Random hypergraphs and property B

In 1964 Erd\H{o}s proved that $(1+\oh{1) is sufficient to build a $k$-graph which is not two colorable . To thisday, it is not known whether there exist such $k#graphs with smaller number ofedges . In the second part of the paper we consider the problem of algorithmmic coloring of random $k-graphs .…

IIE NLP Eyas at SemEval 2021 Task 4 Enhancing PLM for ReCAM with Special Tokens Re Ranking Siamese Encoders and Back Translation

This paper introduces our systems for all three subtasks of SemEval-2021 Task4: Reading Comprehension of Abstract Meaning . To help our model better understand abstract concepts in natural language, we well-design many simple and effective approaches adapted to the backbone model (RoBERTa) Our approaches achieve eighth rank onsubtask-1 and tenth rank on subtask-2 .…

Statistical Testing for Efficient Out of Distribution Detection in Deep Neural Networks

Commonly, Deep Neural Networks generalize well on samples drawn from adistribution similar to that of the training set . However, DNNs’ predictions are brittle and unreliable when the test samples are drawn from a dissimilardistribution . This presents a major concern for deployment in real-world applications, where such behavior may come at a great cost — as in the case of autonomous vehicles or healthcare applications .…

Cloud Broker A Systematic Mapping Study

The current systematic review includes a comprehensive 3-tier strategy(manual search, backward snowballing, and database search). The accuracy of thesearch methodology has been analyzed in terms of extracting related studies and collecting comprehensive and complete information in a supplementary file . The current review includes .…

Truncated Log concave Sampling with Reflective Hamiltonian Monte Carlo

Reflective Hamiltonian Monte Carlo (ReHMC) is an HMC-based algorithm to sample from a log-concave distribution restricted to a convexpolytope . We prove that, starting from a warm start, it mixes in $\widetildeO$ steps for a well-rounded polytope,ignoring logarithmic factors . We alsodeveloped an open source implementation of ReHMC and we performed an experimental study on various high-dimensional data-sets .…

Active Modular Environment for Robot Navigation

This paper presents a novel robot-environment interaction in navigation tasks such that robots have neither a representation of their working space nor planning function, instead, an active environment takes charge of these aspects . This is realized by spatially deploying computing units, called cells, and making cells manage traffic in their respective physical region .…

Task Agnostic Morphology Evolution

Task-Agnostic Morphology Evolution (TAME) evolved morphologies that match multi-task performance of those learned with task supervised algorithms . TAME evolves morphologies by applying randomly sampled action primitives on a population of agents . This is accomplished using an information-theoretic objective that efficientlyranks agents by their ability to reach diverse states in the environment and the causality of their actions .…

On continual single index learning

In this paper, we generalize the problem of single index model to the context of continual learning in which a learner is challenged with a sequence of tasks one by one and the dataset of each task is revealed in an online fashion .…

Lie Group integrators for mechanical systems

Lie group integrators have become amethod of choice in many application areas . They include multibody dynamics, shape analysis, data science, image registration and biophysical simulations . The theory is illustrated by applying the methods totwo nontrivial applications in mechanics .…

Approximate Privacy Preserving Neighbourhood Estimations

Anonymous social networks present a number of new and challenging problems for existing Social Network Analysis techniques . Traditionally, existing methods for analysing graph structure, such as community detection, required global knowledge of the graph structure . Exchanging this data structure infuture decentralised learning deployments gives away no information about theneighbours of the node and therefore does preserve the privacy .…

High Capacity Reversible Data Hiding in Encrypted Images using Adaptive Encoding

RDHEI has gradually become the research hotspot of privacy protection in cloud storage . The proposed method outperforms the state-of-the-art methods inembedding rate and can extract the embedded information correctly and recover the original image losslessly . The new method uses adaptive encoding to embed sufficient additional information in the encrypted domain of an encrypted image, such as additional information embedded in the reserved room of marked pixels by bit substitution .…

Fast Minimum norm Adversarial Attacks through Adaptive Norm Constraints

The inherent complexity of the optimization requires current gradient-based attacks to be carefullytuned, initialized, and possibly executed for many computationally-demandingiterations . In this work, we propose a fast minimum-norm (FMN) attack that works with different $ell_p$-norm perturbation models ($p=0, 1, 2, \infty$) FMN significantly outperforms existing attacks in termsof convergence speed and computation time .…

QNLP in Practice Running Compositional Models of Meaning on a Quantum Computer

Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware . In thispaper, we present results on the first NLP experiments conducted on NoisyIntermediate-Scale Quantum (NISQ) computers for datasets of size < 100sentences . Exploiting the formal similarity of the compositional model of meaning by Coecke et al. with quantum theory, we create representationsfor sentences that have a natural mapping to quantum circuits . We use theserepresentations to implement and successfully train two models that solvesimple sentence classification tasks . …

Optimized Memoryless Fair Share HPC Resources Scheduling using Transparent Checkpoint Restart Preemption

Common resource management methods in supercomputing systems usually include hard divisions, capping, and quota allotment . Those methods involve bad supply-and-demand management rather than a free market playground that will eventually increase system utilization and productivity . In this work, we propose the newly Optimized Memoryless Fair-Share HPCResources Scheduling using Transparent Checkpoint-Restart Preemption, in which the social welfare increases using a free-of-cost interchangeable proprietarypossession scheme .…

Self Tuning for Data Efficient Deep Learning

Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets . However, it is prohibitively time-costly and labor-expensive to collect sufficient labeled data in most realistic scenarios . To mitigate the requirement for labeled data, SSL focuses on exploring both labeled and unlabeled data, while transfer learning popularizes a favorable practice of fine-tuning a pre-trained model to the target data .…

Sentiment Analysis of Persian English Code mixed Texts

The rapid production of data on the internet and the need to understand howusers are feeling from a business and research perspective has prompted the need for an automatic monolingual sentiment detection systems . We then introduce a model which uses BERT pretrained embeddings as well as translation models to automaticallylearn the polarity scores of these Tweets .…

Fragmented Objects Boosting Concurrency of SharedLarge Objects

This work examines strategies to handle large shared data objects indistributed storage systems (DSS) While boosting the number of concurrentaccesses, maintaining strong consistency guarantees, and ensuring goodoperation performance, we define the notion of fragmentedobjects:con-current objects composed of a list of fragments (or blocks) As thefragments belong to the same object, it is not enough that each fragment is linearizable to have useful consistency guarantees in the composed object .…

Persistent Homology and Graphs Representation Learning

This article aims to study the topological invariant properties encoded innode graph representational embeddings by utilizing tools available in persistent homology . Our construction effectivelydefines a unique persistence-based graph descriptor, on both the graph and nodelevels, for every node representation algorithm .…

Real Time Ellipse Detection for Robotics Applications

We propose a new algorithm for real-time detection and tracking of ellipticpatterns suitable for real world robotics applications . The method fitssellipses to each contour in the image frame and rejects ellipses that do not yield a good fit . It can detect complete, partial, and imperfect ellipse inextreme weather and lighting conditions and is lightweight enough to be used on robots’ resource-limited onboard computers .…

SCD A Stacked Carton Dataset for Detection and Segmentation

Carton detection is an important technique in the automatic logistics system . Images are collected from the internet and several warehourses, and objects are labeled using per-instancesegmentation for precise localization . There are totally 250,000 instance masksfrom 16,136 images . The improvement of AP on MS COCO and PASCAL VOC is 1.8% – 2.2% and 3.4% – 4.3% respectively.…

Benchmarking and Survey of Explanation Methods for Black Box Models

The widespread adoption of black-box models in Artificial Intelligence has increased the need for explanation methods to reveal how these obscure models reach specific decisions . We provide acategorization of explanation methods based on the type of explanation returned . We present the most recent and widely used explainers, and we show avisual comparison among explanations and a quantitative benchmarking .…

Generalized Parametric Path Problems

Parametric path problems arise independently in diverse domains, ranging from transportation to finance, where they are studied under various assumptions . We show that when the parametric weights are linear, algorithms remain tractable even under relaxed assumptions . If the weights are allowed to be non-linear, the problem becomes NP-hard.…

Quantization Algorithms for Random Fourier Features

Method of random projection (RP) is the standard technique in machinelearning and many other areas, for dimensionality reduction, approximate nearneighbor search, compressed sensing, etc. The method of random Fourierfeatures (RFF) applies a specific nonlinear transformation on the projected data from random projections .…