PIDSIA Seminar by Danupon Nanongkai
by Announcements of talks＠IDSIA
Speaker: Danupon Nanongkai, KTH Royal Institute of Technology
Title: Distributed Shortest Paths, Exactly
Abstract:
This talk concerns the problem of quickly computing distances and
shortest paths on distributed networks (the CONGEST model). There have
been many developments for this problem in the last few years,
resulting in tight approximation schemes. This left widely open
whether exact algorithms can perform equally well. In this talk, we
will discuss some recent progress in answering this question. The talk
will focus on surveying the state of the art, and will discuss some
recent algorithms in details as time permits.
Based on joint work with Chien-Chung Huang and Thatchaphol Saranurak
(FOCS 2017) and with Sebastian Krinninger (work in progress).
Bio:
Danupon Nanongkai is currently an associate professor in the School of
Electrical Engineering and Computer Science (EECS), KTH Royal
Institute of Technology, Sweden. He received a Ph.D. in Algorithms,
Combinatorics, and Optimization (ACO) from Georgia Tech, USA, in 2011
and a docent in Computer Science from KTH in 2017. He was a recipient
of the Principles of Distributed Computing Doctoral Dissertation Award
in 2013 and the ERC Starting Grant in 2017. His research interest is
generally on graph algorithms and particularly on distributed,
dynamic, and approximation graph algorithms.
When:
Monday 9th of April 2018, 12:00-13:00
Location:
Manno, Galleria 1, 2nd floor, room G1-204
Registration:
Pizza (or alternative food) and drinks will be offered
at the end of the talk. If you plan to attend, please register in a
timely fashion at the following link so that we will have no shortage of food:
https://doodle.com/poll/6qp9hc99m5p42gw5
6 years
Semantic networks: applications and modelling
by Announcements of talks＠IDSIA
*Who:* Corrado Monti, post-doc candidate
*Title:* /*Semantic networks: applications and modelling*/
***Abstract: *
Semantic networks are one of the most valuable tools in today's NLP.
They power the intelligence behind conversational interfaces, help
search engines answer queries, and are one the most used ways to
represent the knowledge present in natural language. In this talk, I
will present some of my work related to them.
Firstly, we will see an example of a simple use case for semantic
networks within opinion mining. I will show how we used them to build a
model able to detect political disaffection in Twitter messages in
Italian language. Semantic networks here helped, e.g., in distinguishing
general political disaffection from a sentiment against a specific
political party. We applied this model to 35 millions tweets, and – in
order to validate the quality of the generated time-series – we compared
our results to opinion surveys.
Secondly, I will illustrate a theoretical model for semantic networks.
In a semantic network, each node is usually tagged with different
categories. How does the presence or absence of such categories
interplay with the network link structure? I will present a model that
is able to describe complex interactions between categories and links,
while at the same time being simple enough to derive scalable
algorithms. Finally, I will show a practical application for this model
on semantic networks, presenting an algorithm to mine surprising links.
***When:* Thursday, 29th of March 2018, 10:15-10:45 *
Where: *Manno, Galleria 1, room G1-204 *
*
6 years
PIDSIA Seminar by Scanagatta Mauro (28.03.2018)
by Announcements of talks＠IDSIA
*Who:* Scanagatta Mauro, Ph.D. Candidate @ IDSIA
*Title:* Advancements in Bayesian network structure learning
***Abstract: *A Bayesian network (BN) is a versatile and well-known probabilistic graphical model with applications in a variety of fields.
Their graphical nature makes BNs excellent models for representing the complex probabilistic relationships existing in many real problems ranging from bioinformatics to law, from image processing to economic risk analysis.
In this talk, we will present the difficult task of learning their dependency graph, also known as their structure, from data.
***When:* Wednesday, 28th of March 2018, 12:00-13:00
*Where: *Manno, Galleria 1, 2nd floor, room G1-204
*Registration: *Pizza kebab (or alternative food) and drinks will be offered at the end of the talk.
If you plan to attend, please register in a timely fashion at the following link so that we will have no shortage of food:
https://doodle.com/poll/cv5c57q94m76rt2i
--
Scanagatta Mauro
Ph.D. Student @ IDSIA -
Institute Dalle Molle for Artificial Intelligence
www.idsia.ch
6 years, 1 month
PIDSIA Seminar by Cristina Rottondi (21.3.2018)
by Announcements of talks＠IDSIA
*Speaker:
Cristina Rottondi, IDSIA
*Title:
Future Optical Network Design And Management Assisted by Machine Learning
*Abstract:
Recently, machine learning methods have started to enter the field of photonics, ranging from quantum mechanics, nanophotonics, optical communication and optical networks.
Though machine learning offers many powerful techniques, linking it to optical communication may not be trivial, due to the inherent peculiarities of optical technologies.
In this talk, we will discuss open challenges and benefits that machine learning methods can bring to optical communications, especially in the field of optical networks design and management.
*Bio:
Cristina Rottondi is a researcher at IDSIA. Her research interests include data privacy and security in the Automatic Metering Infrastructure of Smart Grids, optical networks planning, and networked music performance.
*When:
Wednesday, 21st of March 2018, 12:00-13:00
*Location:
Manno, Galleria 1, 2nd floor, room G1-204
*Registration:
Pizza (or alternative food) and drinks will be offered at the end of the talk. If you plan to attend, please register in a timely fashion at the following link so that we will have no shortage of food:
https://doodle.com/poll/gxidzmuu3i2dmafa
6 years, 1 month
22.03 J Jamal: Industrial cluster symbiosis optimization based on linear programming
by Announcements of talks＠IDSIA
MSE thesis defence
Industrial cluster symbiosis optimization based on linear
programming
Jafar Jamal, IDSIA
Abstract:
Industrial Symbiosis is a symbiotic relationship between
dissimilar industries that is achieved by the flow of
waste and byproducts as an output from one production
units to another as resource to be used for production.
The described symbiosis has many environmental and
economical benefits generated from reducing the cost of
the resources used in the production process. The main
concern with implementing such a system is the guarantee
for an even distribution of the extra profit generated
through the trading process among the participating
production units. Otherwise, it would be intuitively
difficult to convince them to implement the system we
suggest. In this work, we model the problem through
mathematical programming and we propose a solution based
on a series of linear programs that maximizes and balances
the profit between the production units. A first model
focuses on pure bybroducts trading, a more advanced one
also considers the trading process within a dynamic
electricity market where electricity costs vary overtime.
We show that an industrial cluster can take advantage of a
symbiotic system based on the exchange of byproducts
between the cluster's production units in order to
increase the total profit, whether operating in isolation
or within a dynamic electricity market, and that the
approach proposed does indeed spread the extra profit
guaranteed by the exchange of byproducts evenly among the
participating production units.
When: 22nd March 2018, 16:30-17:30
Where: Manno, Galleria 1, 2nd floor, room G1-204
6 years, 1 month
PIDSIA Seminar by Manuela Fisher
by Announcements of talks＠IDSIA
Speaker: Manuela Fisher, ETH Zurich
Title: The Locality of Maximal Matching
Abstract:
Many systems in the world are vast networks consisting of autonomous
nodes that communicate with each other in order to jointly solve a
task. One common feature of these complex networks is that due to
their size it is impossible for an individual node to have a global
view. Instead, nodes have to base their decisions on local information
about nearby nodes only. Despite this intrinsic locality, the network
as a whole is supposed to arrive at a global solution. Understanding
capabilities and limitations of such local algorithms is the key
challenge of distributed graph algorithms. The LOCAL model---the
standard synchronous message-passing model of distributed computing
introduced by Linial in 1987---is designed to abstract the pure
concept of locality.
In this talk, we discuss the maximal matching problem in the LOCAL
model. In particular, we introduce a new rounding technique that leads
to a deterministic O(log^2 Delta log n)-round algorithm on any n-node
graph with maximum degree Delta. This is the first improvement in
almost 20 years over the celebrated O(log^4 n)-round algorithm by
Hanckowiak, Karonski, and Panconesi.
Bio:
Manuela Fischer is a PhD student at ETH Zurich under the supervision
of Mohsen Ghaffari. Her research interests include distributed graph
algorithms, combinatorics, and stochastic processes.
When: 7th of March 2018, 12:00-13:00
Location: Manno, Galleria 1, 2nd floor, room G1-204
Registration: Pizza (or alternative food) and drinks will be offered
at the end of the talk. If you plan to attend, please register in a
timely fashion at the following link so that we will have no shortage of food:
https://doodle.com/poll/yuq8q4433p4df7bi
6 years, 1 month