HURRA Human readable router anomaly detection

This paper presents HURRA, a system that aims to reduce the time spent by network operators in the process of network troubleshooting . It consists of two modules that are plugged after any anomaly detection algorithm . The main difficulty in live deployment concerns the automated selection of the algorithm and tuning of its hyper-parameters .…

Constellation Learning relational abstractions over objects for compositional imagination

Constellation is anetwork that learns relational abstractions of static visual scenes, and generalises these abstractions over sensory particularities . We further show that thisbasis, along with language association, provides a means to imagine sensorycontent in new ways . This work is a first step in the explicit representation of visual relationships and using them for complex cognitive procedures .…

RGB Image Classification with Quantum Convolutional Ansaetze

Many quantum (convolutional) circuit ansaetze are proposed forgrayscale images classification tasks with promising empirical results . But the intra-channel information that is useful for vision tasks is not extracted effectively . This is the first work of a quantum convolutional circuit to deal with RGB images with a higher test accuracy compared to the purely classical CNNs .…

Lower Bounds for Symmetric Circuits for the Determinant

Dawar and Wilsenach (ICALP 2020) show an exponential separation between the sizes of symmetric arithmeticcircuits for computing the determinant and the permanent . The symmetryrestriction is that the circuits which take a matrix input are unchanged by a permutation applied simultaneously to the rows and columns of the matrix .…

Comprehending nulls

The Nested Relational Calculus (NRC) has been an influential high-level query language . It has been used as a basis for language-integrated queries in programming languages such as F#, Scala, and Links . However, NRC’s treatment of incomplete information, using nulls and three-valued logic, is not compatible with `standard’ NRC based on two-valued .…

Standardized Max Logits A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban Scene Segmentation

Identifying unexpected objects on roads in semantic segmentation is crucial in safety-critical applications . Existing approaches use images of unexpected objects from external datasets or require additional training . We propose a simple yet effective approach that standardizes the max logits in order to align the different distributions and reflect the relative meanings of each predicted class .…

Estimating Predictive Uncertainty Under Program Data Distribution Shift

Deep learning (DL) techniques have achieved great success in predictive accuracy in a variety of tasks, but deep neural networks (DNNs) are shown toproduce highly overconfident scores for even abnormal samples . Well-defineduncertainty indicates whether a model’s output should (or should not) betrusted and thus becomes critical in real-world scenarios which typicallyinvolves shifted input distributions due to many factors .…

Lower Bounds for Symmetric Circuits for the Determinant

Dawar and Wilsenach (ICALP 2020) show an exponential separation between the sizes of symmetric arithmeticcircuits for computing the determinant and the permanent . The symmetryrestriction is that the circuits which take a matrix input are unchanged by a permutation applied simultaneously to the rows and columns of the matrix .…

Pruning Ternary Quantization

The method significantly compressesneural network weights to a sparse ternary of [-1,0,1 . It can compress aResNet-18 model from 46 MB to 955KB and a ResNet-50 model from 99 MB to 3.3MB (~30x) The top-1 accuracy on ImageNet drops slightly from 69.7% to65.3% and from 76.15% to 74.47% .…

Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

Current algorithms share one limitation: They rely on directly visible objects . This is a major drawback compared to human behavior, where indirect visual cues caused by the actual object (e.g.,shadows) are already used intuitively to retrieve information . Humans already process light artifacts caused by oncoming vehicles to assume their future appearance, whereas current objectdetection systems rely on the oncoming vehicle’s direct visibility .…

3D Radar Velocity Maps for Uncertain Dynamic Environments

Future urban transportation concepts include a mixture of ground and air vehicles with varying degrees of autonomy in a congested environment . Safe and efficient transportation requires reasoning about the 3Dflow of traffic and properly modeling uncertainty . This paper explores a Bayesian approach that captures our uncertainty in the map given training data .…

MCDAL Maximum Classifier Discrepancy for Active Learning

Recent state-of-the-art active learning methods have mostly leveragedGenerative Adversarial Networks (GAN) for sample acquisition . However, GAN isusually known to suffer from instability and sensitivity to hyper-parameters . In contrast to these methods, we propose in this paper a novel active learningframework that we call Maximum Classifier Discrepancy for Active Learning(MCDAL) which takes the prediction discrepancies between multiple classifiers .…

VisDA 2021 Competition Universal Domain Adaptation to Improve Performance on Out of Distribution Data

Visual DomainAdaptation (VisDA) 2021 competition tests models’ ability to adapt to novel test distributions and handle distributional shift . Ourchallenge draws on large-scale publicly available datasets but constructs the evaluation across domains . Performance will be measured using a rigorous protocol,comparing to state-of-the-art domain adaptation methods with the help ofestablished metrics .…

A simple yet consistent constitutive law and mortar based layer coupling schemes for thermomechanical part scale simulations of metal additive manufacturing processes

This article proposes a coupled thermomechanical finite element modeltailored to the part-scale simulation of metal additive manufacturing process . A first focus lies on the derivation of aconsistent constitutive law on basis of a Voigt-type spatial homogenizationprocedure across the relevant phases, powder, melt and solid .…

Bias Loss for Mobile Neural Networks

Compact convolutional neural networks (CNNs) have witnessed exceptionalimprovements in performance in recent years . However, they still fail toprovide the same predictive power as CNNs with a large number of parameters . Diverse features present in activation maps indicate presence of unique descriptors necessary to distinguish between objects of differentclasses .…

Machine Learning with a Reject Option A survey

Machine learning models always make a prediction, even when it is likely to be inaccurate . This behavior should be avoided in many decision support applications, where mistakes can have severe consequences . This machine learning subfield enables machine learning models toabstain from making a prediction when likely to make a mistake .…

AD GAN End to end Unsupervised Nuclei Segmentation with Aligned Disentangling Training

Aligned Disentangling Generative AdversarialNetwork (AD-GAN) introduces representationdisentanglement to separate content representation from style representation . With this framework, spatial structure can be preserved explicitly, enabling asignificant reduction of macro-level lossy transformation . AD-GAN leads to significant improvement over the current best unsupervised methods by an average 17.8% relatively (w.r.t.…

Finite Element Formulations for Beam to Solid Interaction From Embedded Fibers Towards Contact

Contact and related phenomena, such as friction, wear or elastohydrodynamiclubrication, remain as one of the most challenging problem classes in nonlinearsolid and structural mechanics . The inherentnon-smoothness of contact conditions, the design of robust discretization approaches and the implementation of efficient solution schemes seem toprovide a never ending source of hard nuts to crack .…

Knowledge Rocks Adding Knowledge Assistance to Visualization Systems

Knowledge Rocks is an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems . Its centerpiece is an ontology that is able to automatically analyze andclassify input data, linked to a database to store classified instances . We provide adetailed case study by augmenting an it-security system with knowledge-assistance facilities.…

Exploring Deep Registration Latent Spaces

Explainability of deep neural networks is one of the most challenging and interesting problems in the field . We show that such an approach can decompose the highly convoluted latent latent spaces of registration pipelines in an orthogonal space with several interesting properties .…

When a crisis strikes Emotion analysis and detection during COVID 19

Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about theemotional states of the population . We present CovidEmo, ~1K tweets labeled withemotions. We examine how well large pre-trained language models generalizeacross domains and crises in the task of perceived emotion prediction .…

Data driven deep density estimation

Density estimation plays a crucial role in many data analysis tasks . It is used in tasks as diverse as analyzing population data, spatiallocations in 2D sensor readings, or reconstructing scenes from 3D scans . In this paper, we introduce a learned, data-driven deep density estimation (DDE)to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size .…

Exploring Deep Registration Latent Spaces

Explainability of deep neural networks is one of the most challenging and interesting problems in the field . We show that such an approach can decompose the highly convoluted latent latent spaces of registration pipelines in an orthogonal space with several interesting properties .…

Effective and Interpretable fMRI Analysis via Functional Brain Network Generation

Recent studies in neuroscience show great potential of functional brainnetworks constructed from fMRI data for popularity modeling and clinical predictions . However, existing functional brain networks are noisy and unaware of downstream prediction tasks . In this work, we develop an end-to-endtrainable pipeline to extract prominent fMRI features, generate brain networks, and make predictions with GNNs, all under the guidance of downstream predictions .…

On data lake architectures and metadata management

So-called big data generally come from transactionalsystems, and even more so from the Internet of Things and social media . A data lake is a large, raw data repository that stores and manages all company databearing any format . The data lake concept remains ambiguous or fuzzy for many researchers and practitioners, who often confuse it with the Hadoop technology .…

Exploring Deep Registration Latent Spaces

Explainability of deep neural networks is one of the most challenging and interesting problems in the field . We show that such an approach can decompose the highly convoluted latent latent spaces of registration pipelines in an orthogonal space with several interesting properties .…

Finite Bit Quantization For Distributed Algorithms With Linear Convergence

This paper studies distributed algorithms for (strongly convex) compositeoptimization problems over mesh networks . Instead of focusing on a specific algorithmic design, we propose a black-boxmodel casting distributed algorithms in the form of fixed-point iterates,converging at linear rate . The algorithmic model is coupled with a novel(random) Biased Compression (BC-)rule on the quantizer design, which preserves linear convergence .…

Modelling Latent Translations for Cross Lingual Transfer

We report gains for both zero-shot and few-shot learning setups, up to 2.7 accuracy points on average, which are even more prominent for low-resource languages (e.g., HaitianCreole) We evaluate our novel latent translation-based model on a series ofmultilingual NLU tasks, including commonsense reasoning, paraphraseidentification, and natural language inference .…

Resolution Adaptive All Digital Spatial Equalization for mmWave Massive MU MIMO

All-digital basestation (BS) architectures for millimeter-wave (mmWave)massive multi-user multiple-input multiple- input multiple-output (MU-MIMO) have advantages inspectral efficiency, flexibility, and baseband-processing simplicity overhybrid analog-digital solutions . We demonstrate that adapting the resolution of the analog-to-digitalconverters (ADCs) and spatial equalizer of an all-digital system to the communication scenario enables orders-of-magnitude power savings for realisticmmWave channels .…