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Zeit: Montag, 13. November 2023, 09.30 Uhr
Ort: Informatikzentrum, E3, 2. Etage, Raum 9222
Referent: Henri Lotze M.Sc.
Lehr- und Forschungsgebiet Theoretische Informatik
Thema: Going Offline -- Delays, Reservations and Predictions in Online
Computation
Abstract:
The study of classical online computation builds upon a rather simple
model. The input to a given problem is not known in advance,
decisions have to made immediately upon the arrival of a new element
and these decisions are irrevocable.
While this model is very well suitable to compute worst case bounds to
a broad range of problems, there are some problems which are not well
suited for this analysis. Naturally, problems that do not admit a
constant approximation ratio in their offline formulation do not admit
a constant competitive ratio in the worst case in the online
formulation. However, some problems that are approximable still do
not admit a constant competitive ratio when analyzed in the online
case. For a subset of these problems, such as the Online Simple
Knapsack problem and the class of general F-Node- and F-Edge-Deletion
problems, the reason for this seems to be that a single, specific bad
decision by an algorithm can be arbitrarily punished by an adversary.
In this talk, we explore modifications of the classical online model
with the aim to find natural models that on the one hand cover a large
range of problems and on the other hand work against pathological
kinds of instances that restrict an algorithm from obtaining a
constant competitive ratio. To this end, we study three modifications
of the classical online model.
The models that we study are that of "Late Accepts" -- with Advice --,
which allows to postpone decisions in online minimization problems
until a current partial solution does not uphold a certain property
anymore. The second one is that of "Reservations", in which any
decision may be postponed for a cost proportional to the gain of an
item. Finally, the model of "Bounded Predictions" allows an online
algorithm to see the complete instance in advance, with a caveat:
There is noise on the instance -- controlled by an adversary -- and
each element may actually deviate from its prediction, up to factor
that is known to the algorithm.
We are able to partially classify the advice complexity of the
complete class of both F-Node-Deletion and F-Edge-Deletion problems.
We give tight bounds on the competitive ratio for the Online Simple
Knapsack problem with Reservations for the whole range of possible
reservation costs, i.e. for a factor between 0 and 1. Finally, we give
partially tight bounds for the Online Simple Knapsack problem with
Bounded Predictions for the whole range of possible noise factors on
the size of the items.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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* Informatik-Oberseminar
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Zeit: Freitag, 08.12.2023, 13:00-14:00 Uhr
Ort: Raum 5053.1 (kleiner B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55
Referent: Herr Fabian Ohler, M. Sc.
Lehrstuhl Informatik 5
Thema: Die Rolle funktionaler Domänenmodelle in der Entwicklung allianzgetriebener Softwareplattformen
Abstract:
Organisationen können durch die Bildung von Allianzen gemeinsame Ziele verfolgen. Gestalten sie dabei zusammen eine Plattform, müssen bereits in einem frühen Stadium eine Vielzahl an Prozessen zusammengeführt und die Verteilung der Funktionen auf die Akteure geklärt und koordiniert werden.
Im Vortrag wird die methodische und technische Unterstützung für die unternehmensübergreifenden, von Kollaboration geprägten Entwurfsaktivitäten der zugehörigen Systemarchitektur am Beispiel einer Plattformentwicklung zur Kooperation öffentlicher Verkehrsträger und Verkehrsverbünde vorgestellt. Dafür wird eine empirisch ausgearbeitete Methode zur Herleitung eines funktionalen Domänenmodells und für die Weiterentwicklung zu einer Systemarchitekturbeschreibung präsentiert. Die Rolle des funktionalen Domänenmodells in diesem Kontext wird beleuchtet. Ergänzend werden Software-Werkzeuge zur Unterstützung der Entwicklungsphasen der Feinspezifikation und Validierung vorgestellt. Diese basieren auf in Experteninterviews erhobenen Erkenntnissen über die Defizite bislang eingesetzter Werkzeuge sowie daraus abgeleiteten Anforderungen. Die erarbeitete Methode wurde bereits erfolgreich in zwei Domänen eingesetzt, so dass eine Übertragbarkeit auf weitere Domänen zu erwarten ist.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D., JunProf. Dr. Sandra Geisler
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
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Zeit: Dienstag, 5. Dezember 2023, 13.00 Uhr
Ort: Großer b-it Raum (5053.2), Geb. E2, Informatikzentrum, Ahornstr. 55
Der Vortrag findet hybrid statt.
Zoom:
https://rwth.zoom-x.de/j/63672895840?pwd=L2ZoLzRDMzV3dzIrZ2hsNTAwaFNldz09
Meeting ID: 636 7289 5840, Passcode: 964382
Referent: Istvan Sarandi, M.Sc.
Lehrstuhl Informatik 13
Thema: Robust and Efficient Methods in Visual 3D Human Pose Estimation
Abstract:
Computer vision algorithms for perceiving humans in the real world are
crucial for several impactful emerging technologies, including self-driving
cars and mobile service robots.
In this talk, I will present three contributions to improving the state of
the art in deep learning-based 3D human pose estimation, that is,
localizing major anatomical landmarks of the human body in 3D space from
RGB images only. The central themes are robustness and efficiency, which
constitute the main challenges in robotics applications.
We start by addressing robustness to occlusions, i.e., when objects block
the line of sight between the person and the camera. After presenting the
first systematic study of how occlusions deteriorate 3D pose estimation
accuracy, we propose to mitigate the problem using an effective synthetic
occlusion data augmentation strategy.
We then turn to the problem of truncation, i.e., when only a part of the
body is within the camera's field of view. We develop a truncation-robust
heatmap representation, which also allows learned recovery of the metric
scale. Building upon this capability, I present an end-to-end learned
absolute pose estimation method called MeTRAbs, for robustly reconstructing
human poses in the camera's reference frame at state-of-the-art accuracy.
In the third part, I present the largest-scale experiment reported in the
3D pose literature to date, by merging a total of 32 datasets, in the
pursuit of improved in-the-wild generalization, outside the controlled
environments of motion capture studios. We overcome the challenge of
differently annotated datasets through a novel affine-combining autoencoder
formulation for capturing the common information in different landmark
annotation formats. Importantly, these methods can run on low-powered robot
hardware in real time.
I conclude with a discussion of possible extensions to the presented works,
as well as exciting future challenges for the field as a whole.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Freitag, 01. Dezember 2023, 09.00 Uhr
Ort: IT Center, Kopernikusstraße 6, Seminarraum 003
Referent: Jannis Klinkenberg M.Sc.
Lehrstuhl Informatik 12
Thema: Reactive Runtimes for Parallel Programming Models on Shared,
Distributed, and Heterogeneous Memory Systems
Abstract:
The persistent drive to enhance the scale and precision of scientific
simulations over the past decades has created an ever-growing demand for
computational resources. This, in turn, has catalyzed several innovations in
the architectural design and manufacturing procedures of computer systems,
such as the introduction of multi-core processors and multi-socket shared
memory systems, which are typically operated in High Performance Computing
(HPC) installations. Historically, scientific applications have primarily
been developed with the presumption of a uniform execution environment,
where every compute node and core within an installation operates at a
consistent, unchanging speed and where the execution time can be accurately
predicted. However, in the last decades, both hardware and software have
become increasingly complex and often exhibit dynamic execution behavior,
causing performance fluctuations and run time variability. Consequently, a
priori load balancing within and between compute nodes becomes increasingly
challenging in such environments. Applications as well as runtime systems
therefore demand for new techniques to be able to dynamically react to
changing execution conditions and efficiently balance the load.
This thesis presents reactive concepts, runtime implementations, and runtime
extensions designed to address the growing complexity of both hardware and
software. The objective is to provide portable, vendor-independent solutions
to improve application performance, minimize run time variability and
mitigate impending load imbalances. Locality-aware task scheduling
extensions in OpenMP improve the data locality on contemporary shared memory
NUMA architectures by dynamically identifying physical data locations and
reactively adjusting the task distribution and scheduling. Further,
continuous performance introspection combined with reactively migrating or
replicating tasks in distributed memory can efficiently detect and mitigate
emerging imbalances at execution time. Lastly, abstracting regular memory
allocation allows specifying additional requirements or hints regarding how
the data is used throughout the execution, which can be exploited to
dynamically guide the data placement on systems with heterogeneous memory.
Systematic evaluations demonstrate the effectiveness of the presented
concepts and show that placing the burden on runtime systems to proficiently
handle these low-level aspects is crucial to achieve high performance on
current and future architectures.
Es laden ein: die Dozentinnen und Dozenten der Informatik