pwc-archive/papers-with-abstracts huggingface dataset:
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms
Authors: Jakob Bossek, Aneta Neumann, Frank Neumann
Date: 2021-04-27 00:00:00
Tasks: Diversity, Evolutionary Algorithms

In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least $(1-\varepsilon)\cdot OPT$, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple $(\mu+1)$-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.
A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems
Authors: Urban Škvorc, Tome Eftimov, Peter Korošec
Date: 2021-04-27 00:00:00
Tasks: Benchmarking

When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems. One possible way of easing this difficult task is by using artificially generated problems. In this paper, one such single-objective continuous problem generation approach is analyzed and compared with the COCO benchmark problem set, a well know problem set for benchmarking numerical optimization algorithms. Using Exploratory Landscape Analysis and Singular Value Decomposition, we show that such representations allow us to further explore the relations between the problems by applying visualization and correlation analysis techniques, with the goal of decreasing the bias in benchmark problem assessment.
Geographic ratemaking with spatial embeddings
Authors: Christopher Blier-Wong, Hélène Cossette, Luc Lamontagne, Etienne Marceau
Date: 2021-04-26 00:00:00
Tasks: Management, Spatial Interpolation

Spatial data is a rich source of information for actuarial applications: knowledge of a risk's location could improve an insurance company's ratemaking, reserving or risk management processes. Insurance companies with high exposures in a territory typically have a competitive advantage since they may use historical losses in a region to model spatial risk non-parametrically. Relying on geographic losses is problematic for areas where past loss data is unavailable. This paper presents a method based on data (instead of smoothing historical insurance claim losses) to construct a geographic ratemaking model. In particular, we construct spatial features within a complex representation model, then use the features as inputs to a simpler predictive model (like a generalized linear model). Our approach generates predictions with smaller bias and smaller variance than other spatial interpolation models such as bivariate splines in most situations. This method also enables us to generate rates in territories with no historical experience.
Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework
Authors: Wufan Zhao, Claudio Persello, Alfred Stein
Date: 2021-02-14 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

ep learning methods based upon convolutional neural networks (CNNs) have demonstrated impressive performance in the task of building outline delineation from very high resolution (VHR) remote sensing (RS) imagery. In this paper, we introduce an improved method that is able to predict regularized building outline in a 
vector format within an end-to-end deep learning framework. The main idea of our framework is to learn to 
predict the location of key vertices of the buildings and connect them in sequence. The proposed method is based 
on PolyMapper. We upgrade the feature extraction by introducing global context and boundary refinement 
blocks and add channel and spatial attention modules to improve the effectiveness of the detection module. In 
addition, we introduce stacked conv-GRU to further preserve the geometric relationship between vertices and 
accelerate inference. We tested our method on two large-scale VHR-RS building extraction dataset. The results on 
both COCO and PoLiS metrics demonstrate better performance compared with Mask R-CNN and PolyMapper. 
Specifically, we achieve 4.2 mask mean average precision (mAP) and 3.7 mean average recall (mAR) absolute 
improvements compared to PolyMapper. Also, the qualitative comparison shows that our method significantly 
improves the instance segmentation of buildings of various shapes
Visualization of Linear Operations in the Spherical Harmonics Domain
Authors: Maximilian Kentgens, Peter Jax
Date: 2021-04-27 00:00:00
Tasks: Audio Signal Processing

Linear operations on coefficients in the spherical harmonics (SH) transform domain that again yield SH-domain coefficients are an important toolset in many disciplines of research and engineering. They comprise rotations, spatially selective filters, and many other modifications for various applications, or describe the response of a MIMO system to an excitation. It is of particular importance to characterize these operations both qualitatively and quantitatively, and make them accessible for people to work with. In this paper, we identify different key properties of such operations and propose a method for their visualization. With our proposed method, we succeed to show many important aspects of an operation in a single plot and give rise to a comprehensive interpretation of the behavior of a system. In our evaluation, we show the potential of the proposed method on the basis of various practical examples from spatial audio signal processing, where SH-domain filtering is used to modify acoustic scenes given by higher-order Ambisonics signals.
A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma
Authors: Muhammad Shaban, Shan E Ahmed Raza, Mariam Hassan, Arif Jamshed, Sajid Mushtaq, Asif Loya, Nikolaos Batis, Jill Brooks, Paul Nankivell, Neil Sharma, Max Robinson, Hisham Mehanna, Syed Ali Khurram, Nasir Rajpoot
Date: 2021-04-16 00:00:00
Tasks: Clinical Knowledge, whole slide images

The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim is to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI based automated method. A deep learning based automated method was employed to segment tumour, stroma and lymphocytes in digitally scanned whole slide images of HNSCC tissue slides. The spatial patterns of lymphocytes and tumour-associated stroma were digitally quantified to compute the TASIL-score. Finally, prognostic significance of the TASIL-score for disease-specific and disease-free survival was investigated with the Cox proportional hazard analysis. Three different cohorts of Haematoxylin & Eosin (H&E) stained tissue slides of HNSCC cases (n=537 in total) were studied, including publicly available TCGA head and neck cancer cases. The TASIL-score carries prognostic significance (p=0.002) for disease-specific survival of HNSCC patients. The TASIL-score also shows a better separation between low- and high-risk patients as compared to the manual TIL scoring by pathologists for both disease-specific and disease-free survival. A positive correlation of TASIL-score with molecular estimates of CD8+ T cells was also found, which is in line with existing findings. To the best of our knowledge, this is the first study to automate the quantification of TASIL from routine H&E slides of head and neck cancer. Our TASIL-score based findings are aligned with the clinical knowledge with the added advantages of objectivity, reproducibility and strong prognostic value. A comprehensive evaluation on large multicentric cohorts is required before the proposed digital score can be adopted in clinical practice.
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
Authors: Hengli Wang, Peide Cai, Yuxiang Sun, Lujia Wang, Ming Liu
Date: 2021-04-18 00:00:00
Tasks: Autonomous Driving, Motion Planning, Optical Flow Estimation

Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretability, making the behaviors of the deep networks difficult to explain. Hence, their potential applications could be limited in practice. To address this problem, we propose an interpretable end-to-end vision-based motion planning approach for autonomous driving, referred to as IVMP. Given a set of past surrounding-view images, our IVMP first predicts future egocentric semantic maps in bird's-eye-view space, which are then employed to plan trajectories for self-driving vehicles. The predicted future semantic maps not only provide useful interpretable information, but also allow our motion planning module to handle objects with low probability, thus improving the safety of autonomous driving. Moreover, we also develop an optical flow distillation paradigm, which can effectively enhance the network while still maintaining its real-time performance. Extensive experiments on the nuScenes dataset and closed-loop simulation show that our IVMP significantly outperforms the state-of-the-art approaches in imitating human drivers with a much higher success rate. Our project page is available at https://sites.google.com/view/ivmp.
Advances on image interpolation based on ant colony algorithm
Authors: Olivier Rukundo, Hanqiang Cao
Date: 2021-04-12 00:00:00

This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high-resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses a local weighting scheme. The strength of the proposed global weighting of the AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper.
Multi-Density Attention Network for Loop Filtering in Video Compression
Authors: Zhao Wang, Changyue Ma, Yan Ye
Date: 2021-04-08 00:00:00
Tasks: Video Compression

Video compression is a basic requirement for consumer and professional video applications alike. Video coding standards such as H.264/AVC and H.265/HEVC are widely deployed in the market to enable efficient use of bandwidth and storage for many video applications. To reduce the coding artifacts and improve the compression efficiency, neural network based loop filtering of the reconstructed video has been developed in the literature. However, loop filtering is a challenging task due to the variation in video content and sampling densities. In this paper, we propose a on-line scaling based multi-density attention network for loop filtering in video compression. The core of our approach lies in several aspects: (a) parallel multi-resolution convolution streams for extracting multi-density features, (b) single attention branch to learn the sample correlations and generate mask maps, (c) a channel-mutual attention procedure to fuse the data from multiple branches, (d) on-line scaling technique to further optimize the output results of network according to the actual signal. The proposed multi-density attention network learns rich features from multiple sampling densities and performs robustly on video content of different resolutions. Moreover, the online scaling process enhances the signal adaptability of the off-line pre-trained model. Experimental results show that 10.18% bit-rate reduction at the same video quality can be achieved over the latest Versatile Video Coding (VVC) standard. The objective performance of the proposed algorithm outperforms the state-of-the-art methods and the subjective quality improvement is obvious in terms of detail preservation and artifact alleviation.
Analyze Gauss: Optimal Bounds for Privacy-Preserving Principal Component Analysis
Authors: Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, Li Zhang
Date: 2014-05-01 00:00:00
Tasks: Attribute, Privacy Preserving

We consider the problem of privately releasing a low dimensional approximation to a set of data records, represented as a matrix A
in which each row corresponds to an individual and each column to an attribute. Our goal is to compute a subspace that captures the covariance of A as much as possible, classically known as principal component analysis (PCA). We assume that each row of A has L2 norm bounded by one, and the privacy guarantee is defined with respect to addition or removal of any single row. We show that the well-known, but misnamed, randomized response algorithm, with properly tuned parameters, provides a nearly optimal additive quality gap compared to the best possible singular subspace of A. We further show that when A^TA has a large eigenvalue gap – a reason often cited for PCA – the quality improves significantly. Optimality (up to logarithmic factors) is proved using techniques inspired by the recent work of Bun, Ullman, and Vadhan on applying Tardos’s fingerprinting codes to the construction of hard instances for private mechanisms for 1-way marginal queries. Along the way, we define a list culling game that may be of independent interest. By combining the randomized response mechanism with the well-known following the perturbed leader algorithm of Kalai and Vempala we obtain a private online algorithm with nearly optimal regret. The regret of our algorithm even outperforms all the previously known online non-private algorithms of this type. We achieve this better bound by, satisfyingly, borrowing insights and tools from differential privacy!
Ancillary Services Acquisition Model: considering market interactions in policy design
Authors: Samuel Glismann
Date: 2021-04-27 00:00:00

A rapidly changing electricity sector requires adjusted and new ancillary services, which enable the secure and reliable operation of the electricity system. However, assessments and policy advice regarding ancillary services and market design lack methods to evaluate the complex interaction of markets and services. Therefore, this paper contributes an open-source agent-based model to test design options for ancillary services and electricity markets. The Ancillary Services Acquisition Model (ASAM) combines the agent-based modeling framework MESA with the toolbox Python for Power System Analysis (PyPSA). The model provides various design parameters per market and agent-specific strategies as well as detailed clearing algorithms for the day-ahead market, intra-day continuous trading, redispatch, and imbalances. Moreover, ASAM includes numerous policy performance indicators, including a novel price mark-up indicator and novel redispatch performance indicators. A stylized simulation scenario verified and validated the model and addressed a redispatch design question. The results displayed the following implications from order types in redispatch markets with multi-period all-or-none design: (1) The order design provides few risks for market parties, as orders are fully cleared. (2) Large orders may lead to dispatch ramps before and after the delivery period and may cause ramp-risk mark-ups as well as additional trading of imbalances on intra-day. (3) All-or-none design in a liquid situation leads to the over-procurement of redispatch by the grid operator, as orders cannot be partially activated. Moreover, it is likely that the grid operator induces imbalances to the system by incomplete redispatch activation (i.e. upward and downward redispatch volumes are not equal).
Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank Transactions
Authors: Chris Carmona, Serafin Martinez-Jaramillo
Date: 2019-11-25 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

We propose a general network model suited for longitudinal data of multi-layer networks with directed and weighted edges. Our formulation built upon the latent social space representation of networks. It consists of a hierarchical formulation: deep levels of the model represent latent coordinates of agents in the social space, evolving in continuous time via Gaussian Processes; meanwhile, top levels jointly manage incidence and strength of interactions by considering a Zero-Inflated Gaussian response. Learning of the model is performed through Bayesian Inference. We develop an efficient MCMC algorithm targeting the posterior distribution of model parameters and missing data (available in GitHub). The motivation for our model lies in the context of Financial Networks, specifically the analysis of transactions between commercial banks. We evaluate the model in synthetic data, as well as our main case study: the network of inter-bank transactions in the Mexican financial system. Accurate predictions are obtained in both cases estimating out-of-sample link incidence and link strength.
The Role of General Intelligence in Mathematical Reasoning
Authors: Aviv Keren
Date: 2021-04-27 00:00:00
Tasks: Mathematical Reasoning

Objects are a centerpiece of the mathematical realm and our interaction with and reasoning about it, just as they are of the physical one (if not more). And humans' mathematical reasoning must ultimately be grounded in our general intelligence. Yet in contemporary cognitive science and A.I., the physical and mathematical domains are customarily explored separately, which allows for baking in assumptions for what objects are for the system - and missing potential connections. In this paper, I put the issue into its philosophical and cognitive context. I then describe an abstract theoretical framework for learning object representations, that makes room for mathematical objects on par with non-mathematical ones. Finally, I describe a case study that builds on that view to show how our general ability for integrating different aspects of objects effects our conception of the natural numbers.
Learning from Łukasiewicz and Meredith: Investigations into Proof Structures (Extended Version)
Authors: Christoph Wernhard, Wolfgang Bibel
Date: 2021-04-28 00:00:00
Tasks: LEMMA

The material presented in this paper contributes to establishing a basis deemed essential for substantial progress in Automated Deduction. It identifies and studies global features in selected problems and their proofs which offer the potential of guiding proof search in a more direct way. The studied problems are of the wide-spread form of "axiom(s) and rule(s) imply goal(s)". The features include the well-known concept of lemmas. For their elaboration both human and automated proofs of selected theorems are taken into a close comparative consideration. The study at the same time accounts for a coherent and comprehensive formal reconstruction of historical work by {\L}ukasiewicz, Meredith and others. First experiments resulting from the study indicate novel ways of lemma generation to supplement automated first-order provers of various families, strengthening in particular their ability to find short proofs.
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Authors: Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Date: 2021-04-28 00:00:00
Tasks: Robot Navigation

Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. The lack of an explicit representation however hinders policy interpretability and makes policy verification very complex. In this work, we propose two contributions. The first is a method for identifying unexpected actions selected by POMCP with respect to expert prior knowledge of the task. The second is a shielding approach that prevents POMCP from selecting unexpected actions. The first method is based on Satisfiability Modulo Theory (SMT). It inspects traces (i.e., sequences of belief-action-observation triplets) generated by POMCP to compute the parameters of logical formulas about policy properties defined by the expert. The second contribution is a module that uses online the logical formulas to identify anomalous actions selected by POMCP and substitutes those actions with actions that satisfy the logical formulas fulfilling expert knowledge. We evaluate our approach on Tiger, a standard benchmark for POMDPs, and a real-world problem related to velocity regulation in mobile robot navigation. Results show that the shielded POMCP outperforms the standard POMCP in a case study in which a wrong parameter of POMCP makes it select wrong actions from time to time. Moreover, we show that the approach keeps good performance also if the parameters of the logical formula are optimized using trajectories containing some wrong actions.
Symbolic Abstractions From Data: A PAC Learning Approach
Authors: Alex Devonport, Adnane Saoud, Murat Arcak
Date: 2021-04-28 00:00:00
Tasks: PAC learning

Symbolic control techniques aim to satisfy complex logic specifications. A critical step in these techniques is the construction of a symbolic (discrete) abstraction, a finite-state system whose behaviour mimics that of a given continuous-state system. The methods used to compute symbolic abstractions, however, require knowledge of an accurate closed-form model. To generalize them to systems with unknown dynamics, we present a new data-driven approach that does not require closed-form dynamics, instead relying only the ability to evaluate successors of each state under given inputs. To provide guarantees for the learned abstraction, we use the Probably Approximately Correct (PAC) statistical framework. We first introduce a PAC-style behavioural relationship and an appropriate refinement procedure. We then show how the symbolic abstraction can be constructed to satisfy this new behavioural relationship. Moreover, we provide PAC bounds that dictate the number of data required to guarantee a prescribed level of accuracy and confidence. Finally, we present an illustrative example.