pwc-archive/papers-with-abstracts huggingface dataset:
Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement
Authors: Mukesh Gautam, Narayan Bhusal, Mohammed Benidris
Date: 2023-05-02 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning

Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire analysis and computation to find the optimal network configuration with a change in system operating states. Contrary to this, if properly trained, deep reinforcement learning (DRL)-based DNR can determine optimal or near-optimal configuration quickly even with changes in system states. In this paper, a Deep Q Learning-based framework is proposed for the optimal DNR to improve reliability of the system. An optimization problem is formulated with an objective function that minimizes the average curtailed power. Constraints of the optimization problem are radial topology constraint and all nodes traversing constraint. The distribution network is modeled as a graph and the optimal network configuration is determined by searching for an optimal spanning tree. The optimal spanning tree is the spanning tree with the minimum value of the average curtailed power. The effectiveness of the proposed framework is demonstrated through several case studies on 33-node and 69-node distribution test systems.
Reserve Allocation in Active Distribution Systems for Tertiary Frequency Regulation: A Coalitional Game Theory-based Approach
Authors: Mukesh Gautam, Mohammad MansourLakouraj, Rakib Hossain, Narayan Bhusal, Mohammed Benidris, Hanif Livani
Date: 2023-05-02 00:00:00

This paper proposes a coalitional game theory-based approach for reserve optimization to enable DERs participate in tertiary frequency regulation. A two-stage approach is proposed to effectively and precisely allocate spinning reserve requirements from each DER in distribution systems. In the first stage, two types of characteristic functions: worthiness index (WI) and power loss reduction (PLR) of each coalition are computed. In the second stage, the equivalent Shapley values are computed based on the characteristic functions, which are used to determine distribution factors for reserve allocation among DERs.
Multiplicity Boost Of Transit Signal Classifiers: Validation of 69 New Exoplanets Using The Multiplicity Boost of ExoMiner
Authors: Hamed Valizadegan, Miguel J. S. Martinho, Jon M. Jenkins, Douglas A. Caldwell, Joseph D. Twicken, Stephen T. Bryson
Date: 2023-05-04 00:00:00

Most existing exoplanets are discovered using validation techniques rather than being confirmed by complementary observations. These techniques generate a score that is typically the probability of the transit signal being an exoplanet (y(x)=exoplanet) given some information related to that signal (represented by x). Except for the validation technique in Rowe et al. (2014) that uses multiplicity information to generate these probability scores, the existing validation techniques ignore the multiplicity boost information. In this work, we introduce a framework with the following premise: given an existing transit signal vetter (classifier), improve its performance using multiplicity information. We apply this framework to several existing classifiers, which include vespa (Morton et al. 2016), Robovetter (Coughlin et al. 2017), AstroNet (Shallue & Vanderburg 2018), ExoNet (Ansdel et al. 2018), GPC and RFC (Armstrong et al. 2020), and ExoMiner (Valizadegan et al. 2022), to support our claim that this framework is able to improve the performance of a given classifier. We then use the proposed multiplicity boost framework for ExoMiner V1.2, which addresses some of the shortcomings of the original ExoMiner classifier (Valizadegan et al. 2022), and validate 69 new exoplanets for systems with multiple KOIs from the Kepler catalog.
Structures of Neural Network Effective Theories
Authors: Ian Banta, Tianji Cai, Nathaniel Craig, Zhengkang Zhang
Date: 2023-05-03 00:00:00

We develop a diagrammatic approach to effective field theories (EFTs) corresponding to deep neural networks at initialization, which dramatically simplifies computations of finite-width corrections to neuron statistics. The structures of EFT calculations make it transparent that a single condition governs criticality of all connected correlators of neuron preactivations. Understanding of such EFTs may facilitate progress in both deep learning and field theory simulations.
Quantifying the Dissimilarity of Texts
Authors: Benjamin Shade, Eduardo G. Altmann
Date: 2023-05-03 00:00:00
Tasks: Clustering, Information Retrieval, Retrieval, Topic Classification

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures $D$ using three different representations of texts -- vocabularies, word frequency distributions, and vector embeddings -- and three simple tasks -- clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen--Shannon divergence applied to word frequencies performed strongly across all tasks, that $D$'s based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different $D$'s when the two texts varied in length by a factor $h$. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the $h$-dependency of the bias of the estimator of the generalised Jensen--Shannon divergence applied to word frequencies. We also found numerically that the Jensen--Shannon divergence and embedding-based approaches were robust to changes in $h$, while the Jaccard distance was not.
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Authors: Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Date: 2023-05-03 00:00:00

Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions. We report new algorithmic developments of gauge-equivariant flow architectures facilitating the generalization to higher-dimensional lattice geometries. Specifically, we discuss masked autoregressive transformations with tractable and unbiased Jacobian determinants, a key ingredient for scalable and asymptotically exact flow-based sampling algorithms. For concreteness, results from a proof-of-principle application to SU(3) lattice gauge theory in four space-time dimensions are reported.
Accuracy in readout of glutamate concentrations by neuronal cells
Authors: Swoyam Biswal, Vaibhav Wasnik
Date: 2023-05-02 00:00:00

Glutamate and glycine are important neurotransmitters in the brain. An action potential prop- agating in the terminal of a presynatic neuron causes the release of glutamate and glycine in the synapse by vesicles fusing with the cell membrane, which then activate various receptors on the cell membrane of the post synaptic neuron. Entry of Ca2+ through the activated NMDA receptors leads to a host of cellular processes of which long term potentiation is of crucial importance because it is widely considered to be one of the major mechanisms behind learning and memory. By analysing the readout of glutamate concentration by the post synaptic neurons during Ca2+ signaling, we find that the average receptor density in hippocampal neurons has evolved to allow for accurate measurement of the glutamate concentration in the synaptic cleft.
Emergent cooperative behavior in transient compartments
Authors: Jeferson J. Arenzon, Luca Peliti
Date: 2023-05-01 00:00:00
Tasks: Diversity

We introduce a minimal model of multilevel selection on structured populations, considering the interplay between game theory and population dynamics. Through a bottleneck process, finite groups are formed with cooperators and defectors sampled from an infinite pool. After the fragmentation, these transient compartments grow until the carrying capacity is attained. Eventually, all compartments are merged, well mixed and the whole process is repeated. We show that cooperators, even if interacting only through mean-field intra-group interactions that favor defectors, may perform well because of the inter-group competition and the size diversity among the compartments. These cycles of isolation and coalescence may therefore be important in maintaining diversity among different species or strategies and may help to understand the underlying mechanisms of the scaffolding processes in the transition to multicellularity.
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning
Authors: Xinyang Huang, Chuang Zhu, Wenkai Chen
Date: 2023-05-04 00:00:00
Tasks: Domain Adaptation, Pseudo Label, Semi-supervised Domain Adaptation, Transfer Learning

In semi-supervised domain adaptation (SSDA), a few labeled target samples of each class help the model to transfer knowledge representation from the fully labeled source domain to the target domain. Many existing methods ignore the benefits of making full use of the labeled target samples from multi-level. To make better use of this additional data, we propose a novel Prototype-based Multi-level Learning (ProML) framework to better tap the potential of labeled target samples. To achieve intra-domain adaptation, we first introduce a pseudo-label aggregation based on the intra-domain optimal transport to help the model align the feature distribution of unlabeled target samples and the prototype. At the inter-domain level, we propose a cross-domain alignment loss to help the model use the target prototype for cross-domain knowledge transfer. We further propose a dual consistency based on prototype similarity and linear classifier to promote discriminative learning of compact target feature representation at the batch level. Extensive experiments on three datasets, including DomainNet, VisDA2017, and Office-Home demonstrate that our proposed method achieves state-of-the-art performance in SSDA.
Trainability barriers and opportunities in quantum generative modeling
Authors: Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp, Oriel Kiss, Sofia Vallecorsa, Michele Grossi, Zoë Holmes
Date: 2023-05-04 00:00:00

Quantum generative models, in providing inherently efficient sampling strategies, show promise for achieving a near-term advantage on quantum hardware. Nonetheless, important questions remain regarding their scalability. In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration. We explore the interplay between explicit and implicit models and losses, and show that using implicit generative models (such as quantum circuit-based models) with explicit losses (such as the KL divergence) leads to a new flavour of barren plateau. In contrast, the Maximum Mean Discrepancy (MMD), which is a popular example of an implicit loss, can be viewed as the expectation value of an observable that is either low-bodied and trainable, or global and untrainable depending on the choice of kernel. However, in parallel, we highlight that the low-bodied losses required for trainability cannot in general distinguish high-order correlations, leading to a fundamental tension between exponential concentration and the emergence of spurious minima. We further propose a new local quantum fidelity-type loss which, by leveraging quantum circuits to estimate the quality of the encoded distribution, is both faithful and enjoys trainability guarantees. Finally, we compare the performance of different loss functions for modelling real-world data from the High-Energy-Physics domain and confirm the trends predicted by our theoretical results.
Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions
Authors: Matthias Plock, Martin Hammerschmidt, Sven Burger, Philipp-Immanuel Schneider, Christof Schütte
Date: 2023-05-04 00:00:00

In optical nano metrology numerical models are used widely for parameter reconstructions. Using the Bayesian target vector optimization method we fit a finite element numerical model to a Grazing Incidence X-Ray fluorescence data set in order to obtain the geometrical parameters of a nano structured line grating. Gaussian process, stochastic machine learning surrogate models, were trained during the reconstruction and afterwards sampled with a Markov chain Monte Carlo sampler to determine the distribution of the reconstructed model parameters. The numerical discretization parameters of the used finite element model impact the numerical discretization error of the forward model. We investigated the impact of the polynomial order of the finite element ansatz functions on the reconstructed parameters as well as on the model parameter distributions. We showed that such a convergence study allows to determine numerical parameters which allows for efficient and accurate reconstruction results.
Tensorizing flows: a tool for variational inference
Authors: Yuehaw Khoo, Michael Lindsey, Hongli Zhao
Date: 2023-05-03 00:00:00
Tasks: Tensor Networks, Variational Inference

Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples. Normalizing flows have also been applied successfully to variational inference, wherein one attempts to learn a sampler based on an expression for the log-likelihood or energy function of the distribution, rather than on data. In variational inference, the unimodality of the reference Gaussian distribution used within the normalizing flow can cause difficulties in learning multimodal distributions. We introduce an extension of normalizing flows in which the Gaussian reference is replaced with a reference distribution that is constructed via a tensor network, specifically a matrix product state or tensor train. We show that by combining flows with tensor networks on difficult variational inference tasks, we can improve on the results obtained by using either tool without the other.
Critical heat flux diagnosis using conditional generative adversarial networks
Authors: UngJin Na, Moonhee Choi, HangJin Jo
Date: 2023-05-04 00:00:00
Tasks: Image-to-Image Translation

The critical heat flux (CHF) is an essential safety boundary in boiling heat transfer processes employed in high heat flux thermal-hydraulic systems. Identifying CHF is vital for preventing equipment damage and ensuring overall system safety, yet it is challenging due to the complexity of the phenomena. For an in-depth understanding of the complicated phenomena, various methodologies have been devised, but the acquisition of high-resolution data is limited by the substantial resource consumption required. This study presents a data-driven, image-to-image translation method for reconstructing thermal data of a boiling system at CHF using conditional generative adversarial networks (cGANs). The supervised learning process relies on paired images, which include total reflection visualizations and infrared thermometry measurements obtained from flow boiling experiments. Our proposed approach has the potential to not only provide evidence connecting phase interface dynamics with thermal distribution but also to simplify the laborious and time-consuming experimental setup and data-reduction procedures associated with infrared thermal imaging, thereby providing an effective solution for CHF diagnosis.
Comparison of retinal regions-of-interest imaged by OCT for the classification of intermediate AMD
Authors: Danilo A. Jesus, Eric F. Thee, Tim Doekemeijer, Daniel Luttikhuizen, Caroline Klaver, Stefan Klein, Theo van Walsum, Hans Vingerling, Luisa Sanchez
Date: 2023-05-04 00:00:00
Tasks: Sensitivity, Specificity

To study whether it is possible to differentiate intermediate age-related macular degeneration (AMD) from healthy controls using partial optical coherence tomography (OCT) data, that is, restricting the input B-scans to certain pre-defined regions of interest (ROIs). A total of 15744 B-scans from 269 intermediate AMD patients and 115 normal subjects were used in this study (split on subject level in 80% train, 10% validation and 10% test). From each OCT B-scan, three ROIs were extracted: retina, complex between retinal pigment epithelium (RPE) and Bruch membrane (BM), and choroid (CHO). These ROIs were obtained using two different methods: masking and cropping. In addition to the six ROIs, the whole OCT B-scan and the binary mask corresponding to the segmentation of the RPE-BM complex were used. For each subset, a convolutional neural network (based on VGG16 architecture and pre-trained on ImageNet) was trained and tested. The performance of the models was evaluated using the area under the receiver operating characteristic (AUROC), accuracy, sensitivity, and specificity. All trained models presented an AUROC, accuracy, sensitivity, and specificity equal to or higher than 0.884, 0.816, 0.685, and 0.644, respectively. The model trained on the whole OCT B-scan presented the best performance (AUROC = 0.983, accuracy = 0.927, sensitivity = 0.862, specificity = 0.913). The models trained on the ROIs obtained with the cropping method led to significantly higher outcomes than those obtained with masking, with the exception of the retinal tissue, where no statistically significant difference was observed between cropping and masking (p = 0.47). This study demonstrated that while using the complete OCT B-scan provided the highest accuracy in classifying intermediate AMD, models trained on specific ROIs such as the RPE-BM complex or the choroid can still achieve high performance.
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction
Authors: Qi Wang, Zhijie Wen, Jun Shi, Qian Wang, Dinggang Shen, Shihui Ying
Date: 2023-05-04 00:00:00
Tasks: Image Reconstruction, MRI Reconstruction

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis. To address this issue, we propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. While image pre-processing is capable of mitigating misalignment, improper parameter selection leads to adverse pre-processing effects, requiring iterative experimentation and adjustment. To overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis, effectively mitigating spatial misalignment effects. Furthermore, we adopt an alternating iteration framework between the reconstruction task and the cross-modal synthesis task to optimize the final results. Then, we prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing, and further illustrate that the improved reconstruction result enhances the synthesis process, whereas the enhanced synthesis result improves the reconstruction process. Finally, experimental results from FastMRI and internal datasets confirm the effectiveness of our method, demonstrating significant improvements in image reconstruction quality even at low sampling rates.
Big Data and Large Numbers. Interpreting Zipf's Law
Authors: Horia-Nicolai L. Teodorescu
Date: 2023-05-04 00:00:00

It turns out that some empirical facts in Big Data are the effects of properties of large numbers. Zipf's law 'noise' is an example of such an artefact. We expose several properties of the power law distributions and of similar distribution that occur when the population is finite and the rank and counts of elements in the population are natural numbers. We are particularly concerned with the low-rank end of the graph of the law, the potential of noise in the law, and with the approximation of the number of types of objects at various ranks. Approximations instead of exact solutions are the center of attention. Consequences in the interpretation of Zipf's law are discussed.
3D Reconstruction of Objects in Hands without Real World 3D Supervision
Authors: Aditya Prakash, Matthew Chang, Matthew Jin, Ruisen Tu, Saurabh Gupta
Date: 2023-05-04 00:00:00
Tasks: 3D Reconstruction, Object, Object Reconstruction

Prior works for reconstructing hand-held objects from a single image train models on images paired with 3D shapes. Such data is challenging to gather in the real world at scale. Consequently, these approaches do not generalize well when presented with novel objects in in-the-wild settings. While 3D supervision is a major bottleneck, there is an abundance of a) in-the-wild raw video data showing hand-object interactions and b) synthetic 3D shape collections. In this paper, we propose modules to leverage 3D supervision from these sources to scale up the learning of models for reconstructing hand-held objects. Specifically, we extract multiview 2D mask supervision from videos and 3D shape priors from shape collections. We use these indirect 3D cues to train occupancy networks that predict the 3D shape of objects from a single RGB image. Our experiments in the challenging object generalization setting on in-the-wild MOW dataset show 11.6% relative improvement over models trained with 3D supervision on existing datasets.
What changes when you randomly choose BPE merge operations? Not much
Authors: Jonne Sälevä, Constantine Lignos
Date: 2023-05-04 00:00:00
Tasks: Machine Translation, Sensitivity, Translation

We introduce three simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task. We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords. Analysis using a Bayesian linear model indicates that two of the variants perform nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated. We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating. Our code is available at: https://github.com/bltlab/random-bpe.
Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models
Authors: Fangkai Jiao, Bosheng Ding, Tianze Luo, Zhanfeng Mo
Date: 2023-05-04 00:00:00
Tasks: Instruction Following

This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance. We explore how various training data factors, such as quantity, quality, and linguistic distribution, influence the performance of instruction-tuned models trained on publicly accessible high-quality instruction datasets for both English and Chinese languages. Our goal is to supplement evaluation with quantitative analyses, providing valuable insights for the continued advancement of open-source chat models. Our model, data, and code are publicly available for others to use and build upon.