DOES BILATERAL TRADE LEAD TO INCOME CONVERGENCE? PANEL EVIDENCE

Through panel-data regressions, we found that both per capita income level and growth turn out to converge when the trade intensity ratio increases between the countries. Geographical proximity and language similarities also turn out to be associated with convergence in both income level and growth.Trade, Convergence, Distance, Language

Portfolio-Flow Volatility and Demand for International Reserves

This paper examines the importance of portfolio-flow volatility as a determinant of the demand for international reserves over the 1980-99 period, Using panel data, we find that portfolio-flow volatility significantly raises the level of reserve holdings. Especially reserve accumulation is most sensitive to the volatility of portfolio balance (net flows). Capital account liberalization has increased uncertainty in the world economy, thereby making open economies more vulnerable to international financial crises. The regression results imply that monetary authorities have accumulated more precautionary reserve balances against increased uncertainty in portfolio flows as capital account liberalization progresses. As in previous studies, real openness is an important explanatory factor in determining the demand for reserves

Addressing Label Shift in Distributed Learning via Entropy Regularization​

Source at https://openreview.net/forum?id=kuYxecnlv2.We address the challenge of minimizing "true risk" in multi-node distributed learning.\footnote{We use the term node to refer to a client, FPGA, APU, CPU, GPU, or worker.} These systems are frequently exposed to both inter-node and intra-node "label shifts", which present a critical obstacle to effectively optimizing model performance while ensuring that data remains confined to each node. To tackle this, we propose the Versatile Robust Label Shift (VRLS) method, which enhances the maximum likelihood estimation of the test-to-train label importance ratio. VRLS incorporates Shannon entropy-based regularization and adjusts the importance ratio during training to better handle label shifts at the test time. In multi-node learning environments, VRLS further extends its capabilities by learning and adapting importance ratios across nodes, effectively mitigating label shifts and improving overall model performance. Experiments conducted on MNIST, Fashion MNIST, and CIFAR-10 demonstrate the effectiveness of VRLS, outperforming baselines by up to 20% in imbalanced settings. These results highlight the significant improvements VRLS offers in addressing label shifts. Our theoretical analysis further supports this by establishing high-probability bounds on estimation errors

Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks

Poster presented at the Northern Lights Deep Learning Workshop (NLDL) 2020, 19.01.20 - 21.01.20, UiT The Arctic University of Norway, TromsøForecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems. Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform conventional models that include AutoRegressive Integrated Moving Average(ARIMA). However, due to the structural limitation of vanilla RNN which holds unit-length internal connections, learning the representation of time series with missing data can be severely biased. The goal of this paper is to provide a robust RNN architecture against the bias from missing data. We propose Dilated Recurrent Attention Networks (DRAN). The proposed model has a stacked structure of multiple RNNs which layer of each having a different length of internal connections. This structure allows incorporating previous information at different time scales. DRAN updates its state by a weighted average of the layers. In order to focus more on the layer that carries reliable information against bias from missing data, it leverages attention mechanism which learns the distribution of attention weights among the layers. We report that our model outperforms conventional ones with respect to the forecast accuracy from two benchmark datasets, including a real-world electricity load dataset

Leveraging Foundation Model Adapters to Enable Robust and Semantic Underwater Exploration

This position paper presents a framework for intelligent underwater exploration by marrying foundation models (FMs) with multi‑frequency echosounder data. Echosounder data capture backscattered acoustic signals across a range of frequencies, providing rich insights into underwater environments by exploiting the frequency‑dependent scattering properties of underwater targets. However, their heterogeneity and complex structure complicate analysis. To address these challenges, the paper introduces four key innovations aimed at improving echosounder data analysis under dynamic ocean conditions: (1) aligning multi‑frequency echosounder data with FMs via lightweight FM adapters, (2) enabling continual adaptation to temporal distribution shifts in dynamic marine environments, (3) designing semantic tokenizers that preserve spatial structures, and (4) effectively leveraging sparse annotations to minimize dependence on costly labeled data. For each research direction, we map recent artificial intelligence (AI) methodologies to marine acoustic challenges and outline concrete pathways for technology transfer. Preliminary experiments demonstrate that a Vision Transformer (ViT), pretrained on natural images in a self-supervised manner, can segment sandeel schools from multi‑frequency echosounder data without task‑specific retraining. These results substantiate the proposed framework and illustrate the potential of cross‑disciplinary AI methods for ecologically informative underwater exploration.publishedVersio

Leveraging Foundation Model Adapters to Enable Robust and Semantic Underwater Exploration

Conference proceedings at https://ceur-ws.org/Vol-3975/.This position paper presents a framework for intelligent underwater exploration by marrying foundation models (FMs) with multi‑frequency echosounder data. Echosounder data capture backscattered acoustic signals across a range of frequencies, providing rich insights into underwater environments by exploiting the frequency‑dependent scattering properties of underwater targets. However, their heterogeneity and complex structure complicate analysis. To address these challenges, the paper introduces four key innovations aimed at improving echosounder data analysis under dynamic ocean conditions: (1) aligning multi‑frequency echosounder data with FMs via lightweight FM adapters, (2) enabling continual adaptation to temporal distribution shifts in dynamic marine environments, (3) designing semantic tokenizers that preserve spatial structures, and (4) effectively leveraging sparse annotations to minimize dependence on costly labeled data. For each research direction, we map recent artificial intelligence (AI) methodologies to marine acoustic challenges and outline concrete pathways for technology transfer. Preliminary experiments demonstrate that a Vision Transformer (ViT), pretrained on natural images in a self-supervised manner, can segment sandeel schools from multi‑frequency echosounder data without task‑specific retraining. These results substantiate the proposed framework and illustrate the potential of cross‑disciplinary AI methods for ecologically informative underwater exploration

Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data

Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. Semantic segmentation, a deep learning based analysis method predicting the class attribute of each acoustic intensity, has recently been in the spotlight of the fisheries and aquatic industry since its result can be used to estimate the abundance of the marine organisms. However, a fundamental problem with current methods is the massive reliance on the availability of large amounts of annotated training data, which can only be acquired through expensive handcrafted annotation processes, making such approaches unrealistic in practice. As a solution to this challenge, we propose a novel approach, where we leverage a small amount of annotated data (supervised deep learning) and a large amount of readily available unannotated data (unsupervised learning), yielding a new data-efficient and accurate semi-supervised semantic segmentation method, all embodied into a single end-to-end trainable convolutional neural networks architecture. Our method is evaluated on representative data from a sandeel survey in the North Sea conducted by the Norwegian Institute of Marine Research. The rigorous experiments validate that our method achieves comparable results utilizing only 40 percent of the annotated data on which the supervised method is trained, by leveraging unannotated data. The code is available at https://github.com/SFI-Visual-Intelligence/PredKlus-semisup-segmentation.Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder DatasubmittedVersionsubmittedVersionsubmittedVersionacceptedVersio

Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data

Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. Semantic segmentation, a deep learning based analysis method predicting the class attribute of each acoustic intensity, has recently been in the spotlight of the fisheries and aquatic industry since its result can be used to estimate the abundance of the marine organisms. However, a fundamental problem with current methods is the massive reliance on the availability of large amounts of annotated training data, which can only be acquired through expensive handcrafted annotation processes, making such approaches unrealistic in practice. As a solution to this challenge, we propose a novel approach, where we leverage a small amount of annotated data (supervised deep learning) and a large amount of readily available unannotated data (unsupervised learning), yielding a new data-efficient and accurate semi-supervised semantic segmentation method, all embodied into a single end-to-end trainable convolutional neural networks architecture. Our method is evaluated on representative data from a sandeel survey in the North Sea conducted by the Norwegian Institute of Marine Research. The rigorous experiments validate that our method achieves comparable results utilizing only 40 percent of the annotated data on which the supervised method is trained, by leveraging unannotated data. The code is available at https://github.com/SFI-Visual-Intelligence/PredKlus-semisup-segmentation.Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder DatasubmittedVersionsubmittedVersionsubmittedVersio