## Rethinking Point Cloud Filtering A Non Local Position Based Approach

Existing position based point cloud filtering methods can hardly preservesharp geometric features . We propose a novel positionbased approach for feature-preserving point cloud filter . Unlike normalbased techniques, our method does not require the normal information . The coreidea is to first design a similarity metric to search the non-local similarpatches of a queried local patch .…

## Time Series Clustering for Human Behavior Pattern Mining

Existing pattern mining techniqueseither assume human dynamics is strictly periodic, or require the number of modes as input, or do not consider uncertainty in the sensor data . In this paper, we propose a novel clustering approach formodeling human behavior from time-series data .…

## Quantum Rényi divergences and the strong converse exponent of state discrimination in operator algebras

The sandwiched R\’enyi divergences of two finite-dimensional quantumstates play a distinguished role among the many quantum versions of R\’enidivergences . They are tight quantifiers of the trade-off between the two errorprobabilities in the strong converse domain of state discrimination . We show the same for the sandwiched divergence of two normalstates on an injective von Neumann algebra, thereby establishing theoperational significance of these quantities .…

## DeepSSM A Blueprint for Image to Shape Deep Learning Models

Statistical shape modeling (SSM) characterizes anatomical variations in apopulation of shapes generated from medical images . SSM requires consistentshape representation across samples in shape cohort . Theseshape representations are then used to extract low-dimensional shapedescriptors that facilitate subsequent analyses in different applications .…

## Retrieval guided Counterfactual Generation for QA

Deep NLP models have been shown to learn spurious correlations, leaving them brittle to input perturbations . We develop a Retrieve-Generate-Filter-Filter technique to create counterfactualevaluation and training data with minimal human supervision . Using anopen-domain QA framework and question generation model trained on original task data, we create counterfactsuals that are fluent, semantically diverse, andautomatically labeled.…

## Evaluating Off the Shelf Machine Listening and Natural Language Models for Automated Audio Captioning

Automated audio captioning (AAC) is the task of automatically generating descriptions for general audio signals . We evaluate the performance of off-the-shelf models with aTransformer-based captioning approach . We show that sequences of audio embeddings can be processed using a Transformer encoder to produce higher-qualitycaptions .…

## Understanding Model Robustness to User generated Noisy Texts

Sensitivity of deep-neural models to input noise is known to be a challenging problem . In NLP, model performance often deteriorates with naturally occurring noise, such as spelling errors . We present a thoroughevaluation of several state-of-the-art NLP systems’ robustness in multiple languages .…

## Representation Decoupling for Open Domain Passage Retrieval

Training dense passage representations via contrastive learning (CL) has been effective for Open-Domain Passage Retrieval (ODPR) We call such conflicts Contrastive Conflicts . We propose to solve it with a representation decoupling method, bydecoupling the passage representations into contextual sentence-level ones .…

## Inverse analysis of material parameters in coupled multi physics biofilm models

In this article we propose an inverse analysis algorithm to find the best fit of multiple material parameters in different coupled multi-physics biofilmmodels . We use a nonlinear continuum mechanical approach to model biofilmdeformation that occurs in flow cell experiments .…

## Possibilistic Fuzzy Local Information C Means with Automated Feature Selection for Seafloor Segmentation

The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS)imagery into distinct regions of the sea-floor . The chosen features and resulting segmentation from the image will be assessed based on a select quantitative clustering validitycriterion and a subset of the features that reach a desired threshold will be used for the segmentation process .…

## Building Chinese Biomedical Language Models via Multi Level Text Discrimination

Pre-trained language models (PLMs) have revolutionized the field of NLP, not only in the general domain but also in the biomedical domain . In this work we introduceeHealth, a biomedical PLM in Chinese built with a new pre-training framework . This new framework trains eHealth as a discriminator through both token-leveland sequence-level discrimination .…

## Carousel Memory Rethinking the Design of Episodic Memory for Continual Learning

Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from previous tasks . To avoid performance decrease caused by forgetting, prior studies exploit episodic memory (EM), which stores a subset of the pastobserved samples while learning from new non-i.i.d.…

## Transferring Semantic Knowledge Into Language Encoders

We introduce semantic form mid-tuning, an approach for transferring semanticknowledge from semantic meaning representations into transformer-based languageencoders . We learn to align the text of general sentences with structured semantic representations of those sentences . We show that this alignment can be learnedimplicitly via classification or directly via triplet loss .…

## Considering user agreement in learning to predict the aesthetic quality

There is a growing interest in estimating the user agreement by considering the standard deviation of the scores, instead of only predicting the mean aestheticopinion score . With such loss, the model is encouraged to learn theuncertainty of the content that is relevant to the diversity of observers’opinions, i.e.,…

## Online Bipartite Matching with Reusable Resources

We study the classic online bipartite matching problem with a twist: offlinenodes are reusable any number of times . Every offline node $i$ becomesavailable $d$ steps after it was assigned to. We give the first approximation factor beating$0.5$ namely a $0.505$ approximation, by adapting and interpreting the powerful technique of Online Correlated Selection .…

## Scalable Anytime Algorithms for Learning Formulas in Linear Temporal Logic

Linear temporal logic (LTL) is a specification language for finite sequences called traces . It is widely used in program verification, motion planning inrobotics, process mining, and many other areas . Existing solutions suffer from two limitations: they do notscale beyond small formulas, and they may exhaust computational resources .…

## Knowledge Graph enhanced Sampling for Conversational Recommender System

Conversational Recommendation System (CRS) uses theinteractive form of the dialogue systems to solve the intrinsic problems of traditional recommendation systems . The existing CRS models are unable to deal with theexploitation and exploration (E&E) problem well, resulting in the heavy burdenon users .…

## State of Security and Privacy Practices of Top Websites in the East African Community EAC

Growth in technology has resulted in the large-scale collection and processing of Personally Identifiable Information by organizations that rundigital services such as websites . Several African countries have recently started enacting new dataprotection regulations due to recent technological innovations . Only 16 percent of third-party tracking companies that track users on a particular website are disclosed in the site’s privacy policy statements .…

## The core of housing markets from an agent s perspective Is it worth sprucing up your home

We study housing markets as introduced by Shapley and Scarf (1974) We prove that the core of housingmarkets respects improvement in the following sense: given an allocation in thecore of $H$ where agent $a$ receives a house $h$ if the value of the house owned by $a$.…

## SAR Net A Scenario Aware Ranking Network for PersonalizedFair Recommendation in Hundreds of Travel Scenarios

Alibaba serves an indispensable role forhundreds of different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized recommendation service for users visiting differentscenarios, there are two critical issues to be carefully addressed . In this paper, we propose a novelScenario-Aware Ranking Network (SAR-Net) to address these issues .…