How likely is a random graph shift enabled

The shift-enabled property of an underlying graph is essential in designing distributed filters . In particular, popular graph models ER, WS, BA random graph are used, weighted and unweighted, as well as signed graphs . The behaviour is not observed for weighted connected graphs, but very dense graphs are not shifted-enabled .…

Visible Rank and Codes with Locality

We propose a framework to study the effect of local recovery requirements of codeword symbols on the dimension of linear codes . The locality constraints of a linearcode are stipulated by a matrix $H$ of $H $ of $0$’s and $H’s .…

A denotational semantics for PROMELA addressing arbitrary jumps

PROMELA (Process Meta Language) is a high-level specification languagedesigned for modeling interactions in distributed systems . It is used as an input language for the model checker SPIN (Simple Promela INterpreter) The main characteristics are non-determinism, process communication through synchronous as well as asynchronous channels .…

Targeting Underrepresented Populations in Precision Medicine A Federated Transfer Learning Approach

The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research has become a barrier to translating precision medicine research into practice . Risk prediction models are often found to be underperformed inthese underrepresented populations . In this paper, we propose a two-way data integration strategy that integrates heterogeneous data from diverse populations and from multiple healthcare institutions via a federated transfer learning approach .…

A comparison of approaches to improve worst case predictive model performance over patient subpopulations

Predictive models for clinical outcomes that are accurate on average in apatient population may underperform drastically for some subpopulations . No approach performs better, for each patient subpopulation examined, than standardlearning procedures using the entire training dataset . These results imply that when it is of interest to improve model performance, it may be necessary to doso via techniques that implicitly or explicitly increase the effective samplesize, such as increasing the number of patient sub-populations that are of interest .…

The Devil is in the Detail Simple Tricks Improve Systematic Generalization of Transformers

Recently, many datasets have been proposed to test the systematicgeneralization ability of neural networks . The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are shownto fail dramatically . Here we demonstrate that by revisiting modelconfigurations as basic as scaling of embeddings, early stopping, relativepositional embedding, and Universal Transformer variants, we can drasticallyimprove the performance of Transformers on systematic generalization .…

CharmFL A Fault Localization Tool for Python

Fault localization is one of the most time-consuming and error-prone parts of software debugging . The tool employsSpectrum-based fault localization (SBFL) to help Python developers analyze their programs and generate useful data at run-time to beused, then to produce a ranked list of potentially faulty program elements .…

Artificial Neural Networks Based Analysis of BLDC Motor Speed Control

Brushless DirectCurrent motor (BLDC motor) uses electronic closed-loop controllers to switch DCcurrent to the motor windings and produces the magnetic fields . The motor is modeled in the MATLAB/Simulink and the speedcontrol is obtained with a PI controller . The acquired data is then fed into binary artificial neuralnetworks and as a result, the ANN model predicts the corresponding parameters close to the simulation results .…

Prior Signal Editing for Graph Filter Posterior Fairness Constraints

Graph filters are an emerging paradigm that systematizes informationpropagation in graphs as transformation of prior node values, called graphsignals, to posterior scores . In this work, we study the problem of mitigatingdisparate impact, i.e. posterior score differences between a protected set ofsensitive nodes and the rest, while minimally editing scores to preserverecommendation quality .…

COVID 19 reproduction number estimated from SEIR model association with people s mobility in 2020

This paper is an exploratory study of two epidemiological questions on aworldwide basis . How fast is the disease spreading? Are the restrictions(especially mobility restrictions) for people bring the expected effect? To answer the first question, we propose a tool for estimating the reproductionnumber of epidemic (the number of secondary infections $R_t$) based on the SEIR model .…

Robustness Disparities in Commercial Face Detection

Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade . We present the first of its kind detailedbenchmark of the robustness of three such systems: Amazon Rekognition,Microsoft Azure, and Google Cloud Platform .…

A functional skeleton transfer

The animation community has spent significant effort trying to ease riggingprocedures . The increasing availability of 3D datamakes manual rigging infeasible . However, object animations involve understanding elaborate geometry and dynamics . This paper proposes a functional approach for skeletontransfer that uses limited information and does not require a complete match between the geometries .…

SAUCE Truncated Sparse Document Signature Bit Vectors for Fast Web Scale Corpus Expansion

When a sufficient amount of within-domain text may not be available, expanding a seed corpus of relevant documents from large-scale web data poses several challenges . The authors propose a novel truncated sparse document bit-vectorrepresentation, termed Signature Assisted Unsupervised Corpus Expansion(SAUCE) The SAUCE can reduce the computational burden while ensuring high within-Domain lexical coverage, especially under limited seed corpora scenarios.…

Quantum Sub Gaussian Mean Estimator

We present a new quantum algorithm for estimating the mean of a real-valued random variable obtained as the output of a quantum computation . Our estimatorachieves a nearly-optimal quadratic speedup over the number of classical i.i.d.samples needed . We obtain new quantum algorithms for the .…

GLocal K Global and Local Kernels for Recommender Systems

Recommender systems typically operate on high-dimensional sparse user-item matrix matrix . We propose a Global-Local Kernel-based matrix completionframework, named GLocal-K . Our model outperforms the state-of-the-artbaselines on three collaborative filtering benchmarks: ML-100K, ML-1M, andDouban. We apply our model under the extreme low-resource setting, which includes only a user item rating matrix, with no side information, to an extreme low resource setting .…

Quantum Sub Gaussian Mean Estimator

We present a new quantum algorithm for estimating the mean of a real-valued random variable obtained as the output of a quantum computation . Our estimatorachieves a nearly-optimal quadratic speedup over the number of classical i.i.d.samples needed . We obtain new quantum algorithms for the .…

Learning to Give Checkable Answers with Prover Verifier Games

Prover-Verifier Games (PVGs) is a game-theoretic framework to encourage learningagents to solve decision problems in a verifiable manner . The PVG consists of two learners with competing objectives: a trusted verifier network tries tochoose the correct answer, and a more powerful but untrusted prover network attempts to persuade the verifier of a particular answer .…

Quantum Sub Gaussian Mean Estimator

We present a new quantum algorithm for estimating the mean of a real-valued random variable obtained as the output of a quantum computation . Our estimatorachieves a nearly-optimal quadratic speedup over the number of classical i.i.d.samples needed . We obtain new quantum algorithms for the .…

Quantum Sub Gaussian Mean Estimator

We present a new quantum algorithm for estimating the mean of a real-valued random variable obtained as the output of a quantum computation . Our estimatorachieves a nearly-optimal quadratic speedup over the number of classical i.i.d.samples needed . We obtain new quantum algorithms for the .…