pwc-archive/papers-with-abstracts huggingface dataset:
Modelling coevolutionary dynamics in heterogeneous SI epidemiological systems across scales
Authors: Tommaso Lorenzi, Elisa Paparelli, Andrea Tosin
Date: 2023-06-28 00:00:00

We develop a new structured compartmental model for the coevolutionary dynamics between susceptible and infectious individuals in heterogeneous SI epidemiological systems. In this model, the susceptible compartment is structured by a continuous variable that represents the level of resistance to infection of susceptible individuals, while the infectious compartment is structured by a continuous variable that represents the viral load of infectious individuals. We first formulate an individual-based model wherein the dynamics of single individuals is described through stochastic processes, which permits a fine-grain representation of individual dynamics and captures stochastic variability in evolutionary trajectories amongst individuals. Next we formally derive the mesoscopic counterpart of this model, which consists of a system of coupled integro-differential equations for the population density functions of susceptible and infectious individuals. Then we consider an appropriately rescaled version of this system and we carry out formal asymptotic analysis to derive the corresponding macroscopic model, which comprises a system of coupled ordinary differential equations for the proportions of susceptible and infectious individuals, the mean level of resistance to infection of susceptible individuals, and the mean viral load of infectious individuals. Overall, this leads to a coherent mathematical representation of the coevolutionary dynamics between susceptible and infectious individuals across scales. We provide well-posedness results for the mesoscopic and macroscopic models, and we show that there is excellent agreement between analytical results on the long-time behaviour of the components of the solution to the macroscopic model, the results of Monte Carlo simulations of the individual-based model, and numerical solutions of the macroscopic model.
Nonparametric causal discovery with applications to cancer bioinformatics
Authors: Jean Pierre Gomez
Date: 2023-06-28 00:00:00
Tasks: Causal Discovery

Many natural phenomena are intrinsically causal. The discovery of the cause-effect relationships implicit in these processes can help us to understand and describe them more effectively, which boils down to causal discovery about the data and variables that describe them. However, causal discovery is not an easy task. Current methods for this are extremely complex and costly, and their usefulness is strongly compromised in contexts with large amounts of data or where the nature of the variables involved is unknown. As an alternative, this paper presents an original methodology for causal discovery, built on essential aspects of the main theories of causality, in particular probabilistic causality, with many meeting points with the inferential approach of regularity theories and others. Based on this methodology, a non-parametric algorithm is developed for the discovery of causal relationships between binary variables associated to data sets, and the modeling in graphs of the causal networks they describe. This algorithm is applied to gene expression data sets in normal and cancerous prostate tissues, with the aim of discovering cause-effect relationships between gene dysregulations leading to carcinogenesis. The gene characterizations constructed from the causal relationships discovered are compared with another study based on principal component analysis (PCA) on the same data, with satisfactory results.
Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
Authors: Erik Altman, Jovan Blanuša, Luc von Niederhäusern, Béni Egressy, Andreea Anghel, Kubilay Atasu
Date: 2023-06-22 00:00:00
Proceeding: NeurIPS 2023 11

With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5\% of global GDP or \$0.8 - \$2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.
Neural networks can detect model-free static arbitrage strategies
Authors: Ariel Neufeld, Julian Sester
Date: 2023-06-19 00:00:00

In this paper we demonstrate both theoretically as well as numerically that neural networks can detect model-free static arbitrage opportunities whenever the market admits some. Due to the use of neural networks, our method can be applied to financial markets with a high number of traded securities and ensures almost immediate execution of the corresponding trading strategies. To demonstrate its tractability, effectiveness, and robustness we provide examples using real financial data. From a technical point of view, we prove that a single neural network can approximately solve a class of convex semi-infinite programs, which is the key result in order to derive our theoretical results that neural networks can detect model-free static arbitrage strategies whenever the financial market admits such opportunities.
Discount Models
Authors: Damir Filipovic
Date: 2023-06-29 00:00:00

Discount is the difference between the face value of a bond and its present value. I propose an arbitrage-free dynamic framework for discount models, which provides an alternative to the Heath--Jarrow--Morton framework for forward rates. I derive general consistency conditions for factor models, and discuss affine term structure models in particular. There are several open problems, and I outline possible directions for further research.
Divergence Based Quadrangle and Applications
Authors: Anton Malandii, Siddhartha Gupte, Cheng Peng, Stan Uryasev
Date: 2023-06-28 00:00:00
Tasks: Decision Making, Management

This paper introduces a novel framework for assessing risk and decision-making in the presence of uncertainty, the \emph{$\varphi$-Divergence Quadrangle}. This approach expands upon the traditional Risk Quadrangle, a model that quantifies uncertainty through four key components: \emph{risk, deviation, regret}, and \emph{error}. The $\varphi$-Divergence Quadrangle incorporates the $\varphi$-divergence as a measure of the difference between probability distributions, thereby providing a more nuanced understanding of risk. Importantly, the $\varphi$-Divergence Quadrangle is closely connected with the distributionally robust optimization based on the $\varphi$-divergence approach through the duality theory of convex functionals. To illustrate its practicality and versatility, several examples of the $\varphi$-Divergence Quadrangle are provided, including the Quantile Quadrangle. The final portion of the paper outlines a case study implementing regression with the Entropic Value-at-Risk Quadrangle. The proposed $\varphi$-Divergence Quadrangle presents a refined methodology for understanding and managing risk, contributing to the ongoing development of risk assessment and management strategies.
weighted CapsuleNet networks for Persian multi-domain sentiment analysis
Authors: Mahboobeh Sadat Kobari, Nima Karimi, Benyamin Pourhosseini, Ramin Mousa
Date: 2023-06-12 00:00:00
Tasks: Classification, domain classification, Sentiment Analysis, Sentiment Classification

Sentiment classification is a fundamental task in natural language processing, assigning one of the three classes, positive, negative, or neutral, to free texts. However, sentiment classification models are highly domain dependent; the classifier may perform classification with reasonable accuracy in one domain but not in another due to the Semantic multiplicity of words getting poor accuracy. This article presents a new Persian/Arabic multi-domain sentiment analysis method using the cumulative weighted capsule networks approach. Weighted capsule ensemble consists of training separate capsule networks for each domain and a weighting measure called domain belonging degree (DBD). This criterion consists of TF and IDF, which calculates the dependency of each document for each domain separately; this value is multiplied by the possible output that each capsule creates. In the end, the sum of these multiplications is the title of the final output, and is used to determine the polarity. And the most dependent domain is considered the final output for each domain. The proposed method was evaluated using the Digikala dataset and obtained acceptable accuracy compared to the existing approaches. It achieved an accuracy of 0.89 on detecting the domain of belonging and 0.99 on detecting the polarity. Also, for the problem of dealing with unbalanced classes, a cost-sensitive function was used. This function was able to achieve 0.0162 improvements in accuracy for sentiment classification. This approach on Amazon Arabic data can achieve 0.9695 accuracies in domain classification.
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets
Authors: Vadim Lomshakov, Sergey Kovalchuk, Maxim Omelchenko, Sergey Nikolenko, Artem Aliev
Date: 2023-06-26 00:00:00
Proceeding: ICCS: International Conference on Computational Science 2023 6
Tasks: Code Generation, Language Modelling, Program Synthesis, Prompt Engineering, Question Answering, Text-to-Code Generation

We study the ability of pretrained large language models (LLM) to answer questions from online question answering fora such as Stack Overflow. We consider question-answer pairs where the main part of the answer consists of source code. On two benchmark datasets—CoNaLa and a newly collected dataset based on Stack Overflow—we investigate how a closed-book question answering system can be improved by fine-tuning the LLM for the downstream task, prompt engineering, and data preprocessing. We use publicly available autoregressive language models such as GPT-Neo, CodeGen, and PanGu-Coder, and after the proposed fine-tuning achieve a BLEU score of 0.4432 on the CoNaLa test set, significantly exceeding previous state of the art for this task.
Near-Optimal 3-D Visual Coverage for Quadrotor Unmanned Aerial Vehicles Under Photogrammetric Constraints
Authors: Hongpeng Wang
Date: 2022-02-02 00:00:00
Proceeding: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022 2
Tasks: Trajectory Planning

In this article, we propose a complete visual
coverage trajectory planning framework for quadrotor un-
manned aerial vehicles (UAVs) in three-dimensional (3-D)
terrain environment under photogrammetric constraints. To
ensure the accuracy of the 3-D reconstruction, the air-to-
ground projective geometric constraints are established.
To optimize exploration efﬁciency, a novel two-step hier-
archical coverage planning algorithm is implemented. The
algorithm is veriﬁed in both simulation and practical ex-
periments. The coverage trajectory that satisﬁes the con-
straints is generated, and the 3-D reconstruction from the
images collected along the way is obtained. The results
show that the proposed method can improve the perfor-
mance of quadrotor UAV-based 3-D visual coverage in
terms of trajectory length and traversal time, as well as the
quality of 3-D reconstruction.
Geometrical interpretation and improvements of the Blahut-Arimoto's algorithm
Authors: Ziad NAJA(1), Florence ALBERGE(1), Pierre DUHAMEL(2)
Date: 2009-04-01 00:00:00
Proceeding: ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing 2009 4

The paper first recalls the Blahut Arimoto algorithm for computing the capacity of arbitrary discrete memoryless channels, as an example of an iterative algorithm working with probability density estimates. Then, a geometrical interpretation of this algorithm based on projections onto linear and exponential families of probabilities is provided. Finally, this understanding allows also to propose to write the Blahut-Arimoto algorithm, as a true proximal point algorithm. it is shown that the corresponding version has an improved convergence rate, compared to the initial algorithm, as well as in comparison with other improved versions.
Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing
Authors: Kaihong Huang 1, Chunshu Li 2, Jiaqi Zhang 3 and Beilun Wang 2, *
Date: 2021-08-13 00:00:00
Proceeding: journal 2021 8
Tasks: Decoder, Object, object-detection, Object Detection

The demand for the sensor-based detection of camouflage objects widely exists in biological
research, remote sensing, and military applications. However, the performance of traditional object
detection algorithms is limited, as they are incapable of extracting informative parts from low signal-to-noise ratio features. To address this problem, we propose Camouflaged Object Detection with
Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor
that leverages a cascaded structure with Feedback Partial Decoders (FPD) instead of a traditional
encoder–decoder structure. Through a selective fusion strategy and feedback loop, FPD reduces the
loss of information and the interference of noises in the process of feature interweaving. Furthermore,
we introduce Pixel Perception Fusion (PPF) loss, which aims to pay more attention to local pixels that
might become the edges of an object. Experimental results on an edge device show that CODCEF
achieved competitive results compared with 10 state-of-the-art methods.
The birth properties of Galactic millisecond radio pulsars
Authors: Lilia Ferrario, Dayal Wickramasinghe
Date: 2007-01-16 00:00:00

We model the population characteristics of the sample of millisecond pulsars within a distance of 1.5kpc.We find that for a braking index n=3, the birth magnetic field distribution of the neutron stars as they switch on as radio MSPs can be represented by a Gaussian with mean $\log B(G)= 8.1$ and $\sigma_{\log B}=0.4$ and their birth spin period by a Gaussian with mean $P_0=4$ ms and $\sigma_{P_0}=1.3$ ms. Our study, which takes into consideration acceleration effects on the observed spin-down rate, shows that most MSPs are born with periods that are close to the currently observed values and with average characteristic ages typically larger by a factor 1.5 compared to the true age. The Galactic birth rate of the MSPs is deduced to be $\gsimeq 3.2 \times 10^{-6}$ yr$^{-1}$ near the upper end of previous estimates and larger than the semi-empirical birth rate $\sim 10^{-7}$ yr$^{-1}$ of the LMXBs. The mean birth spin period deduced by us for the radio MSPs is a factor 2 higher than the mean spin period observed for the accretion and nuclear powered X-ray pulsars, although this discrepancy can be resolved if we use a braking index $n=5$, the value appropriate to spin down caused by angular momentum losses by gravitational radiation or magnetic multipolar radiation. We discuss the arguments for and against the hypothesis that accretion induced collapse may constitute the main route to the formation of the MSPs, pointing out that on the AIC scenario the low magnetic fields of the MSPs may simply reflect the field distribution in isolated magnetic white dwarfs which has recently been shown to be bi-modal with a dominant component that is likely to peak at fields below $10^3$ G which would scale to neutron star fields below $10^9$ G.
Audio Embeddings as Teachers for Music Classification
Authors: Yiwei Ding, Alexander Lerch
Date: 2023-06-30 00:00:00
Tasks: Classification, Information Retrieval, Instrument Recognition, Knowledge Distillation, Music Auto-Tagging, Music Classification, Music Information Retrieval, Retrieval, Transfer Learning

Music classification has been one of the most popular tasks in the field of music information retrieval. With the development of deep learning models, the last decade has seen impressive improvements in a wide range of classification tasks. However, the increasing model complexity makes both training and inference computationally expensive. In this paper, we integrate the ideas of transfer learning and feature-based knowledge distillation and systematically investigate using pre-trained audio embeddings as teachers to guide the training of low-complexity student networks. By regularizing the feature space of the student networks with the pre-trained embeddings, the knowledge in the teacher embeddings can be transferred to the students. We use various pre-trained audio embeddings and test the effectiveness of the method on the tasks of musical instrument classification and music auto-tagging. Results show that our method significantly improves the results in comparison to the identical model trained without the teacher's knowledge. This technique can also be combined with classical knowledge distillation approaches to further improve the model's performance.
Stay on topic with Classifier-Free Guidance
Authors: Guillaume Sanchez, Honglu Fan, Alexander Spangher, Elad Levi, Pawan Sasanka Ammanamanchi, Stella Biderman
Date: 2023-06-30 00:00:00
Tasks: Code Generation, Common Sense Reasoning, Image Generation, LAMBADA, Language Modeling, Language Modelling, Machine Translation, Sentence Completion, Text Generation, Text to Image Generation, Text-to-Image Generation, Zero-Shot Learning

Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75\% preference for GPT4All using CFG over baseline.
Breaking the Metric Voting Distortion Barrier
Authors: Moses Charikar, Prasanna Ramakrishnan, Kangning Wang, Hongxun Wu
Date: 2023-06-30 00:00:00

We consider the following well-studied problem of metric distortion in social choice. Suppose we have an election with $n$ voters and $m$ candidates located in a shared metric space. We would like to design a voting rule that chooses a candidate whose average distance to the voters is small. However, instead of having direct access to the distances in the metric space, the voting rule obtains, from each voter, a ranked list of the candidates in order of distance. Can we design a rule that regardless of the election instance and underlying metric space, chooses a candidate whose cost differs from the true optimum by only a small factor (known as the distortion)? A long line of work culminated in finding optimal deterministic voting rules with metric distortion $3$. However, for randomized voting rules, there is still a gap in our understanding: Even though the best lower bound is $2.112$, the best upper bound is still $3$, attained even by simple rules such as Random Dictatorship. Finding a randomized rule that guarantees distortion $3 - \epsilon$ has been a major challenge in computational social choice, as prevalent approaches to designing voting rules are known to be insufficient. Such a voting rule must use information beyond aggregate comparisons between pairs of candidates, and cannot only assign positive probability to candidates that are voters' top choices. In this work, we give a rule that guarantees distortion less than $2.753$. To do so we study a handful of voting rules that are new to the problem. One is Maximal Lotteries, a rule based on the Nash equilibrium of a natural zero-sum game which dates back to the 60's. The others are novel rules that can be thought of as hybrids of Random Dictatorship and the Copeland rule. Though none of these rules can beat distortion $3$ alone, a randomization between Maximal Lotteries and any of the novel rules can.
Resetting the Optimizer in Deep RL: An Empirical Study
Date: 2023-06-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Deep Reinforcement Learning

We focus on the task of approximating the optimal value function in deep reinforcement learning. This iterative process is comprised of solving a sequence of optimization problems where the loss function changes per iteration. The common approach to solving this sequence of problems is to employ modern variants of the stochastic gradient descent algorithm such as Adam. These optimizers maintain their own internal parameters such as estimates of the first-order and the second-order moments of the gradient, and update them over time. Therefore, information obtained in previous iterations is used to solve the optimization problem in the current iteration. We demonstrate that this can contaminate the moment estimates because the optimization landscape can change arbitrarily from one iteration to the next one. To hedge against this negative effect, a simple idea is to reset the internal parameters of the optimizer when starting a new iteration. We empirically investigate this resetting idea by employing various optimizers in conjunction with the Rainbow algorithm. We demonstrate that this simple modification significantly improves the performance of deep RL on the Atari benchmark.
Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetables
Authors: Chin-Yun Yu, György Fazekas
Date: 2023-06-29 00:00:00
Tasks: Singing Voice Synthesis

This paper introduces GlOttal-flow LPC Filter (GOLF), a novel method for singing voice synthesis (SVS) that exploits the physical characteristics of the human voice using differentiable digital signal processing. GOLF employs a glottal model as the harmonic source and IIR filters to simulate the vocal tract, resulting in an interpretable and efficient approach. We show it is competitive with state-of-the-art singing voice vocoders, requiring fewer synthesis parameters and less memory to train, and runs an order of magnitude faster for inference. Additionally, we demonstrate that GOLF can model the phase components of the human voice, which has immense potential for rendering and analysing singing voices in a differentiable manner. Our results highlight the effectiveness of incorporating the physical properties of the human voice mechanism into SVS and underscore the advantages of signal-processing-based approaches, which offer greater interpretability and efficiency in synthesis.