pwc-archive/papers-with-abstracts huggingface dataset:
The effect of host population heterogeneity on epidemic outbreaks
Authors: Martin Bootsma, Danny Chan, Odo Diekmann, Hisashi Inaba
Date: 2023-08-12 00:00:00

In the first part of this paper, we review old and new results about the influence of host population heterogeneity on (various characteristics of) epidemic outbreaks. In the second part we highlight a modelling issue that so far has received little attention: how do contact patterns, and hence transmission opportunities, depend on the size and the composition of the host population? Without any claim on completeness, we offer a range of potential (quasi-mechanistic) submodels. The overall aim of the paper is to describe the state-of-the-art and to catalyse new work.
Automated rendering of multi-stranded DNA complexes with pseudoknots
Authors: Malgorzata Nowicka, Vinay K. Gautam, Pekka Orponen
Date: 2023-08-11 00:00:00

We present a general method for rendering representations of multi-stranded DNA complexes from textual descriptions into 2D diagrams. The complexes can be arbitrarily pseudoknotted, and if a planar rendering is possible, the method will determine one in time which is essentially linear in the size of the textual description. (That is, except for a final stochastic fine-tuning step.) If a planar rendering is not possible, the method will compute a visually pleasing approximate rendering in quadratic time. Examples of diagrams produced by the method are presented in the paper.
The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions
Authors: Gal Haspel, Ben Baker, Isabel Beets, Edward S Boyden, Jeffrey Brown, George Church, Netta Cohen, Daniel Colon-Ramos, Eva Dyer, Christopher Fang-Yen, Steven Flavell, Miriam B Goodman, Anne C Hart, Eduardo J Izquierdo, Konstantinos Kagias, Shawn Lockery, Yangning Lu, Adam Marblestone, Jordan Matelsky, Brett Mensh, Talmo D Pereira, Hanspeter Pfister, Kanaka Rajan, Horacio G Rotstein, Monika Scholz, Joshua W. Shaevitz, Eli Shlizerman, Quilee Simeon, Michael A Skuhersky, Vineet Tiruvadi, Vivek Venkatachalam, Donglai Wei, Brock Wester, Guangyu Robert Yang, Eviatar Yemini, Manuel Zimmer, Konrad P Kording
Date: 2023-08-12 00:00:00

Just like electrical engineers understand how microprocessors execute programs in terms of how transistor currents are affected by their inputs, neuroscientists want to understand behavior production in terms of how neuronal outputs are affected by their inputs and internal states. This dependency of neuronal outputs on inputs can be described by a state-dependent input-output (IO)-function. However, to reliably identify these IO-functions, we need to perturb each input and combinations of inputs while observing all the outputs. Here, we argue that such completeness is possible in C. elegans; a complete description that goes all the way from the activity of every neuron to predict behavior. The established and growing toolkit of optophysiology can non-invasively capture and control every neuron's activity and scale to countless experiments. The information from many such experiments can be pooled while capturing the inter-individual variability because neuronal identity and function are largely conserved across individuals. Just like electrical engineers use transistor IO-functions to simulate program execution, we argue that neuronal IO-functions could be used to simulate the impressive breadth of brain states and behaviors of C. elegans.
Intestinal Microecology in Pediatric Surgery-Related Gastrointestinal Diseases Current Insights and Future Perspectives
Authors: Yingchao Li, Yuqing Wu, Suolin Li, Lin Liu, Xiaoyi Zhang, Jiaxun Lv, Qinqin Li
Date: 2023-08-14 00:00:00

Intestinal microecology is established from birth and is constantly changing until homeostasis is reached. Intestinal microecology is involved in the immune inflammatory response of the intestine and regulates the intestinal barrier function. The imbalance of intestinal microecology is closely related to the occurrence and development of digestive system diseases. In some gastrointestinal diseases related to pediatric surgery, intestinal microecology and its metabolites undergo a series of changes, which can provide a certain basis for the diagnosis of diseases. The continuous development of microecological agents and fecal microbiota transplantation technology has provided a new means for its clinical treatment. We review the relationship between pathogenesis, diagnosis and treatment of pediatric surgery-related gastrointestinal diseases and intestinal microecology, in order to provide new ideas and methods for clinical diagnosis, treatment and research.
Loan portfolio management and Liquidity Risk: The impact of limited liability and haircut
Authors: Deb Narayan Barik, Siddhartha P. Chakrabarty
Date: 2023-08-12 00:00:00
Tasks: Management

In this article, we consider the problem of a bank's loan portfolio in the context of liquidity risk, while allowing for the limited liability protection enjoyed by the bank. Accordingly, we construct a novel loan portfolio model with limited liability, while maintaining a threshold level of haircut in the portfolio. For the constructed three-time step loan portfolio, at the initial time, the bank raises capital via debt and equity, investing the same in several classes of loans, while at the final time, the bank either meets its liabilities or becomes insolvent. At the intermediate time step, a fraction of the deposits are withdrawn, resulting in liquidation of some of the bank's assets. The liquidated portfolio is designed with the goal of minimizing the liquidation cost. Our theoretical results show that model with the haircut constraint leads to lesser liquidity risk, as compared to the scenario of no haircut constraint being imposed. Finally, we present numerical results to illustrate the theoretical results which were obtained.
DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning
Authors: Bocheng Chen, Nikolay Ivanov, Guangjing Wang, Qiben Yan
Date: 2023-07-16 00:00:00
Tasks: Federated Learning, Scheduling

Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a global model by exploiting the decentralized data across millions of edge devices. Compared with centralized learning, FL preserves the clients' privacy by refraining from explicitly downloading their data. However, given the geo-distributed edge devices (e.g., mobile, car, train, or subway) with highly dynamic networks in the wild, aggregating all the model updates from those participating devices will result in inevitable long-tail delays in FL. This will significantly degrade the efficiency of the training process. To resolve the high system heterogeneity in time-sensitive FL scenarios, we propose a novel FL framework, DynamicFL, by considering the communication dynamics and data quality across massive edge devices with a specially designed client manipulation strategy. \ours actively selects clients for model updating based on the network prediction from its dynamic network conditions and the quality of its training data. Additionally, our long-term greedy strategy in client selection tackles the problem of system performance degradation caused by short-term scheduling in a dynamic network. Lastly, to balance the trade-off between client performance evaluation and client manipulation granularity, we dynamically adjust the length of the observation window in the training process to optimize the long-term system efficiency. Compared with the state-of-the-art client selection scheme in FL, \ours can achieve a better model accuracy while consuming only 18.9\% -- 84.0\% of the wall-clock time. Our component-wise and sensitivity studies further demonstrate the robustness of \ours under various real-life scenarios.
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Authors: Owen Melia, Eric Jonas, Rebecca Willett
Date: 2023-07-27 00:00:00
Tasks: 3D Shape Classification, Inductive Bias, Molecular Property Prediction, Property Prediction

Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant machine learning methods set the state of the art for many tasks, including molecular property prediction and 3D shape classification. These methods generally either rely on task-specific rotation-invariant features, or they use general-purpose deep neural networks which are complicated to design and train. However, it is unclear whether the success of these methods is primarily due to the rotation invariance or the deep neural networks. To address this question, we suggest a simple and general-purpose method for learning rotation-invariant functions of three-dimensional point cloud data using a random features approach. Specifically, we extend the random features method of Rahimi & Recht 2007 by deriving a version that is invariant to three-dimensional rotations and showing that it is fast to evaluate on point cloud data. We show through experiments that our method matches or outperforms the performance of general-purpose rotation-invariant neural networks on standard molecular property prediction benchmark datasets QM7 and QM9. We also show that our method is general-purpose and provides a rotation-invariant baseline on the ModelNet40 shape classification task. Finally, we show that our method has an order of magnitude smaller prediction latency than competing kernel methods.
An Efficient Privacy-Preserving Multi-Keyword Query Scheme in Location Based Services
Authors: SHIWEN ZHANG 1, 2, (Member, TINGTING YAO3, WEI LIANG 4, VOUNDI KOE ARTHUR SANDOR4, AND KUAN-CHING LI 5, (Senior Member, IEEE)
Date: 2020-08-21 00:00:00
Proceeding: IEEE 2020 8
Tasks: Privacy Preserving

With the proliferation of location-aware mobile devices and the prevalence of wireless
communications, location-based services (LBS) have attracted much particular attention in recent years.
For flexibility and cost savings, the LBS provider outsources the LBS data to the cloud in order to serve
the increasing number of mobile users. To guarantee users’ privacy and data confidentiality, some excellent
works have been proposed which focus on secure query over the location server. However, these existing
works have two limitations. On the one hand, they cannot preserve users’ location and query content privacy
simultaneously. On the other hand, they fail to support multi-keyword queries. In this article, aiming at a
multi-keywords query in LBS, we propose a novel efficient and privacy-preserving multi-keyword query
scheme (PPMQ) over the outsourced cloud, which satisfies the requirements of the location and query
content privacy protection, query efficiency, the confidentiality of LBS data and scalability regarding the
data users. To improve the efficiency of our proposed scheme, we utilize the linear quad-tree technique to
build a grid system to represent the location information in the query condition as well as a searchable
index. To protect the location privacy, we combine decimal Morton code and public-key cryptography
techniques to build a searchable index or to generate a trapdoor. To enable the cloud server to perform a
secure multi-keyword query, we systematically construct a privacy-preserving query scheme with bilinear
pairing-based cryptography. In particular, our proposed scheme is scalable and very suitable for multi-user
environments due to the flexible user registration and revocation mechanisms. Furthermore, a detailed
security analysis shows that the proposed scheme can ensure the confidentiality of LBS data, and protect
the location and query content privacy. Extensive experiments are conducted on a real LBS dataset, and the
simulation results confirm the security and efficiency of our scheme.
Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network
Authors: Hyeon-Ju Jeon, Min-Woo Choi, O-Joun Lee
Date: 2022-09-21 00:00:00
Proceeding: Sensors 2022 9
Tasks: Node Property Prediction, Solar Irradiance Forecasting

Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.
SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition
Authors: Fabian David Schmidt, Ivan Vulić, Goran Glavaš
Date: 2022-10-01 00:00:00
Proceeding: Proceedings of the Conference on Empirical Methods in Natural Language Processing 2022 10
Tasks: Cross-Lingual Transfer, Multilingual text classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Zero-Shot Cross-Lingual Transfer

Large multilingual language models generally demonstrate impressive results in zero-shot cross-lingual transfer, yet often fail to successfully transfer to low-resource languages, even for token-level prediction tasks like named entity recognition (NER). In this work, we introduce a simple yet highly effective approach for improving zero-shot transfer for NER to low-resource languages. We observe that NER fine-tuning in the source language decontextualizes token representations, i.e., tokens increasingly attend to themselves. This increased reliance on token information itself, we hypothesize, triggers a type of overfitting to properties that NE tokens within the source languages share, but are generally not present in NE mentions of target languages. As a remedy, we propose a simple yet very effective sliced fine-tuning for NER (SLICER) that forces stronger token contextualization in the Transformer: we divide the transformed token representations and classifier into disjoint slices that are then independently classified during training. We evaluate SLICER on two standard benchmarks for NER that involve low-resource languages, WikiANN and MasakhaNER, and show that it (i) indeed reduces decontextualization (i.e., extent to which NE tokens attend to themselves), consequently (ii) yielding consistent transfer gains, especially prominent for low-resource target languages distant from the source language.
Learning multi-resolution representations of research patterns in bibliographic networks
Authors: O-Joun Lee, Hyeon-Ju Jeon, Jason J. Jung
Date: 2021-02-01 00:00:00
Proceeding: Journal of Informetrics 2021 2
Tasks: Graph Embedding, Graph Representation Learning, Network Embedding, Node Property Prediction, Research Performance Prediction

This study aims at representing research patterns of bibliographic entities (e.g., scholars, papers, and venues) with a fixed-length vector. Bibliographic network structures rooted in the entities are incredibly diverse, and this diversity increases in the outstanding entities. Thus, despite their significant volume, the outstanding entities obtain minimal learning opportunities, whereas low-performance entities are over-represented. This study solves the problem by representing the patterns of the entities rather than depicting individual entities in a precise manner. First, we describe structures rooted in the entities using the Weisfeiler–Lehman (WL) relabeling process. Each subgraph generated by the relabeling process provides information on the scholars, kinds of papers they published, standards of venues in which the papers were published, and types of their collaborators. We assume that a subgraph depicts the research patterns of bibliographic entities, such as the preference of a scholar in choosing either a few highly impactful papers or numerous papers of moderate impact. Then, we simplify the subgraphs according to multiple levels of detailedness. Original subgraphs represent the individuality of the entities, and simplified subgraphs represent the entities sharing the same research patterns. In addition, simplified subgraphs balance the learning opportunities of high- and low-performance entities by co-occurring with both types of entities. We embed the subgraphs using the Skip-Gram method. If the results of the embedding represent the research patterns of the entities, the obtained vectors should be able to represent various aspects of the research performance in both the short-term and long-term durations regardless of the performances of the entities. Therefore, we conducted experiments for predicting 23 performance indicators during four time periods for four performance groups (top 1%, 5%, 10%, and all entities) using only the vector representations. The proposed model outperformed the existing network embedding methods in terms of both accuracy and variance.
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?
Authors: Hyeon-Ju Jeon, O-Joun Lee, Jason. J. Jung
Date: 2019-11-05 00:00:00
Proceeding: Frontiers in Big Data 2019 11
Tasks: Community Detection, Graph Embedding, Graph Representation Learning, Node Clustering

This study aims to validate whether the research performance of scholars correlates with how the scholars work together. Although the most straightforward approaches are centrality measurements or community detection, scholars mostly participate in multiple research groups and have different roles in each group. Thus, we concentrate on the subgraphs of co-authorship networks rooted in each scholar that cover (i) overlapping of the research groups on the scholar and (ii) roles of the scholar in the groups. This study calls the subgraphs “collaboration patterns” and applies subgraph embedding methods to discover and represent the collaboration patterns. Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles. Then, we have examined whether scholars in each cluster have similar research performance, using the quantitative indicators. The coherence of the indicators cannot be solid proofs for validating the correlation between collaboration and performance. Nevertheless, the examination for clusters has exhibited that the collaboration patterns can reflect research styles of scholars. This information will enable us to predict the research performance more accurately since the research styles are more consistent and sustainable features of scholars than a few high-impact publications.
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
Authors: Honggu Kang, Seohyeon Cha, Jinwoo Shin, Jongmyeong Lee, Joonhyuk Kang
Date: 2023-08-15 00:00:00
Tasks: Federated Learning

Federated learning (FL) enables distributed training while preserving data privacy, but stragglers-slow or incapable clients-can significantly slow down the total training time and degrade performance. To mitigate the impact of stragglers, system heterogeneity, including heterogeneous computing and network bandwidth, has been addressed. While previous studies have addressed system heterogeneity by splitting models into submodels, they offer limited flexibility in model architecture design, without considering potential inconsistencies arising from training multiple submodel architectures. We propose nested federated learning (NeFL), a generalized framework that efficiently divides deep neural networks into submodels using both depthwise and widthwise scaling. To address the inconsistency arising from training multiple submodel architectures, NeFL decouples a subset of parameters from those being trained for each submodel. An averaging method is proposed to handle these decoupled parameters during aggregation. NeFL enables resource-constrained devices to effectively participate in the FL pipeline, facilitating larger datasets for model training. Experiments demonstrate that NeFL achieves performance gain, especially for the worst-case submodel compared to baseline approaches (7.63% improvement on CIFAR-100). Furthermore, NeFL aligns with recent advances in FL, such as leveraging pre-trained models and accounting for statistical heterogeneity. Our code is available online.
Link-Context Learning for Multimodal LLMs
Authors: Yan Tai, Weichen Fan, Zhao Zhang, Feng Zhu, Rui Zhao, Ziwei Liu
Date: 2023-08-15 00:00:00
Proceeding: CVPR 2024 1
Tasks: Few-Shot Learning, In-Context Learning, Novel Concepts

The ability to learn from context with novel concepts, and deliver appropriate responses are essential in human conversations. Despite current Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being trained on mega-scale datasets, recognizing unseen images or understanding novel concepts in a training-free manner remains a challenge. In-Context Learning (ICL) explores training-free few-shot learning, where models are encouraged to ``learn to learn" from limited tasks and generalize to unseen tasks. In this work, we propose link-context learning (LCL), which emphasizes "reasoning from cause and effect" to augment the learning capabilities of MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal relationship between the support set and the query set. By providing demonstrations with causal links, LCL guides the model to discern not only the analogy but also the underlying causal associations between data points, which empowers MLLMs to recognize unseen images and understand novel concepts more effectively. To facilitate the evaluation of this novel approach, we introduce the ISEKAI dataset, comprising exclusively of unseen generated image-label pairs designed for link-context learning. Extensive experiments show that our LCL-MLLM exhibits strong link-context learning capabilities to novel concepts over vanilla MLLMs. Code and data will be released at https://github.com/isekai-portal/Link-Context-Learning.