pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling
Authors: Berk Iskender, Marc L. Klasky, Yoram Bresler
Date: 2022-04-21 00:00:00
Tasks: Dynamic Reconstruction, Object

In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time. The resulting inconsistent set of projections cannot be used directly to reconstruct an object corresponding to a time instant. Instead, the objective is to reconstruct a spatio-temporal representation of the object, which can be displayed as a movie. We analyze conditions for unique and stable solution of this ill-posed inverse problem, and present a recovery algorithm, validating it experimentally. We compare our approach to one based on the recently proposed GMLR variation on deep prior for video, demonstrating the advantages of the proposed approach.
Reconfigurable Intelligent Surface for Near Field Communications: Beamforming and Sensing
Authors: Yuhua Jiang, Feifei Gao, Mengnan Jian, Shun Zhang, Wei zhang
Date: 2022-04-21 00:00:00

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. We then propose the maximum likelihood (ML) method and the focal scanning (FS) method to sense the location of the receiver based on the analytic expression of the reflection coefficient, and derive the corresponding position error bound (PEB). Simulation results demonstrate that the proposed scheme can reduce energy leakage and thus enlarge the channel capacity compared to the conventional scheme. Moreover, the location of the receiver could be accurately sensed by the ML method with large computation complexity or be roughly sensed by the FS method with small computation complexity.
Robust Phase Retrieval via Reverse Kullback-Leibler Divergence
Authors: Nazia Afroz Choudhury, Bariscan Yonel, Birsen Yazıcı
Date: 2022-04-20 00:00:00
Tasks: Retrieval

Robustness to noise and outliers is a desirable trait in phase retrieval algorithms for many applications in imaging and signal processing. In this paper, we develop novel robust phase retrieval algorithms based on the minimization of reverse Kullback-Leibler divergence (RKLD) within the Wirtinger Flow (WF) framework. We use RKLD over intensity-only measurements in two distinct ways: i) to design a novel initial estimate based on minimum distortion design of spectral estimates, and ii) as a loss function for iterative refinement based on WF. The RKLD-based loss function offers implicit regularization by processing data at the logarithmic scale and provides the following benefits: suppressing the influence of outliers and promoting projections orthogonal to noise subspace. We perform a quantitative analysis demonstrating the robustness of RKLD-based minimization as compared to that of the $\ell_2$ and Poisson loss-based minimization. We present three algorithms based on RKLD minimization, including two with truncation schemes to enhance the robustness to significant contamination. Our numerical study uses data generated based on synthetic coded diffraction patterns and real optical imaging data. The results demonstrate the advantages of our algorithms in terms of sample efficiency, convergence speed, and robustness with respect to outliers over the state-of-the-art techniques.
Smart Interference Management xApp using Deep Reinforcement Learning
Authors: Mahdi Eskandari, Shipra Kapoor, Keith Briggs, Arman Shojaeifard, Huiling Zhu, Alain Mourad
Date: 2022-04-12 00:00:00
Tasks: Computational Efficiency, Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Interference continues to be a key limiting factor in cellular radio access network (RAN) deployments. Effective, data-driven, self-adapting radio resource management (RRM) solutions are essential for tackling interference, and thus achieving the desired performance levels particularly at the cell-edge. In future network architecture, RAN intelligent controller (RIC) running with near-real-time applications, called xApps, is considered as a potential component to enable RRM. In this paper, based on deep reinforcement learning (RL) xApp, a joint sub-band masking and power management is proposed for smart interference management. The sub-band resource masking problem is formulated as a Markov Decision Process (MDP) that can be solved employing deep RL to approximate the policy functions as well as to avoid extremely high computational and storage costs of conventional tabular-based approaches. The developed xApp is scalable in both storage and computation. Simulation results demonstrate advantages of the proposed approach over decentralized baselines in terms of the trade-off between cell-centre and cell-edge user rates, energy efficiency and computational efficiency.
Short-time asymptotics for non self-similar stochastic volatility models
Authors: Giacomo Giorgio, Barbara Pacchiarotti, Paolo Pigato
Date: 2022-04-21 00:00:00
Tasks: Math

We provide a short-time large deviation principle (LDP) for stochastic volatility models, where the volatility is expressed as a function of a Volterra process. This LDP does not require strict self-similarity assumptions on the Volterra process. For this reason, we are able to apply such an LDP to two notable examples of non self-similar rough volatility models: models where the volatility is given as a function of a log-modulated fractional Brownian motion [Bayer et al., Log-modulated rough stochastic volatility models. SIAM J. Financ. Math, 2021, 12(3), 1257-1284], and models where it is given as a function of a fractional Ornstein-Uhlenbeck (fOU) process [Gatheral et al., Volatility is rough. Quant. Finance, 2018, 18(6), 933-949]. In both cases we derive consequences for short-maturity European option prices, implied volatility surfaces and implied volatility skew. In the fOU case we also discuss moderate deviations pricing and simulation results.
Nanodiamond quantum sensors reveal temperature variation associated to hippocampal neurons firing
Authors: G. Petrini, G. Tomagra, E. Bernardi, E. Moreva, P. Traina, A. Marcantoni, F. Picollo, K. Kvakova, P. Cigler, I. P. Degiovanni, V. Carabelli, M. Genovese
Date: 2022-03-31 00:00:00

Temperature is one of the most relevant parameters for the regulation of intracellular processes. Measuring localized subcellular temperature gradients is fundamental for a deeper understanding of cell function, such as the genesis of action potentials, and cell metabolism. Here, we detect for the first time temperature variations (1{\deg}C) associated with potentiation and depletion of neuronal firing, exploiting a nanoscale thermometer based on optically detected magnetic resonance in nanodiamonds. Our results provide a tool for assessing neuronal spiking activity under physiological and pathological conditions and, conjugated with the high sensitivity of this technique (in perspective sensitive to < 0.1{\deg}C variations), pave the way to a systematic study of the generation of localized temperature gradients. Furthermore, they prompt further studies explaining in detail the physiological mechanism originating this effect.
Emergent simulation of cell-like shapes satisfying the conditions of life using lattice-type multiset chemical model
Authors: Takeshi Ishida
Date: 2022-04-21 00:00:00
Tasks: Chemical Process

One of the great challenges in science is determining when, where, why, and how life first arose as well as the form taken by this life. In the present study, life was assumed to be (1) bounded, (2) replicating, (3) able to inherit information, and (4) able to metabolize energy. The various existing hypotheses provide little explanation of how these four conditions for life were established. Indeed, 'how' a chemical process that simultaneously satisfies all four conditions emerged after the materials for life were in place is not always clear. In this study, a 'multiset chemical lattice model', which allows virtual molecules of multiple types to be placed in each cell on a two-dimensional space, was considered. Using only the processes of molecular diffusion, reaction, and polymerization and modeling the chemical reactions of 15 types of molecules and 2 types of polymerized molecules and using the morphogenesis rule of the Turing model, the process of emergence of a cell-like form with the four conditions of life was modeled and demonstrated. Thus, in future research, this model will allow us to revisit and refine each of the hypotheses for the emergence of life.
Guidelines for reporting cell types: the MIRACL standard
Authors: Tiago Lubiana, Paola Roncaglia, Christopher J. Mungall, Ellen M. Quardokus, Joshua D. Fortriede, David Osumi-Sutherland, Alexander D. Diehl
Date: 2022-04-18 00:00:00
Tasks: Articles, Diagnostic

Cell types are at the root of modern biology, and describing them is a core task of the Human Cell Atlas project. Surprisingly, there are no standards for reporting new cell types, leading to a gap between classes mentioned in biomedical literature and the Cell Ontology, the primary registry of cell types. Here we introduce the Minimal Information Reporting About a CelL (MIRACL) standard, a guideline for describing cell types alongside scientific articles. In a MIRACL sheet, authors organize a label, a diagnostic description, a taxon, an anatomical structure, and a parent cell class for each cell type of interest. The MIRACL standard bridges the gap between wet-lab researchers and ontologists, facilitating the integration of biomedical knowledge into ontologies and artificial intelligence systems.
New confidence interval methods for Shannon index
Authors: Gabriel R. Palma, Silvio S. Zocchi, Wesley A. C. Godoy, Jorge A. Wiendl
Date: 2022-04-21 00:00:00
Tasks: Diversity

Several factors affect the structure of communities, including biological, physical and chemical phenomena, impacting the quantification of biodiversity, measured by diversity indexes such as Shannon's entropy. Then, once a point estimate is obtained, confidence intervals methods such as the bootstrap ones are often used. These methods, however, can have different performances, which many authors have revealed in the last decade. Furthermore, problems such as the asymmetry of the distribution of estimates and the possibility of Shannon's diversity index estimator bias can lead to incorrect recommendations to the research community. Thus, we propose two methods and compare them with seven others using their performances to face these problems. The first idea uses the credible interval (CI) method to build a bootstrap confidence interval. The second one starts by correcting the bias and then uses an asymptotic approach. We considered 27 community structures representing scenarios with high dominance, high codominance or moderate dominance, the number of species equal to 4, 20 or 80 and 10, 50 or 500 individuals to compare their performances. Then, we generated 1000 samples, built 95% confidence intervals, and calculated the percentage of times they included the community diversity index (coverage percentage) for each community structure. Our results showed the feasibility of both proposed methods to estimate Shannon's diversity. The simulation study revealed the bootstrap-t technique had the best performance, i.e., best coverage percentage, compared with the other methods. Finally, we illustrate the methodology by applying it to an original aphid and parasitoid species dataset. We recommend the bootstrap-t when the community structure analysed is similar to the simulated ones. Also, the methods provided high performance for the high dominance scenarios.
Optimality of intercellular signaling: direct transport versus diffusion
Authors: Hyunjoong Kim, Yoichiro Mori, Joshua B. Plotkin
Date: 2022-04-20 00:00:00

Intercellular signaling has an important role in organism development, but not all communication occurs using the same mechanism. Here, we analyze the energy efficiency of intercellular signaling by two canonical mechanisms: diffusion of signaling molecules and direct transport mediated by signaling cellular protrusions. We show that efficient contact formation for direct transport can be established by an optimal rate of projecting protrusions, which depends on the availability of information about the location of the target cell. The optimal projection rate also depends on how signaling molecules are transported along the protrusion, in particular the ratio of the energy cost for contact formation and molecule synthesis. Also, we compare the efficiency of the two signaling mechanisms, under various model parameters. We find that the direct transport is favored over the diffusion when transporting a large amount of signaling molecules. There is a critical number of signaling molecules at which the efficiency of the two mechanisms are the same. The critical number is small when the distance between cells is far, which helps explain why protrusion-based mechanisms are observed in long-range cellular communications.
ProTranslator: zero-shot protein function prediction using textual description
Authors: Hanwen Xu, Sheng Wang
Date: 2022-04-20 00:00:00
Tasks: Machine Translation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Prediction, Protein Function Prediction

Accurately finding proteins and genes that have a certain function is the prerequisite for a broad range of biomedical applications. Despite the encouraging progress of existing computational approaches in protein function prediction, it remains challenging to annotate proteins to a novel function that is not collected in the Gene Ontology and does not have any annotated proteins. This limitation, a side effect from the widely-used multi-label classification problem setting of protein function prediction, hampers the progress of studying new pathways and biological processes, and further slows down research in various biomedical areas. Here, we tackle this problem by annotating proteins to a function only based on its textual description so that we do not need to know any associated proteins for this function. The key idea of our method ProTranslator is to redefine protein function prediction as a machine translation problem, which translates the description word sequence of a function to the amino acid sequence of a protein. We can then transfer annotations from functions that have similar textual description to annotate a novel function. We observed substantial improvement in annotating novel functions and sparsely annotated functions on CAFA3, SwissProt and GOA datasets. We further demonstrated how our method accurately predicted gene members for a given pathway in Reactome, KEGG and MSigDB only based on the pathway description. Finally, we showed how ProTranslator enabled us to generate the textual description instead of the function label for a set of proteins, providing a new scheme for protein function prediction. We envision ProTranslator will give rise to a protein function "search engine" that returns a list of proteins based on the free text queried by the user.
A Structured Survey of Quantum Computing for the Financial Industry
Authors: Franco D. Albareti, Thomas Ankenbrand, Denis Bieri, Esther Hänggi, Damian Lötscher, Stefan Stettler, Marcel Schöngens
Date: 2022-04-21 00:00:00
Tasks: Management

Quantum computers can solve specific problems that are not feasible on "classical" hardware. Harvesting the speed-up provided by quantum computers therefore has the potential to change any industry which uses computation, including finance. First quantum applications for the financial industry involving optimization, simulation, and machine learning problems have already been proposed and applied to use cases such as portfolio management, risk management, and pricing derivatives. This survey reviews platforms, algorithms, methodologies, and use cases of quantum computing for various applications in finance in a structured way. It is aimed at people working in the financial industry and serves to gain an overview of the current development and capabilities and understand the potential of quantum computing in the financial industry.
Generative Pre-Trained Transformers for Biologically Inspired Design
Authors: Qihao Zhu, Xinyu Zhang, Jianxi Luo
Date: 2022-03-31 00:00:00
Tasks: Language Modelling

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a novel form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a computational approach to bridge the gap. This paper proposes a generative design approach based on the pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the correlation between the domains within the generated BID concepts. The approach is then tested via a case study in which the fine-tuned models are applied to generate and evaluate light-weighted flying car concepts inspired by nature. The results show our approach can generate BID concepts with good performance.
Scaling Language Model Size in Cross-Device Federated Learning
Authors: Jae Hun Ro, Theresa Breiner, Lara McConnaughey, Mingqing Chen, Ananda Theertha Suresh, Shankar Kumar, Rajiv Mathews
Date: 2022-03-31 00:00:00
Proceeding: FL4NLP (ACL) 2022 5
Tasks: Federated Learning, Language Modeling, Language Modelling, Quantization, Transfer Learning

Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larger language models in cross-device federated learning. With systematic applications of partial model training, quantization, efficient transfer learning, and communication-efficient optimizers, we are able to train a $21$M parameter Transformer and $20.2$M parameter Conformer that achieve the same or better perplexity as that of a similarly sized LSTM with $\sim10\times$ smaller client-to-server communication cost and $11\%$ lower perplexity than smaller LSTMs commonly studied in literature.
Matching Writers to Content Writing Tasks
Authors: Narayana Darapaneni, Chandrashekhar Bhakuni, Ujjval Bhatt, Khamir Purohit, Vikas Sardna, Prabir Chakraborty, Anwesh Reddy Paduri
Date: 2022-04-07 00:00:00
Tasks: Articles, Marketing

Businesses need content. In various forms and formats and for varied purposes. In fact, the content marketing industry is set to be worth $412.88 billion by the end of 2021. However, according to the Content Marketing Institute, creating engaging content is the #1 challenge that marketers face today. We under-stand that producing great content requires great writers who understand the business and can weave their message into reader (and search engine) friendly content. In this project, the team has attempted to bridge the gap between writers and projects by using AI and ML tools. We used NLP techniques to analyze thou-sands of publicly available business articles (corpora) to extract various defining factors for each writing sample. Through this project we aim to automate the highly time-consuming, and often biased task of manually shortlisting the most suitable writer for a given content writing requirement. We believe that a tool like this will have far reaching positive implications for both parties - businesses looking for suitable talent for niche writing jobs as well as experienced writers and Subject Matter Experts (SMEs) wanting to lend their services to content marketing projects. The business gets the content they need, the content writer/ SME gets a chance to leverage his or her talent, while the reader gets authentic content that adds real value.
Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization
Authors: Brydon Parker, Alik Sokolov, Mahtab Ahmed, Matt Kalebic, Sedef Akinli Kocak, Ofer Shai
Date: 2022-04-06 00:00:00
Tasks: Data Augmentation, Denoising

Summarization of long-form text data is a problem especially pertinent in knowledge economy jobs such as medicine and finance, that require continuously remaining informed on a sophisticated and evolving body of knowledge. As such, isolating and summarizing key content automatically using Natural Language Processing (NLP) techniques holds the potential for extensive time savings in these industries. We explore applications of a state-of-the-art NLP model (BART), and explore strategies for tuning it to optimal performance using data augmentation and various fine-tuning strategies. We show that our end-to-end fine-tuning approach can result in a 5-6\% absolute ROUGE-1 improvement over an out-of-the-box pre-trained BART summarizer when tested on domain specific data, and make available our end-to-end pipeline to achieve these results on finance, medical, or other user-specified domains.
LSTM-RASA Based Agri Farm Assistant for Farmers
Authors: Narayana Darapaneni, Selvakumar Raj, Raghul V, Venkatesh Sivaraman, Sunil Mohan, Anwesh Reddy Paduri
Date: 2022-04-07 00:00:00
Tasks: Chatbot

The application of Deep Learning and Natural Language based ChatBots are growing rapidly in recent years. They are used in many fields like customer support, reservation system and as personal assistant. The Enterprises are using such ChatBots to serve their customers in a better and efficient manner. Even after such technological advancement, the expert advice does not reach the farmers on timely manner. The farmers are still largely dependent on their peers knowledge in solving the problems they face in their field. These technologies have not been effectively used to give the required information to farmers on timely manner. This project aims to implement a closed domain ChatBot for the field of Agriculture Farmers Assistant. Farmers can have conversation with the Chatbot and get the expert advice in their field. Farmers Assistant is based on RASA Open Source Framework. The Chatbot identifies the intent and entity from user utterances and retrieve the remedy from the database and share it with the user. We tested the Bot with existing data and it showed promising results.
Recent Progress in Conversational AI
Authors: Zijun Xue, Ruirui Li, Mingda Li
Date: 2022-04-08 00:00:00

Conversational artificial intelligence (AI) is becoming an increasingly popular topic among industry and academia. With the fast development of neural network-based models, a lot of neural-based conversational AI system are developed. We will provide a brief review of the recent progress in the Conversational AI, including the commonly adopted techniques, notable works, famous competitions from academia and industry and widely used datasets.