## Pseudo supervised Deep Subspace Clustering

Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achievedimpressive performance due to the powerful representation extracted using deepneural networks . However, self-reconstruction loss of an AE ignores rich useful relation information and might lead to indiscriminative representation . Pseudo-graphs and pseudo-labels, which allow benefiting from uncertain knowledge acquired during network training, are further employed to supervise similarity learning .…

## Side Channel Attacks on Triple Modular Redundancy Schemes

Thispaper shows how triple modular redundancy affects a side-channel attack (SCA) Our counterintuitive findings show that modular redundancy can increase SCAresiliency .…

## Model LineUpper Supporting Interactive Model Comparison at Multiple Levels for AutoML

Automated Machine Learning (AutoML) is a rapidly growing set of technologiesthat automate the model development pipeline by searching model space andgenerating candidate models . A critical, final step of AutoML is humanselection of a final model from dozens of candidates .…

## First order natural deduction in Agda

Agda is a dependently-typed functional programming language . It is based on an extension of intuitionistic Martin-L\”of type theory . We implement first order natural deduction in Agda . We use Agda’s type checker to verify the correctness of natural deduction proves properties of natural deduction .…

## A Theory of Heap for Constrained Horn Clauses Extended Technical Report

Constrained Horn Clauses (CHCs) are an intermediate program representation that can be generated by several verification tools . They can be processed and solved by a number of Horn solvers . The CHCs represent an intermediate representation that is language-independent and agnostic of the algorithm implemented by the Horn solver .…

## Risk Aware Lane Selection on Highway with Dynamic Obstacles

When lane-changing is discretionary, it is advised not to change lanes unless highly beneficial, e.g., reducing traveltime significantly or securing higher safety . We propose a real-time lane-selection algorithm with careful cost considerations and with modularity in design . The algorithm is asearch-based optimization method that evaluates uncertain dynamic positions of other vehicles under a continuous time and space domain .…

## GrASP A Library for Extracting and Exploring Human Interpretable Textual Patterns

We provide a Python library for GrASP, an algorithm for drawing patterns from textual data . The library is equipped with a web-based interface empowering human usersto conveniently explore the data and the extracted patterns . We alsodemonstrate the use of the library in two settings (spam detection and argumentmining) and discuss future deployments of the .…

## Deep Indexed Active Learning for Matching Heterogeneous Entity Representations

DIAL uses an Index-By-Committee framework . Each committee member learns representations based on powerfultransformer models . We highlight surprising differences between the matcher and the blocker in the creation of the training data and the objective used totrain their parameters .…

## Handling Climate Change Using Counterfactuals Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future

Climate change poses a major challenge to humanity, especially in its impacton agriculture, a challenge that a responsible AI should meet . In this paper, we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by supporting grassland management, through accurate crop growth prediction .…

## Generative Landmarks

Most sparse landmark detectionmethods rely on laborious, manually labelled landmarks . High-quality landmarks with personalization is often hard to achieve . We propose a general purpose approach to detect landmarks with improvedtemporal consistency . We capture two setsof unpaired marked (with paint) and unmarked videos .…

## Learning What To Do by Simulating the Past

Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences . Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state .…

## Towards End to End Neural Face Authentication in the Wild Quantifying and Compensating for Directional Lighting Effects

The recent availability of low-power neural accelerator hardware, combined with improvements in end-to-end neural facial recognition algorithms provides,enabling technology for on-device facial authentication . The present researchwork examines the effects of directional lighting on a State-of-Art(SoA) neuralface recognizer . Top lighting and its variants (top-left, top-right) arefound to have minimal effect on accuracy .…

## CARRNN A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data

Anovel deep learning-based model is developed for modeling multiple temporalfeatures in sporadic data . The CARRNN uses a generalizeddiscrete-time autoregressive model that is trainable end-to-end using neuralnetworks modulated by time lags to describe the changes caused by their regularity and asynchronicity .…

## Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance

Study presents an enhanced 2D objectdetector based on Faster R-CNN that is better suited for autonomous vehicles . Two main aspects are improved: the anchor generationprocedure and the performance drop in minority classes . The default uniformanchor configuration is not suitable in this scenario due to the perspectiveprojection of the vehicle cameras .…

## BR NS an Archive less Approach to Novelty Search

Novelty Search traditionallyrelies on k-nearest neighbours search and an archive of previously visited behavior descriptors which are assumed to live in a Euclidean space . The performance of NoveltySearch depends on a number of algorithmic choices and hyperparameters, such as the number of elements to add or remove elements to the archive .…

## On tuning consistent annealed sampling for denoising score matching

Score-based generative models provide state-of-the-art quality for image and audio synthesis . Sampling from these models is performed iteratively, typically using a discretized series of noise levels and a predefined scheme . In thisnote, we first overview three common sampling schemes for models trained withdenoising score matching .…

## HindSight A Graph Based Vision Model Architecture For Representing Part Whole Hierarchies

This paper presents a model architecture for encoding the representations of part-whole hierarchies in images in form of a graph . The idea is to divide the image into patches of different levels and treat all of these patches as nodes for a fully connected graph .…

## Spatial Imagination With Semantic Cognition for Mobile Robots

The imagination of the surrounding environment based on experience and semantic cognition has great potential to extend the limited observations and provide more information for mapping, collision avoidance, and path planning . We demonstrate that the algorithm can perform imagination for unseen parts of the object, by recalling the images and experience .…

## Laplace Beltrami based Multi Resolution Shape Reconstruction on Subdivision Surfaces

The eigenfunctions of the Laplace-Beltrami operator have widespread applications in engineering, computervision/graphics, machine learning, etc. They provide the means to smoothly interpolate data on a manifold . The method relies on subdivision basis sets to construct both boundary element isogeometric methods for analysis and surfacefinite elements to construct manifold harmonics.…

## Re designing cities with conditional adversarial networks

This paper introduces a conditional generative adversarial network to redesign a street-level image of urban scenes by generating an urban intervention policy, an attention map that localises where intervention is needed . The trained model shows strong performance in re-modellingcities, outperforming existing methods that apply image-to-image translation in other domains that is computed in a single GPU .…

## A Reinforcement Learning Environment For Job Shop Scheduling

Finding schedules often intractable and cannot be achieved by Combinatorial OptimizationProblem (COP) methods within a given time limit . Recent advances of DeepReinforcement Learning (DRL) in learning complex behavior enable new COPapplication possibilities . We demonstrate that our approach significantlyoutperforms existing DRL methods on classic benchmark instances, coming closeto state-of-the-art COP approaches .…

## XFORMAL A Benchmark for Multilingual Formality Style Transfer

XFORMAL is a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian . Results suggest state-of-the-art style transfer approaches perform close to simpleelines, indicating that style transfer is even more challenging when movingmultilingual .…

## A Bayesian Approach to Reinforcement Learning of Vision Based Vehicular Control

In this paper, we present a state-of-the-art reinforcement learning method for autonomous driving . Our approach employs temporal difference learning in aBayesian framework to learn vehicle control signals from sensor data . The agenthas access to images from a forward facing camera, which are preprocessed togenerate semantic segmentation maps .…

## Uncertainty aware Remaining Useful Life predictor

Remaining Useful Life (RUL) estimation is the problem of inferring how long an industrial asset can be expected to operate within its defined specifications . Machine Learning (ML) algorithms are natural candidates totackle the challenges involved in the design of intelligent maintenancesystems .…

## Support Target Protocol for Meta Learning

The support/query (S/Q) training protocol is widely used in meta-learning . S/Q protocol trains a task-specific model on S and then evaluates it on Q tooptimize the meta-model using query loss, which depends on size and quality of Q . The new S/T protocol offers a more accurate evaluation since it does not rely on possibly biased and noisy query instances .…

## Few Shot Action Recognition with Compromised Metric via Optimal Transport

Few-shot learning algorithms extract a transferable embedding from seen classes and reuse it on unseen classes by constructing a metric-based classifier . Compromised Metric via Optimal Transport (CMOT) combinhes advantages of these two solutions . Empirical results on benchmark datasets demonstrate the superiority of CMOT.…

## Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control

For lower arm amputees, robotic prosthetic hands offer the promise to regain the capability to perform fine object manipulation in activities of dailyliving . Current control methods based on physiological signals such as EEG and EMG are prone to poor inference outcomes due to motion artifacts, variabilityof skin electrode junction impedance over time, muscle fatigue, and other factors .…

## Connecting Deep Reinforcement Learning based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators

Deep Reinforcement Learning has emerged as an efficient dynamic obstacleavoidance method in highly dynamic environments . It has the potential toreplace overly conservative or inefficient navigation approaches . However, the integration of the method into existing navigation systems is still an open frontier due to the myopic nature of Deep Reinforcer Learning .…

## Just Label What You Need Fine Grained Active Selection for Perception and Prediction through Partially Labeled Scenes

Self-driving vehicles must perceive and predict the future positions of nearby actors in order to avoid collisions and drive safely . Active learning techniques leverage the state of the current model to iteratively select examples for labeling . Wethus introduce generalizations that ensure that our approach is both cost-awareand allows for fine-grained selection of examples through partially labeled scenes .…

## A Sketch Based Neural Model for Generating Commit Messages from Diffs

In this paper we apply neural machine translation (NMT) techniques to convert code diffs into commit messages . The results highlight that thisimprovement is relevant especially for Java source code files, by examining twodifferent datasets introduced in recent years for this task .…

## Towards Deployment of Deep Reinforcement Learning Based Obstacle Avoidance into Conventional Autonomous Navigation Systems

Deep reinforcement learning emerged as an alternativeplanning method to replace overly conservative approaches and promises more efficient and flexible navigation . However, deep reinforcement learning approaches are not suitable for long-range navigation due to their proneness to local minima and lack of long-term memory .…

## Software Hardware Co design for Multi modal Multi task Learning in Autonomous Systems

Optimizing quality of result (QoR) and the quality of service (QoS) of AI-empowered autonomous systems simultaneously is very challenging . Autonomous systems essentially require multi-modalmulti-task (MMMT) learning which must be aware of hardware performance and implementation strategies . We advocate for further explorations of MMMT in autonomous systems and software/hardware co-design solutions .…

## Flavored Tacotron Conditional Learning for Prosodic linguistic Features

Yasuda et. al. show that Tacotron-2’s encoder doesn’t fully represent prosodicfeatures (e.g. syllable stress in English) from characters, and result in flatfundamental frequency variations . They propose a novel carefully designed strategy for conditioningTacotron 2 on two fundamental prosodic features in English — stress syllableand pitch accent — that help achieve more natural prosody .…

## Unitary Subgroup Testing

We present a novel structural property of Clifford unitaries . Namely, that their (normalized) trace is bounded by $1/\sqrt{2$ in absolute value . We show a similar property for the $q$-aryCliffords . This allows us to analyze a simple single-query identity test under the Clifford promise and show that it has (at least) constant soundness .…

## On Mixed Iterated Revisions

The ten operators considered in this article are shownto be all reducible to three: lexicographic revision, refinement and severewithdrawal . The complexity of mixed sequences of belief change operators is alsoanalyzed . Most of them require only a polynomial number of calls to asatisfiability checker, some are even easier.…

## Voluntary safety commitments provide an escape from over regulation in AI development

With the introduction of Artificial Intelligence (AI) and relatedtechnologies in our daily lives, fear and anxiety about their misuse as well asthe hidden biases in their creation have led to a demand for regulation . Yet blindly regulating an innovation process that is notwell understood, may stifle this process and reduce benefits that society may gain from the generated technology, even under the best intentions .…

## Residual Gaussian Process A Tractable Nonparametric Bayesian Emulator for Multi fidelity Simulations

Challenges in multi-fidelity modeling relate to accuracy, uncertaintyestimation and high-dimensionality . A novel additive structure is introduced in which the highest fidelity solution is written as a sum of the lowest fidelity solution and residuals between the solutions at successive fidelity levels, Gaussian process priors placed over the low fidelity solution .…

## Half Truth A Partially Fake Audio Detection Dataset

A paper develops a dataset for half-truth audio detection (HAD) Partially fake audio in the HAD dataset involves only changing a few words in an utterance . We can not only detect fakeuttrances but also localize manipulated regions in a speech using this dataset .…

## Grapheme to Phoneme Transformer Model for Transfer Learning Dialects

Grapheme-to-Phoneme (G2P) models convert words to their phoneticpronunciations . Modern G2P systems incorporate learning, suchas, LSTM and Transformer-based attention models . We show that our method has potential applications for accent transfer for speech . We experiment with two English dialects: Indian and British .…

## Post Hoc Domain Adaptation via Guided Data Homogenization

Addressing shifts in data distributions is an important prerequisite for the deployment of deep learning models to real-world settings . We demonstrate the potential of data homogenization through experiments on the CIFAR-10 and MNIST data sets . This approach makes use of information about the training data contained implicitly in the deep learning model to learn a domain transfer function .…

## Computation and Bribery of Voting Power in Delegative Simple Games

Weighted voting games is one of the most important classes of cooperative games . Zhang and Grossi proposed a variant of this class, calleddelegative simple games . They defined a power index, called the delagative Banzhaf index to compute the importance of each agent in a delegation graph .…

## The virtual element method for the coupled system of magneto hydrodynamics

Virtual Element Method allows us to construct noveldiscretizations for simulating realistic phenomenon in magneto-hydrodynamics . We show that this VEM approximation will yield divergence freediscrete magnetic fields, an important property in any simulation in MHD . We present a model for magneticreconnection in a mesh that includes a series of hanging nodes, which we use tocalibrate the resolution of the method .…

## Extended Parallel Corpus for Amharic English Machine Translation

This paper describes the acquisition, preprocessing, segmentation, andalignment of an Amharic-English parallel corpus . It will be useful for machinetranslation of an under-resourced language . The corpus is larger thanpreviously compiled corpora; it is released for research purposes .…

## Improving Solar Cell Metallization Designs using Convolutional Neural Networks

SolarNet modifies the optimization domain such that theweights of a CNN model are optimized . The design generated by CNN is thenevaluated using the physics equations, which in turn generates gradients forbackpropagation . SolarNet improves the performance of solar cells compared to the traditional TO approach .…

## A unified Abaqus implementation of the phase field fracture method using only a user material subroutine

We present a simple and robust implementation of the phase field fracture method in Abaqus . Unlike previous works, only a user material (UMAT) subroutine is used . This is achieved by exploiting the analogy between the Phase Fieldbalance equation and heat transfer, which avoids the need for a user elementmesh .…

## Advances in Metric Ramsey Theory and its Applications

Metric Ramsey theory is concerned with finding large well-structured subsetsof more complex metric spaces . For finite metric spaces this problem was first studied by Bourgain, Figiel and Milman . In this paper we provide deterministicconstructions for this problem via a novel notion of \emph{metric Ramseydecomposition}.…

## AlephBERT A Hebrew Large Pre Trained Language Model to Start off your Hebrew NLP Application With

AlephBERT is a large pre-trained language model for ModernHebrew . It is trained on larger vocabulary and a larger dataset than any PLM before . We present new state-of-the-art results on multiple Hebrew tasks and benchmarks . We make our model publicly available, providing a single point of entry for the development of Hebrew NLPapplications.…

## Sound Probabilistic Inference via Guide Types

Probabilistic programming languages aim to describe and automate Bayesianmodeling and inference . Modern languages support programmable inference, which allows users to customize inference algorithms by incorporating guide programs . For Bayesian inference to be sound, guide programs must be compatible with model programs .…

## Large Deviations and Information theory for Sub Critical SINR Randon Network Models

The article obtains large deviation asymptotic for sub-critical communicationnetworks modeled as signal-interference-noise-ratio networks . We define the empirical mark measure and the empirical link measure, as well asprove joint large deviation principles(LDPs) for the two empirical measures on two different scales, i.e.,…

## Evolutionary rates of information gain and decay in fluctuating environments

In this paper, we wish to investigate the dynamics of information transfer inevolutionary dynamics . We use information theoretic tools to track how much information an evolving population has obtained and managed to retain about different environments that it is exposed to .…