pwc-archive/papers-with-abstracts huggingface dataset:
Identifiability of total effects from abstractions of time series causal graphs
Authors: Charles K. Assaad, Emilie Devijver, Eric Gaussier, Gregor Gössler, Anouar Meynaoui
Date: 2023-10-23 00:00:00
Tasks: Time Series

We study the problem of identifiability of the total effect of an intervention from observational time series in the situation, common in practice, where one only has access to abstractions of the true causal graph. We consider here two abstractions: the extended summary causal graph, which conflates all lagged causal relations but distinguishes between lagged and instantaneous relations, and the summary causal graph which does not give any indication about the lag between causal relations. We show that the total effect is always identifiable in extended summary causal graphs and provide sufficient conditions for identifiability in summary causal graphs. We furthermore provide adjustment sets allowing to estimate the total effect whenever it is identifiable.
An updated State-of-the-Art Overview of transcriptomic Deconvolution Methods
Authors: Bastien Chassagnol, Grégory Nuel, Etienne Becht
Date: 2023-10-23 00:00:00

Although bulk transcriptomic analyses have significantly contributed to an enhanced comprehension of multifaceted diseases, their exploration capacity is impeded by the heterogeneous compositions of biological samples. Indeed, by averaging expression of multiple cell types, RNA-Seq analysis is oblivious to variations in cellular changes, hindering the identification of the internal constituents of tissues, involved in disease progression. On the other hand, single-cell techniques are still time, manpower and resource-consuming analyses.To address the intrinsic limitations of both bulk and single-cell methodologies, computational deconvolution techniques have been developed to estimate the frequencies of cell subtypes within complex tissues. These methods are especially valuable for dissecting intricate tissue niches, with a particular focus on tumour microenvironments (TME).In this paper, we offer a comprehensive overview of deconvolution techniques, classifying them based on their methodological characteristics, the type of prior knowledge required for the algorithm, and the statistical constraints they address. Within each category identified, we delve into the theoretical aspects for implementing the underlying method, while providing an in-depth discussion of their main advantages and disadvantages in supplementary materials.Notably, we emphasise the advantages of cutting-edge deconvolution tools based on probabilistic models, as they offer robust statistical frameworks that closely align with biological realities. We anticipate that this review will provide valuable guidelines for computational bioinformaticians in order to select the appropriate method in alignment with their statistical and biological objectives.We ultimately end this review by discussing open challenges that must be addressed to accurately quantify closely related cell types from RNA sequencing data, and the complementary role of single-cell RNA-Seq to that purpose.
On a model of online analog computation in the cell with absolute functional robustness: algebraic characterization, function compiler and error control
Authors: Mathieu Hemery, François Fages
Date: 2023-10-23 00:00:00

The Turing completeness of continuous Chemical Reaction Networks (CRNs) states that any computable real function can be computed by a continuous CRN on a finite set of molecular species, possibly restricted to elementary reactions, i.e. with at most two reactants and mass action law kinetics. In this paper, we introduce a more stringent notion of robust online analog computation, and Absolute Functional Robustness (AFR), for the CRNs that stabilize the concentration values of some output species to the result of one function of the input species concentrations, in a perfectly robust manner with respect to perturbations of both intermediate and output species. We prove that the set of real functions stabilized by a CRN with mass action law kinetics is precisely the set of real algebraic functions. Based on this result, we present a compiler which takes as input any algebraic function (defined by one polynomial and one point for selecting one branch of the algebraic curve defined by the polynomial) and generates an abstract CRN to stabilize it. Furthermore, we provide error bounds to estimate and control the error of an unperturbed system, under the assumption that the environment inputs are driven by k-Lipschitz functions.
Neural representation in active inference: using generative models to interact with -- and understand -- the lived world
Authors: Giovanni Pezzulo, Leo D'Amato, Francesco Mannella, Matteo Priorelli, Toon Van de Maele, Ivilin Peev Stoianov, Karl Friston
Date: 2023-10-23 00:00:00
Tasks: Navigate

This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action-perception cycles to those that underwrite planning and imagination; namely, from "explicit" models that entail variables for predicting concurrent sensations, like objects, faces, or people - to "action-oriented models" that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities - and the gradual transition from "pragmatic" to "detached" neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation.
Effective and anatomical connectivity of the dorso-central insula during the processing of action forms
Authors: Di Cesare G., Zeidman P., Urgen B. A., Lombardi G., Sciutti A
Date: 2023-10-22 00:00:00

In both human and monkeys the observation and execution of actions produced the activation of a network consisting of parietal and frontal areas. Although this network is involved in the encoding of the action goal, it does not consider the affective component of the action: vitality form (VF). Several studies showed that the observation and execution of actions conveying VFs selectively activated the dorso-central insula (DCI). In the present study, we aimed to clarify, by using Dynamic Causal Modeling (DCM), the direction of the information flow across DCI, parieto-frontal areas (PMv, IPL) and posterior superior temporal sulcus (pSTS) during both observation and execution of actions conveying VFs. Results indicate that, during observation, DCI receives the visual input from pSTS, and, in turn, sends it to the fronto-parietal network. Moreover, DCI significantly modulates PMv. Conversely, during execution, the motor input starts from PMv, reaches DCI and IPL, with a significant modulation from PMv to DCI. The reciprocal exchange of information between PMv and DCI suggests that these areas work closely together in the VFs action processing. An additional tractography analysis corroborates our DCM models, showing a correspondence between functional connections and anatomical tracts.
Form, function, mind: what doesn't compute (and what might)
Authors: Stuart A. Newman
Date: 2023-10-21 00:00:00
Tasks: Form

The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, dynamicism is similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life.
Computational and Systems Biology Advances to Enable Bioagent-Agnostic Signatures
Authors: Andy Lin, Cameron Torres, Errett C. Hobbs, Jaydeep Bardhan, Stephen B. Aley, Charles T. Spencer, Karen L. Taylor, Tony Chiang
Date: 2023-10-21 00:00:00

Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen interactions directed by a human immunological model. Although well-developed technical assays exist for many aspects of human-pathogen interaction, the analytic methods and pipelines to combine and holistically interpret the results of such assays are immature and require further investments to exploit new technologies. In this manuscript, we discuss potential immunologically based bioagent-agnostic approaches and the computational tool gaps the community should prioritize filling.
Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes
Authors: Yaroslav Balytskyi, Nataliia Kalashnyk, Inna Hubenko, Alina Balytska, Kelly McNear
Date: 2023-10-19 00:00:00

The combination of Deep Learning techniques and Raman spectroscopy shows great potential offering precise and prompt identification of pathogenic bacteria in clinical settings. However, the traditional closed-set classification approaches assume that all test samples belong to one of the known pathogens, and their applicability is limited since the clinical environment is inherently unpredictable and dynamic, unknown or emerging pathogens may not be included in the available catalogs. We demonstrate that the current state-of-the-art Neural Networks identifying pathogens through Raman spectra are vulnerable to unknown inputs, resulting in an uncontrollable false positive rate. To address this issue, first, we developed a novel ensemble of ResNet architectures combined with the attention mechanism which outperforms existing closed-world methods, achieving an accuracy of $87.8 \pm 0.1\%$ compared to the best available model's accuracy of $86.7 \pm 0.4\%$. Second, through the integration of feature regularization by the Objectosphere loss function, our model achieves both high accuracy in identifying known pathogens from the catalog and effectively separates unknown samples drastically reducing the false positive rate. Finally, the proposed feature regularization method during training significantly enhances the performance of out-of-distribution detectors during the inference phase improving the reliability of the detection of unknown classes. Our novel algorithm for Raman spectroscopy enables the detection of unknown, uncatalogued, and emerging pathogens providing the flexibility to adapt to future pathogens that may emerge, and has the potential to improve the reliability of Raman-based solutions in dynamic operating environments where accuracy is critical, such as public safety applications.
Unwinding Stochastic Order Flow: When to Warehouse Trades
Authors: Marcel Nutz, Kevin Webster, Long Zhao
Date: 2023-10-22 00:00:00

We study how to unwind stochastic order flow with minimal transaction costs. Stochastic order flow arises, e.g., in the central risk book (CRB), a centralized trading desk that aggregates order flows within a financial institution. The desk can warehouse in-flow orders, ideally netting them against subsequent opposite orders (internalization), or route them to the market (externalization) and incur costs related to price impact and bid-ask spread. We model and solve this problem for a general class of in-flow processes, enabling us to study in detail how in-flow characteristics affect optimal strategy and core trading metrics. Our model allows for an analytic solution in semi-closed form and is readily implementable numerically. Compared with a standard execution problem where the order size is known upfront, the unwind strategy exhibits an additive adjustment for projected future in-flows. Its sign depends on the autocorrelation of orders; only truth-telling (martingale) flow is unwound myopically. In addition to analytic results, we present extensive simulations for different use cases and regimes, and introduce new metrics of practical interest.
A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market
Authors: Jaydip Sen, Arup Dasgupta, Partha Pratim Sengupta, Sayantani Roy Choudhury
Date: 2023-10-23 00:00:00
Tasks: Portfolio Optimization

This chapter presents a comparative study of the three portfolio optimization methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing on the stocks chosen from 15 sectors listed on the National Stock Exchange of India. The top stocks of each cluster are identified based on their free-float market capitalization from the report of the NSE published on July 1, 2022 (NSE Website). For each sector, three portfolios are designed on stock prices from July 1, 2019, to June 30, 2022, following three portfolio optimization approaches. The portfolios are tested over the period from July 1, 2022, to June 30, 2023. For the evaluation of the performances of the portfolios, three metrics are used. These three metrics are cumulative returns, annual volatilities, and Sharpe ratios. For each sector, the portfolios that yield the highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio over the training and the test periods are identified.
Human Reliability Assessment method applied to investigate human factors in NDT -- The case of the interpretation of radiograms in the French nuclear sector
Authors: Justin Larouze, Etienne Martin, Pierre Calmon
Date: 2023-10-23 00:00:00
Tasks: Management

This communication reports on a study carried out in the context of the collaborative FOEHN project (Human and Organizational Factors in Non-Destructive Evaluation) supported by the French National Research Agency. The motivation of this project comes from the observation that human and Organizational factors (HOF) are not sufficiently considered by the NDT community. Its goal is to analyse and model the influence of the HOF on selected cases of study in the perspective of a better evaluation of the performance of inspections. The communication is focused on a radiographic test (RT) case of study in which it appeared that several successive inspections had failed to detect an existing in-service defect. The analysis and modelling of HOF related to interpretation of films has been achieved in the framework of the CREAM (Cognitive and Reliability and Error Analysis Method). A survey has been conducted during the training and the maintaining of the proficiency of NDT (Non Destructive Testing) operators. This was followed by a non-participant observation of operators on site and several individual interviews including a sample of people covering the main organizational and hierarchical roles (eg. project management, management, operations, invigilation). The exchange with the HOF experts resulted in a hierarchical analysis of ''radiogram interpretation'' tasks (31 sub-tasks) and a list of contextual and organizational factors that may affect the performance of interpretation of films by the operator. From such a description the CREAM method allows to determine critical tasks and probability of ``errors'' linked to a limited set of ``Common Performance Conditions'' (CPC). The first conclusions of this study are that the model CREAM seems well-adapted to the estimation of the impact of HOF on NDT performances. The next phases should be to apply it to other tasks (here only radiograph interpretation) and techniques. The expected benefit of this study is to provide tools for the evaluation and optimisation of NDT implementation.
Reconciling Open Interest with Traded Volume in Perpetual Swaps
Authors: Ioannis Giagkiozis, Emilio Said
Date: 2023-10-23 00:00:00

Perpetual swaps are derivative contracts that allow traders to speculate on, or hedge, the price movements of cryptocurrencies. Unlike futures contracts, perpetual swaps have no settlement or expiration in the traditional sense. The funding rate acts as the mechanism that tethers the perpetual swap to its underlying with the help of arbitrageurs. Open interest, in the context of perpetual swaps and derivative contracts in general, refers to the total number of outstanding contracts at a given point in time. It is a critical metric in derivatives markets as it can provide insight into market activity, sentiment and overall liquidity. It also provides a way to estimate a lower bound on the collateral required for every cryptocurrency market on an exchange. This number, cumulated across all markets on the exchange in combination with proof of reserves, can be used to gauge whether the exchange in question operates with unsustainable levels of leverage, which could have solvency implications. We find that open interest in Bitcoin perpetual swaps is systematically misquoted by some of the largest derivatives exchanges; however, the degree varies, with some exchanges reporting open interest that is wholly implausible to others that seem to be delaying messages of forced trades, i.e., liquidations. We identify these incongruities by analyzing tick-by-tick data for two time periods in $2023$ by connecting directly to seven of the most liquid cryptocurrency derivatives exchanges.
Cognitive Energy Cost of Informed Decisions
Authors: Michele Vodret
Date: 2023-10-23 00:00:00
Tasks: Decision Making

Time irreversibility in neuronal dynamics has recently been demonstrated to correlate with various indicators of cognitive effort in living systems. Using Landauer's principle, which posits that time-irreversible information processing consumes energy, we establish a thermodynamically consistent measure of cognitive energy cost associated with belief dynamics. We utilize this concept to analyze a two-armed bandit game, a standard decision-making framework under uncertainty, considering exploitation, finite memory, and concurrent allocation to both game options or arms. Through exploitative, prediction-error-based belief dynamics, the decision maker incurs a cognitive energy cost. Initially, we observe the rise of dissipative structures in the steady state of the belief space due to time-reversal symmetry breaking at intermediate exploitative levels. To delve deeper into the belief dynamics, we liken it to the behavior of an active particle subjected to state-dependent noise. This analogy enables us to relate emergent risk aversion to standard thermophoresis, connecting two apparently unrelated concepts. Finally, we numerically compute the time irreversibility of belief dynamics in the steady state, revealing a strong correlation between elevated - yet optimized - cognitive energy cost and optimal decision-making outcomes. This correlation suggests a mechanism for the evolution of living systems towards maximally out-of-equilibrium structures.
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Authors: Yingwei Ma, Yue Yu, Shanshan Li, Yu Jiang, Yong Guo, Yuanliang Zhang, Yutao Xie, Xiangke Liao
Date: 2023-10-16 00:00:00
Tasks: Code Generation, HumanEval, In-Context Learning, mbpp

Large language models (LLMs) have showcased remarkable prowess in code generation. However, automated code generation is still challenging since it requires a high-level semantic mapping between natural language requirements and codes. Most existing LLMs-based approaches for code generation rely on decoder-only causal language models often treate codes merely as plain text tokens, i.e., feeding the requirements as a prompt input, and outputing code as flat sequence of tokens, potentially missing the rich semantic features inherent in source code. To bridge this gap, this paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT. Our motivation is that the semantic information of the source code (\eg data flow and control flow) describes more precise program execution behavior, intention and function. By guiding LLM consider and integrate semantic information, we can achieve a more granular understanding and representation of code, enhancing code generation accuracy. Meanwhile, while traditional techniques leveraging such semantic information require complex static or dynamic code analysis to obtain features such as data flow and control flow, SeCoT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (i.e., in-context learning), while being generalizable and applicable to challenging domains. While SeCoT can be applied with different LLMs, this paper focuses on the powerful GPT-style models: ChatGPT(close-source model) and WizardCoder(open-source model). The experimental study on three popular DL benchmarks (i.e., HumanEval, HumanEval-ET and MBPP) shows that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.
Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data
Authors: Sergio F. Chevtchenko, Monalisa C. M. dos Santos, Diego M. Vieira, Ricardo L. Mota, Elisson Rocha, Bruna V. Cruz, Danilo Araújo, Ermeson Andrade
Date: 2023-10-15 00:00:00
Tasks: Anomaly Detection, Multiobjective Optimization

With the support of Internet of Things (IoT) devices, it is possible to acquire data from degradation phenomena and design data-driven models to perform anomaly detection in industrial equipment. This approach not only identifies potential anomalies but can also serve as a first step toward building predictive maintenance policies. In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines. This work evaluates a combination of pre-processing techniques and machine learning (ML) models with a low computational cost. We use a combination of pre-processing techniques such as Fast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are well-known approaches for extracting features from raw data. We also aim to guarantee an optimal balance between multiple conflicting parameters, such as anomaly detection rate, false positive rate, and inference speed of the solution. To this end, multiobjective optimization and analysis are performed on the evaluated models. Pareto-optimal solutions are presented to select which models have the best results regarding classification metrics and computational effort. Differently from most works in this field that use publicly available datasets to validate their models, we propose an end-to-end solution combining low-cost and readily available IoT sensors. The approach is validated by acquiring a custom dataset from induction motors. Also, we fuse vibration, temperature, and noise data from these sensors as the input to the proposed ML model. Therefore, we aim to propose a methodology general enough to be applied in different industrial contexts in the future.