pwc-archive/papers-with-abstracts huggingface dataset:
Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations
Authors: Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp
Date: 2021-07-12 00:00:00
Tasks: Topic Models

This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in topic models. The data consists of observed multinomial counts of $p$ words across $n$ independent documents. In topic models, the $p\times n$ expected word frequency matrix is assumed to be factorized as a $p\times K$ word-topic matrix $A$ and a $K\times n$ topic-document matrix $T$. Since columns of both matrices represent conditional probabilities belonging to probability simplices, columns of $A$ are viewed as $p$-dimensional mixture components that are common to all documents while columns of $T$ are viewed as the $K$-dimensional mixture weights that are document specific and are allowed to be sparse. The main interest is to provide sharp, finite sample, $\ell_1$-norm convergence rates for estimators of the mixture weights $T$ when $A$ is either known or unknown. For known $A$, we suggest MLE estimation of $T$. Our non-standard analysis of the MLE not only establishes its $\ell_1$ convergence rate, but reveals a remarkable property: the MLE, with no extra regularization, can be exactly sparse and contain the true zero pattern of $T$. We further show that the MLE is both minimax optimal and adaptive to the unknown sparsity in a large class of sparse topic distributions. When $A$ is unknown, we estimate $T$ by optimizing the likelihood function corresponding to a plug in, generic, estimator $\hat{A}$ of $A$. For any estimator $\hat{A}$ that satisfies carefully detailed conditions for proximity to $A$, the resulting estimator of $T$ is shown to retain the properties established for the MLE. The ambient dimensions $K$ and $p$ are allowed to grow with the sample sizes. Our application is to the estimation of 1-Wasserstein distances between document generating distributions. We propose, estimate and analyze new 1-Wasserstein distances between two probabilistic document representations.
Asking Clarifying Questions Based on Negative Feedback in Conversational Search
Authors: Keping Bi, Qingyao Ai, W. Bruce Croft
Date: 2021-07-12 00:00:00
Tasks: Conversational Search, Question Selection, Retrieval

Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users' search intents. This, however, can take significant effort to answer a series of questions starting with "what/why/how". To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns. In this task, it is essential to use negative feedback about the previous questions in the conversation history. To this end, we propose a Maximum-Marginal-Relevance (MMR) based BERT model (MMR-BERT) to leverage negative feedback based on the MMR principle for the next clarifying question selection. Experiments on the Qulac dataset show that MMR-BERT outperforms state-of-the-art baselines significantly on the intent identification task and the selected questions also achieve significantly better performance in the associated document retrieval tasks.
COPER: a Query-adaptable Semantics-based Search Engine for Persian COVID-19 Articles
Authors: Reza Khanmohammadi, Mitra Sadat Mirshafiee, Mehdi Allahyari
Date: 2021-07-12 00:00:00
Tasks: Articles, Semantic Textual Similarity

With the surge of pretrained language models, a new pathway has been opened to incorporate Persian text contextual information. Meanwhile, as many other countries, including Iran, are fighting against COVID-19, a plethora of COVID-19 related articles has been published in Iranian Healthcare magazines to better inform the public of the situation. However, finding answers in this sheer volume of information is an extremely difficult task. In this paper, we collected a large dataset of these articles, leveraged different BERT variations as well as other keyword models such as BM25 and TF-IDF, and created a search engine to sift through these documents and rank them, given a user's query. Our final search engine consists of a ranker and a re-ranker, which adapts itself to the query. We fine-tune our models using Semantic Textual Similarity and evaluate them with standard task metrics. Our final method outperforms the rest by a considerable margin.
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
Authors: Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant
Date: 2021-07-12 00:00:00
Tasks: Information Retrieval, Open-Domain Question Answering, Retrieval

In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors methods has proven to work well. Meanwhile, there has been a growing interest in learning sparse representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes. In this work, we present a new first-stage ranker based on explicit sparsity regularization and a log-saturation effect on term weights, leading to highly sparse representations and competitive results with respect to state-of-the-art dense and sparse methods. Our approach is simple, trained end-to-end in a single stage. We also explore the trade-off between effectiveness and efficiency, by controlling the contribution of the sparsity regularization.
A New Approach for Semantic Web Matching
Authors: Kamran Zamanifar, Golsa Heidari, Naser Nematbakhsh, Farhad Mardookhi
Date: 2021-07-06 00:00:00
Tasks: Management

In this work we propose a new approach for semantic web matching to improve the performance of Web Service replacement. Because in automatic systems we should ensure the self-healing, self-configuration, self-optimization and self-management, all services should be always available and if one of them crashes, it should be replaced with the most similar one. Candidate services are advertised in Universal Description, Discovery and Integration (UDDI) all in Web Ontology Language (OWL). By the help of bipartite graph, we did the matching between the crashed service and a Candidate one. Then we chose the best service, which had the maximum rate of matching. In fact we compare two services` functionalities and capabilities to see how much they match. We found that the best way for matching two web services, is comparing the functionalities of them.
Challenges for machine learning in clinical translation of big data imaging studies
Authors: Nicola K Dinsdale, Emma Bluemke, Vaanathi Sundaresan, Mark Jenkinson, Stephen Smith, Ana IL Namburete
Date: 2021-07-07 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Epidemiology, Translation

The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology. However, despite the success of deep learning when applied to many neuroimaging tasks, there remain barriers to the clinical translation of large-scale datasets and processing tools. Here, we explore the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation and logistical challenges, and discuss the challenges we believe are still to be overcome to enable the full success of big data deep learning approaches to be experienced outside of the research field.
AI Algorithm for Mode Classification of PCF SPR Sensor Design
Authors: Prasunika Khare, Mayank Goswami
Date: 2021-07-09 00:00:00
Tasks: Classification, Specificity

Photonic Crystal Fiber design based on surface plasmon resonance phenomenon (PCF SPR) is optimized before it is fabricated for a particular application. An artificial intelligence algorithm is evaluated here to increase the ease of the simulation process for common users. COMSOL MultiPhysics is used. The algorithm suggests best among eight standard machine learning and one deep learning model to automatically select the desired mode, chosen visually by the experts otherwise. Total seven performance indices: namely Precision, Recall, Accuracy, F1-Score, Specificity, Matthew correlation coefficient, are utilized to make the optimal decision. Robustness towards variations in sensor geometry design is also considered as an optimal parameter. Several PCF-SPR based Photonic sensor designs are tested, and a large range optimal (based on phase matching) design is proposed. For this design algorithm has selected Support Vector Machine (SVM) as the best option with an accuracy of 96%, F1-Score is 95.83%, and MCC of 92.30%. The average sensitivity of the proposed sensor design with respect to change in refractive index (1.37-1.41) is 5500 nm/RIU. Resolution is 2.0498x10^(-5) RIU^(-1). The algorithm can be integrated into commercial software as an add-on or as a module in academic codes. The proposed novel step has saved approximately 75 minutes in the overall design process. The present work is equally applicable for mode selection of sensor other than PCF-SPR sensing geometries.
A Self-Regulated and Reconfigurable CMOS Physically Unclonable Function Featuring Zero-Overhead Stabilization
Authors: Dai Li, Kaiyuan Yang
Date: 2021-07-07 00:00:00

This article presents a reconfigurable physically unclonable function (PUF) design fabricated using 65-nm CMOS technology. A subthreshold-inverter-based static PUF cell achieves 0.3% native bit error rate (BER) at 0.062-fJ per bit core energy efficiency. A flexible, native transistor-based voltage regulation scheme achieves low-overhead supply regulation with 6-mV/V line sensitivity, making the PUF resistant against voltage variations. Additionally, the PUF cell is designed to be reconfigurable with no area overhead, which enables stabilization without redundancy on chip. Thanks to the highly stable and self-regulated PUF cell and the zero-overhead stabilization scheme, a 0.00182% native BER is obtained after reconfiguration. The proposed design shows 0.12%/10 {\deg}C and 0.057%/0.1-V bit error across the military-grade temperature range from -55 {\deg}C to 125 {\deg}C and supply voltage variation from 0.7 to 1.4 V. The total energy per bit is 15.3 fJ. Furthermore, the unstable bits can be detected by sweeping the body bias instead of temperature during enrollment, thereby significantly reducing the testing costs. Last but not least, the prototype exhibits almost ideal uniqueness and randomness, with a mean inter-die Hamming distance (HD) of 0.4998 and a 1020x inter-/intra-die HD separation. It also passes both NIST 800-22 and 800-90B randomness tests.
Making Auctions Robust to Aftermarkets
Authors: Moshe Babaioff, Nicole Immorlica, Yingkai Li, Brendan Lucier
Date: 2021-07-13 00:00:00
Tasks: Fairness

A prevalent assumption in auction theory is that the auctioneer has full control over the market and that the allocation she dictates is final. In practice, however, agents might be able to resell acquired items in an aftermarket. A prominent example is the market for carbon emission allowances. These allowances are commonly allocated by the government using uniform-price auctions, and firms can typically trade these allowances among themselves in an aftermarket that may not be fully under the auctioneer's control. While the uniform-price auction is approximately efficient in isolation, we show that speculation and resale in aftermarkets might result in a significant welfare loss. Motivated by this issue, we consider three approaches, each ensuring high equilibrium welfare in the combined market. The first approach is to adopt smooth auctions such as discriminatory auctions. This approach is robust to correlated valuations and to participants acquiring information about others' types. However, discriminatory auctions have several downsides, notably that of charging bidders different prices for identical items, resulting in fairness concerns that make the format unpopular. Two other approaches we suggest are either using posted-pricing mechanisms, or using uniform-price auctions with anonymous reserves. We show that when using balanced prices, both these approaches ensure high equilibrium welfare in the combined market. The latter also inherits many of the benefits from uniform-price auctions such as price discovery, and can be introduced with a minor modification to auctions currently in use to sell carbon emission allowances.
A Rational Entailment for Expressive Description Logics via Description Logic Programs
Authors: Giovanni Casini, Umberto Straccia
Date: 2021-06-28 00:00:00

Lehmann and Magidor's rational closure is acknowledged as a landmark in the field of non-monotonic logics and it has also been re-formulated in the context of Description Logics (DLs). We show here how to model a rational form of entailment for expressive DLs, such as SROIQ, providing a novel reasoning procedure that compiles a non-monotone DL knowledge base into a description logic program (dl-program).
Design of a Smooth Landing Trajectory Tracking System for a Fixed-wing Aircraft
Authors: Solomon Gudeta, Ali Karimoddini
Date: 2021-07-13 00:00:00

This paper presents a landing controller for a fixed-wing aircraft during the landing phase, ensuring the aircraft reaches the touchdown point smoothly. The landing problem is converted to a finite-time linear quadratic tracking (LQT) problem in which an aircraft needs to track the desired landing path in the longitudinal-vertical plane while satisfying performance requirements and flight constraints. First, we design a smooth trajectory that meets flight performance requirements and constraints. Then, an optimal controller is designed to minimize the tracking error, while landing the aircraft within the desired time frame. For this purpose, a linearized model of an aircraft developed under the assumption of a small flight path angle and a constant approach speed is used. The resulting Differential Riccati equation is solved backward in time using the Dormand Prince algorithm. Simulation results show a satisfactory tracking performance and the finite-time convergence of tracking errors for different initial conditions of the flare-out phase of landing.
Raspberry PI for compact autonomous home farm control
Authors: R. Ildar
Date: 2021-07-06 00:00:00

This manuscript presented an autonomous home farm for predicting metrological characteristics that can not only automate the process of growing crops but also, due to a neural network, significantly increase the productivity of the farm. The developed farm monitors and manages the following indicators: illumination, soil PH, air temperature, ground temperature, air humidity, CO2 concentration, and soil moisture. The presented farm can also be considered as a device for testing various weather conditions to determine the optimal temperature characteristics for different crops. This farm as a result is completely autonomous grows tomatoes at home.
AUC Optimization for Robust Small-footprint Keyword Spotting with Limited Training Data
Authors: Menglong Xu, Shengqiang Li, Chengdong Liang, Xiao-Lei Zhang
Date: 2021-07-13 00:00:00
Tasks: Keyword Spotting, Small-Footprint Keyword Spotting

Deep neural networks provide effective solutions to small-footprint keyword spotting (KWS). However, if training data is limited, it remains challenging to achieve robust and highly accurate KWS in real-world scenarios where unseen sounds that are out of the training data are frequently encountered. Most conventional methods aim to maximize the classification accuracy on the training set, without taking the unseen sounds into account. To enhance the robustness of the deep neural networks based KWS, in this paper, we introduce a new loss function, named the maximization of the area under the receiver-operating-characteristic curve (AUC). The proposed method not only maximizes the classification accuracy of keywords on the closed training set, but also maximizes the AUC score for optimizing the performance of non-keyword segments detection. Experimental results on the Google Speech Commands dataset v1 and v2 show that our method achieves new state-of-the-art performance in terms of most evaluation metrics.
An Ecological Robustness-Oriented Approach for Power System Network Expansion
Authors: Hao Huang, Zeyu Mao, Varuneswara Panyam, Astrid Layton, Katherine Davis
Date: 2021-07-13 00:00:00

Power systems are critical infrastructure for reliable and secure electric energy delivery. Incidents are increasing, as unexpected multiple hazards ranging from natural disasters to cyberattacks threaten the security and functionality of society. Inspired by resilient ecosystems, this paper presents a resilient network design approach with an ecological robustness (R ECO )-oriented optimization to improve power systems' ability to maintain a secure operating state throughout unknown hazards. The approach uses R ECO , a surprisal -based metric that captures key features of an ecosystem's resilient structure, as an objective to strategically design the electrical network. The approach enables solvability and practicality by introducing a stochastic-based candidate branch creation algorithm and a Taylor series expansion for relaxation of the R ECO formulation. Finally, studies are conducted on the R ECO -oriented approach using the IEEE 24 Bus RTS and the ACTIVSg200 systems. Results demonstrate improvement of the system's reliability under multiple hazards, network properties of robust structure and equally distributed power flows, and survivability against cascading failures. From the analysis, we observe that a more redundant network structure with equally distributed power flows benefits its resilience.
Adaptive dynamic programming-based adaptive-gain sliding mode tracking control for fixed-wing UAV with disturbances
Authors: Chaofan Zhang, Guoshan Zhang, Qi Dong
Date: 2021-07-13 00:00:00

This paper proposes an adaptive dynamic programming-based adaptive-gain sliding mode control (ADP-ASMC) scheme for a fixed-wing unmanned aerial vehicle (UAV) with matched and unmatched disturbances. Starting from the dynamic of fixed-wing UAV, the control-oriented model composed of attitude subsystem and airspeed subsystem is established. According to the different issues in two subsystems, two novel adaptive-gain generalized super-twisting (AGST) algorithms are developed to eliminate the effects of disturbances in two subsystems and make the system trajectories tend to the designed integral sliding manifolds (ISMs) in finite time. Then, based on the expected equivalent sliding-mode dynamics, the modified adaptive dynamic programming (ADP) approach with actor-critic (AC) structure is utilized to generate the nearly optimal control laws and achieve the nearly optimal performance of the sliding-mode dynamics. Furthermore, through the Lyapunov stability theorem, the tracking errors and the weight estimation errors of two neural networks (NNs) are all uniformly ultimately bounded (UUB). Finally, comparative simulations demonstrate the superior performance of the proposed control scheme for the fixed-wing UAV.
Dynamic State Estimation for Integrated Natural Gas and Electric Power Systems
Authors: Liang Chen, Xinxin Hui, Songlin Gu, Manyun Huang, Yang Li
Date: 2021-07-13 00:00:00
Tasks: State Estimation

A dynamic state estimation method of integrated natural gas and electric power systems (IGESs) in proposed. Firstly, the coupling model of gas pipeline networks and power systems by gas turbine units (GTUs) is established. Secondly, the Kalman filter based linear DSE model for the IGES is built. The gas density and mass flow rate, as well as the real and imaginary parts of bus voltages are taken as states, which are predicted by the linearized fluid dynamic equations of gases and exponential smoothing techniques. Boundary conditions of pipeline networks are used as supplementary constraints in the system model. At last, the proposed method is applied to an IGES including a 30-node pipeline network and IEEE 39-bus system coupled by two GTUs. Two indexes are used to evaluate the DSE performance under three measurement error conditions, and the results show that the DSE can obtain the accurate dynamic states in different conditions.
Minimizing the Risk of Spreading Processes via Surveillance Schedules and Sparse Control
Authors: Vera L. J. Somers, Ian R. Manchester
Date: 2021-07-13 00:00:00

In this paper, we propose an optimization framework that combines surveillance schedules and sparse control to bound the risk of spreading processes such as epidemics and wildfires. Here, risk is considered the risk of an undetected outbreak, i.e. the product of the probability of an outbreak and the impact of that outbreak, and we can bound or minimize the risk by resource allocation and persistent monitoring schedules. The presented framework utilizes the properties of positive systems and convex optimization to provide scalable algorithms for both surveillance and intervention purposes. We demonstrate with different spreading process examples how the method can incorporate different parameters and scenarios such as a vaccination strategy for epidemics and the effect of vegetation, wind and outbreak rate on a wildfire in persistent monitoring scenarios.
A new method for vehicle system safety design based on data mining with uncertainty modeling
Authors: Xianping Du, Binhui Jiang, Feng Zhu
Date: 2021-07-12 00:00:00

In this research, a new data mining-based design approach has been developed for designing complex mechanical systems such as a crashworthy passenger car with uncertainty modeling. The method allows exploring the big crash simulation dataset to design the vehicle at multi-levels in a top-down manner (main energy absorbing system, components, and geometric features) and derive design rules based on the whole vehicle body safety requirements to make decisions towards the component and sub-component level design. Full vehicle and component simulation datasets are mined to build decision trees, where the interrelationship among parameters can be revealed and the design rules are derived to produce designs with good performance. This method has been extended by accounting for the uncertainty in the design variables. A new decision tree algorithm for uncertain data (DTUD) is developed to produce the desired designs and evaluate the design performance variations due to the uncertainty in design variables. The framework of this method is implemented by combining the design of experiments (DOE) and crash finite element analysis (FEA) and then demonstrated by designing a passenger car subject to front impact. The results show that the new methodology could achieve the design objectives efficiently and effectively. By applying the new method, the reliability of the final designs is also increased greatly. This approach has the potential to be applied as a general design methodology for a wide range of complex structures and mechanical systems.