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 .…

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 .…

OLR 2021 Challenge Datasets Rules and Baselines

This paper introduces the sixth Oriental Language Recognition (OLR) 2021Challenge . It intends to improve the performance of language recognitionsystems and speech recognition systems within multilingual scenarios . The dataprofile, four tasks, two baselines, and the evaluation principles are presented .…

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 .…

Designing Mobile EEG Neurofeedback Games for Children with Autism Implications from Industry Practice

Neurofeedback games are an effective and playful approach to enhance certainsocial and attentional capabilities in children with autism . The games are promising to become widely accessible along with the commercialization of mobile EEG modules . However, little industry-based experiences are shared on how to better design neurofeedbacks games to fine-tune their playability and user experiences for autistic children .…

Photon Starved Scene Inference using Single Photon Cameras

Single-photon cameras (SPCs) are an emergingsensing modality that are capable of capturing images with high sensitivity . Despite having minimal read-noise, images captured by SPCs in photon-starved conditions still suffer from strong shot noise . We propose photon scale-space a collection of high-SNR imagesspanning a wide range of photons-per-pixel (PPP) levels as guides to train inference model on low photon flux images .…

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 .…

Resource Efficient Mountainous Skyline Extraction using Shallow Learning

Skyline plays a pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented realityapplications . We present a novel mountainous skyline detection approach wherewe adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions .…

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% .…

Cardiac CT segmentation based on distance regularized level set

A paper uses distanceregularized level set (DRL SE) to explore the segmentation effect of epicardiumand endocardium . Five CT images are used to verify the proposedmethod, and image quality evaluation indexes such as dice score and Hausdorffdistance are used . The results showed that the researchers could separate the inner and outer membrane very well (endocardiumdice = 0.9253, Hausorfff = 7.8740) and epicocardium Hausdice= 0.9687 .…

Reservoir Computing Approach for Gray Images Segmentation

The paper proposes a novel approach for gray scale images segmentation . It is based on multiple features extraction from single feature per image pixel, using Echo state network . The newly extractedfeatures — reservoir equilibrium states — reveal hidden image characteristicsthat improve its segmentation via a clustering algorithm .…

An Adaptive State Aggregation Algorithm for Markov Decision Processes

Value iteration is a well-known method of solving Markov Decision Processes . However, the computational cost of value iteration quickly becomesfeasible as the size of the state space increases . In this paper, we propose an intuitive algorithm for solving MDPsthat reduces the cost of updates by dynamically grouping together states with similar cost-to-go values .…

Aggressive Visual Perching with Quadrotors on Inclined Surfaces

Autonomous Micro Aerial Vehicles (MAVs) have the potential to be employed for surveillance and monitoring tasks . By perching and staring on one or multiplelocations aerial robots can save energy while concurrently increasing theiroverall mission time without actively flying . The onboard cameras and IMU are concurrently used for state estimation and to inferthe relative robot/target localization .…

Human Pose Estimation from Sparse Inertial Measurements through Recurrent Graph Convolution

The AAGC-LSTM combines spatial and temporal dependency in a single network operation . This is made possible by equipping graph convolutions with adjacency adaptivity, which allows for learning unknown dependencies of the human body joints . Tofurther boost accuracy, we propose longitudinal loss weighting to considernatural movement patterns, as well as body-aware contralateral dataaugmentation .…

Making Reads in BFT State Machine Replication Fast Linearizable and Live

Practical Byzantine Fault Tolerance (PBFT) is a seminal state machinereplication protocol that achieves a performance comparable to non-replicated systems in realistic environments . One of these optimizations is read-only requests, a particular type of client request which avoids running the three-step agreement protocol and allows replicas to respond directly, thus reducing the latency of reads from five to two communication steps .…

Robust Adaptive Submodular Maximization

Most of existing studies on adaptive submodular optimization focus on theaverage-case, i.e., their objective is to find a policy that maximizes theexpected utility over a known distribution of realizations . We introduce a new class ofstochastic functions, called \emph{worst-case submodular function .…

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 .…

Hash Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression

We introduce a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees . We propose a simple diversity-preservationmechanism with promising results on a collection of symbolic regressionbenchmark problems . The algorithm has two important applications: fast calculation ofpopulation diversity and algebraic simplification of symbolic expression trees .…

Formalizing Galois Theory

We describe a project to formalize Galois theory using the Lean theoremprover . We discuss some of the challenges we faced and the decisions we made in the course of this project . The maintheorems we formalized are the primitive element theorem, the fundamentaltheorem of Galois Theory, and the equivalence of several characterizations offinite degree Galois extensions .…

LocalGLMnet interpretable deep learning for tabular data

Deep learning models have gained great popularity in statistical modeling . Theadvantage of deep learning models is that their solutions are difficult tointerpret and explain . We propose a new network architecture that sharessimilar features as generalized linear models, but provides superior predictivepower benefiting from the art of representation learning .…

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 .…

Minimal Session Types for the π calculus Extended Version

Session types enable the static verification of message-passing programs . Asession type specifies a channel’s protocol as sequences of messages . This paper establishes a minimality result but now for the session\pi-calculus, the language in which values are names and for which sessiontypes have been more widely studied .…

Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection under Domain Shift Conditions

The goal of Unsupervised Anomaly Detection (UAD) is to detect anomalous signals under the condition that only non-anomalous (normal) data is available before . In UAD-S, data is exposed to contextual changes that are usually unknown beforehand . In our exploratory investigation, we look for two qualities, Separability (SEP) and Discriminative Support (DSUP) and formulate several hypotheses that could facilitate diagnosis and development of further representation and detection approaches .…

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 .…

Unsupervised Domain Adaptive 3D Detection with Multi Level Consistency

Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets . However, drasticperformance degradation remains a critical challenge for cross-domain deployment . We propose anovel and unified framework, Multi-Level Consistency Network (MLC-Net), whichemploys a teacher-student paradigm to generate adaptive and reliablepseudo-targets .…

LARGE Latent Based Regression through GAN Semantics

We propose a novel method for solving regression tasks using few-shot or weaksupervision . At the core of our method is the fundamental observation that GANsare incredibly successful at encoding semantic information within their latentspace, even in a completely unsupervised setting .…

ArchaeoDAL A Data Lake for Archaeological Data Management and Analytics

Archaeological data have many different formats (images,texts, sensor data) and can be structured, semi-structured and unstructured . Such variety makes data difficult to collect, store, manage, search and analyze . We propose a generic, flexible andcomplete data lake architecture . Our metadata management system exploitsgoldMEDAL, which is the most complete metadata model currently available .…

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.…

Spinning Sequence to Sequence Models with Meta Backdoors

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to “spin” their output and support acertain sentiment . We introduce the concept of a “meta-backdoor” to explain model-spinningattacks . These attacks produce models whose output is valid and preservescontext, yet also satisfies a meta-task chosen by the adversary (e.g.,…

Confidence Aware Scheduled Sampling for Neural Machine Translation

Scheduled sampling is an effective method to alleviate the exposure biasproblem of neural machine translation . It simulates the inference scene by replacing ground-truth target input tokens with predicted ones during training . Despite its success, its critical schedule strategies are merelybased on training steps, ignoring the real-time model competence .…