## Self Supervised Learning by Estimating Twin Class Distributions

TWIST is a novel self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way . We employ asiamese network terminated by a softmax operation to produce twin class distributions of two augmented images . Without supervision, we enforce theclass distributions of different augmentations to be consistent .…

## Automatic Generation of Grover Quantum Oracles for Arbitrary Data Structures

Quantum oracles that are frequently masked asblack boxes play an important role in Grover’s algorithm . The automaticgeneration of the corresponding circuits for a Grover quantum oracle is deeplylinked to the synthesis of reversible quantum logic . Despite numerous advances in the field, quantum computing still remains a challenge till today in terms ofsynthesizing efficient and scalable circuits for complex boolean functions .…

## 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 .…

## Region Semantically Aligned Network for Zero Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes . Previous methods focused on learning directembeddings from global features to the semantic space in hope of knowledgetransfer from seen classes to unseen classes . Instead of using globalfeatures which are obtained by an average pooling layer after an image encoder, we directly utilize the output of the image .…

## UniPELT A Unified Framework for Parameter Efficient Language Model Tuning

Conventional fine-tuning of pre-trained language models tunes all model parameters and stores a full model copy for each downstream task . UniPELT incorporates different PELT methods assub modules and learns to activate the ones that best suit the current data or setup .…

## Compressibility of Distributed Document Representations

We propose CoRe, a straightforward, representation learner-agnostic framework suitable for representation compression . The CoRe’s performance was studied on a collection of 17 real-life corpora from biomedical,news, social media, and literary domains . We explored the behavior whenconsidering contextual and non-contextual document representations, differentcompression levels, and 9 different compression algorithms .…

## Capacity of Group invariant Linear Readouts from Equivariant Representations How Many Objects can be Linearly Classified Under All Possible Views

Equivariance has emerged as a desirable property of representations of objects subject to identity-preserving transformations that constitute a group . However, the expressivity of arepresentation constrained by group equivariance is still not fully understood . We provide a generalization of Cover’s Function CountingTheorem that quantifies the number of linearly separable and group-invariantbinary dichotomies that can be assigned to equivariant representations .…

## On the ESL algorithm for solving energy games

We propose a variant of an algorithm introduced by Schewe and also studied by Luttenberger for solving parity or mean-payoff games . We present it over energy games and call our variant the ESL algorithm . We find that using potential reductions as introduced by Gurvich, Karzanov andKhachiyan allows for a simple and elegant presentation of the algorithm .…

## EPROACH A Population Vaccination Game for Strategic Information Design to Enable Responsible COVID Reopening

A population of players decides whether to vaccinate or not based on the public and private information they receive . The equilibrium vaccination decisions depend on the information received by the agents . The insightsobtained from our framework include the appropriate vaccination coveragethreshold for safe-reopening and information-based methods to incentivize individual vaccination decisions .…

## Comparative Opinion Summarization via Collaborative Decoding

Opinion summarization focuses on generating summaries that reflect popularopinions of multiple reviews for a single entity . We propose a task to generate two contrastive summaries and onecommon summary from two given sets of reviews from different entities . Wedeveloped a comparative summarization framework CoCoSum, which consists of twofew-shot summarization models that are jointly used to generate contrastive andcommon summaries .…

## A Robotic Antenna Alignment and Tracking System for Millimeter Wave Propagation Modeling

In this paper, we discuss the design of a sliding-correlator channel sounder for 28 GHz propagation modeling on the NSF POWDER testbed in Salt Lake City,UT . Beam-alignment is mechanically achieved via a fully autonomous roboticantenna tracking platform, designed using commercial off-the-shelf components .…

## Graph Condensation for Graph Neural Networks

We aim to condense large, original graph into a small, synthetic and highly-informative graph . We are able to approximate the original test accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer . Extensive experiments have demonstrated the effectiveness of the proposed framework in condensing different graph datasets into informativesmaller graphs .…

## Order Constraints in Optimal Transport

Recent works have aimed toimprove optimal transport plans through the introduction of various forms of structure . We introduce novel order constraints into the optimal transportformulation to allow for structure . While there will are now quadratically many constraints as before, we prove a .roximatesolution…

## Adversarial examples by perturbing high level features in intermediate decoder layers

Instead ofperturbing pixels, we use an encoder-decoder representation of the input image and perturb intermediate layers in the decoder . This changes the high-levelfeatures provided by the generative model . We formulatethis task as an optimization problem by minimizing the Wasserstein distancebetween the adversarial and initial images under a misclassificationconstraint .…

## Offline Reinforcement Learning with Soft Behavior Regularization

Most prior approaches to offline reinforcement learning (RL) utilize behavior regularization, typically augmenting existing off-policyactor critic algorithms with a penalty measuring divergence between the policy and the offline data . We propose a practical way to compute the density ratio and demonstrate its equivalence to a state-dependentbehavior regularization .…

## A Functional Abstraction of Typed Invocation Contexts

Danvy andFilinski show how this method scales to Felleisen’scontrol and prompt operators . Compared to shift and reset, control and promptexhibit a more dynamic behavior, in that they can manipulate a trail ofcontexts surrounding the invocation of previously captured continuations .…

## SpeechT5 Unified Modal Encoder Decoder Pre training for Spoken Language Processing

A unified-modalSpeechT5 framework explores the encoder-decoder pre-training for self-supervised speech/text representation learning . SpeechT5 canpre-train on a large scale of unlabeled speech and text data to improve the ability of the speech and textual modeling . To align the textual and speech information into a unified semantic space, we propose a random mixing-up method .…

## Sub word Level Lip Reading With Visual Attention

The goal of this paper is to learn strong lip reading models that can recognise speech in silent videos . We use sub-word units for lip reading for the first time to better model the ambiguities of the task . Our best lip reading model achieves 22.6% word error rate on the LRS2 dataset, a performanceunprecedented for lip-reading models, significantly reducing the performance gap between lip reading and automatic speech recognition .…

## MReD A Meta Review Dataset for Controllable Text Generation

Using existing text generation datasets for controllable text summarization, we are facing the problem of not having the domain knowledge and thus the aspects that could be controlled are limited . MReD consists of 7,089 meta-reviews and all its 45k sentences are manually annotated as one of the 9 categories, including abstract, strength, decision, etc.…

## SGoLAM Simultaneous Goal Localization and Mapping for Multi Object Goal Navigation

SGoLAM is ranked 2nd in the CVPR 2021 MultiON(Multi-Object Goal Navigation) challenge . It does not require any training of neural networks, it could be used in an off-the-shelfmanner, and amenable for fast generalization in new, unseen environments . The mapping module converts observations into an occupancy map, and the goal localization modulemarks the locations of goal objects .…

## SpongeCake A Layered Microflake Surface Appearance Model

In this paper, we propose SpongeCake: a layered BSDF model where each layer is a volumetric scattering medium . We omit any reflecting and refracting interfaces between the layers . Despite theabsence of layer interfaces, we demonstrate that many common material effectscan be achieved with layers of SGGX microflake and other volumes with appropriate parameters .…

## sMGC A Complex Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs

Recent advancements in Graph Neural Networks have led to state-of-the-art performance on representation learning of graphs for node classification . However, the majority of existing works process directed graphs bysymmetrization, which may cause loss of directional information . In this paper, we propose the magnetic Laplacian that preserves edge directionality by encoding it into complex phase as a deformation of the combinatorial LaPlacian .…

## 6G Connectivity in the Era of Distributed Intelligence

The confluence of 5G and AI is transforming wireless networks to deliver diverse services at the Edge, driving towards a vision of pervasive distributedintelligence . Future 6G networks will need to deliver quality of . experiencethrough seamless integration of communication, computation and AI .…

## 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 .…

## Bugs in our Pockets The Risks of Client Side Scanning

Some in industry and government now advocate a new technology to access targeted data: client-side scanning . CSS would enable on-device analysis of data in the clear . CSS by its nature createsserious security and privacy risks for all society while it can provide assistance for law enforcement is at best problematic, authors say .…

## Learning Temporal 3D Human Pose Estimation with Pseudo Labels

We present a simple, yet effective, approach for self-supervised 3D humanpose estimation . During training, we rely ontriangulating 2D body pose estimates of a multiple-view camera system . Atemporal convolutional neural network is trained with the generated 3Dground-truth and the geometric multi-view consistency loss, imposinggeometrical constraints on the predicted 3D body skeleton .…

## Socially assistive robots deployment in healthcare settings a global perspective

Social robots are finding their place in societyis for healthcare-related applications . Yet, very little research has mapped the deployment of socially assistive robots in real settings . Using adocumentary research method, we were able to trace back 279 experiences of SARsdeployments in hospitals, elderly care centers, occupational health centers, private homes, and educational institutions worldwide .…

## Improve Cross lingual Voice Cloning Using Low quality Code switched Data

To synthesize speech for multiple languages usually requires multi-lingual speech from the target speaker . But it is both laborious and expensive to collect high-quality data for the target speakers . Instead, we proposed to use low-quality code-switched found data from thenon-target speakers to achieve crosslingual voice cloning for the targetspeakers .…

## A Dual Attention Neural Network for Pun Location and Using Pun Gloss Pairs for Interpretation

Pun location is to identify the punning word (usually a word or phrase that makes the text ambiguous) in a given short text . Pun interpretation is to find out two different meanings of the word . DANN (Dual-Attentive Neural Network) is proposed for pun location, effectively integrates word senses and pronunciation with context information to address two kinds of pun at the same time .…

## ClonalNet Classifying Better by Focusing on Confusing Categories

A pre-trained baseline network has paid attention to the target image region even though it incorrectly predicts the image . This observationmotivates us to consider inter-category correlations . We propose aclonal network, named ClonalNet, which learns to discriminate between confusing categories derived from the baseline .…

## FedSpeech Federated Text to Speech with Continual Learning

Federated text-to-speech aims tosynthesize natural speech of multiple users with a few audio training samples . However, very few training samples from each speaker are available, training samples are all stored in local device of each user, and global model is vulnerable to various attacks .…

## Domain Adaptation on Semantic Segmentation with Separate Affine Transformation in Batch Normalization

In recent years, unsupervised domain adaptation (UDA) for semanticsegmentation has brought many researchers’attention . The proposed SEAT is simple, easily implemented and easy to integrate into existing adversarial learning based UDA methods . We introduce multi level adaptation by adding thelower-level features to the higher-level ones before feeding them to the discriminator, without adding extra discriminator like others.…

## Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

In this paper, we introduce a variational Bayesian algorithm (VBA) for imageblind deconvolution . Our generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel . One of our main contributions is the integration of VBA withina neural network paradigm, following an unrolling methodology .…

## The Neural MMO Platform for Massively Multiagent Research

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular gamesystems . We present Neural MMO as free and opensource software with active support, ongoing development, documentation, and training, logging, and visualization tools to help users adapt to the new setting .…

## Automatic Modeling of Social Concepts Evoked by Art Images as Multimodal Frames

Social concepts referring to non-physical objects are powerful tools to describe, index, and query the content of visual data . We propose the translation of recent theories about social concept representation into a software approach to represent them as multimodal frames, by integrating multisensory data .…

## Mind the Style of Text Adversarial and Backdoor Attacks Based on Text Style Transfer

Adversarial attacks and backdoor attacks harness task-irrelevant features of datain their implementation . Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial attacks . The attack success rates can exceed 90% without much effort .…

## HUMAN4D A Human Centric Multimodal Dataset for Motions and Immersive Media

We introduce HUMAN4D, a large and multimodal 4D dataset that contains avariety of human activities simultaneously captured by a professionalmarker-based MoCap, a volumetric capture and an audio recording system . By capturing 2 female and $2$ male professional actors performing variousfull-body movements and expressions, we provide a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities .…

A piecewise Pad\’e-Chebyshev type (PiPCT) approximation method is proposed to minimize Gibbs phenomenon . Atheorem on $L^1$-error estimate is proved for sufficiently smooth functions . Numerical experiments are performed to show that the PiPCT method accurately captures isolatedsingularities of a function without using the positions and the types of singularities .…

## A more direct and better variant of New Q Newton s method Backtracking for m equations in m variables

In this paper we propose a variant of New Q-Newton’s method Backtracking . The update rule of our method is $x\mapsto x-\gamma (x)w(x)$. Good theoretical guarantees are proven, in particular for systems ofpolynomial equations . In “generic situations”, we will also discuss a way to avoid that the limit of the constructed sequence is a solution of $H(x), but not of$F(x)=0\$ The limit is a .…

## The Neural MMO Platform for Massively Multiagent Research

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular gamesystems . We present Neural MMO as free and opensource software with active support, ongoing development, documentation, and training, logging, and visualization tools to help users adapt to the new setting .…

## Robust monolithic solvers for the Stokes Darcy problem with the Darcy equation in primal form

We construct mesh-independent and parameter-robust monolithic solvers for the primal Stokes-Darcy problem . Numerical experiments demonstrate the parameters of the proposed solvers . The suggested preconditioners utilize operators in fractionalSobolev spaces . In each case, robustpreconditioners are derived using a unified theoretical framework .…

## UniPELT A Unified Framework for Parameter Efficient Language Model Tuning

Conventional fine-tuning of pre-trained language models tunes all model parameters and stores a full model copy for each downstream task . UniPELT incorporates different PELT methods assub modules and learns to activate the ones that best suit the current data or setup .…

## Conformer Based Self Supervised Learning for Non Speech Audio Tasks

In this paper, we propose a self-supervised audio representationlearning method and apply it to a variety of downstream non-speech audio tasks . We achieve a mean average precision(mAP) score of 0.415, which is a new state-of-the-art on this dataset . Our fine-tuned conformers also surpass ormatch the performance of previous systems pre-trained in a supervised way on several downstream tasks, we say .…

## Algebraic Reasoning of Quantum Programs via Non Idempotent Kleene Algebra

We investigate the algebraic reasoning of quantum programs inspired by the success of classical program analysis based on Kleene algebra . We propose the Non-idempotent Kleena Algebra (NKA) as a natural alternative and identify complete and sound semantic models for NKA as well astheir appropriate quantum interpretations .…

## M2MeT The ICASSP 2022 Multi Channel Multi Party Meeting Transcription Challenge

The meeting scenario is one of the most valuable and, at the sametime, most challenging scenarios for speech technologies . We will launch the Multi-channel Multi-partyMeeting Transcription Challenge (M2MeT) as an ICASSP2022 Signal ProcessingGrand Challenge . The challenge consists of two tracks, namely speakerdiarization and multi-speaker ASR.…

## Self Supervised Domain Adaptation for Visual Navigation with Global Map Consistency

Given an embodied agent trained in anoiseless environment, our objective is to transfer the agent to a noisy environment where actuation and odometry sensor noise is present . We propose a light-weight, self-supervised adaptation for a visual navigationagent to generalize to unseen environment .…

## Secure Precoding in MIMO NOMA A Deep Learning Approach

A novel signaling design for secure transmission over two-user multiple-inputmultiple-output non-orthogonal multiple access channel using deep neuralnetworks (DNNs) is proposed . The goal of the DNN is to form the covariancematrix of users’ signals such that the message of each user is transmitted reliably while being confidential from its counterpart .…

## Adaptive Differentially Private Empirical Risk Minimization

We propose an adaptive (stochastic) gradient perturbation method for empirical risk minimization . At each iteration, therandom noise added to the gradient is optimally adapted to the stepsize . We prove that the ADP method considerably improves the efficiency guarantee compared to the standard differentially private method inwhich vanilla random noise is added .…

## Semantically Distributed Robust Optimization for Vision and Language Inference

Analysis of vision-and-language models has revealed their brittleness underlinguistic phenomena such as paraphrasing, negation, textual entailment, and textual substitutions with synonyms or antonyms . In this paper, we present a model-agnosticmethod that utilizes a set linguistic transformations in a distributed robustoptimization setting .…

## HAVEN Hierarchical Cooperative Multi Agent Reinforcement Learning with Dual Coordination Mechanism

Multi-agent reinforcement learning often suffers from the exponentially larger action space caused by a large number of agents . We propose a novel value decomposition framework HAVEN based on hierarchicalreinforcement learning for the fully cooperative multi-agent problems . Ourmethod is demonstrated to achieve superior results to many baselines onStarCraft II micromanagement tasks and offers an efficient solution tomulti-agent hierarchical reinforcement learning in fully cooperative scenarios .…