pwc-archive/papers-with-abstracts huggingface dataset:
On Estimating the Autoregressive Coefficients of Time-Varying Fading Channels
Authors: Julia Vinogradova, Gábor Fodor, Peter Hammarberg
Date: 2022-03-31 00:00:00
Tasks: parameter estimation

As several previous works have pointed out, the evolution of the wireless channels in multiple input multiple output systems can be advantageously modeled as an autoregressive process. Therefore, estimating the coefficients, and, in particular, the state transition matrix of this autoregressive process is a key to accurate channel estimation, tracking, and prediction in fast fading environments. In this paper we assume a time varying spatially uncorrelated channel, which is approximately the case with proper antenna spacing at the base station in rich scattering environments. We propose a method for autoregressive parameter estimation for the single input multiple output (SIMO) channel. We show an almost sure convergence of the estimated coefficients to the true autoregressive coefficients in large dimensions. We apply the proposed method to SIMO channel tracking.
Joint Sensing, Communication and Localization of a Silent Abnormality Using Molecular Diffusion
Authors: Ladan Khaloopour, Mahtab Mirmohseni, Masoumeh Nasiri-Kenari
Date: 2022-03-30 00:00:00
Tasks: Decision Making

In this paper, we propose a molecular communication system to localize an abnormality in a diffusion based medium. We consider a general setup to perform joint sensing, communication and localization. This setup consists of three types of devices, each for a different task: mobile sensors for navigation and molecule releasing (for communication), fusion centers (FC)s for sampling, amplifying and forwarding the signal, and a gateway for making decision or exchanging the information with an external device. The sensors move randomly in the environment to reach the abnormality. We consider both collaborative and non-collaborative sensors that simultaneously release their molecules to the FCs when the number of activated sensors or the moving time reach a certain threshold, respectively. The FCs amplify the received signal and forward it to the gateway for decision making using either an ideal or a noisy communication channel. A practical application of the proposed model is drug delivery in a tissue of human body, in order to guide the nanomachine bound drug to the exact location. The decision rules and probabilities of error are obtained for two considered sensors types in both ideal and noisy communication channels.
Multi Antenna Radar System for American Sign Language (ASL) Recognition Using Deep Learning
Authors: Gavin MacLaughlin, Jack Malcolm, Syed Ali Hamza
Date: 2022-03-30 00:00:00

This paper investigates RF-based system for automatic American Sign Language (ASL) recognition. We consider radar for ASL by joint spatio-temporal preprocessing of radar returns using time frequency (TF) analysis and high-resolution receive beamforming. The additional degrees of freedom offered by joint temporal and spatial processing using a multiple antenna sensor can help to recognize ASL conversation between two or more individuals. This is performed by applying beamforming to collect spatial images in an attempt to resolve individuals communicating at the same time through hand and arm movements. The spatio-temporal images are fused and classified by a convolutional neural network (CNN) which is capable of discerning signs performed by different individuals even when the beamformer is unable to separate the respective signs completely. The focus group comprises individuals with varying expertise with sign language, and real time measurements at 77 GHz frequency are performed using Texas Instruments (TI) cascade radar.
Multi-source data processing and fusion method for power distribution internet of things based on edge intelligence
Authors: Quande Yuan, Yuzhen Pi, Lei Kou, Fangfang Zhang, Yang Li, Zhenming Zhang
Date: 2022-03-30 00:00:00

With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this paper, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, to realize the uniform conversion of various sensor data sources in the distribution network in terms of magnitude and order of magnitude. By introducing the Box-Cox transform to improve the data offset problem in the Zscore normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box-Cox transform Zscore is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.
Parallel framework for Dynamic Domain Decomposition of Data Assimilation problems a case study on Kalman Filter algorithm
Authors: Rosalba Cacciapuoti, Luisa D'Amore
Date: 2022-01-09 00:00:00
Tasks: State Estimation

We focus on Partial Differential Equation (PDE) based Data Assimilatio problems (DA) solved by means of variational approaches and Kalman filter algorithm. Recently, we presented a Domain Decomposition framework (we call it DD-DA, for short) performing a decomposition of the whole physical domain along space and time directions, and joining the idea of Schwarz' methods and parallel in time approaches. For effective parallelization of DD-DA algorithms, the computational load assigned to subdomains must be equally distributed. Usually computational cost is proportional to the amount of data entities assigned to partitions. Good quality partitioning also requires the volume of communication during calculation to be kept at its minimum. In order to deal with DD-DA problems where the observations are nonuniformly distributed and general sparse, in the present work we employ a parallel load balancing algorithm based on adaptive and dynamic defining of boundaries of DD -- which is aimed to balance workload according to data location. We call it DyDD. As the numerical model underlying DA problems arising from the so-called discretize-then-optimize approach is the constrained least square model (CLS), we will use CLS as a reference state estimation problem and we validate DyDD on different scenarios.
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-Scans
Authors: Sanskar Hasija, Peddaputha Akash, Maganti Bhargav Hemanth, Ankit KumarSanjeev Sharma
Date: 2022-03-28 00:00:00
Proceeding: Neuroscience Informatics 2022 3
Tasks: COVID-19 Diagnosis, Diagnostic, image-classification, Image Classification, Multi-class Classification

The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multi class Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that uses two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances were compared using different classification metrics.
Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks
Authors: Nway Nway Ei, Madyan Alsenwi, Yan Kyaw Tun
Date: 2022-02-23 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2022 2
Tasks: Edge-computing

Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) has become one promising solution
for energy-constrained devices to run the applications with high
computation demand and stringent delay requirement in beyond
5G era. In this work, we study a multi-UAV-assisted two-stage
MEC system in which UAVs provide the computing and relaying
services to the mobile devices. Due to the limited computing
resources, each UAV executes only a portion of the offloaded tasks
from its associated MDs in the first stage. Hence, in the second
stage, each UAV relays the portions of the tasks to the terrestrial
base station (TBS) which has rich computing resources enough
to handle all the tasks relayed to it. In this regard, we formulate
a joint task offloading, communication and computation resource
allocation problem to minimize the energy consumption of MDs
and UAVs by considering the limited resources of UAVs and
the tolerable latency of the tasks. The formulated problem is a
mixed-integer non-convex problem which is NP hard. To solve the
formulated optimization problem, we apply the Block Successive
Upper-bound Minimization (BSUM) method which guarantees to
obtain the stationary points of the non-convex objective function.
Finally, the extensive evaluation results are conducted to show
the superior performance of our proposed framework.
Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent
Authors: Wei Xu
Date: 2011-07-13 00:00:00

For large scale learning problems, it is desirable if we can obtain the optimal model parameters by going through the data in only one pass. Polyak and Juditsky (1992) showed that asymptotically the test performance of the simple average of the parameters obtained by stochastic gradient descent (SGD) is as good as that of the parameters which minimize the empirical cost. However, to our knowledge, despite its optimal asymptotic convergence rate, averaged SGD (ASGD) received little attention in recent research on large scale learning. One possible reason is that it may take a prohibitively large number of training samples for ASGD to reach its asymptotic region for most real problems. In this paper, we present a finite sample analysis for the method of Polyak and Juditsky (1992). Our analysis shows that it indeed usually takes a huge number of samples for ASGD to reach its asymptotic region for improperly chosen learning rate. More importantly, based on our analysis, we propose a simple way to properly set learning rate so that it takes a reasonable amount of data for ASGD to reach its asymptotic region. We compare ASGD using our proposed learning rate with other well known algorithms for training large scale linear classifiers. The experiments clearly show the superiority of ASGD.
PYSKL: a toolbox for skeleton-based video understanding
Authors: Haodong Duan
Date: 2022-04-02 00:00:00
Proceeding: arXiv 2022 4
Tasks: Skeleton Based Action Recognition, Video Understanding

A placeholder for the tech report.
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
Authors: Chuanyu Pan, Yanchao Yang, Kaichun Mo, Yueqi Duan, Leonidas Guibas
Date: 2021-12-15 00:00:00
Proceeding: ICLR 2022 4
Tasks: Disentanglement, Object

We propose a framework to continuously learn object-centric representations for visual learning and understanding. Existing object-centric representations either rely on supervisions that individualize objects in the scene, or perform unsupervised disentanglement that can hardly deal with complex scenes in the real world. To mitigate the annotation burden and relax the constraints on the statistical complexity of the data, our method leverages interactions to effectively sample diverse variations of an object and the corresponding training signals while learning the object-centric representations. Throughout learning, objects are streamed one by one in random order with unknown identities, and are associated with latent codes that can synthesize discriminative weights for each object through a convolutional hypernetwork. Moreover, re-identification of learned objects and forgetting prevention are employed to make the learning process efficient and robust. We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations. Furthermore, we demonstrate the capability of the proposed framework in learning representations that can improve label efficiency in downstream tasks. Our code and trained models are made publicly available at: https://github.com/pptrick/Object-Pursuit.
From Statistical to Causal Learning
Authors: Bernhard Schölkopf, Julius von Kügelgen
Date: 2022-04-01 00:00:00
Tasks: BIG-bench Machine Learning

We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data.
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Authors: Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence
Date: 2022-04-01 00:00:00
Tasks: Diversity, Image Captioning, Multimodal Reasoning, Retrieval, Text Retrieval, Video Retrieval, Video to Text Retrieval, Zero-Shot Video Retrieval

Large pretrained (e.g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g., spreadsheets, SAT questions, code). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this diversity is symbiotic, and can be leveraged through Socratic Models (SMs): a modular framework in which multiple pretrained models may be composed zero-shot i.e., via multimodal-informed prompting, to exchange information with each other and capture new multimodal capabilities, without requiring finetuning. With minimal engineering, SMs are not only competitive with state-of-the-art zero-shot image captioning and video-to-text retrieval, but also enable new applications such as (i) answering free-form questions about egocentric video, (ii) engaging in multimodal assistive dialogue with people (e.g., for cooking recipes) by interfacing with external APIs and databases (e.g., web search), and (iii) robot perception and planning.
Fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing
Authors: Manuela Geiß, Martin Baresch, Georgios Chasparis, Edwin Schweiger, Nico Teringl, Michael Zwick
Date: 2022-04-01 00:00:00
Tasks: Image Generation, Object, object-detection, Object Detection

We present an end-to-end framework for fast retraining of object detection models in human-robot-collaboration. Our Faster R-CNN based setup covers the whole workflow of automatic image generation and labeling, model retraining on-site as well as inference on a FPGA edge device. The intervention of a human operator reduces to providing the new object together with its label and starting the training process. Moreover, we present a new loss, the intraspread-objectosphere loss, to tackle the problem of open world recognition. Though it fails to completely solve the problem, it significantly reduces the number of false positive detections of unknown objects.
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy
Authors: Banafsheh Rafiee, Jun Jin, Jun Luo, Adam White
Date: 2022-04-01 00:00:00
Tasks: Representation Learning

Auxiliary tasks have been argued to be useful for representation learning in reinforcement learning. Although many auxiliary tasks have been empirically shown to be effective for accelerating learning on the main task, it is not yet clear what makes useful auxiliary tasks. Some of the most promising results are on the pixel control, reward prediction, and the next state prediction auxiliary tasks; however, the empirical results are mixed, showing substantial improvements in some cases and marginal improvements in others. Careful investigations of how auxiliary tasks help the learning of the main task is necessary. In this paper, we take a step studying the effect of the target policies on the usefulness of the auxiliary tasks formulated as general value functions. General value functions consist of three core elements: 1) policy 2) cumulant 3) continuation function. Our focus on the role of the target policy of the auxiliary tasks is motivated by the fact that the target policy determines the behavior about which the agent wants to make a prediction and the state-action distribution that the agent is trained on, which further affects the main task learning. Our study provides insights about questions such as: Does a greedy policy result in bigger improvement gains compared to other policies? Is it best to set the auxiliary task policy to be the same as the main task policy? Does the choice of the target policy have a substantial effect on the achieved performance gain or simple strategies for setting the policy, such as using a uniformly random policy, work as well? Our empirical results suggest that: 1) Auxiliary tasks with the greedy policy tend to be useful. 2) Most policies, including a uniformly random policy, tend to improve over the baseline. 3) Surprisingly, the main task policy tends to be less useful compared to other policies.
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression
Authors: Mohammadmahdi Nouriborji, Omid Rohanian, David Clifton
Date: 2022-04-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Multi-Task Learning, regression

This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subtasks A and B. Using a pre-trained transformer as a backbone, the model targeted the task of multi-task classification and ranking in the context of finding the best fillers for a cloze task related to instructional texts on the website Wikihow. The system employed a combination of two ordinal regression components to tackle this task in a multi-task learning scenario. According to the official leaderboard of the shared task, this system was ranked 5th in the ranking and 7th in the classification subtasks out of 21 participating teams. With additional experiments, the models have since been further optimised.
A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Small Group Learning
Authors: Tanmay Sinha, Zhen Bai, Justine Cassell
Date: 2022-04-01 00:00:00
Tasks: Lifelong learning

Curiosity is a vital metacognitive skill in educational contexts, leading to creativity, and a love of learning. And while many school systems increasingly undercut curiosity by teaching to the test, teachers are increasingly interested in how to evoke curiosity in their students to prepare them for a world in which lifelong learning and reskilling will be more and more important. One aspect of curiosity that has received little attention, however, is the role of peers in eliciting curiosity. We present what we believe to be the first theoretical framework that articulates an integrated socio-cognitive account of curiosity that ties observable behaviors in peers to underlying curiosity states. We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions. We validate the proposed framework by leveraging a longitudinal latent variable modeling approach. Findings confirm a positive predictive relationship between the latent variables of individual and interpersonal functions and curiosity, with the interpersonal functions exercising a comparatively stronger influence. Prominent behavioral realizations of these functions are also discovered in a data-driven manner. We instantiate the proposed theoretical framework in a set of strategies and tactics that can be incorporated into learning technologies to indicate, evoke, and scaffold curiosity. This work is a step towards designing learning technologies that can recognize and evoke moment-by-moment curiosity during learning in social contexts and towards a more complete multimodal learning analytics. The underlying rationale is applicable more generally for developing computer support for other metacognitive and socio-emotional skills.
Leveraging Privacy Profiles to Empower Users in the Digital Society
Authors: Davide Di Ruscio, Paola Inverardi, Patrizio Migliarini, Phuong T. Nguyen
Date: 2022-04-01 00:00:00
Tasks: Ethics, Recommendation Systems

Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. Profiling users is standard practice for software applications triggering the need for users, also enforced by laws, to properly manage privacy settings. Users need to manage software privacy settings properly to protect personally identifiable information and express personal ethical preferences. AI technologies that empower users to interact with the digital world by reflecting their personal ethical preferences can be key enablers of a trustworthy digital society. We focus on the privacy dimension and contribute a step in the above direction through an empirical study on an existing dataset collected from the fitness domain. We find out which set of questions is appropriate to differentiate users according to their preferences. The results reveal that a compact set of semantic-driven questions (about domain-independent privacy preferences) helps distinguish users better than a complex domain-dependent one. This confirms the study's hypothesis that moral attitudes are the relevant piece of information to collect. Based on the outcome, we implement a recommender system to provide users with suitable recommendations related to privacy choices. We then show that the proposed recommender system provides relevant settings to users, obtaining high accuracy.