pwc-archive/papers-with-abstracts huggingface dataset:
Hammer PDF: An Intelligent PDF Reader for Scientific Papers
Authors: Sheng-Fu Wang, Shu-Hang Liu, Tian-Yi Che, Yi-Fan Lu, Song-Xiao Yang, Heyan Huang, Xian-Ling Mao
Date: 2022-04-06 00:00:00

It is the most important way for researchers to acquire academic progress via reading scientific papers, most of which are in PDF format. However, existing PDF Readers like Adobe Acrobat Reader and Foxit PDF Reader are usually only for reading by rendering PDF files as a whole, and do not consider the multi-granularity content understanding of a paper itself. Specifically, taking a paper as a basic and separate unit, existing PDF Readers cannot access extended information about the paper, such as corresponding videos, blogs and codes. Meanwhile, they cannot understand the academic content of a paper, such as terms, authors, and citations. To solve these problems, we introduce Hammer PDF, an intelligent PDF Reader for scientific papers. Apart from basic reading functions, Hammer PDF has the following four innovative features: (1) information extraction ability, which can locate and mark spans like terms and other entities; (2) information extension ability, which can present relevant academic content of a paper, such as citations, references, codes, videos, blogs; (3) built-in Hammer Scholar, an academic search engine based on academic information collected from major academic databases; (4) built-in Q&A bot, which can find helpful conference information. The proposed Hammer PDF Reader can help researchers, especially those studying computer science, to improve the efficiency and experience of reading scientific papers. We have released Hammer PDF, available at https://pdf.hammerscholar.net/face.
Towards Better Understanding of User Satisfaction in Open-Domain Conversational Search
Authors: Zhumin Chu, Qingyao Ai, Zhihong Wang, Yiqun Liu, Yingye Huang, Rui Zhang, Min Zhang, Shaoping Ma
Date: 2022-04-06 00:00:00
Tasks: Conversational Search, Semantic Similarity, Semantic Textual Similarity

With the increasing popularity of conversational search, how to evaluate the performance of conversational search systems has become an important question in the IR community. Existing works on conversational search evaluation can mainly be categorized into two streams: (1) constructing metrics based on semantic similarity (e.g. BLUE, METEOR and BERTScore), or (2) directly evaluating the response ranking performance of the system using traditional search methods (e.g. nDCG, RBP and nERR). However, these methods either ignore the information need of the user or ignore the mixed-initiative property of conversational search. This raises the question of how to accurately model user satisfaction in conversational search scenarios. Since explicitly asking users to provide satisfaction feedback is difficult, traditional IR studies often rely on the Cranfield paradigm (i.e., third-party annotation) and user behavior modeling to estimate user satisfaction in search. However, the feasibility and effectiveness of these two approaches have not been fully explored in conversational search. In this paper, we dive into the evaluation of conversational search from the perspective of user satisfaction. We build a novel conversational search experimental platform and construct a Chinese open-domain conversational search behavior dataset containing rich annotations and search behavior data. We also collect third-party satisfaction annotation at the session-level and turn-level, to investigate the feasibility of the Cranfield paradigm in the conversational search scenario. Experimental results show both some consistency and considerable differences between the user satisfaction annotations and third-party annotations. We also propose dialog continuation or ending behavior models (DCEBM) to capture session-level user satisfaction based on turn-level information.
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Authors: Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, Meng Wang
Date: 2022-04-06 00:00:00
Tasks: Decision Making

As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.
Fault Diagnosis of Discrete-Event Systems under Non-Deterministic Observations with Output Fairness
Authors: Weijie Dong, Shang Gao, Xiang Yin, ShaoYuan Li
Date: 2022-04-06 00:00:00
Tasks: Fairness, Fault Diagnosis

In this paper, we revisit the fault diagnosis problem of discrete-event systems (DES) under non-deterministic observations. Non-deterministic observation is a general observation model that includes the case of intermittent loss of observations. In this setting, upon the occurrence of an event, the sensor reading may be non-deterministic such that a set of output symbols are all possible. Existing works on fault diagnosis under non-deterministic observations require to consider all possible observation realizations. However, this approach includes the case where some possible outputs are permanently disabled. In this work, we introduce the concept of output fairness by requiring that, for any output symbols, if it has infinite chances to be generated, then it will indeed be generated infinite number of times. We use an assume-guarantee type of linear temporal logic formulas to formally describe this assumption. A new notion called output-fair diagnosability (OF-diagnosability) is proposed. An effective approach is provided for the verification of OF-diagnosability. We show that the proposed notion of OF-diagnosability is weaker than the standard definition of diagnosability under non-deterministic observations, and it better captures the physical scenario of observation non-determinism or intermittent loss of observations.
Evidence for positive long- and short-term effects of vaccinations against COVID-19 in wearable sensor metrics -- Insights from the German Corona Data Donation Project
Authors: Marc Wiedermann, Annika H. Rose, Benjamin F. Maier, Jakob J. Kolb, David Hinrichs, Dirk Brockmann
Date: 2022-04-06 00:00:00
Tasks: ICU Admission

Vaccines are among the most powerful tools used to combat the COVID-19 pandemic. They are highly effective against infection and substantially reduce the risk of severe disease, hospitalization, ICU admission, and death. However, their potential for attenuating long-term effects of a SARS-CoV-2 infection, commonly denoted as Long COVID, remains elusive and is still subject of debate. Such long-term effects can be effectively monitored at the individual level by analyzing physiological data collected by consumer-grade wearable sensors. Here, we investigate changes in resting heart rate, daily physical activity, and sleep duration in response to a SARS-CoV-2 infection stratified by vaccination status. Data was collected over a period of two years in the context of the German Corona Data Donation Project with currently around 190,000 monthly active donors. Compared to their unvaccinated counterparts, we find that vaccinated individuals on average experience smaller changes in their vital data that also return to normal levels more quickly. Likewise, extreme changes in vitals during the acute phase of the disease occur less frequently in vaccinated individuals. Our results solidify evidence that vaccines can mitigate long-term detrimental effects of SARS-CoV-2 infections both in terms of duration and magnitude. Furthermore, they demonstrate the value of large scale, high-resolution wearable sensor data in public health research.
Towards Better Test Coverage: Merging Unit Tests for Autonomous Systems
Authors: Josefine Graebener, Apurva Badithela, Richard M. Murray
Date: 2022-04-06 00:00:00

We present a framework for merging unit tests for autonomous systems. Typically, it is intractable to test an autonomous system for every scenario in its operating environment. The question of whether it is possible to design a single test for multiple requirements of the system motivates this work. First, we formally define three attributes of a test: a test specification that characterizes behaviors observed in a test execution, a test environment, and a test policy. Using the merge operator from contract-based design theory, we provide a formalism to construct a merged test specification from two unit test specifications. Temporal constraints on the merged test specification guarantee that non-trivial satisfaction of both unit test specifications is necessary for a successful merged test execution. We assume that the test environment remains the same across the unit tests and the merged test. Given a test specification and a test environment, we synthesize a test policy filter using a receding horizon approach, and use the test policy filter to guide a search procedure (e.g. Monte-Carlo Tree Search) to find a test policy that is guaranteed to satisfy the test specification. This search procedure finds a test policy that maximizes a pre-defined robustness metric for the test while the filter guarantees a test policy for satisfying the test specification. We prove that our algorithm is sound. Furthermore, the receding horizon approach to synthesizing the filter ensures that our algorithm is scalable. Finally, we show that merging unit tests is impactful for designing efficient test campaigns to achieve similar levels of coverage in fewer test executions. We illustrate our framework on two self-driving examples in a discrete-state setting.
Spectral Denoising for Microphone Classification
Authors: L. Cuccovillo, A. Giganti, P. Bestagini, P. Aichroth, S. Tubaro
Date: 2022-04-06 00:00:00
Tasks: Classification, Denoising

In this paper, we propose the use of denoising for microphone classification, to enable its usage for several key application domains that involve noisy conditions. We describe the proposed analysis pipeline and the baseline algorithm for microphone classification, and discuss various denoising approaches which can be applied to it within the time or spectral domain; finally, we determine the best-performing denoising procedure, and evaluate the performance of the overall, integrated approach with several SNR levels of additive input noise. As a result, the proposed method achieves an average accuracy increase of about 25% on denoised content over the reference baseline.
Global HRTF Interpolation via Learned Affine Transformation of Hyper-conditioned Features
Authors: Jin Woo Lee, Sungho Lee, Kyogu Lee
Date: 2022-04-06 00:00:00
Tasks: Position

Estimating Head-Related Transfer Functions (HRTFs) of arbitrary source points is essential in immersive binaural audio rendering. Computing each individual's HRTFs is challenging, as traditional approaches require expensive time and computational resources, while modern data-driven approaches are data-hungry. Especially for the data-driven approaches, existing HRTF datasets differ in spatial sampling distributions of source positions, posing a major problem when generalizing the method across multiple datasets. To alleviate this, we propose a deep learning method based on a novel conditioning architecture. The proposed method can predict an HRTF of any position by interpolating the HRTFs of known distributions. Experimental results show that the proposed architecture improves the model's generalizability across datasets with various coordinate systems. Additional demonstrations show that the model robustly reconstructs the target HRTFs from the spatially downsampled HRTFs in both quantitative and perceptual measures.
A Convex Optimization Approach for Control of Linear Quadratic Systems with Multiplicative Noise via System Level Synthesis
Authors: Majid Mazouchi, Farzaneh Tatari, Hamidreza Modares
Date: 2022-04-06 00:00:00

This paper presents a convex optimization-based solution to the design of state-feedback controllers for solving the linear quadratic regulator (LQR) problem of uncertain discrete-time systems with multiplicative noise. To synthesize a tractable solution, the recently developed system level synthesis (SLS) framework is leveraged. It is shown that SLS shifts the controller synthesis task from the design of a robust controller to the design of the entire set-valued closed-loop system responses. To this end, the closed-loop system response is entirely characterized by probabilistic set-valued maps from the additive noise to control actions and states. A bi-level convex optimization over the achievable set-valued closed-loop responses is then developed to optimize the expected value of the LQR cost against the worst-case closed-loop system response. The solution to this robust optimization problem may be too conservative since it aims at enforcing the design constraints for all possible system realizations. To deal with this issue, the presented optimization problem is next reformulated as a chance-constrained program (CCP) in which the guarantees are not intended in a deterministic sense of satisfaction against all possible closed-loop system responses, but are instead intended in a probabilistic sense of satisfaction against all but a small fraction of the system responses. To approximately solve the CCP without the requirement of knowing the probabilistic description of the uncertainty in the system matrices, the so-called scenario optimization approach is employed, which provides probabilistic guarantees based on a finite number of system realizations and results in a convex optimization program with moderate computational complexity. Finally, numerical simulations are presented to illustrate the theoretical findings.
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Authors: Wenhan Cao, Jingliang Duan, Shengbo Eben Li, Chen Chen, Chang Liu, Yu Wang
Date: 2022-04-06 00:00:00

This paper proposes a primal-dual framework to learn a stable estimator for linear constrained estimation problems leveraging the moving horizon approach. To avoid the online computational burden in most existing methods, we learn a parameterized function offline to approximate the primal estimate. Meanwhile, a dual estimator is trained to check the suboptimality of the primal estimator during execution time. Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality. This in turn allows us to bound the probability of the learned estimator being infeasible or suboptimal. Furthermore, we analyze the stability of the resulting estimator with a bounded error in the minimization of the cost function. Since our algorithm does not require the solution of an optimization problem during runtime, state estimates can be generated online almost instantly. Simulation results are presented to show the accuracy and time efficiency of the proposed framework compared to online optimization of moving horizon estimation and Kalman filter. To the best of our knowledge, this is the first learning-based state estimator with feasibility and near-optimality guarantees for linear constrained systems.
Online Feedback Droop Scheduling in Distribution Grids for Frequency and Local Voltage Control
Authors: Ognjen Stanojev, Yi Guo, Gabriela Hug
Date: 2022-04-06 00:00:00
Tasks: Scheduling

This paper presents a novel framework for collective control of Distributed Energy Resources (DERs) in active Distribution Networks (DNs). The proposed approach unifies the commonly employed local (i.e., decentralized) voltage and frequency droop control schemes into a transfer matrix relating frequency and voltage magnitude measurements to active and reactive power injection adjustments. Furthermore, the transfer matrices of individual DER units are adaptively tuned in real-time via slow communication links using a novel online gain scheduling approach to enable primary frequency support provision to the transmission system and ensure that the DN voltages are kept within the allowable limits. A global asymptomatic stability condition of the analyzed droop-controlled DN is analytically established. The considered gain scheduling problem is solved by leveraging an online primal-dual gradient-based method and a suitable linearized power flow model. Additional ancillary service providers can be trivially incorporated into the proposed framework in a plug-and-play fashion. Numerical simulations of the 37-bus IEEE test system and a realistic Swedish 533-bus DN confirm the validity and the scalability of the approach and demonstrate numerous advantages of the proposed scheme over the state-of-the-art.
Cluster Synchronization of Kuramoto Oscillators and the Method of Averaging
Authors: Rui Kato, Hideaki Ishii
Date: 2022-04-06 00:00:00
Tasks: Functional Connectivity

Rigorous conditions for cluster synchronization of Kuramoto oscillators are presented. The method of averaging plays an important role in stability analysis, but the standard Lyapunov's second method is not applicable due to the lack of uniform continuity. This paper contributes to overcoming this difficulty with the help of nonmonotonic Lyapunov functions. Our extensions of averaging in stability theory are key to derive the two interrelated cluster synchronization conditions: (i) the coupling strengths between clusters are sufficiently weak and/or (ii) the natural frequencies are largely different between clusters. Cluster phase cohesiveness in the absence of network partitions ensuring the existence of invariant manifolds is also investigated. Moreover, we apply our theoretical findings to brain networks and exhibit certain relations among network parameters and functional connectivity.
Co-optimization of power line shutoff and restoration under high wildfire ignition risk
Authors: Noah Rhodes, Line Roald
Date: 2022-04-05 00:00:00

Electric power infrastructure has ignited several of the most destructive wildfires in recent history. Preemptive power shutoffs are an effective tool to mitigate the risk of ignitions from power lines, but at the same time can cause widespread power outages. This work proposes a mathematical optimization problem to help utilities decide where and when to implement these shutoffs, as well as how to most efficiently restore power once the wildfire risk is lower. Specifically, our model co-optimizes the power shutoff (considering both wildfire risk reduction and power outages) as well as the post-event restoration efforts given constraints related to inspection and energization of lines, and is implemented as a rolling horizon optimization problem that is resolved whenever new forecasts of load and wildfire risk become available. We demonstrate our method on the IEEE RTS-GMLC test case using real wildfire risk data and forecasts from US Geological Survey, and investigate the sensitivity of the results to the forecast quality, decision horizon and system restoration budget. The software implementation is available in the open source software package PowerModelsWildfire.jl.
Recursive Restoration Refinement: A Fast Heuristic for Near-Optimal Restoration Prioritization in Power Systems
Authors: Noah Rhodes, Carleton Coffrin, Line Roald
Date: 2022-04-05 00:00:00

The prioritization of restoration actions after large power system outages plays a key role in how quickly power can be restored. It has been shown that fast and intuitive heuristics for restoration prioritization most often result in low-quality restoration plans. Meanwhile, mathematical optimization tools that find high-quality restoration plans are too slow to be applied to restoration planning problems of practical interest. This work makes a significant step in closing this quality vs compute time gap by proposing the Recursive Restoration Refinement heuristic for power system restoration. This heuristic is shown to produce near-optimal restoration plans up to 1,000 times faster than other state-of-the-art solution methods on a range of test cases with up to 500 buses and 700 damaged components. The potential impact of this new heuristic is demonstrated by a preliminary analysis of the key features of high-quality restoration plans. The recursive restoration refinement algorithm and other methods explored in this work have been made available as part of the open-source software package, PowerModelsRestoration, to support ongoing research in power restoration algorithms.
A Comprehensive Framework based on Dynamic and Steady State Analysis to Evaluate Power System Resiliency to Extreme Weather Conditions
Authors: Giritharan Vijay Iswaran, Ramin Vakili, Mojdeh Khorsand
Date: 2022-04-05 00:00:00

Power system robustness against high impact low probability events is becoming a major concern. To depict distinct phases of a system response during these disturbances, an irregular polygon model is derived from the conventional trapezoid model and the model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same. Furthermore, the system resiliency to windstorm is evaluated on the IEEE reliability test system (RTS) by performing steady state and dynamic security assessment incorporating protection modelling and corrective action schemes using the software Power System Simulator for Engineering (PSS/E). Based on the results of steady state and dynamic analysis, modified resiliency metrics are quantified. Finally, this paper quantifies the interdependency of operational and infrastructure resiliency as they cannot be considered as discrete characteristics of the system.