt für die Klassenstufe 5 konzipiert mit der bundesweit einmaligen Verbindung von Informatik mit anwendungsbezogenen und gesellschaftlich relevanten Bezügen zur Ökologie und Ökonomie. Der Technische Report gibt eine Handreichung zur Einführung des neuen Faches.
When I started my PhD, I wanted to do something related to systems but I wasn't sure exactly what. I didn't consider data management systems initially, because I was unaware of the richness of the systems work that data management systems were build on. I thought the field was mainly about SQL. Luckily, that view changed quickly.
The Hasso Plattner Institute (HPI), academically structured as the independent Faculty of Digital Engineering at the University of Potsdam, unites computer science research and teaching with the advantages of a privately financed institute and a tuition-free study program. Founder and namesake of the institute is the SAP co-founder Hasso Plattner, who also heads the Enterprise Platform and Integration Concepts (EPIC) research center which focuses on the technical aspects of business software with a vision to provide the fastest way to get insights out of enterprise data. Founded in 2006, the EPIC combines three research groups comprising autonomous data management, enterprise software engineering, and data-driven decision support.
Schelling's classical segregation model gives a coherent explanation for the wide-spread phenomenon of residential segregation. We introduce an agent-based saturated open-city variant, the Flip Schelling Process (FSP), in which agents, placed on a graph, have one out of two types and, based on the predominant type in their neighborhood, decide whether to change their types; similar to a new agent arriving as soon as another agent leaves the vertex. We investigate the probability that an edge {u,v} is monochrome, i.e., that both vertices u and v have the same type in the FSP, and we provide a general framework for analyzing the influence of the underlying graph topology on residential segregation. In particular, for two adjacent vertices, we show that a highly decisive common neighborhood, i.e., a common neighborhood where the absolute value of the difference between the number of vertices with different types is high, supports segregation and, moreover, that large common neighborhoods are more decisive. As an application, we study the expected behavior of the FSP on two common random graph models with and without geometry: (1) For random geometric graphs, we show that the existence of an edge {u,v} makes a highly decisive common neighborhood for u and v more likely. Based on this, we prove the existence of a constant c>0 such that the expected fraction of monochrome edges after the FSP is at least 1/2+c. (2) For Erdős–Rényi graphs we show that large common neighborhoods are unlikely and that the expected fraction of monochrome edges after the FSP is at most 1/2+o(1). Our results indicate that the cluster structure of the underlying graph has a significant impact on the obtained segregation strength.
Massive Open Online Courses (MOOCs) remarkably attracted global media attention, but the spotlight has been concentrated on a handful of English-language providers. While Coursera, edX, Udacity, and FutureLearn received most of the attention and scrutiny, an entirely new ecosystem of local MOOC providers was growing in parallel. This ecosystem is harder to study than the major players: they are spread around the world, have less staff devoted to maintaining research data, and operate in multiple languages with university and corporate regional partners. To better understand how online learning opportunities are expanding through this regional MOOC ecosystem, we created a research partnership among 15 different MOOC providers from nine countries. We gathered data from over eight million learners in six thousand MOOCs, and we conducted a large-scale survey with more than 10 thousand participants. From our analysis, we argue that these regional providers may be better positioned to meet the goals of expanding access to higher education in their regions than the better-known global providers. To make this claim we highlight three trends: first, regional providers attract a larger local population with more inclusive demographic profiles; second, students predominantly choose their courses based on topical interest, and regional providers do a better job at catering to those needs; and third, many students feel more at ease learning from institutions they already know and have references from. Our work raises the importance of local education in the global MOOC ecosystem, while calling for additional research and conversations across the diversity of MOOC providers.
The automotive industry is a prime example of digital technologies reshaping mobility. Connected, autonomous, shared, and electric (CASE) trends lead to new emerging players that threaten existing industrial-aged companies. To respond, incumbents need to bridge the gap between contrasting product architecture and organizational principles in the physical and digital realms. Over-the-air (OTA) technology, that enables seamless software updates and on-demand feature additions for customers, is an example of CASE-driven digital product innovation. Through an extensive longitudinal case study of an OTA initiative by an industrial- aged automaker, this dissertation explores how incumbents accomplish digital product innovation. Building on modularity, liminality, and the mirroring hypothesis, it presents a process model that explains the triggers, mechanisms, and outcomes of this process. In contrast to the literature, the findings emphasize the primacy of addressing product architecture challenges over organizational ones and highlight the managerial implications for success.
Homomorphisms are a fundamental concept in mathematics expressing the similarity of structures. They provide a framework that captures many of the central problems of computer science with close ties to various other fields of science. Thus, many studies over the last four decades have been devoted to the algorithmic complexity of homomorphism problems. Despite their generality, it has been found that non-uniform homomorphism problems, where the target structure is fixed, frequently feature complexity dichotomies. Exploring the limits of these dichotomies represents the common goal of this line of research.
We investigate the problem of counting homomorphisms to a fixed structure over a finite field of prime order and its algorithmic complexity. Our emphasis is on graph homomorphisms and the resulting problem #_{p}Hom[H] for a graph H and a prime p. The main research question is how counting over a finite field of prime order affects the complexity.
In the first part of this thesis, we tackle the research question in its generality and develop a framework for studying the complexity of counting problems based on category theory. In the absence of problem-specific details, results in the language of category theory provide a clear picture of the properties needed and highlight common ground between different branches of science. The proposed problem #Mor^{C}[B] of counting the number of morphisms to a fixed object B of C is abstract in nature and encompasses important problems like constraint satisfaction problems, which serve as a leading example for all our results. We find explanations and generalizations for a plethora of results in counting complexity. Our main technical result is that specific matrices of morphism counts are non-singular. The strength of this result lies in its algebraic nature. First, our proofs rely on carefully constructed systems of linear equations, which we know to be uniquely solvable. Second, by exchanging the field that the matrix is defined by to a finite field of order p, we obtain analogous results for modular counting. For the latter, cancellations are implied by automorphisms of order p, but intriguingly we find that these present the only obstacle to translating our results from exact counting to modular counting. If we restrict our attention to reduced objects without automorphisms of order p, we obtain results analogue to those for exact counting. This is underscored by a confluent reduction that allows this restriction by constructing a reduced object for any given object. We emphasize the strength of the categorial perspective by applying the duality principle, which yields immediate consequences for the dual problem of counting the number of morphisms from a fixed object.
In the second part of this thesis, we focus on graphs and the problem #_{p}Hom[H]. We conjecture that automorphisms of order p capture all possible cancellations and that, for a reduced graph H, the problem #_{p}Hom[H] features the complexity dichotomy analogue to the one given for exact counting by Dyer and Greenhill. This serves as a generalization of the conjecture by Faben and Jerrum for the modulus 2. The criterion for tractability is that H is a collection of complete bipartite and reflexive complete graphs. From the findings of part one, we show that the conjectured dichotomy implies dichotomies for all quantum homomorphism problems, in particular counting vertex surjective homomorphisms and compactions modulo p. Since the tractable cases in the dichotomy are solved by trivial computations, the study of the intractable cases remains. As an initial problem in a series of reductions capable of implying hardness, we employ the problem of counting weighted independent sets in a bipartite graph modulo prime p. A dichotomy for this problem is shown, stating that the trivial cases occurring when a weight is congruent modulo p to 0 are the only tractable cases. We reduce the possible structure of H to the bipartite case by a reduction to the restricted homomorphism problem #_{p}Hom^{bip}[H] of counting modulo p the number of homomorphisms between bipartite graphs that maintain a given order of bipartition. This reduction does not have an impact on the accessibility of the technical results, thanks to the generality of the findings of part one. In order to prove the conjecture, it suffices to show that for a connected bipartite graph that is not complete, #_{p}Hom^{bip}[H] is #_{p}P-hard. Through a rigorous structural study of bipartite graphs, we establish this result for the rich class of bipartite graphs that are (K_{3,3}\{e}, domino)-free. This overcomes in particular the substantial hurdle imposed by squares, which leads us to explore the global structure of H and prove the existence of explicit structures that imply hardness.
Health app policy
(2022)
An abundant and growing supply of digital health applications (apps) exists in the commercial tech-sector, which can be bewildering for clinicians, patients, and payers. A growing challenge for the health care system is therefore to facilitate the identification of safe and effective apps for health care practitioners and patients to generate the most health benefit as well as guide payer coverage decisions. Nearly all developed countries are attempting to define policy frameworks to improve decision-making, patient care, and health outcomes in this context. This study compares the national policy approaches currently in development/use for health apps in nine countries. We used secondary data, combined with a detailed review of policy and regulatory documents, and interviews with key individuals and experts in the field of digital health policy to collect data about implemented and planned policies and initiatives. We found that most approaches aim for centralized pipelines for health app approvals, although some countries are adding decentralized elements. While the countries studied are taking diverse paths, there is nevertheless broad, international convergence in terms of requirements in the areas of transparency, health content, interoperability, and privacy and security. The sheer number of apps on the market in most countries represents a challenge for clinicians and patients. Our analyses of the relevant policies identified challenges in areas such as reimbursement, safety, and privacy and suggest that more regulatory work is needed in the areas of operationalization, implementation and international transferability of approvals. Cross-national efforts are needed around regulation and for countries to realize the benefits of these technologies.
“Ick bin een Berlina”
(2024)
Background: Robots are increasingly used as interaction partners with humans. Social robots are designed to follow expected behavioral norms when engaging with humans and are available with different voices and even accents. Some studies suggest that people prefer robots to speak in the user’s dialect, while others indicate a preference for different dialects.
Methods: Our study examined the impact of the Berlin dialect on perceived trustworthiness and competence of a robot. One hundred and twenty German native speakers (Mage = 32 years, SD = 12 years) watched an online video featuring a NAO robot speaking either in the Berlin dialect or standard German and assessed its trustworthiness and competence.
Results: We found a positive relationship between participants’ self-reported Berlin dialect proficiency and trustworthiness in the dialect-speaking robot. Only when controlled for demographic factors, there was a positive association between participants’ dialect proficiency, dialect performance and their assessment of robot’s competence for the standard German-speaking robot. Participants’ age, gender, length of residency in Berlin, and device used to respond also influenced assessments. Finally, the robot’s competence positively predicted its trustworthiness.
Discussion: Our results inform the design of social robots and emphasize the importance of device control in online experiments.
V-Edge
(2022)
As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: Instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays lower-bounded by propagation speed, edge servers deployed in close proximity to the user (e.g., at some base station) serve as proxy for the cloud. This is particularly interesting for upcoming machine-learning-based intelligent services, which require substantial computational and networking performance for continuous model training. However, this promising idea is hampered by the limited number of such edge servers. In this article, we discuss a way forward, namely the V-Edge concept. V-Edge helps bridge the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture, and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services.
Fetal alcohol-spectrum disorder (FASD) is underdiagnosed and often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Here, we develop a screening tool for FASD in youth with ADHD symptoms. To develop the prediction model, medical record data from a German University outpatient unit are assessed including 275 patients aged 0-19 years old with FASD with or without ADHD and 170 patients with ADHD without FASD aged 0-19 years old. We train 6 machine learning models based on 13 selected variables and evaluate their performance. Random forest models yield the best prediction models with a cross-validated AUC of 0.92 (95% confidence interval [0.84, 0.99]). Follow-up analyses indicate that a random forest model with 6 variables - body length and head circumference at birth, IQ, socially intrusive behaviour, poor memory and sleep disturbance - yields equivalent predictive accuracy. We implement the prediction model in a web-based app called FASDetect - a user-friendly, clinically scalable FASD risk calculator that is freely available at https://fasdetect.dhc-lab.hpi.de.
Purpose
Due to the increasing application of genome analysis and interpretation in medical disciplines, professionals require adequate education. Here, we present the implementation of personal genotyping as an educational tool in two genomics courses targeting Digital Health students at the Hasso Plattner Institute (HPI) and medical students at the Technical University of Munich (TUM).
Methods
We compared and evaluated the courses and the students ' perceptions on the course setup using questionnaires.
Results
During the course, students changed their attitudes towards genotyping (HPI: 79% [15 of 19], TUM: 47% [25 of 53]). Predominantly, students became more critical of personal genotyping (HPI: 73% [11 of 15], TUM: 72% [18 of 25]) and most students stated that genetic analyses should not be allowed without genetic counseling (HPI: 79% [15 of 19], TUM: 70% [37 of 53]). Students found the personal genotyping component useful (HPI: 89% [17 of 19], TUM: 92% [49 of 53]) and recommended its inclusion in future courses (HPI: 95% [18 of 19], TUM: 98% [52 of 53]).
Conclusion
Students perceived the personal genotyping component as valuable in the described genomics courses. The implementation described here can serve as an example for future courses in Europe.
Deep learning has seen widespread application in many domains, mainly for its ability to learn data representations from raw input data. Nevertheless, its success has so far been coupled with the availability of large annotated (labelled) datasets. This is a requirement that is difficult to fulfil in several domains, such as in medical imaging. Annotation costs form a barrier in extending deep learning to clinically-relevant use cases. The labels associated with medical images are scarce, since the generation of expert annotations of multimodal patient data at scale is non-trivial, expensive, and time-consuming. This substantiates the need for algorithms that learn from the increasing amounts of unlabeled data. Self-supervised representation learning algorithms offer a pertinent solution, as they allow solving real-world (downstream) deep learning tasks with fewer annotations. Self-supervised approaches leverage unlabeled samples to acquire generic features about different concepts, enabling annotation-efficient downstream task solving subsequently.
Nevertheless, medical images present multiple unique and inherent challenges for existing self-supervised learning approaches, which we seek to address in this thesis: (i) medical images are multimodal, and their multiple modalities are heterogeneous in nature and imbalanced in quantities, e.g. MRI and CT; (ii) medical scans are multi-dimensional, often in 3D instead of 2D; (iii) disease patterns in medical scans are numerous and their incidence exhibits a long-tail distribution, so it is oftentimes essential to fuse knowledge from different data modalities, e.g. genomics or clinical data, to capture disease traits more comprehensively; (iv) Medical scans usually exhibit more uniform color density distributions, e.g. in dental X-Rays, than natural images. Our proposed self-supervised methods meet these challenges, besides significantly reducing the amounts of required annotations.
We evaluate our self-supervised methods on a wide array of medical imaging applications and tasks. Our experimental results demonstrate the obtained gains in both annotation-efficiency and performance; our proposed methods outperform many approaches from related literature. Additionally, in case of fusion with genetic modalities, our methods also allow for cross-modal interpretability. In this thesis, not only we show that self-supervised learning is capable of mitigating manual annotation costs, but also our proposed solutions demonstrate how to better utilize it in the medical imaging domain. Progress in self-supervised learning has the potential to extend deep learning algorithms application to clinical scenarios.
Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77%. They show that these metrics are more effective than demographic and clinical variables.
Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.