pwc-archive/papers-with-abstracts huggingface dataset:
Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer Programs
Authors: Charly Robinson La Rocca, Emma Frejinger, Jean-François Cordeau
Date: 2022-02-07 00:00:00

Current state-of-the-art solvers for mixed-integer programming (MIP) problems are designed to perform well on a wide range of problems. However, for many real-world use cases, problem instances come from a narrow distribution. This has motivated the development of specialized methods that can exploit the information in historical datasets to guide the design of heuristics. Recent works have shown that machine learning (ML) can be integrated with an MIP solver to inject domain knowledge and efficiently close the optimality gap. This hybridization is usually done with deep learning (DL), which requires a large dataset and extensive hyperparameter tuning to perform well. This paper proposes an online heuristic that uses the notion of entropy to efficiently build a model with minimal training data and tuning. We test our method on the locomotive assignment problem (LAP), a recurring real-world problem that is challenging to solve at scale. Experimental results show a speed up of an order of magnitude compared to a general purpose solver (CPLEX) with a relative gap of less than 2%. We also observe that for some instances our method can discover better solutions than CPLEX within the time limit.
Embeddability of centrosymmetric matrices capturing the double-helix structure in natural and synthetic DNA
Authors: Muhammad Ardiyansyah, Dimitra Kosta, Jordi Roca-Lacostena
Date: 2022-02-11 00:00:00

In this paper, we discuss the embedding problem for centrosymmetric matrices, which are higher order generalizations of the matrices occurring in Strand Symmetric Models. These models capture the substitution symmetries arising from the double helix structure of the DNA. Deciding whether a transition matrix is embeddable or not enables us to know if the observed substitution probabilities are consistent with a homogeneous continuous time substitution model, such as the Kimura models, the Jukes-Cantor model or the general time-reversible model. On the other hand, the generalization to higher order matrices is motivated by the setting of synthetic biology, which works with different sizes of genetic alphabets.
Automated Aircraft Dent Inspection via a Modified Fourier Transform Profilometry Algorithm
Authors: Pasquale Lafiosca, Ip-Shing Fan, Nicolas P. Avdelidis
Date: 2022-01-07 00:00:00
Proceeding: Sensors 2022 1
Tasks: 3D Reconstruction

The search for dents is a consistent part of the aircraft inspection workload. The engineer is required to find, measure, and report each dent over the aircraft skin. This process is not only hazardous, but also extremely subject to human factors and environmental conditions. This study discusses the feasibility of automated dent scanning via a single-shot triangular stereo Fourier transform algorithm, designed to be compatible with the use of an unmanned aerial vehicle. The original algorithm is modified introducing two main contributions. First, the automatic estimation of the pass-band filter removes the user interaction in the phase filtering process. Secondly, the employment of a virtual reference plane reduces unwrapping errors, leading to improved accuracy independently of the chosen unwrapping algorithm. Static experiments reached a mean absolute error of ∼0.1 mm at a distance of 60 cm, while dynamic experiments showed ∼0.3 mm at a distance of 120 cm. On average, the mean absolute error decreased by ∼34%, proving the validity of the proposed single-shot 3D reconstruction algorithm and suggesting its applicability for future automated dent inspections.
Two is better than one: Regularized shrinkage of large minimum variance portfolio
Authors: Taras Bodnar, Nestor Parolya, Erik Thorsén
Date: 2022-02-14 00:00:00

In this paper we construct a shrinkage estimator of the global minimum variance (GMV) portfolio by a combination of two techniques: Tikhonov regularization and direct shrinkage of portfolio weights. More specifically, we employ a double shrinkage approach, where the covariance matrix and portfolio weights are shrunk simultaneously. The ridge parameter controls the stability of the covariance matrix, while the portfolio shrinkage intensity shrinks the regularized portfolio weights to a predefined target. Both parameters simultaneously minimize with probability one the out-of-sample variance as the number of assets $p$ and the sample size $n$ tend to infinity, while their ratio $p/n$ tends to a constant $c>0$. This method can also be seen as the optimal combination of the well-established linear shrinkage approach of Ledoit and Wolf (2004, JMVA) and the shrinkage of the portfolio weights by Bodnar et al. (2018, EJOR). No specific distribution is assumed for the asset returns except of the assumption of finite $4+\varepsilon$ moments. The performance of the double shrinkage estimator is investigated via extensive simulation and empirical studies. The suggested method significantly outperforms its predecessor (without regularization) and the nonlinear shrinkage approach in terms of the out-of-sample variance, Sharpe ratio and other empirical measures in the majority of scenarios. Moreover, it obeys the most stable portfolio weights with uniformly smallest turnover.
Fluctuation scaling in Lévy-stable recruitment of marine fishes in randomly varying environments
Authors: Hiro-Sato Niwa
Date: 2022-02-13 00:00:00

This paper studies the scaling properties of recruitment fluctuations in randomly varying environments, for abundant marine species with extreme reproductive behavior. Fisheries stock-recruitment data from the North Atlantic display fluctuation scaling, a proportionality between the standard deviation and the average recruitment among stocks. The proportionality covers over five orders of magnitude in the range studied. A linear-scaling behavior can be a sign of a universal distribution of the normalized data across stocks. In light of this conjecture, it is demonstrated that the L\'evy-stable model offers a better effective description of the recruitment distribution than the log-normal model. Care is devoted to the problem of random sums of random variables. Recruitment is calculated by summing random offspring numbers with infinite variance, where the number of summands (i.e. spawning population size) is also a random process with infinite variance.
Estimation of symbiotic bacterial structure in a sustainable seagrass ecosystem on recycled management
Authors: Hirokuni Miyamoto, Nobuhiro Kawachi, Atsushi Kurotani, Shigeharu Moriya, Wataru Suda, Kenta Suzuki, Makiko Matsuura, Naoko Tsuji, Teruno Nakaguma, Chitose Ishii, Arisa Tsuboi, Chie Shindo, Tamotsu Kato, Motoaki Udagawa, Takashi Satoh, Satoshi Wada, Hiroshi Masuya, Hisashi Miyamoto, Hiroshi Ohno, Jun Kikuchi
Date: 2022-02-13 00:00:00
Tasks: Management

Seagrass meadows play an essential role in blue carbon and aquatic ecosystem services. However, methods for the flourishing of seagrass are still being explored. Here, data from 49 public coastal surveys on the distribution of seagrass and seaweed around the onshore aquaculture facilities are revalidated, and an exceptional area where the seagrass Zostera marina thrives was found. The bacterial population there showed an apparent decrease in the pathogen candidates belonging to the order Flavobacteriales. Moreover, structure equation modeling and a linear non-Gaussian acyclic model based on the machine learning data estimated an optimal symbiotic bacterial group candidate for seagrass growth as follows: the Lachnospiraceae and Ruminococcaceae families as gut-inhabitant bacteria, Rhodobacteraceae as photosynthetic bacteria, and Desulfobulbaceae as cable bacteria modulating oxygen or nitrate reduction and oxidation of sulfide. These observations confer a novel perspective on the seagrass symbiotic bacterial structures critical for blue carbon and low-pathogenic marine ecosystems in aquaculture.
An Optimal Control Model of transmission dynamics of COVID-19 in Ghana
Authors: Samuel Okyere, Joseph Ackora-Prah, Ebenezer Bonyah, Kwaku Forkuoh Darkwah
Date: 2022-01-26 00:00:00

Almost every country in the world is battling to limit the spread of COVID-19. As the world strives to get an effective medication to control the disease, appropriate intervention measures, for now, remains one of the effective methods to reduce the spread of the disease. Optimal control strategies have proven to be an effective method in curtailing the spread of infectious diseases. In this paper, a model has been formulated to study transmission dynamics of the disease. Basic properties of the model such as the basic reproduction number, equilibrium points and stability of the equilibrium points have been determined. Sensitivity analysis was carried on to determine the impact of the model parameters on the basic reproduction number. We also introduced a compartment for the deceased and examined the behaviour of COVID-19 related deaths. The numerical simulation prediction is consistent with real data from Ghana for the period March 2020 to March 2021. The simulation revealed the disease had less impact on the population during the first seven months of the outbreak. To help contain the spread of the disease, time dependent optimal controls were incorporated into the model and Pontryagin maximum principle was used to characterize vital conditions of the optimal control model. Numerical simulations of the optimal control model showed that, combination of optimal preventive strategies such as nose mask and vaccination are effective to significantly decrease the number of COVID-19 cases in different compartments of the model. Vaccination decreases the susceptibility to the disease whereas mask usage preserved the susceptible population from extinction.
Novel prediction methods for virtual drug screening
Authors: Josip Mesarić
Date: 2022-02-14 00:00:00
Tasks: Drug Discovery, Prediction

Drug development is an expensive and time-consuming process where thousands of chemical compounds are being tested in order to find those possessing drug-like properties while being safe and effective. One of key parts of the early drug discovery process has become virtual drug screening -- a method used to narrow down search for potential drugs by running computer simulations of drug-target interactions. As these methods are known to demand huge amounts of computational power to get accurate results, prediction models based on machine learning techniques became a popular solution requiring less computational power as well as offering the ability to generate novel chemical structures for further research. Deep learning is to stay in drug discovery but has a long way to go. Only in the past few years with increases in computing power have researchers really started to embrace the potential of neural networks in various stages of the drug discovery process. While prediction methods promise great perspective in the future development of drug discovery they open new questions and challenges that still have to be solved.
Semi-analytical pricing of barrier options in the time-dependent Heston model
Authors: P. Carr, A. Itkin, D. Muravey
Date: 2022-02-13 00:00:00

We develop the general integral transforms (GIT) method for pricing barrier options in the time-dependent Heston model (also with a time-dependent barrier) where the option price is represented in a semi-analytical form as a two-dimensional integral. This integral depends on yet unknown function $\Phi(t,v)$ which is the gradient of the solution at the moving boundary $S = L(t)$ and solves a linear mixed Volterra-Fredholm equation of the second kind also derived in the paper. Thus, we generalize the one-dimensional GIT method, developed in (Itkin, Lipton, Muravey, Generalized integral transforms in mathematical finance, WS, 2021) and the corresponding papers, to the two-dimensional case. In other words, we show that the GIT method can be extended to stochastic volatility models (two drivers with inhomogeneous correlation). As such, this 2D approach naturally inherits all advantages of the corresponding 1D methods, in particular, their speed and accuracy. This result is new and has various applications not just in finance but also in physics. Numerical examples illustrate high speed and accuracy of the method as compared with the finite-difference approach.
Super-replication prices with multiple-priors in discrete time
Authors: Romain Blanchard, Laurence Carassus
Date: 2022-02-14 00:00:00

In the frictionless discrete time financial market of Bouchard and Nutz (2015), we propose a full characterization of the quasi-sure super-replication price: as the supremum of the mono-prior super-replication prices, through an extreme prior and through martingale measures.
Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
Authors: Tao Wang, Rui Wang, Di Jin, Dongxiao He, Yuxiao Huang
Date: 2021-12-27 00:00:00
Tasks: Attribute

Graph Convolutional Networks (GCNs) have been widely applied in various fields due to their significant power on processing graph-structured data. Typical GCN and its variants work under a homophily assumption (i.e., nodes with same class are prone to connect to each other), while ignoring the heterophily which exists in many real-world networks (i.e., nodes with different classes tend to form edges). Existing methods deal with heterophily by mainly aggregating higher-order neighborhoods or combing the immediate representations, which leads to noise and irrelevant information in the result. But these methods did not change the propagation mechanism which works under homophily assumption (that is a fundamental part of GCNs). This makes it difficult to distinguish the representation of nodes from different classes. To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs. To adaptively learn the propagation process, we introduce two measurements of homophily degree between node pairs, which is learned based on topological and attribute information, respectively. Then we incorporate the learnable homophily degree into the graph convolution framework, which is trained in an end-to-end schema, enabling it to go beyond the assumption of homophily. More importantly, we theoretically prove that our model can constrain the similarity of representations between nodes according to their homophily degree. Experiments on seven real-world datasets demonstrate that this new approach outperforms the state-of-the-art methods under heterophily or low homophily, and gains competitive performance under homophily.
Regulation toward Self-organized Criticality in a Recurrent Spiking Neural Reservoir
Authors: Simon Brodeur, Jean Rouat
Date: 2012-09-14 00:00:00
Proceeding: Artificial Neural Networks and Machine Learning – ICANN 2012 9

Generating stable yet performant spiking neural reservoirs for classification applications is still an open issue. This is due to the extremely non-linear dynamics of recurrent spiking neural networks. In this perspective, a local and unsupervised learning rule that tunes the reservoir toward self-organized criticality is proposed, and applied to networks of leaky integrate-and-fire neurons with random and small-world topologies. Longer sustained activity for both topologies was elicited after learning compared to spectral radius normalization (global rescaling scheme). The ability to control the desired regime of the reservoir was shown and quick convergence toward it was observed for speech signals. Proposed regulation method can be applied online and leads to reservoirs more strongly adapted to the task at hand.
Predicting on the Edge: Identifying Where a Larger Model Does Better
Authors: Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar
Date: 2022-02-15 00:00:00
Tasks: Decoder

Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity. In this paper, we demonstrate and analyze the surprisingly tight link between a model's predictive uncertainty on individual examples and the likelihood that larger models will improve prediction on them. Through extensive numerical studies on the T5 encoder-decoder architecture, we show that large models have the largest improvement on examples where the small model is most uncertain. On more certain examples, even those where the small model is not particularly accurate, large models are often unable to improve at all, and can even perform worse than the smaller model. Based on these findings, we show that a switcher model which defers examples to a larger model when a small model is uncertain can achieve striking improvements in performance and resource usage. We also explore committee-based uncertainty metrics that can be more effective but less practical.
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs
Authors: Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong
Date: 2022-02-15 00:00:00
Tasks: Density Estimation, Knowledge Graphs, Link Prediction

How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from time-evolving KGs, is crucial for many applications to provide intelligent services. However, despite the prevalence of real-world data that can be represented as TKGs, most methods focus on reasoning over static knowledge graphs, or cannot predict future events. In this paper, we present a problem formulation that unifies the two major problems that need to be addressed for an effective reasoning over TKGs, namely, modeling the event time and the evolving network structure. Our proposed method EvoKG jointly models both tasks in an effective framework, which captures the ever-changing structural and temporal dynamics in TKGs via recurrent event modeling, and models the interactions between entities based on the temporal neighborhood aggregation framework. Further, EvoKG achieves an accurate modeling of event time, using flexible and efficient mechanisms based on neural density estimation. Experiments show that EvoKG outperforms existing methods in terms of effectiveness (up to 77% and 116% more accurate time and link prediction) and efficiency.
Wireless Resource Management in Intelligent Semantic Communication Networks
Authors: Le Xia, Yao Sun, Xiaoqian Li, Gang Feng, Muhammad Ali Imran
Date: 2022-02-15 00:00:00
Tasks: Management, Semantic Communication

The prosperity of artificial intelligence (AI) has laid a promising paradigm of communication system, i.e., intelligent semantic communication (ISC), where semantic contents, instead of traditional bit sequences, are coded by AI models for efficient communication. Due to the unique demand of background knowledge for semantic recovery, wireless resource management faces new challenges in ISC. In this paper, we address the user association (UA) and bandwidth allocation (BA) problems in an ISC-enabled heterogeneous network (ISC-HetNet). We first introduce the auxiliary knowledge base (KB) into the system model, and develop a new performance metric for the ISC-HetNet, named system throughput in message (STM). Joint optimization of UA and BA is then formulated with the aim of STM maximization subject to KB matching and wireless bandwidth constraints. To this end, we propose a two-stage solution, including a stochastic programming method in the first stage to obtain a deterministic objective with semantic confidence, and a heuristic algorithm in the second stage to reach the optimality of UA and BA. Numerical results show great superiority and reliability of our proposed solution on the STM performance when compared with two baseline algorithms.
Defending against Reconstruction Attacks with Rényi Differential Privacy
Authors: Pierre Stock, Igor Shilov, Ilya Mironov, Alexandre Sablayrolles
Date: 2022-02-15 00:00:00

Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can reconstruct data samples from language models, making this threat scenario an important aspect of model release. Differential privacy is a known solution to such attacks, but is often used with a relatively large privacy budget (epsilon > 8) which does not translate to meaningful guarantees. In this paper we show that, for a same mechanism, we can derive privacy guarantees for reconstruction attacks that are better than the traditional ones from the literature. In particular, we show that larger privacy budgets do not protect against membership inference, but can still protect extraction of rare secrets. We show experimentally that our guarantees hold against various language models, including GPT-2 finetuned on Wikitext-103.
Fairness Indicators for Systematic Assessments of Visual Feature Extractors
Authors: Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier
Date: 2022-02-15 00:00:00
Tasks: Fairness

Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can spark major concerns when they exhibit vast performance discrepancies between people from various demographic and social backgrounds. Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems. To initiate an effort towards standardized fairness audits, we propose three fairness indicators, which aim at quantifying harms and biases of visual systems. Our indicators use existing publicly available datasets collected for fairness evaluations, and focus on three main types of harms and bias identified in the literature, namely harmful label associations, disparity in learned representations of social and demographic traits, and biased performance on geographically diverse images from across the world.We define precise experimental protocols applicable to a wide range of computer vision models. These indicators are part of an ever-evolving suite of fairness probes and are not intended to be a substitute for a thorough analysis of the broader impact of the new computer vision technologies. Yet, we believe it is a necessary first step towards (1) facilitating the widespread adoption and mandate of the fairness assessments in computer vision research, and (2) tracking progress towards building socially responsible models. To study the practical effectiveness and broad applicability of our proposed indicators to any visual system, we apply them to off-the-shelf models built using widely adopted model training paradigms which vary in their ability to whether they can predict labels on a given image or only produce the embeddings. We also systematically study the effect of data domain and model size.
A General Framework for Modelling Conditional Reasoning -- Preliminary Report
Authors: Giovanni Casini, Umberto Straccia
Date: 2022-02-15 00:00:00

We introduce and investigate here a formalisation for conditionals that allows the definition of a broad class of reasoning systems. This framework covers the most popular kinds of conditional reasoning in logic-based KR: the semantics we propose is appropriate for a structural analysis of those conditionals that do not satisfy closure properties associated to classical logics.