pwc-archive/papers-with-abstracts huggingface dataset:
Phase-Shift Design and Channel Modeling for Focused Beams in IRS-Assisted FSO Systems
Authors: Junghoon Noh, Byungju Lee
Date: 2022-04-20 00:00:00

Interest in free-space optics (FSO) is rapidly growing as a potential solution for the backhaul of next-generation mobile or low-orbit satellite communications. Various techniques have been suggested for employing an intelligent reflecting surface (IRS) in FSO systems, such as anomalous reflection, power amplification, and beam splitting. It is possible to deliver more power to the receiver (Rx) by collimating or focusing the reflected beam at the Rx lens. In this study, we propose a phase-shift design of an IRS for beam focusing. In addition, we propose a new pointing error model and an outage performance analysis applicable when the beam width is comparable to or less than the aperture size of the Rx. The analytical results are validated by Monte Carlo simulations. This study provides essential preliminary results for future researches that assume a focused beam in FSO systems.
A Scalable Deep Learning Framework for Multi-rate CSI Feedback under Variable Antenna Ports
Authors: Yu-Chien Lin, Ta-Sung Lee, Zhi Ding
Date: 2022-04-20 00:00:00

Channel state information (CSI) at transmitter is crucial for massive MIMO downlink systems to achieve high spectrum and energy efficiency. Existing works have provided deep learning architectures for CSI feedback and recovery at the eNB/gNB by reducing user feedback overhead and improving recovery accuracy. However, existing DL architectures tend to be inflexible and non-scalable as models are often trained according to a preset number of antennas for a given compression ratio. In this work, we develop a flexible and scalable learning framework based on a divide-and-conquer approach (DCA). This new DCA architecture can flexibly accommodate different numbers of 3GPP antenna ports and dynamic levels of feedback compression. Importantly, it also significantly reduces computational complexity and memory size by allowing UEs to feedback segmented downlink CSI. We further propose a multi-rate successive convolution encoder with fewer than 1000 parameters. Test results demonstrate superior performance, good scalability, and low complexity for both indoor and outdoor channels.
Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies
Authors: Jiahui Chen, Guo-Wei Wei
Date: 2022-04-20 00:00:00

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective to variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.
Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision
Authors: Yueming Li, Ying Jiang, Lu Lan, Xiaowei Ge, Ran Cheng, Yuewei Zhan, Guo Chen, Linli Shi, Runyu Wang, Nan Zheng, Chen Yang, Ji-Xin Cheng
Date: 2022-04-19 00:00:00

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological diseases. Current non-invasive neuromodulation methods offer limited precision at the millimeter level. Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision. OFUS is generated by a soft optoacoustic pad (SOAP) fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane film. SOAP generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83 um, which is two orders of magnitude smaller than that of conventional transcranial-focused ultrasound (tFUS). Here, we show effective OFUS neurostimulation in vitro with a single ultrasound cycle. We demonstrate submillimeter transcranial stimulation of the mouse motor cortex in vivo. An acoustic energy of 0.6 mJ/cm^2, four orders of magnitude less than that of tFUS, is sufficient for successful OFUS neurostimulation. OFUS offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision non-invasively.
Digging into Primary Financial Market: Challenges and Opportunities of Adopting Blockchain
Authors: Ji Liu, Zheng Xu, Yanmei Zhang, Wei Dai, Hao Wu, Shiping Chen
Date: 2022-04-20 00:00:00
Tasks: Survey

Since the emergence of blockchain technology, its application in the financial market has always been an area of focus and exploration by all parties. With the characteristics of anonymity, trust, tamper-proof, etc., blockchain technology can effectively solve some problems faced by the financial market, such as trust issues and information asymmetry issues. To deeply understand the application scenarios of blockchain in the financial market, the issue of securities issuance and trading in the primary market is a problem that must be studied clearly. We conducted an empirical study to investigate the main difficulties faced by primary market participants in their business practices and the potential challenges of the deepening application of blockchain technology in the primary market. We adopted a hybrid method combining interviews (qualitative methods) and surveys (quantitative methods) to conduct this research in two stages. In the first stage, we interview 15 major primary market participants with different backgrounds and expertise. In the second phase, we conducted a verification survey of 54 primary market practitioners to confirm various insights from the interviews, including challenges and desired improvements. Our interviews and survey results revealed several significant challenges facing blockchain applications in the primary market: complex due diligence, mismatch, and difficult monitoring. On this basis, we believe that our future research can focus on some aspects of these challenges.
Homogeneity and heterogeneity of cryptocurrencies
Authors: Xiao Fan Liu, Zeng-Xian Lin, Xiao-Pu Han
Date: 2019-10-03 00:00:00

Thousands of cryptocurrencies have been issued and publicly exchanged since Bitcoin was invented in 2008. The total cryptocurrency market value exceeds 300 billion US dollars as of 2019. This paper analyzes the prices, volumes, blockchain transactions, coin difficulties and public opinion popularities of 3607 actively exchanged cryptocurrencies. We aim to reveal and explain the homogeneity, i.e., the strong correlation of market performance, and the heterogeneity, i.e., the imbalance of popularities and sophistications, of the cryptocurrencies.
Analysis of the Relation between Quadratic Unconstrained Binary Optimization (QUBO) and the Spin Glass Ground-State Problem
Authors: Stefan Boettcher
Date: 2019-06-24 00:00:00

We analyze the transformation of QUBO from its conventional Boolean presentation into an equivalent spin glass problem with coupled $\pm1$ spin variables exposed to a site-dependent external field. We find that in a widely used testbed for QUBO these fields tend to be rather large compared to the typical coupling and many spins in each optimal configurations simply align with the fields irrespective of their constraints. Thereby, the testbed instances tend to exhibit large redundancies - seemingly independent variables which contribute little to the hardness of the problem, however. We demonstrate various consequences of this insight, for QUBO solvers as well as for heuristics developed for finding spin glass ground states. To this end, we implement the Extremal Optimization (EO) heuristic, in a new adaptation for the QUBO problem. We also propose a novel way to assess the quality of heuristics for increasing problem sizes based on asymptotic scaling.
A Survey on Bias and Fairness in Natural Language Processing
Authors: Rajas Bansal
Date: 2022-03-06 00:00:00
Tasks: Fairness, Survey

As NLP models become more integrated with the everyday lives of people, it becomes important to examine the social effect that the usage of these systems has. While these models understand language and have increased accuracy on difficult downstream tasks, there is evidence that these models amplify gender, racial and cultural stereotypes and lead to a vicious cycle in many settings. In this survey, we analyze the origins of biases, the definitions of fairness, and how different subfields of NLP mitigate bias. We finally discuss how future studies can work towards eradicating pernicious biases from NLP algorithms.
COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks
Authors: Fangyi Zhu, See-Kiong Ng, Stéphane Bressan
Date: 2022-04-01 00:00:00
Tasks: Question Answering

Vision outlooker improves the performance of vision transformers, which implements a self-attention mechanism by adding an outlook attention, a form of local attention. In natural language processing, as has been the case in computer vision and other domains, transformer-based models constitute the state-of-the-art for most processing tasks. In this domain, too, many authors have argued and demonstrated the importance of local context. We present an outlook attention mechanism, COOL, for natural language processing. COOL, added on top of the self-attention layers of a transformer-based model, encodes local syntactic context considering word proximity and more pair-wise constraints than dynamic convolution used by existing approaches. A comparative empirical performance evaluation of an implementation of COOL with different transformer-based models confirms the opportunity for improvement over a baseline using the original models alone for various natural language processing tasks, including question answering. The proposed approach achieves competitive performance with existing state-of-the-art methods on some tasks.
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Authors: Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang
Date: 2022-03-20 00:00:00
Proceeding: ACL 2022 5
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games

Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real world. In this paper, we address the challenges by introducing world-perceiving modules, which automatically decompose tasks and prune actions by answering questions about the environment. We then propose a two-phase training framework to decouple language learning from reinforcement learning, which further improves the sample efficiency. The experimental results show that the proposed method significantly improves the performance and sample efficiency. Besides, it shows robustness against compound error and limited pre-training data.
Build a Robust QA System with Transformer-based Mixture of Experts
Authors: Yu Qing Zhou, Xixuan Julie Liu, Yuanzhe Dong
Date: 2022-03-20 00:00:00
Tasks: Data Augmentation, Mixture-of-Experts, Question Answering

In this paper, we aim to build a robust question answering system that can adapt to out-of-domain datasets. A single network may overfit to the superficial correlation in the training distribution, but with a meaningful number of expert sub-networks, a gating network that selects a sparse combination of experts for each input, and careful balance on the importance of expert sub-networks, the Mixture-of-Experts (MoE) model allows us to train a multi-task learner that can be generalized to out-of-domain datasets. We also explore the possibility of bringing the MoE layers up to the middle of the DistilBERT and replacing the dense feed-forward network with a sparsely-activated switch FFN layers, similar to the Switch Transformer architecture, which simplifies the MoE routing algorithm with reduced communication and computational costs. In addition to model architectures, we explore techniques of data augmentation including Easy Data Augmentation (EDA) and back translation, to create more meaningful variance among the small out-of-domain training data, therefore boosting the performance and robustness of our models. In this paper, we show that our combination of best architecture and data augmentation techniques achieves a 53.477 F1 score in the out-of-domain evaluation, which is a 9.52% performance gain over the baseline. On the final test set, we reported a higher 59.506 F1 and 41.651 EM. We successfully demonstrate the effectiveness of Mixture-of-Expert architecture in a Robust QA task.
Radiology Text Analysis System (RadText): Architecture and Evaluation
Authors: Song Wang, Mingquan Lin, Ying Ding, George Shih, Zhiyong Lu, Yifan Peng
Date: 2022-03-19 00:00:00
Tasks: De-identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Negation, Negation Detection, Sentence

Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis. In this work, we present RadText, an open-source radiology text analysis system developed by Python. RadText offers an easy-to-use text analysis pipeline, including de-identification, section segmentation, sentence split and word tokenization, named entity recognition, parsing, and negation detection. RadText features a flexible modular design, provides a hybrid text processing schema, and supports raw text processing and local processing, which enables better usability and improved data privacy. RadText adopts BioC as the unified interface, and also standardizes the input / output into a structured representation compatible with Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This allows for a more systematic approach to observational research across multiple, disparate data sources. We evaluated RadText on the MIMIC-CXR dataset, with five new disease labels we annotated for this work. RadText demonstrates highly accurate classification performances, with an average precision of, a recall of 0.94, and an F-1 score of 0.92. We have made our code, documentation, examples, and the test set available at https://github.com/bionlplab/radtext .
Hierarchical BERT for Medical Document Understanding
Authors: Ning Zhang, Maciej Jankowski
Date: 2022-03-11 00:00:00
Tasks: document understanding, Sentence

Medical document understanding has gained much attention recently. One representative task is the International Classification of Disease (ICD) diagnosis code assignment. Existing work adopts either RNN or CNN as the backbone network because the vanilla BERT cannot handle well long documents (>2000 to kens). One issue shared across all these approaches is that they are over specific to the ICD code assignment task, losing generality to give the whole document-level and sentence-level embedding. As a result, it is not straight-forward to direct them to other downstream NLU tasks. Motivated by these observations, we propose Medical Document BERT (MDBERT) for long medical document understanding tasks. MDBERT is not only effective in learning representations at different levels of semantics but efficient in encoding long documents by leveraging a bottom-up hierarchical architecture. Compared to vanilla BERT solutions: 1, MDBERT boosts the performance up to relatively 20% on the MIMIC-III dataset, making it comparable to current SOTA solutions; 2, it cuts the computational complexity on self-attention modules to less than 1/100. Other than the ICD code assignment, we conduct a variety of other NLU tasks on a large commercial dataset named as TrialTrove, to showcase MDBERT's strength in delivering different levels of semantics.
Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing
Authors: Sonish Sivarajkumar, Thomas Yu CHow Tam, Haneef Ahamed Mohammad, Samual Viggiano, David Oniani, Shyam Visweswaran, Yanshan Wang
Date: 2022-03-08 00:00:00
Tasks: Language Modelling, Large Language Model, Sleep Quality

Alzheimer's Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192,000 de-identified clinical notes of 7,266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based Natural Language Processing (NLP) algorithm, machine learning models, and Large Language Model(LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of Positive Predictive Value (PPV), rule-based NLP algorithm achieved 1.00 for daytime sleepiness and sleep duration, machine learning models: 0.95 and for napping, 0.86 for bad sleep quality and 0.90 for snoring; and LLAMA2 with finetuning achieved PPV of 0.93 for Night Wakings, 0.89 for sleep problem, and 1.00 for sleep duration. The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD, but could be extended to general sleep information extraction for other diseases.
The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves
Authors: Abhijit Suresh, Jennifer Jacobs, Charis Harty, Margaret Perkoff, James H. Martin, Tamara Sumner
Date: 2022-04-06 00:00:00
Proceeding: LREC 2022 6
Tasks: Math, Sentence

Transcripts of teaching episodes can be effective tools to understand discourse patterns in classroom instruction. According to most educational experts, sustained classroom discourse is a critical component of equitable, engaging, and rich learning environments for students. This paper describes the TalkMoves dataset, composed of 567 human-annotated K-12 mathematics lesson transcripts (including entire lessons or portions of lessons) derived from video recordings. The set of transcripts primarily includes in-person lessons with whole-class discussions and/or small group work, as well as some online lessons. All of the transcripts are human-transcribed, segmented by the speaker (teacher or student), and annotated at the sentence level for ten discursive moves based on accountable talk theory. In addition, the transcripts include utterance-level information in the form of dialogue act labels based on the Switchboard Dialog Act Corpus. The dataset can be used by educators, policymakers, and researchers to understand the nature of teacher and student discourse in K-12 math classrooms. Portions of this dataset have been used to develop the TalkMoves application, which provides teachers with automated, immediate, and actionable feedback about their mathematics instruction.
Syntax-informed Question Answering with Heterogeneous Graph Transformer
Authors: Fangyi Zhu, Lok You Tan, See-Kiong Ng, Stéphane Bressan
Date: 2022-04-01 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering

Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks. These models are expensive to train. We propose to evaluate whether such pre-trained models can benefit from the addition of explicit linguistics information without requiring retraining from scratch. We present a linguistics-informed question answering approach that extends and fine-tunes a pre-trained transformer-based neural language model with symbolic knowledge encoded with a heterogeneous graph transformer. We illustrate the approach by the addition of syntactic information in the form of dependency and constituency graphic structures connecting tokens and virtual vertices. A comparative empirical performance evaluation with BERT as its baseline and with Stanford Question Answering Dataset demonstrates the competitiveness of the proposed approach. We argue, in conclusion and in the light of further results of preliminary experiments, that the approach is extensible to further linguistics information including semantics and pragmatics.
The MIT Voice Name System
Authors: Brian Subirana, Harry Levinson, Ferran Hueto, Prithvi Rajasekaran, Alexander Gaidis, Esteve Tarragó, Peter Oliveira-Soens
Date: 2022-03-28 00:00:00
Tasks: Speech-to-Text

This RFC white Paper summarizes our progress on the MIT Voice Name System (VNS) and Huey. The VNS, similar in name and function to the DNS, is a system to reserve and use "wake words" to activate Artificial Intelligence (AI) devices. Just like you can say "Hey Siri" to activate Apple's personal assistant, we propose using the VNS in smart speakers and other devices to route wake requests based on commands such as "turn off", "open grocery shopping list" or "271, start flash card review of my computer vision class". We also introduce Huey, an unambiguous Natural Language to interact with AI devices. We aim to standardize voice interactions to a universal reach similar to that of other systems such as phone numbering, with an agreed world-wide approach to assign and use numbers, or the Internet's DNS, with a standard naming system, that has helped flourish popular services including the World-Wide-Web, FTP, and email. Just like these standards are "neutral", we also aim to endow the VNS with "wake neutrality" so that each participant can develop its own digital voice. We focus on voice as a starting point to talk to any IoT object and explain briefly how the VNS may be expanded to other AI technologies enabling person-to-machine conversations (really machine-to-machine), including computer vision or neural interfaces. We also describe briefly considerations for a broader set of standards, MIT Open AI (MOA), including a reference architecture to serve as a starting point for the development of a general conversational commerce infrastructure that has standard "Wake Words", NLP commands such as "Shopping Lists" or "Flash Card Reviews", and personalities such as Pi or 271. Privacy and security are key elements considered because of speech-to-text errors and the amount of personal information contained in a voice sample.