pwc-archive/papers-with-abstracts huggingface dataset:
Unified neural field theory of brain dynamics underlying oscillations in Parkinson's disease and generalized epilepsies
Authors: Eli J Müller, Sacha J van Albada, Jong-Won Kim, Peter A Robinson
Date: 2024-01-24 00:00:00
Tasks: EEG

The mechanisms underlying pathologically synchronized neural oscillations in Parkinson's disease (PD) and generalized epilepsies are jointly explored via a neural field model of the corticothalamic-basal ganglia (CTBG) system. The basal ganglia (BG) are approximated as a single effective population and their roles in modulating oscillatory corticothalamic (CT) dynamics and vice versa are analyzed. Besides normal EEG rhythms, enhanced activity around 4 Hz and 20 Hz exists in the model, consistent with characteristic frequencies in PD. These rhythms result from resonances in loops between the BG and CT populations, analogous to those underlying epileptic oscillations in a previous CT model. Dopamine depletion is argued to weaken the dampening of these resonances in PD, and network connections explain the significant coherence between BG, thalamic, and cortical activity around 4-8 Hz and 20 Hz. Parallels between the afferent and efferent connection sites of the thalamic reticular nucleus (TRN) and BG predict low dopamine to correspond to a reduced likelihood of tonic-clonic (grand mal) seizures, agreeing with experimental findings. Further, the model predicts an increased likelihood of absence (petit mal) seizure resulting from low dopamine levels matching experimental findings. Suppression of absence seizure activity is shown when afferent and efferent BG connections to the CT system are strengthened, consistent with other CTBG modeling studies. The BG are demonstrated to suppress activity of the CTBG system near tonic-clonic seizure states, providing insight into the reported efficacy of current treatments in BG circuits. Sleep states of the TRN are also found to suppress pathological PD activity matching observations. Overall, the findings demonstrate strong parallels between coherent oscillations in generalized epilepsies and PD, and provide insights into possible comorbidities.
A generic model of consciousness
Authors: Mark J. Hadley
Date: 2024-01-02 00:00:00
Tasks: model

This is a model of consciousness. The hard problem of consciousness, what it feels like, is answered. The work builds on medical research analyzing the source and mechanisms associated with our feelings. It goes further by describing a generic model with wide applicability. The model is fully consistent with medical pathways in humans, but easily extends to animals and AI. The essence of the model is the interplay between associative memory and physiology. The model is a clear and concrete counterexample to the famous philosophical objections to a scientific explanation.
Enabling Global Image Data Sharing in the Life Sciences
Authors: Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner, Beth A Cimini, Lucy Collinson, Jan Ellenberg, Reto Fiolka, Maryellen Giger, Wojtek Goscinski, Matthew Hartley, Nathan Hotaling, Rick Horwitz, Florian Jug, Anna Kreshuk, Emma Lundberg, Aastha Mathur, Kedar Narayan, Shuichi Onami, Anne L. Plant, Fred Prior, Jason Swedlow, Adam Taylor, Antje Keppler
Date: 2024-01-23 00:00:00

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data
Authors: Nikki Bialy, Frank Alber, Brenda Andrews, Michael Angelo, Brian Beliveau, Lacramioara Bintu, Alistair Boettiger, Ulrike Boehm, Claire M. Brown, Mahmoud Bukar Maina, James J. Chambers, Beth A. Cimini, Kevin Eliceiri, Rachel Errington, Orestis Faklaris, Nathalie Gaudreault, Ronald N. Germain, Wojtek Goscinski, David Grunwald, Michael Halter, Dorit Hanein, John W. Hickey, Judith Lacoste, Alex Laude, Emma Lundberg, Jian Ma, Leonel Malacrida, Josh Moore, Glyn Nelson, Elizabeth Kathleen Neumann, Roland Nitschke, Shuichi Onami, Jaime A. Pimentel, Anne L. Plant, Andrea J. Radtke, Bikash Sabata, Denis Schapiro, Johannes Schöneberg, Jeffrey M. Spraggins, Damir Sudar, Wouter-Michiel Adrien Maria Vierdag, Niels Volkmann, Carolina Wählby, Siyuan, Wang, Ziv Yaniv, Caterina Strambio-De-Castillia
Date: 2024-01-23 00:00:00

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled Enabling Global Image Data Sharing in the Life Sciences, which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data. In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
Validation of Golden Gate assemblies using highly multiplexed Nanopore amplicon sequencing
Authors: Adan A. Ramirez Rojas, Cedric K. Brinkmann, Daniel Schindler
Date: 2024-01-25 00:00:00

Golden Gate cloning has revolutionized synthetic biology. Its concept of modular, highly characterized libraries of parts that can be combined into higher order assemblies allows engineering principles to be applied to biological systems. The basic parts, typically stored in level 0 plasmids, are sequence validated by the method of choice and can be combined into higher order assemblies on demand. Higher order assemblies are typically transcriptional units, and multiple transcriptional units can be assembled into multi-gene constructs. Higher order Golden Gate assembly based on defined and validated parts usually does not introduce sequence changes. Therefore, simple validation of the assemblies, e.g. by colony PCR or restriction digest pattern analysis, is sufficient. However, in many experimental setups, researchers do not use defined parts, but rather part libraries, resulting in assemblies of high combinatorial complexity where sequencing again becomes mandatory. Here we present a detailed protocol for the use of a highly multiplexed dual barcode amplicon sequencing using the Nanopore sequencing platform for in-house sequence validation. The workflow, called DuBA.flow, is a start-to-finish procedure that provides all necessary steps from a single colony to the final easy-to-interpret sequencing report.
Single NV in nanodiamond for quantum sensing of protein dynamics in an ABEL trap
Authors: Ivan Perez, Anke Krueger, Joerg Wrachtrup, Fedor Jelezko, Michael Börsch
Date: 2024-01-24 00:00:00

Enzymes are cellular protein machines using a variety of conformational changes to power fast biochemical catalysis. Our goal is to exploit the single-spin properties of the luminescent NV (nitrogen-vacancy) center in nanodiamonds to reveal the dynamics of an active enzyme complex at physiological conditions with the highest spatio-temporal resolution. Specifically attached to the membrane enzyme FoF1-ATP synthase, the NV sensor will report the adenosine triphosphate (ATP)-driven full rotation of Fo motor subunits in ten consecutive 36{\deg} steps. Conformational dynamics are monitored using either a double electron-electron resonance scheme or NV- magnetometry with optical readout or using NV- relaxometry with a superparamagnetic nanoparticle as the second marker attached to the same enzyme. First, we show how all photophysical parameters like individual size, charge, brightness, spectral range of fluorescence and fluorescence lifetime can be determined for the NV- center in a single nanodiamond held in aqueous solution by a confocal anti-Brownian electrokinetic trap (ABEL trap). Stable photon count rates of individual nanodiamonds and the absence of blinking allow for observation times of single nanodiamonds in solution exceeding hundreds of seconds. For the proposed quantum sensing of nanometer-sized distance changes within an active enzyme, we show that local magnetic field fluctuations can be detected all-optically by analyzing fluorescence lifetime changes of the NV- center in each nanodiamond in solution.
Ready for climate change? The importance of adaptive thermoregulatory flexibility for the Malagasy bat species Triaenops menamena
Authors: Sina Remmers
Date: 2024-01-23 00:00:00
Tasks: energy management

The balance between energy intake and expenditure is essential and crucial for survival for all organisms. The energy management is closely linked to the ecology. Thus, changes in environmental conditions can be challenging, especially for the animals physiology. Different strategies of thermoregulation have evolved and heterothermy seems to be the most efficient way for saving energy. Daily torpor, a temporally controlled reduction of the metabolic rate and body temperature, is one form of heterothermy and recent studies revealed that this physiological strategy is used by many tropical and subtropical species. Yet, little is known about torpor in bats and their intraspecific thermoregulatory flexibility. Therefore, three populations of the Malagasy bat species Triaenops menamena were investigated, to examine their metabolic rate, skin temperature and related energy expenditure during normothermic and torpid states in context of different microclimatic conditions. This study exposed significant physiological differences among these three populations along a gradient of fluctuation in environmental conditions. The greater the fluctuations in ambient temperature and humidity, the higher was the general resting metabolic rate and the rate of its reduction, but the lower was the torpid metabolic rate. This species shows a highly adaptive flexibility in their physiology and are able to cope with unfavorable environmental conditions by using different strategies of thermoregulation and hypometabolism, which is beneficial regarding ongoing climatic changes.
Higher order approximation of option prices in Barndorff-Nielsen and Shephard models
Authors: Álvaro Guinea Juliá, Alet Roux
Date: 2024-01-25 00:00:00

We present an approximation method based on the mixing formula (Hull & White 1987, Romano & Touzi 1997) for pricing European options in Barndorff-Nielsen and Shephard models. This approximation is based on a Taylor expansion of the option price. It is implemented using a recursive algorithm that allows us to obtain closed form approximations of the option price of any order (subject to technical conditions on the background driving L\'evy process). This method can be used for any type of Barndorff-Nielsen and Shephard stochastic volatility model. Explicit results are presented in the case where the stationary distribution of the background driving L\'evy process is inverse Gaussian or gamma. In both of these cases, the approximation compares favorably to option prices produced by the characteristic function. In particular, we also perform an error analysis of the approximation, which is partially based on the results of Das & Langren\'e (2022). We obtain asymptotic results for the error of the $N^{\text{th}}$ order approximation and error bounds when the variance process satisfies an inverse Gaussian Ornstein-Uhlenbeck process or a gamma Ornstein-Uhlenbeck process.
Optimizing Transition Strategies for Small to Medium Sized Portfolios
Authors: Nakul Upadhya, Alexandre Granzer-Guay
Date: 2024-01-23 00:00:00
Tasks: Management

This work discusses the benefits of constrained portfolio turnover strategies for small to medium-sized portfolios. We propose a dynamic multi-period model that aims to minimize transaction costs and maximize terminal wealth levels whilst adhering to strict portfolio turnover constraints. Our results demonstrate that using our framework in combination with a reasonable forecast, can lead to higher portfolio values and lower transaction costs on average when compared to a naive, single-period model. Such results were maintained given different problem cases, such as, trading horizon, assets under management, wealth levels, etc. In addition, the proposed model lends itself to a reformulation that makes use of the column generation algorithm which can be strategically leveraged to reduce complexity and solving times.
Self and mutually exciting point process embedding flexible residuals and intensity with discretely Markovian dynamics
Authors: Kyungsub Lee
Date: 2024-01-25 00:00:00
Tasks: Time Series

This work introduces a self and mutually exciting point process that embeds flexible residuals and intensity with discretely Markovian dynamics. By allowing the integration of diverse residual distributions, this model serves as an extension of the Hawkes process, facilitating intensity modeling. This model's nature enables a filtered historical simulation that more accurately incorporates the properties of the original time series. Furthermore, the process extends to multivariate models with manageable estimation and simulation implementations. We investigate the impact of a flexible residual distribution on the estimation of high-frequency financial data, comparing it with the Hawkes process.
AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles
Authors: M Sabbir Salek, Abdullah Al Mamun, Mashrur Chowdhury
Date: 2023-12-31 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Autonomous Vehicles, Classification, Generative Adversarial Network

This study developed a generative adversarial network (GAN)-based defense method for traffic sign classification in an autonomous vehicle (AV), referred to as the attack-resilient GAN (AR-GAN). The novelty of the AR-GAN lies in (i) assuming zero knowledge of adversarial attack models and samples and (ii) providing consistently high traffic sign classification performance under various adversarial attack types. The AR-GAN classification system consists of a generator that denoises an image by reconstruction, and a classifier that classifies the reconstructed image. The authors have tested the AR-GAN under no-attack and under various adversarial attacks, such as Fast Gradient Sign Method (FGSM), DeepFool, Carlini and Wagner (C&W), and Projected Gradient Descent (PGD). The authors considered two forms of these attacks, i.e., (i) black-box attacks (assuming the attackers possess no prior knowledge of the classifier), and (ii) white-box attacks (assuming the attackers possess full knowledge of the classifier). The classification performance of the AR-GAN was compared with several benchmark adversarial defense methods. The results showed that both the AR-GAN and the benchmark defense methods are resilient against black-box attacks and could achieve similar classification performance to that of the unperturbed images. However, for all the white-box attacks considered in this study, the AR-GAN method outperformed the benchmark defense methods. In addition, the AR-GAN was able to maintain its high classification performance under varied white-box adversarial perturbation magnitudes, whereas the performance of the other defense methods dropped abruptly at increased perturbation magnitudes.
Toward Robust Multimodal Learning using Multimodal Foundational Models
Authors: Xianbing Zhao, Soujanya Poria, Xuejiao Li, Yixin Chen, Buzhou Tang
Date: 2024-01-20 00:00:00
Tasks: Multimodal Sentiment Analysis, Sentiment Analysis

Existing multimodal sentiment analysis tasks are highly rely on the assumption that the training and test sets are complete multimodal data, while this assumption can be difficult to hold: the multimodal data are often incomplete in real-world scenarios. Therefore, a robust multimodal model in scenarios with randomly missing modalities is highly preferred. Recently, CLIP-based multimodal foundational models have demonstrated impressive performance on numerous multimodal tasks by learning the aligned cross-modal semantics of image and text pairs, but the multimodal foundational models are also unable to directly address scenarios involving modality absence. To alleviate this issue, we propose a simple and effective framework, namely TRML, Toward Robust Multimodal Learning using Multimodal Foundational Models. TRML employs generated virtual modalities to replace missing modalities, and aligns the semantic spaces between the generated and missing modalities. Concretely, we design a missing modality inference module to generate virtual modaliites and replace missing modalities. We also design a semantic matching learning module to align semantic spaces generated and missing modalities. Under the prompt of complete modality, our model captures the semantics of missing modalities by leveraging the aligned cross-modal semantic space. Experiments demonstrate the superiority of our approach on three multimodal sentiment analysis benchmark datasets, CMU-MOSI, CMU-MOSEI, and MELD.
Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels
Authors: Liming Xu, Stephen Mak, Yaniv Proselkov, Alexandra Brintrup
Date: 2023-10-13 00:00:00

Recent global disruptions, such as the pandemic and geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Autonomous supply chains (ASCs) have emerged as a potential solution, offering increased visibility, flexibility, and resilience in turbulent trade environments. Despite discussions in industry and academia over several years, ASCs lack well-established theoretical foundations. This paper addresses this research gap by presenting a formal definition of ASC along with its defining characteristics and auxiliary concepts. We propose a layered conceptual framework called the MIISI model. An illustrative case study focusing on the meat supply chain demonstrates an initial ASC implementation based on this conceptual model. Additionally, we introduce a seven-level supply chain autonomy reference model, delineating a trajectory towards achieving a full supply chain autonomy. Recognising that this work represents an initial endeavour, we emphasise the need for continued exploration in this emerging domain. We anticipate that this work will stimulate further research, both theoretical and technical, and contribute to the continual evolution of ASCs.
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
Authors: Michael Hanna, Ollie Liu, Alexandre Variengien
Date: 2023-04-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Language Modeling, Language Modelling, valid

Pre-trained language models can be surprisingly adept at tasks they were not explicitly trained on, but how they implement these capabilities is poorly understood. In this paper, we investigate the basic mathematical abilities often acquired by pre-trained language models. Concretely, we use mechanistic interpretability techniques to explain the (limited) mathematical abilities of GPT-2 small. As a case study, we examine its ability to take in sentences such as "The war lasted from the year 1732 to the year 17", and predict valid two-digit end years (years > 32). We first identify a circuit, a small subset of GPT-2 small's computational graph that computes this task's output. Then, we explain the role of each circuit component, showing that GPT-2 small's final multi-layer perceptrons boost the probability of end years greater than the start year. Finally, we find related tasks that activate our circuit. Our results suggest that GPT-2 small computes greater-than using a complex but general mechanism that activates across diverse contexts.
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Authors: Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long
Date: 2023-02-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Representation Learning, Time Series, Time Series Analysis

Time series analysis is widely used in extensive areas. Recently, to reduce labeling expenses and benefit various tasks, self-supervised pre-training has attracted immense interest. One mainstream paradigm is masked modeling, which successfully pre-trains deep models by learning to reconstruct the masked content based on the unmasked part. However, since the semantic information of time series is mainly contained in temporal variations, the standard way of randomly masking a portion of time points will seriously ruin vital temporal variations of time series, making the reconstruction task too difficult to guide representation learning. We thus present SimMTM, a Simple pre-training framework for Masked Time-series Modeling. By relating masked modeling to manifold learning, SimMTM proposes to recover masked time points by the weighted aggregation of multiple neighbors outside the manifold, which eases the reconstruction task by assembling ruined but complementary temporal variations from multiple masked series. SimMTM further learns to uncover the local structure of the manifold, which is helpful for masked modeling. Experimentally, SimMTM achieves state-of-the-art fine-tuning performance compared to the most advanced time series pre-training methods in two canonical time series analysis tasks: forecasting and classification, covering both in- and cross-domain settings.