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Optimization of the treatment of hydrocephalus by the non-invasive measurement of the intra-cranial pressure

Alec Ginggen

This thesis work aims to optimize the treatment of hydrocephalus. It consists of two distinct objectives: the optimization of the diagnosis of patients implanted with a shunt system for the derivation of cerebro-spinal fluid (CSF) and the optimization of t ...

EPFL2007

Deep MinCut: Learning Node Embeddings by Detecting Communities

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong

We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph -structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide insights into the graph structure ...

ELSEVIER SCI LTD2022

The consensus number of a cryptocurrency (vol 35, pg 1, 2022)

Rachid Guerraoui, Matej Pavlovic, Dragos-Adrian Seredinschi, Matteo Monti

SPRINGER2022

PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition

Boris Bergsma

At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation have shown that analog signal processing (ASP) can be more power-efficient tha ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Special issue on computer graphics international 2022 part 1

Daniel Thalmann

WILEY2022

Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells

Idan Segev, Werner Alfons Hilda Van Geit

Many scientific systems are studied using computer codes that simulate the phenomena of interest. Computer simulation enables scientists to study a broad range of possible conditions, generating large quantities of data at a faster rate than the laboratory ...

FRONTIERS MEDIA SA2022

Learning Bayesian Networks in the Presence of Structural Side Information

Negar Kiyavash, Seyed Jalal Etesami, Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi

We study the problem of learning a Bayesian network (BN) of a set of variables when structural side information about the system is available. It is well known that learning the structure of a general BN is both computationally and statistically challengin ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Deep Reinforcement Learning for Random Access in Machine-Type Communication

Fernando Perez Cruz

Random access (RA) schemes are a topic of high interest in machine-type communication (MTC). In RA protocols, backoff techniques such as exponential backoff (EB) are used to stabilize the system to avoid low throughput and excessive delays. However, these ...

IEEE2022

Detailed Placement for Dedicated LUT-Level FPGA Interconnect

Paolo Ienne, Grace Zgheib, Stefan Nikolic

In this work, we develop timing-driven CAD support for FPGA architectures with direct connections between LUTs. We do so by proposing an efficient ILP-based detailed placer, which moves a carefully selected subset of LUTs from their original positions, so ...

ASSOC COMPUTING MACHINERY2022

fGOT: Graph Distances Based on Filters and Optimal Transport

Pascal Frossard, Mireille El Gheche, Hermina Petric Maretic

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph dis ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

One Fuzz Doesn’t Fit All: Optimizing Directed Fuzzing via Target-tailored Program State Restriction

Mathias Josef Payer

Fuzzing is the de-facto default technique to discover software flaws, randomly testing programs to discover crashing test cases. Yet, a particular scenario may only care about specific code regions (for, e.g., bug reproduction, patch or regression testing) ...

ASSOC COMPUTING MACHINERY2022

Probabilistic Data Association for Semantic SLAM at Scale

Tony Alan Wood

With advances in image processing and machine learning, it is now feasible to incorporate semantic information into the problem of simultaneous localisation and mapping (SLAM). Previously, SLAM was carried out using lower level geometric features (points, ...

IEEE2022

Decentralized Semi-supervised Learning over Multitask Graphs

Ali H. Sayed, Roula Nassif, Elsa Rizk

In network semi-supervised learning problems, only a subset of the network nodes is able to access the data labeling. This paper formulates a decentralized optimization problem where agents have individual decision rules to estimate, subject to the conditi ...

IEEE2022

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Andreas Krause, Maryam Kamgarpour

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Motivating Children to Practice Perspective-Taking Through Playing Games with Cozmo

Pierre Dillenbourg

Recent studies with children have pointed out the importance of spatial thinking as an essential factor in determining later success in STEM-related fields. The current study explores the potential of using embodied activities with robots to aid the develo ...

IEEE2022

BLOOM: Bimodal Lattice One-out-of-Many Proofs and Applications

Ngoc Khanh Karol Nguyen

We give a construction of an efficient one-out-of-many proof system, in which a prover shows that he knows the pre-image for one element in a set, based on the hardness of lattice problems. The construction employs the recent zero-knowledge framework of Ly ...

SPRINGER INTERNATIONAL PUBLISHING AG2022