pwc-archive/papers-with-abstracts huggingface dataset:
Rethinking Conversational Recommendations: Is Decision Tree All You Need?
Authors: A S M Ahsan-Ul Haque, Hongning Wang
Date: 2022-08-31 00:00:00
Tasks: All, Deep Reinforcement Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning (RL)

Conversational recommender systems (CRS) dynamically obtain the user preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinforcement learning algorithms. However, deep reinforcement learning methods are often criticised for lacking interpretability and requiring a large amount of training data to perform. In this paper, we explore a simpler alternative and propose a decision tree based solution to CRS. The underlying challenge in CRS is that the same item can be described differently by different users. We show that decision trees are sufficient to characterize the interactions between users and items, and solve the key challenges in multi-turn CRS: namely which questions to ask, how to rank the candidate items, when to recommend, and how to handle negative feedback on the recommendations. Firstly, the training of decision trees enables us to find questions which effectively narrow down the search space. Secondly, by learning embeddings for each item and tree nodes, the candidate items can be ranked based on their similarity to the conversation context encoded by the tree nodes. Thirdly, the diversity of items associated with each tree node allows us to develop an early stopping strategy to decide when to make recommendations. Fourthly, when the user rejects a recommendation, we adaptively choose the next decision tree to improve subsequent questions and recommendations. Extensive experiments on three publicly available benchmark CRS datasets show that our approach provides significant improvement to the state of the art CRS methods.
When Variable-Length Codes Meet the Field of Error Detection
Authors: Jean Néraud
Date: 2022-08-31 00:00:00
Tasks: Relation

Given a finite alphabet $A$ and a binary relation $\tau\subseteq A^*\times A^*$, a set $X$ is $\tau$-{\it independent} if $ \tau(X)\cap X=\emptyset$. Given a quasi-metric $d$ over $A^*$ (in the meaning of \cite{W31}) and $k\ge 1$, we associate the relation $\tau_{d,k}$ defined by $(x,y)\in\tau_{d,k}$ if, and only if, $d(x,y)\le k$ \cite{CP02}.In the spirit of \cite{JK97,N21}, the error detection-correction capability of variable-length codes can be expressed in term of conditions over $\tau_{d,k}$. With respect to the prefix metric, the factor one, and every quasi-metric associated to (anti-)automorphisms of the free monoid, we examine whether those conditions are decidable for a given regular code.
Cost-Efficient Deployment of a Reliable Multi-UAV Unmanned Aerial System
Authors: Nithin Babu, Petar Popovski, Constantinos B. Papadias
Date: 2022-08-30 00:00:00

In this work, we study the trade-off between the reliability and the investment cost of an unmanned aerial system (UAS) consisting of a set of unmanned aerial vehicles (UAVs) carrying radio access nodes, called portable access points (PAPs)), deployed to serve a set of ground nodes (GNs). Using the proposed algorithm, a given geographical region is equivalently represented as a set of circular regions, where each circle represents the coverage region of a PAP. Then, the steady-state availability of the UAS is analytically derived by modelling it as a continuous time birth-death Markov decision process (MDP). Numerical evaluations show that the investment cost to guarantee a given steady-state availability to a set of GNs can be reduced by considering the traffic demand and distribution of GNs.
Modified Froelich's Equation for Modelling of a Three Phase Self-Excited Synchronous Generator
Authors: Udayan Banerjee, Nayan Kumar, Thotakura NSC Sekhar, Tapas Kumar Saha, Chayan Kumar Paul
Date: 2022-08-31 00:00:00

With advancement in design and analysis of electro-mechanical and electromagnetic devices, the modelling of magnetic saturation of a synchronous generator has emerged to be a subject of interest in number of publications. Most of the existing electrical machine modelling methods does ignore the saturation effect for simplicity. On the other hand, who incorporate saturation effect, are dealing with complex computation of coefficients which involves tedious curve fitting techniques like non-linear regression, least-squares. This paper presents the novel method of modelling of the self-excited synchronous generator along with magnetizing characteristics with ease and good accuracy which is inspired from Froelichs equation. The proposed mathematical model is implemented in simulation environment and validated the results with a practical three phase self-excited synchronous generator in which saturation plays a vital role.
Vulnerability of Distributed Inverter VAR Control in PV Distributed Energy System
Authors: Bo Tu, Wen-Tai Li, Chau Yuen
Date: 2022-08-31 00:00:00

This work studies the potential vulnerability of distributed control schemes in smart grids. To this end, we consider an optimal inverter VAR control problem within a PV-integrated distribution network. First, we formulate the centralized optimization problem considering the reactive power priority and further reformulate the problem into a distributed framework by an accelerated proximal projection method. The inverter controller can curtail the PV output of each user by clamping the reactive power. To illustrate the studied distributed control scheme that may be vulnerable due to the two-hop information communication pattern, we present a heuristic attack injecting false data during the information exchange. Then we analyze the attack impact on the update procedure of critical parameters. A case study with an eight-node test feeder demonstrates that adversaries can violate the constraints of distributed control scheme without being detected through simple attacks such as the proposed attack.
Intelligent detect for substation insulator defects based on CenterMask
Authors: Bo Ye, Feng Li, Mingxuan Li, Peipei Yan, Huiting Yang, Lihua Wang
Date: 2022-08-31 00:00:00
Tasks: Defect Detection

With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing speed and accuracy of defect detection. Based on the end-to-end learning paradigm, this paper proposes an intelligent detection method for substation insulator defects based on CenterMask. First, the backbone network VoVNet is improved according to the residual connection and eSE module, which effectively solves the problems of deep network saturation and gradient information loss. On this basis, an insulator mask generation method based on a spatial attentiondirected mechanism is proposed. Insulators with complex image backgrounds are accurately segmented. Then, three strategies of pixel-wise regression prediction, multi-scale features and centerness are introduced. The anchor-free single-stage target detector accurately locates the defect points of insulators. Finally, an example analysis is carried out with the substation inspection image of a power supply company in a certain area to verify the effectiveness and robustness of the proposed method.
Adaptive Filtering Algorithms for Set-Valued Observations -- Symmetric Measurement Approach to Unlabeled and Anonymized Data
Authors: Vikram Krishnamurthy
Date: 2022-08-31 00:00:00

Suppose $L$ simultaneous independent stochastic systems generate observations, where the observations from each system depend on the underlying parameter of that system. The observations are unlabeled (anonymized), in the sense that an analyst does not know which observation came from which stochastic system. How can the analyst estimate the underlying parameters of the $L$ systems? Since the anonymized observations at each time are an unordered set of L measurements (rather than a vector), classical stochastic gradient algorithms cannot be directly used. By using symmetric polynomials, we formulate a symmetric measurement equation that maps the observation set to a unique vector. By exploiting that fact that the algebraic ring of multi-variable polynomials is a unique factorization domain over the ring of one-variable polynomials, we construct an adaptive filtering algorithm that yields a statistically consistent estimate of the underlying parameters. We analyze the asymptotic covariance of these estimates to quantify the effect of anonymization. Finally, we characterize the anonymity of the observations in terms of the error probability of the maximum aposteriori Bayesian estimator. Using Blackwell dominance of mean preserving spreads, we construct a partial ordering of the noise densities which relates the anonymity of the observations to the asymptotic covariance of the adaptive filtering algorithm.
PMU-based dynamic state and parameter estimation for dynamic security assessment in power systems -- Ultimate boundedness in the presence of measurement noise
Authors: Nicolai Lorenz-Meyer, René Suchantke, Johannes Schiffer
Date: 2022-08-30 00:00:00
Tasks: parameter estimation

Dynamic state and parameter estimation methods for dynamic security assessment in power systems are becoming increasingly important for system operators. Usually, the data used for this type of applications stems from phasor measurement units (PMUs) and is corrupted by noise. In general, the impact of the latter may significantly deteriorate the estimation performance. This motivates the present work, in which it is proven that the state and parameter estimation method proposed by part of the authors in [1] and extended in [2] features the property that the estimation errors are ultimately bounded in the presence of PMU measurement data corrupted by bounded noise. The analysis is conducted for the third-order flux-decay model of a synchronous generator and holds independently of the employed automatic voltage regulator and power system stabilizer (if present). The analysis is illustrated by simulations.
Regulatory motifs: structural and functional building blocks of genetic computation
Authors: Thomas M. A. Fink
Date: 2022-08-31 00:00:00

Developing and maintaining life requires a lot of computation. This is done by gene regulatory networks. But we have little understanding of how this computation is organized. I show that there is a direct correspondence between the structural and functional building blocks of regulatory networks, which I call regulatory motifs. I derive a simple bound on the range of function that these motifs can perform, in terms of the local network structure. I prove that this range is a small fraction of all possible functions, which severely constrains global network behavior. Part of this restriction is due to redundancy in the function that regulatory motifs can achieve - there are many ways to perform the same task. Regulatory motifs help us understanding how genetic computation is organized and what it can achieve.
Mortality equation characterizes the dynamics of an aging population
Authors: Thomas Fink
Date: 2022-08-31 00:00:00

Aging is thought to be a consequence of intrinsic breakdowns in how genetic information is processed. But mounting experimental evidence suggests that aging can be slowed. To help resolve this mystery, I derive a mortality equation which characterizes the dynamics of an evolving population with a given maximum age. Remarkably, while the spectrum of eigenvalues that govern the evolution depends on the fitness, how they change with the maximum age is independent of fitness. This makes it possible to establish the conditions under which programmed aging can provide an evolutionary benefit.
Adaptive altruistic strategy in cyclic models during an epidemic
Authors: J. Menezes, B. Ferreira, E. Rangel, B. Moura
Date: 2022-08-31 00:00:00

We investigate a cyclic game system where organisms face an epidemic beyond being threatened by natural enemies. As a survival strategy, individuals of one out of the species usually safeguard themselves by approaching the enemies of their enemies and performing social distancing to escape contamination when an outbreak affects the neighbourhood. We simulate how the survival movement strategy to local epidemic surges must adapt if a pathogen mutation makes the disease deadlier. We study the spatial distribution of local outbreaks and observe the influence of disease mortality on individuals' spatial organisation. We show that adapting the survival movement strategy for a high mortality disease demands an altruistic behaviour of the organisms since their death risk increases. Despite weakening the disease transmission chain, which benefits the species, abandoning refuges provided by safeguarding social interaction increases the vulnerability to being eliminated in the cyclic game. Considering that not all individuals exhibit altruism, we find the relative growth in the species density as a function of the proportion of individuals behaving altruistically. Our results may be helpful for biologists and data scientists to understand how adaptive altruistic processes can affect population dynamics in complex systems.
A Vector Monotonicity Assumption for Multiple Instruments
Authors: Leonard Goff
Date: 2020-09-01 00:00:00

When a researcher combines multiple instrumental variables for a single binary treatment, the monotonicity assumption of the local average treatment effects (LATE) framework can become restrictive: it requires that all units share a common direction of response even when separate instruments are shifted in opposing directions. What I call vector monotonicity, by contrast, simply assumes treatment uptake to be monotonic in all instruments. I characterize the class of causal parameters that are point identified under vector monotonicity, when the instruments are binary. This class includes, for example, the average treatment effect among units that are in any way responsive to the collection of instruments, or those that are responsive to a given subset of them. The identification results are constructive and yield a simple estimator for the identified treatment effect parameters. An empirical application revisits the labor market returns to college.
Making the Elite: Top Jobs, Disparities, and Solutions
Authors: Soumitra Shukla
Date: 2022-08-31 00:00:00

How do socioeconomically unequal screening practices impact access to elite firms and what policies might reduce inequality? Using personnel data from elite U.S. and European multinational corporations recruiting from an elite Indian college, I show that caste disparities in hiring do not arise in many job search stages, including: applications, application reading, written aptitude tests, large group debates that assess socio-emotional skills, and job choices. Rather, disparities arise in the final round, comprising non-technical personal interviews that screen on family background, neighborhood, and "cultural fit." These characteristics are plausibly weakly correlated with productivity (at the interview round) but strongly correlated with caste. Employer willingness to pay for an advantaged caste is as large as that for a full standard deviation increase in college GPA. A hiring subsidy that eliminates the caste penalty would be more cost-effective in diversifying elite hiring than equalizing the caste distribution of pre-college test scores or enforcing hiring quotas.
A nation-wide experiment: fuel tax cuts and almost free public transport for three months in Germany -- Report 3 Second wave results
Authors: Allister Loder, Fabienne Cantner, Andrea Cadavid, Markus B. Siewert, Stefan Wurster, Sebastian Goerg, Klaus Bogenberger
Date: 2022-08-31 00:00:00
Tasks: Survey

In spring 2022, the German federal government agreed on a set of measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures included among others, a nation-wide public transport ticket for 9\ EUR per month and a fuel tax cut that reduced fuel prices by more than 15\,\%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this third report, we provide first findings from the second survey, conducted during the experiment.
What does machine learning say about the drivers of inflation?
Authors: Emanuel Kohlscheen
Date: 2022-08-31 00:00:00
Tasks: regression

This paper examines the drivers of CPI inflation through the lens of a simple, but computationally intensive machine learning technique. More specifically, it predicts inflation across 20 advanced countries between 2000 and 2021, relying on 1,000 regression trees that are constructed based on six key macroeconomic variables. This agnostic, purely data driven method delivers (relatively) good outcome prediction performance. Out of sample root mean square errors (RMSE) systematically beat even the in-sample benchmark econometric models. Partial effects of inflation expectations on CPI outcomes are also elicited in the paper. Overall, the results highlight the role of expectations for inflation outcomes in advanced economies, even though their importance appears to have declined somewhat during the last 10 years.
Globalisation and the Decoupling of Inflation from Domestic Labour Costs
Authors: Emanuel Kohlscheen, Richhild Moessner
Date: 2022-08-31 00:00:00

We provide novel systematic cross-country evidence that the link between domestic labour markets and CPI inflation has weakened considerably in advanced economies during recent decades. The central estimate is that the short-run pass-through from domestic labour cost changes to core CPI inflation decreased from 0.25 in the 1980s to just 0.02 in the 2010s, while the long-run pass-through fell from 0.36 to 0.03. We show that the timing of the collapse in the pass-through coincides with a steep increase in import penetration from a group of 10 major manufacturing EMEs around the turn of the millennium. This signals increased competition and market contestability. Besides the extent of trade openness, we show that the intensity of the pass-through also depends in a non-linear way on the average level of inflation.
Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix
Authors: Emanuel Kohlscheen, Richhild Moessner
Date: 2022-08-31 00:00:00

We analyse the drivers of European Power Exchange (EPEX) wholesale electricity prices between 2012 and early 2022 using machine learning. The agnostic random forest approach that we use is able to reduce in-sample root mean square errors (RMSEs) by around 50% when compared to a standard linear least square model. This indicates that non-linearities and interaction effects are key in wholesale electricity markets. Out-of-sample prediction errors using machine learning are (slightly) lower than even in-sample least square errors using a least square model. The effects of efforts to limit power consumption and green the energy matrix on wholesale electricity prices are first order. CO2 permit prices strongly impact electricity prices, as do the prices of source energy commodities. And carbon permit prices impact has clearly increased post-2021 (particularly for baseload prices). Among energy sources, natural gas has the largest effect on electricity prices. Importantly, the role of wind energy feed-in has slowly risen over time, and its impact is now roughly on par with that of coal.
The optimality of (stochastic) veto delegation
Authors: Xiaoxiao Hu, Haoran Lei
Date: 2022-08-31 00:00:00

We analyze the optimal delegation problem between a principal and an agent, assuming that the latter has state-independent preferences. We demonstrate that if the principal is more risk-averse than the agent toward non-status quo options, an optimal mechanism is a {\em veto mechanism}. In a veto mechanism, the principal uses veto (i.e., maintaining the status quo) to balance the agent's incentives and does not randomize among non-status quo options. We characterize the optimal veto mechanism in a one-dimensional setting. In the solution, the principal uses veto only when the state surpasses a critical threshold.
The Emergence of Fads in a Changing World
Authors: Wanying Huang
Date: 2022-08-30 00:00:00

We study how fads emerge under social learning in a changing environment. We consider a simple sequential social learning model where rational agents arrive in order, each acting only once, and the underlying unknown state constantly evolves. Each agent receives a private signal, observes all past actions of others, and chooses an action to match the current state. Because the state changes over time, cascades cannot last forever, and actions also fluctuate. We show that despite the rise of temporary information cascades, in the long run, actions change more often than the state. This result provides a theoretical foundation for faddish behavior in which people often change their actions more frequently than necessary.
Optimal dynamic insurance contracts
Authors: Vitor Farinha Luz
Date: 2022-08-30 00:00:00

I analyze long-term contracting in insurance markets with asymmetric information. The buyer privately observes her risk type, which evolves stochastically over time. A long-term contract specifies a menu of insurance policies, contingent on the history of type reports and contractable accident information. The optimal contract offers the consumer in each period a choice between a perpetual complete coverage policy with fixed premium and a risky continuation contract in which current period's accidents may affect not only within-period consumption (partial coverage) but also future policies. The model allows for arbitrary restrictions to the extent to which firms can use accident information in pricing. In the absence of pricing restrictions, accidents as well as choices of partial coverage are used in the efficient provision of incentives. If firms are unable to use accident history, longer periods of partial coverage choices are rewarded, leading to menus with cheaper full-coverage options and more attractive partial-coverage options; and allocative inefficiency decreases along all histories. These results are used to study a model of perfect competition, where the equilibrium is unique whenever it exists, as well as the monopoly problem, where necessary and sufficient conditions for the presence of information rents are given.
Singing Beat Tracking With Self-supervised Front-end and Linear Transformers
Authors: Mojtaba Heydari, Zhiyao Duan
Date: 2022-08-31 00:00:00
Tasks: Beat Tracking

Tracking beats of singing voices without the presence of musical accompaniment can find many applications in music production, automatic song arrangement, and social media interaction. Its main challenge is the lack of strong rhythmic and harmonic patterns that are important for music rhythmic analysis in general. Even for human listeners, this can be a challenging task. As a result, existing music beat tracking systems fail to deliver satisfactory performance on singing voices. In this paper, we propose singing beat tracking as a novel task, and propose the first approach to solving this task. Our approach leverages semantic information of singing voices by employing pre-trained self-supervised WavLM and DistilHuBERT speech representations as the front-end and uses a self-attention encoder layer to predict beats. To train and test the system, we obtain separated singing voices and their beat annotations using source separation and beat tracking on complete songs, followed by manual corrections. Experiments on the 741 separated vocal tracks of the GTZAN dataset show that the proposed system outperforms several state-of-the-art music beat tracking methods by a large margin in terms of beat tracking accuracy. Ablation studies also confirm the advantages of pre-trained self-supervised speech representations over generic spectral features.