A Provably Componentwise Backward Stable O n 2 QR Algorithm for the Diagonalization of Colleague Matrices

The roots of a monic polynomial expressed in a Chebyshev basis are known to be the eigenvalues of the so-called colleague matrix . In this manuscript, we describe an $O(n^2)$ explicitstructured QR algorithm for colleague matrices . We prove that it is componentwise backward stable, in the sense that the backward error in the colleague matrix can be represented as relative perturbations to its components .…

On Unbiased Estimation for Discretized Models

In this article, we consider computing expectations w.r.t. probabilitymeasures which are subject to discretization error . Examples include partiallyobserved diffusion processes or inverse problems, where one may have todiscretize time and/or space, in order to practically work with the probabilityof interest .…

Multichannel LSTM CNN for Telugu Technical Domain Identification

Domain Identification plays a significant role in Machine Translation, Text Summarization, Question Answering,Information Extraction, and Sentiment Analysis . System got 69.9% of the F1score on the test dataset and 90.01% on the validation set . This architecture was used and evaluated in the context of the ICONshared task TechDOfication 2020 (task h) Thematic keywords give a compressedrepresentation of the text, and usually, Domain Identification is used in machine translation .…

Approximation of Manifold valued Functions

We consider the approximation of manifold-valued functions by embedding them into a higher dimensional space, applying a vector-valuedapproximation operator and projecting the resulting vector back to themanifold . We provide explicitconstants that depend on the reach of the embedded manifold .…

Classification of the streaming approximability of Boolean CSPs

A Boolean constraint satisfaction problem (CSP) is amaximization problem specified by a constraint $f:\{-1,1\}^k\to\{0,1$ The goal is to compute the maximum number of constraints that can be satisfied by a Boolean assignment to the $n$~variables . In this work we completely characterize the approximability of all BooleanCSPs in the streaming model .…

Abelian Neural Networks

We study the problem of modeling a binary operation that satisfies somealgebraic requirements . We first construct a neural network architecture for Abelian group operations and derive a universal approximation property . Then,we extend it to Abelian semigroup operations using the characterization ofassociative symmetric polynomials .…

Parameterized Temperature Scaling for Boosting the Expressive Power in Post Hoc Uncertainty Calibration

Standard deepneural networks typically yield uncalibrated predictions, which can be betransformed into confidence scores using post-hoc calibration methods . We address the problem of uncertainty calibration and introduce a novel method, Parametrized Temperature Scaling (PTS) We show that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of modelarchitectures, datasets and metrics .…

Kronecker Products Low Depth Circuits and Matrix Rigidity

The rank $r$ rigidity of $M$ is the smallest number of entries which one must change to make its rank atmost $r$. The $N \times N$ Walsh-Hadamard transform has a linear circuit of size $O(d \cdot N^{1+ 0.96/d) The new rigidity upper bound, showing that the following classes of matrices are not rigid enough to prove circuit lower bounds using Valiant’s approach, generalizes recent results on non-rigidity, using a simpler approach which avoids needing the polynomial method .…

Lossless Compression of Efficient Private Local Randomizers

Locally Differentially Private (LDP) Reports are commonly used for collection of statistics and machine learning in the federated setting . LDP reports are known to have relatively little information about the user’s data due to randomization . Several schemes are known that exploit this fact to design low-communication versions of LDP algorithms but all of them do so at the expense of a significant loss in utility .…

PsiPhi Learning Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

We propose amulti-task inverse reinforcement learning (IRL) algorithm, called \emph{inversetemporal difference learning} (ITD) that learns shared state features and per-agent successor features . We further show how to seamlesslyintegrate ITD with learning from online environment interactions, arriving at anovel algorithm for reinforcement learning with demonstrations, called $\Psi\Phi$-learning (pronounced `Sci-Fi’) We provide empirical evidence for the effectiveness of this method for improving RL, IRL,imitation, and few-shot transfer, and we derive worst-case bounds for its performance in zero-shot transfers to new tasks .…

Trajectory Based Meta Learning for Out Of Vocabulary Word Embedding Learning

Word embedding learning methods require a large number of occurrences of aword to accurately learn its embedding . Out-of-vocabulary (OOV) words do not appear in the training corpus emerge frequently in the smallerdownstream data . We propose the use of Leap, ameta-learning algorithm which leverages the entire trajectory of the learning process instead of just the beginning and the end points .…

Creolizing the Web

The evolution of language has been a hotly debated subject with contradictinghypotheses and unreliable claims . Drawing from signalling games, dynamicpopulation mechanics, machine learning and algebraic topology, we present amethod for detecting evolutionary patterns in a sociological model of languageevolution .…

vrCAPTCHA Exploring CAPTCHA Designs in Virtual Reality

With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA designs for VR devices . In this paper, we present four traditional two-dimensional (2D) CAPTCHAs in VR . Then, based on the three-dimensional interactioncharacteristics of VR devices, we propose two vrCAPTCHA design prototypes .…

AutoAI TS AutoAI for Time Series Forecasting

AutoAI for Time Series Forecasting (AutoAI-TS) provides users with a zeroconfiguration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset . Automatically providing a good set of models to users for a givendataset saves both time and effort from using trial-and-error approaches .…

Research on False Data Injection Attacks in VSC HVDC Systems

The false data injection (FDI) attack is a crucial form of cyber-physical security problems facing cyber power systems . There is noresearch revealing the problem of FDI attacks facing voltage source converterbased high voltage direct current transmission (VSC-HVDC) systems . And finally, the modified IEEE-14 bus system is used to demonstrate that attackers are capable of disrupting the operation security of converter stations in VSC- HVDC systems by FDI attack strategies .…

Directional Bias Amplification

Mitigating bias in machine learning systems requires refining our understanding of bias propagation pathways . A metric formeasuring bias amplification was introduced in the seminal work by Zhao et al. We introduceand analyze a new, decoupled metric for measuring bias amplification,$\text{BiasAmp}_{\rightarrow}$ (Directional Bias Amplification) We provide suggestions about its measurement by cautioning against predicting sensitive attributes, encouraging the use ofconfidence intervals due to fluctuations in the fairness of models across runs,and discussing the limitations of what this metric captures .…

Optimal Control Policies to Address the Pandemic Health Economy Dilemma

Non-pharmaceutical interventions (NPIs) are effective measures to contain apandemic. Yet, such control measures commonly have a negative effect on the economy . Here, we propose a macro-level approach to support resolving thisHealth-Economy Dilemma (HED) This study contributes to pandemicmodeling and simulation by providing a novel concept that elaborates onintegrating economic aspects while exploring the optimal moment to enable NPIs.…

Mobile Recharger Path Planning and Recharge Scheduling in a Multi Robot Environment

In many multi-robot applications, mobile worker robots are often engaged in performing some tasks repetitively by following pre-computed trajectories . Asthese robots are battery-powered, they need to get recharged at regular intervals . We envision that in the future, a few mobile recharger robots will be employed to supply charge to the energy-deficient worker robots recurrently, to keep the overall efficiency of the system optimized .…

Teach Me to Explain A Review of Datasets for Explainable NLP

Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated explanations . These explanations are used downstream in threeways: as data augmentation to improve performance on a predictive task, as aloss signal to train models to produce explanations for their predictions, and as a means to evaluate the quality of model-generated explanations .…

Temporal Energy Analysis of Symbol Sequences for Fiber Nonlinear Interference Modelling via Energy Dispersion Index

The stationary statistical properties of independent, identically distributed(i.i.d.) input symbols provide insights on the induced nonlinear interference(NLI) during fiber transmission . These statistical properties can be used inthe design of probabilistic amplitude shaping (PAS) The effective signal-to-noise ratio (SNR) in PAS has been shown to increase when the shaping blocklength decreases.…

PolicySpace2 modeling markets and endogenous housing policies

Policymakers decide on alternative policies facing restricted budgets anduncertain, ever-changing future . Designing housing policies is difficult giving the heterogeneous characteristics of properties themselves and the intricacy of housing markets . We propose PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model .…