pwc-archive/papers-with-abstracts huggingface dataset:
Naming Schema for a Human Brain-Scale Neural Network
Authors: Morgan Schaefer, Lauren Michelin, Jeremy Kepner
Date: 2021-09-22 00:00:00

Deep neural networks have become increasingly large and sparse, allowing for the storage of large-scale neural networks with decreased costs of storage and computation. Storage of a neural network with as many connections as the human brain is possible with current versions of the high-performance Apache Accumulo database and the Distributed Dimensional Data Model (D4M) software. Neural networks of such large scale may be of particular interest to scientists within the human brain Connectome community. To aid in research and understanding of artificial neural networks that parallel existing neural networks like the brain, a naming schema can be developed to label groups of neurons in the artificial network that parallel those in the brain. Groups of artificial neurons are able to be specifically labeled in small regions for future study.
Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics
Authors: Rüdiger Frey, Verena Köck
Date: 2021-09-23 00:00:00

In recent years a large literature on deep learning based methods for the numerical solution partial differential equations has emerged; results for integro-differential equations on the other hand are scarce. In this paper we study deep neural network algorithms for solving linear and semilinear parabolic partial integro-differential equations with boundary conditions in high dimension. To show the viability of our approach we discuss several case studies from insurance and finance.
High-dimensional regression with potential prior information on variable importance
Authors: Benjamin G. Stokell, Rajen D. Shah
Date: 2021-09-23 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

There are a variety of settings where vague prior information may be available on the importance of predictors in high-dimensional regression settings. Examples include ordering on the variables offered by their empirical variances (which is typically discarded through standardisation), the lag of predictors when fitting autoregressive models in time series settings, or the level of missingness of the variables. Whilst such orderings may not match the true importance of variables, we argue that there is little to be lost, and potentially much to be gained, by using them. We propose a simple scheme involving fitting a sequence of models indicated by the ordering. We show that the computational cost for fitting all models when ridge regression is used is no more than for a single fit of ridge regression, and describe a strategy for Lasso regression that makes use of previous fits to greatly speed up fitting the entire sequence of models. We propose to select a final estimator by cross-validation and provide a general result on the quality of the best performing estimator on a test set selected from among a number $M$ of competing estimators in a high-dimensional linear regression setting. Our result requires no sparsity assumptions and shows that only a $\log M$ price is incurred compared to the unknown best estimator. We demonstrate the effectiveness of our approach when applied to missing or corrupted data, and time series settings. An R package is available on github.
Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment
Authors: Abram Magner, Carolyn Kaminski, Petko Bogdanov
Date: 2021-09-22 00:00:00
Tasks: Epidemiology, Marketing

Spreading processes on graphs arise in a host of application domains, from the study of online social networks to viral marketing to epidemiology. Various discrete-time probabilistic models for spreading processes have been proposed. These are used for downstream statistical estimation and prediction problems, often involving messages or other information that is transmitted along with infections caused by the process. It is thus important to design models of cascade observation that take into account phenomena that lead to uncertainty about the process state at any given time. We highlight one such phenomenon -- temporal distortion -- caused by a misalignment between the rate at which observations of a cascade process are made and the rate at which the process itself operates, and argue that failure to correct for it results in degradation of performance on downstream statistical tasks. To address these issues, we formulate the clock estimation problem in terms of a natural distortion measure. We give a clock estimation algorithm, which we call FastClock, that runs in linear time in the size of its input and is provably statistically accurate for a broad range of model parameters when cascades are generated from the independent cascade process with known parameters and when the underlying graph is Erd\H{o}s-R\'enyi. We further give empirical results on the performance of our algorithm in comparison to the state of the art estimator, a likelihood proxy maximization-based estimator implemented via dynamic programming. We find that, in a broad parameter regime, our algorithm substantially outperforms the dynamic programming algorithm in terms of both running time and accuracy.
Dynamic inference of user context through social tag embedding for music recommendation
Authors: Diego Sánchez-Moreno, Álvaro Lozano Murciego, Vivian F. López Batista, María Dolores Muñoz Vicente, María N. Moreno-García
Date: 2021-09-23 00:00:00
Tasks: Music Recommendation, Recommendation Systems, TAG

Music listening preferences at a given time depend on a wide range of contextual factors, such as user emotional state, location and activity at listening time, the day of the week, the time of the day, etc. It is therefore of great importance to take them into account when recommending music. However, it is very difficult to develop context-aware recommender systems that consider these factors, both because of the difficulty of detecting some of them, such as emotional state, and because of the drawbacks derived from the inclusion of many factors, such as sparsity problems in contextual pre-filtering. This work involves the proposal of a method for the detection of the user contextual state when listening to music based on the social tags of music items. The intrinsic characteristics of social tagging that allow for the description of items in multiple dimensions can be exploited to capture many contextual dimensions in the user listening sessions. The embeddings of the tags of the first items played in each session are used to represent the context of that session. Recommendations are then generated based on both user preferences and the similarity of the items computed from tag embeddings. Social tags have been used extensively in many recommender systems, however, to our knowledge, they have been hardly used to dynamically infer contextual states.
Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model
Authors: Jianling Wang, Ainur Yessenalina, Alireza Roshan-Ghias
Date: 2021-09-22 00:00:00
Tasks: Recommendation Systems, Vocal Bursts Valence Prediction

Online video services acquire new content on a daily basis to increase engagement, and improve the user experience. Traditional recommender systems solely rely on watch history, delaying the recommendation of newly added titles to the right customer. However, one can use the metadata information of a cold-start title to bootstrap the personalization. In this work, we propose to adopt a two-tower model, in which one tower is to learn the user representation based on their watch history, and the other tower is to learn the effective representations for titles using metadata. The contribution of this work can be summarized as: (1) we show the feasibility of using two-tower model for recommendations and conduct a series of offline experiments to show its performance for cold-start titles; (2) we explore different types of metadata (categorical features, text description, cover-art image) and an attention layer to fuse them; (3) with our Amazon proprietary data, we show that the attention layer can assign weights adaptively to different metadata with improved recommendation for warm- and cold-start items.
Recommender system for Weld break prediction
Authors: Bereket Abera Yilma
Date: 2017-10-01 00:00:00
Proceeding: ResearchGate 2017 10
Tasks: Prediction, Recommendation Systems

In this paper, we propose an approach to Weld break prediction challenge. In a production line, a steel strip comes to the cold rolling machine in the form of a coil. When a coil is about to be finished, its end is welded with the head of the next coil. This results in a weld in the steel strip. The weld is smoothed in what is known as a scarfing process. The resulting steel strip is pickled on the pickling line and then thrown onto the tandem mill. At the tandem mill, the strip is pressurized to reduce the thickness. In this particular scenario, we address a weld break prediction for a company that welds the end of steel coils together in their production process. Often their production line succeeds in doing this. However, there are cases after the welding, in a subsequent step of the production line, the weld breaks. This is unfortunate because the company has to stop the production line, get the steel coil out of a machine and fix the broken steel. Visualizing one csv file which corresponds to one coil that might or might not have broken and the additional description files; one can understand that each file contains static parameters and a temporal information of dynamic variables measured during the welding process. We can also understand each row is a one-time step and each time step contains measurements from different sensors. The names corresponding to the values can be found at the top of each CSV file. The “OK” folder containing measurements of healthy coils has 2970 items where the “WB” folder containing measurements of broken coils has 200 items, providing a total of 3170 samples. The provided dataset is highly skewed (exhibits a large imbalance) in the distribution of the target classes. Project implementation can be found here https://github.com/Bekyilma/Weld-break-prediction
Who are the arbitrageurs? Empirical evidence from Bitcoin traders in the Mt. Gox exchange platform
Authors: Pietro Saggese, Alessandro Belmonte, Nicola Dimitri, Angelo Facchini, Rainer Böhme
Date: 2021-09-22 00:00:00

We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, to detect the triangular arbitrage activity conducted within the platform. The availability of user identifiers per trade allows us to focus on the historical record of 440 investors, detected as arbitrageurs, and consequently to describe their trading behavior. We begin by showing that a considerable difference appears between arbitrageurs when indicators of their expertise are taken into account. In particular, we distinguish between those who conducted arbitrage in a single or in multiple markets: using this element as a proxy for trade ability, we find that arbitrage actions performed by expert users are on average non-profitable when transaction costs are accounted for, while skilled investors conduct arbitrage at a positive and statistically significant premium. Next, we show that specific trading strategies, such as splitting orders or conducting arbitrage non aggressively, are further indicators of expertise that increase the profitability of arbitrage. Most importantly, we exploit within-user (across hours and markets) variation and document that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. We present further evidence that such differences are chiefly due to a better ability of the latter in incorporating information, both on the transactions costs and on the exchange rates volatility, eventually resulting in a better timing choice at small time scale intervals. Our results support the hypothesis that arbitrageurs are few and sophisticated users.
Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem
Authors: Cheng Tan, Zhichao Li, Jian Zhang, Yu Cao, Sikai Qi, Zherui Liu, Yibo Zhu, Chuanxiong Guo
Date: 2021-09-18 00:00:00
Tasks: GPU, Scheduling

Multi-Instance GPU (MIG) is a new feature introduced by NVIDIA A100 GPUs that partitions one physical GPU into multiple GPU instances. With MIG, A100 can be the most cost-efficient GPU ever for serving Deep Neural Networks (DNNs). However, discovering the most efficient GPU partitions is challenging. The underlying problem is NP-hard; moreover, it is a new abstract problem, which we define as the Reconfigurable Machine Scheduling Problem (RMS). This paper studies serving DNNs with MIG, a new case of RMS. We further propose a solution, MIG-serving. MIG- serving is an algorithm pipeline that blends a variety of newly designed algorithms and customized classic algorithms, including a heuristic greedy algorithm, Genetic Algorithm (GA), and Monte Carlo Tree Search algorithm (MCTS). We implement MIG-serving on Kubernetes. Our experiments show that compared to using A100 as-is, MIG-serving can save up to 40% of GPUs while providing the same throughput.
Synthesis of Partially Observed Jump-Diffusion Systems via Control Barrier Functions
Authors: Niloofar Jahanshahi, Pushpak Jagtap, Majid Zamani
Date: 2021-09-23 00:00:00

In this paper, we study formal synthesis of control policies for partially observed jump-diffusion systems against complex logic specifications. Given a state estimator, we utilize a discretization-free approach for formal synthesis of control policies by using a notation of control barrier functions without requiring any knowledge of the estimation accuracy. Our goal is to synthesize an offline control policy providing (potentially maximizing) a lower bound on the probability that the trajectories of the partially observed jump-diffusion system satisfy some complex specifications expressed by deterministic finite automata. Finally, we illustrate the effectiveness of the proposed results by synthesizing a policy for a jet engine example.
Necessary and Sufficient Conditions for State Feedback Equivalence to Negative Imaginary Systems
Authors: Kanghong Shi, Ian R. Petersen, Igor G. Vladimirov
Date: 2021-09-23 00:00:00

In this paper, we present necessary and sufficient conditions under which a linear time-invariant (LTI) system is state feedback equivalent to a negative imaginary (NI) system. More precisely, we show that a minimal LTI strictly proper system can be rendered NI using full state feedback if and only if it can be output transformed into a system, which has relative degree less than or equal to two and is weakly minimum phase. We also considered the problems of state feedback equivalence to output strictly negative imaginary systems and strongly strict negative imaginary systems. Then we apply the NI state feedback equivalence result to robustly stabilize an uncertain system with strictly negative imaginary uncertainty. An example is provided to illustrate the proposed results, for the purpose of stabilizing an uncertain system.
Sandboxing Controllers for Stochastic Cyber-Physical Systems
Authors: Bingzhuo Zhong, Majid Zamani, Marco Caccamo
Date: 2021-09-23 00:00:00

Current cyber-physical systems (CPS) are expected to accomplish complex tasks. To achieve this goal, high performance, but unverified controllers (e.g. deep neural network, black-box controllers from third parties) are applied, which makes it very challenging to keep the overall CPS safe. By sandboxing these controllers, we are not only able to use them but also to enforce safety properties over the controlled physical systems at the same time. However, current available solutions for sandboxing controllers are just applicable to deterministic (a.k.a. non-stochastic) systems, possibly affected by bounded disturbances. In this paper, for the first time we propose a novel solution for sandboxing unverified complex controllers for CPS operating in noisy environments (a.k.a. stochastic CPS). Moreover, we also provide probabilistic guarantees on their safety. Here, the unverified control input is observed at each time instant and checked whether it violates the maximal tolerable probability of reaching the unsafe set. If this probability exceeds a given threshold, the unverified control input will be rejected, and the advisory input provided by the optimal safety controller will be used to maintain the probabilistic safety guarantee. The proposed approach is illustrated empirically and the results indicate that the expected safety probability is guaranteed.
Multi-sensor joint target detection, tracking and classification via Bernoulli filter
Authors: Gaiyou Li, Ping Wei, Giorgio Battistelli, Luigi Chisci, Lin Gao
Date: 2021-09-23 00:00:00
Tasks: Sensor Fusion

This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli \textit{random finite set} (RFS) uniquely characterized by existence, classification, class-conditioned mode and class\&mode-conditioned state probability distributions. By designing suitable centralized and distributed rules for fusing information on target existence, class, mode and state from different sensor nodes, novel \textit{centralized} and \textit{distributed} JDTC \textit{Bernoulli filters} (C-JDTC-BF and D-JDTC-BF), are proposed. The performance of the proposed JDTC-BF approach is evaluated by means of simulation experiments.
Data-driven stability analysis of switched affine systems
Authors: Matteo Della Rossa, Zheming Wang, Lucas N. Egidio, Raphaël M. Jungers
Date: 2021-09-23 00:00:00

We consider discrete-time switching systems composed of a finite family of affine sub-dynamics. First, we recall existing results and present further analysis on the stability problem, the existence and characterization of compact attractors, and the relations these problems have with the joint spectral radius of the set of matrices composing the linear part of the subsystems. Second, we tackle the problem of providing probabilistic certificates of stability along with the existence of forward invariant sets, assuming no knowledge on the system data but only observing a finite number of sampled trajectories. Some numerical examples illustrate the advantages and limits of the proposed conditions.
Energy Efficient Control of Electric Motors
Authors: Farhad Aghili
Date: 2021-09-23 00:00:00

This paper presents development of an optimal feedback linearization control (OFLC) for interior permanent magnet (PM) synchronous machines operating in a non steady-sate operating point, i.e., varying torque and speed, to achieve precision tracking performance and energy saving by minimizing the copper loss. An isomorphism mapping between the dq-axes phase voltages and two auxiliary control inputs over full ranges of torque and speed is established by the linearization controller using the notion of orthogonal projection. The auxiliary control inputs are defined to be exclusively responsible for torque generation and power consumption. Subsequently, an analytical solution for the optimal-linearization control is derived in a closed-form by applying the Hamiltonian of optimal control theory in conjunction with the Pontryagin's minimum principle. The optimal controller takes the maximum voltage limit and torque tracking constraint into account while maximizing machine efficiency for non-constant operational load torque and speed. Unlike the convectional quadratic regulator-based control of electric motors, the proposed control approach does not rely on steady-state operation conditions and hence it is suitable for such applications as electric vehicles and robotics.
On the Optimality of Voltage Unbalance Attenuation by Inverters
Authors: Yifei Guo, Bikash C. Pal, Rabih A. Jabr
Date: 2021-09-22 00:00:00

In this paper, we investigate the control of inverter-based resources (IBRs) for optimal voltage unbalance attenuation (OVUA). This problem is formulated as an optimization program under a tailored dq-frame, which minimizes the negative-sequence voltage at the point of common coupling (PCC) subject to the current, active power, synchronization stability, and feasibility constraints. The program is inherently nonconvex and intractable. So, to guarantee the optimality, a rigorous optimality analysis is performed by leveraging analytical optimization. The analysis is divided into two cases: full mitigation of VU and partial attenuation of VU. For the former case, we analytically solve the original program since the resultant VU is immediately deducible. For the latter one, directly solving the problem becomes very hard. Thus, we reformulate the program into an equivalent but more tractable form under certain conditions, by which the analytical optimum can be finally derived. It is found that the optimum trajectory has three stages (O1-O3), depending on two critical boundary conditions (C1 and C2). Finally, we implement the optimum with a photovoltaic (PV)-storage system by developing an OVUA controller. The proposed approach is demonstrated by dynamic simulations under different VU conditions and is compared with several existing practices.
Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology
Authors: Nicholas Sale, Jeffrey Giansiracusa, Biagio Lucini
Date: 2021-09-22 00:00:00

We use persistent homology and persistence images as an observable of three different variants of the two-dimensional XY model in order to identify and study their phase transitions. We examine models with the classical XY action, a topological lattice action, and an action with an additional nematic term. In particular, we introduce a new way of computing the persistent homology of lattice spin model configurations and, by considering the fluctuations in the output of logistic regression and k-nearest neighbours models trained on persistence images, we develop a methodology to extract estimates of the critical temperature and the critical exponent of the correlation length. We put particular emphasis on finite-size scaling behaviour and producing estimates with quantifiable error. For each model we successfully identify its phase transition(s) and are able to get an accurate determination of the critical temperatures and critical exponents of the correlation length.
Treatment Effect Estimation with Noisy Conditioning Variables
Authors: Kenichi Nagasawa
Date: 2018-11-01 00:00:00

I develop a new identification strategy for treatment effects when noisy measurements of unobserved confounding factors are available. I use proxy variables to construct a random variable conditional on which treatment variables become exogenous. The key idea is that, under appropriate conditions, there exists a one-to-one mapping between the distribution of unobserved confounding factors and the distribution of proxies. To ensure sufficient variation in the constructed control variable, I use an additional variable, termed excluded variable, which satisfies certain exclusion restrictions and relevance conditions. I establish asymptotic distributional results for semiparametric and flexible parametric estimators of causal parameters. I illustrate empirical relevance and usefulness of my results by estimating causal effects of attending selective college on earnings.