Miracle-dz/UniFAR-FTUs huggingface dataset:
The Informational Advantage of Foreign Investors: An Empirical

This paper uses a unique dataset on daily capital flows to the Swedish bond market to analyse the relative information endowment of domestic and foreign investors. Using the standard framework of a noisy rational expectations equilibrium , it is concluded that foreign investors are on average better informed than domestic investors about the future value of sek denominated bonds. The better information of foreign investors is shown to be related to both pure Swedish factors as well as international factors. visiting the Haas School of Business. I thank the Finance Group for its hospitality during this stay. The views expressed in this paper are those of the author and do not necessarily reflect those of Sveriges Riksbank. I gratefully acknowledge research funding from Jan Wallanders och Tom Hedelius' Stiftelse and Bankforskningsinstitutet. Abstract This paper uses a unique dataset on daily capital flows to the Swedish bond market to analyse the relative information endowment of domestic and foreign investors. Using the standard framework of a noisy rational expectations equilibrium , it is concluded that foreign investors are on average better informed than domestic investors about the future value of sek denominated bonds. The better information of foreign investors is shown to be related to both pure Swedish factors as well as international factors.
Effect of exchange-rate volatility on foreign direct investment in Sub-Saharan Africa: The case of Ghana

Purpose - The present study aims at using a broader data set and longer time frame coupled with a relatively rigorous and robust methodology to examine the effect of real exchange rate volatility on foreign direct investment (FDI) in a small and developing country such as Ghana. Design/methodology/approach - Time series data covering the period 1970-2002 were used. ARCH and GARCH models were employed for the determination of real exchange rate volatility, and co-integration and ECM were used to determine both the short- and the long-term relationships. Findings - The study showed that the volatility of the real exchange rate has a negative influence on FDI inflow and that the liberalization process has not led to a greater inflow of FDI in Ghana. It is also revealed that while both the stock of FDI and political factors are likely to attract FDI, most foreign investors do not consider the size of the market in making a decision to invest or otherwise in Ghana. Originality/value - The main contribution of the study is its departure from the use of ratios in examining the effect of real exchange rate risk on FDI to a more rigorous and robust methodology, coupled with the fact that studies of this nature are virtually non-existent in Ghana.
Stochastic reservoir optimization using El Niño information: case study of Daule Peripa, Ecuador

Reservoir optimization requires the ability to produce inflow scenarios that are consistent with the available climatic information. We approach stochastic inflow modelling with a Markov-switching model where inflow anomalies are described by a mixture of autoregressive models with exogenous input, each corresponding to a hidden climate state. Climatic information is used as exogenous input and to condition state transitions. We apply the model to the inflow of the Daule Peripa reservoir in western Ecuador, where El Nino events cause anomalously heavy rainfall. El Nino–Southern Oscillation (ENSO) indices constitute the climatic input of the inflow model. The Daule Peripa reservoir serves a hydropower plant and a downstream water supply facility. Based on ENSO forecasts, which are available with 9 month lead time, monthly inflow scenarios are generated to perform stochastic optimization of reservoir releases with monthly time-steps. To account for inflow uncertainty, we generate multiple synthetic inflow time series and apply a multi-objective genetic algorithm to evaluate the objective functions. The results highlight the advantages of using a climate-driven stochastic model to produce inflow scenarios and forecasts for reservoir optimization, and show significant potential improvements with respect to the current reservoir management.
Role of Livestock Sector in the Socio-Economic Uplift of the Rural Pakistan

Livestock sector occupies a very crucial position in the economy of Pakistan. The sector is equally important in irrigation as well as in arid/rain fed areas. Based on the valid reasons and available data, some scientists believe that livestock can strengthen and boost-up the economy of the rain fed/arid lands like Pothowar plateau where other enterprises like crops do not flourish and witness success due to drought and uncertain weather conditions. In this article, an attempt has been made to establish the role of livestock rearing in both the high and low potential areas. Their role in the overall economy of the country has been highlighted. Also many constraints in livestock business have been identified. The livestock sector in Pakistan has tremendous potential for growth. Therefore, a workable strategy for the promotion of the livestock has been outlined to uplift the rural economy and improve the socio-economic conditions of the rural dweller of the country.
A Block-based Convolutional Neural Network for Low-Resolution Image Classification

—The success of CNN-based architecture on image classiﬁcation in learning and extracting features made them so popular these days, but the task of image classiﬁcation becomes more challenging when we use state of art models to classify noisy and low-quality images. To solve this problem, we proposed a novel image classiﬁcation architecture that learns subtle details in low-resolution images that are blurred and noisy. In order to build our new blocks, we used the idea of Res Connections and the Inception module ideas. Using the MNIST datasets, we have conducted extensive experiments that show that the introduced architecture is more accurate and faster than other state-of-the-art Convolutional neural networks. As a result of the special characteristics of our model, it can achieve a better result with fewer parameters. Code is available at this Repository
Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. Established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorization (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility with respect to signalling, microstructural, and metabolic targets. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.
Expressivity and Complexity of the Conjunctive Core of the SIGNAL Process Query Language

This paper presents a formalisation and expressivity and complexity analysis of SIGNAL, an industry-scale query language for analysing business process event logs. The formal analysis shows that the core capabilities of SIGNAL, which we refer to as the SIGNAL Conjunctive Core, are more expressive than relational algebra and thus not captured by standard relational databases. We do provide an upper-bound on the expressiveness via a reduction to semi-positive Datalog, which also leads to an upper bound of P-hard for the data complexity of evaluating SIGNAL Conjunctive Core queries. The findings provide first insights into how real-world process query languages are fundamentally different from the more generally prevalent structured query languages for querying relational databases and provide a rigorous foundation for extending the existing capabilities of the industry-scale state-of-the-art of process data querying.
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks

Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gains can be obtained if decisions are taken as early as possible as a function of the complexity of the input time series. The decision on when to stop inference and produce a decision must rely on an estimate of the current accuracy of the decision. Prior work demonstrated the use of conformal prediction (CP) as a principled way to quantify uncertainty and support adaptive-latency decisions in SNNs. In this paper, we propose to enhance the uncertainty quantification capabilities of SNNs by implementing ensemble models for the purpose of improving the reliability of stopping decisions. Intuitively, an ensemble of multiple models can decide when to stop more reliably by selecting times at which most models agree that the current accuracy level is sufficient. The proposed method relies on different forms of information pooling from ensemble models and offers theoretical reliability guarantees. We specifically show that variational inference-based ensembles with p-variable pooling significantly reduce the average latency of state-of-the-art methods while maintaining reliability guarantees.
Exploring Large Language Models for Code Explanation

Automating code documentation through explanatory text can prove highly beneficial in code understanding. Large Language Models (LLMs) have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs. The findings indicate that Code LLMs outperform their generic counterparts, and zero-shot methods yield superior results when dealing with datasets with dissimilar distributions between training and testing sets.
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference

Variational inference (VI) can be cast as an optimization problem in which the variational parameters are tuned to closely align a variational distribution with the true posterior. The optimization task can be approached through vanilla gradient descent in black-box VI or natural-gradient descent in natural-gradient VI. In this work, we reframe VI as the optimization of an objective that concerns probability distributions defined over a \textit{variational parameter space}. Subsequently, we propose Wasserstein gradient descent for tackling this optimization problem. Notably, the optimization techniques, namely black-box VI and natural-gradient VI, can be reinterpreted as specific instances of the proposed Wasserstein gradient descent. To enhance the efficiency of optimization, we develop practical methods for numerically solving the discrete gradient flows. We validate the effectiveness of the proposed methods through empirical experiments on a synthetic dataset, supplemented by theoretical analyses.
On the Kashaev signature conjecture

In 2018, Kashaev introduced a square matrix indexed by the regions of a link diagram, and conjectured that it provides a novel way of computing the Levine-Tristram signature and Alexander polynomial of the corresponding oriented link. In this article, we show that for the classical signature (i.e. the Levine-Tristram signature at -1), this conjecture follows from the seminal work of Gordon-Litherland. We also relate Kashaev's matrix to Kauffman's"Formal Knot Theory"model of the Alexander polynomial. As a consequence, we establish the Alexander polynomial and classical signature parts of the conjecture for arbitrary links, as well as the full conjecture for definite knots.
SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations

Emotion recognition in conversations (ERC) is a rapidly evolving task within the natural language processing community, which aims to detect the emotions expressed by speakers during a conversation. Recently, a growing number of ERC methods have focused on leveraging supervised contrastive learning (SCL) to enhance the robustness and generalizability of learned features. However, current SCL-based approaches in ERC are impeded by the constraint of large batch sizes and the lack of compatibility with most existing ERC models. To address these challenges, we propose an efficient and model-agnostic SCL framework named Supervised Sample-Label Contrastive Learning with Soft-HGR Maximal Correlation (SSLCL), which eliminates the need for a large batch size and can be seamlessly integrated with existing ERC models without introducing any model-specific assumptions. Specifically, we introduce a novel perspective on utilizing label representations by projecting discrete labels into dense embeddings through a shallow multilayer perceptron, and formulate the training objective to maximize the similarity between sample features and their corresponding ground-truth label embeddings, while minimizing the similarity between sample features and label embeddings of disparate classes. Moreover, we innovatively adopt the Soft-HGR maximal correlation as a measure of similarity between sample features and label embeddings, leading to significant performance improvements over conventional similarity measures. Additionally, multimodal cues of utterances are effectively leveraged by SSLCL as data augmentations to boost model performances. Extensive experiments on two ERC benchmark datasets, IEMOCAP and MELD, demonstrate the compatibility and superiority of our proposed SSLCL framework compared to existing state-of-the-art SCL methods. Our code is available at \url{https://github.com/TaoShi1998/SSLCL}.
An Online Self-calibrating Refractive Camera Model with Application to Underwater Odometry

This work presents a camera model for refractive media such as water and its application in underwater visual-inertial odometry. The model is self-calibrating in real-time and is free of known correspondences or calibration targets. It is separable as a distortion model (dependent on refractive index $n$ and radial pixel coordinate) and a virtual pinhole model (as a function of $n$). We derive the self-calibration formulation leveraging epipolar constraints to estimate the refractive index and subsequently correct for distortion. Through experimental studies using an underwater robot integrating cameras and inertial sensing, the model is validated regarding the accurate estimation of the refractive index and its benefits for robust odometry estimation in an extended envelope of conditions. Lastly, we show the transition between media and the estimation of the varying refractive index online, thus allowing computer vision tasks across refractive media.
Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review

In the realm of data protection, a striking disconnect prevails between traditional domains of doctrinal, legal, theoretical, and policy-based inquiries and a burgeoning body of empirical evidence. Much of the scholarly and regulatory discourse remains entrenched in abstract legal principles or normative frameworks, leaving the empirical landscape uncharted or minimally engaged. Since the birth of EU data protection law, a modest body of empirical evidence has been generated but remains widely scattered and unexamined. Such evidence offers vital insights into the perception, impact, clarity, and effects of data protection measures but languishes on the periphery, inadequately integrated into the broader conversation. To make a meaningful connection, we conduct a comprehensive review and synthesis of empirical research spanning nearly three decades (1995- March 2022), advocating for a more robust integration of empirical evidence into the evaluation and review of the GDPR, while laying a methodological foundation for future empirical research.
Design Space Exploration of Sparsity-Aware Application-Specific Spiking Neural Network Accelerators

Spiking Neural Networks (SNNs) offer a promising alternative to Artificial Neural Networks (ANNs) for deep learning applications, particularly in resource-constrained systems. This is largely due to their inherent sparsity, influenced by factors such as the input dataset, the length of the spike train, and the network topology. While a few prior works have demonstrated the advantages of incorporating sparsity into the hardware design, especially in terms of reducing energy consumption, the impact on hardware resources has not yet been explored. This is where design space exploration (DSE) becomes crucial, as it allows for the optimization of hardware performance by tailoring both the hardware and model parameters to suit specific application needs. However, DSE can be extremely challenging given the potentially large design space and the interplay of hardware architecture design choices and application-specific model parameters. In this paper, we propose a flexible hardware design that leverages the sparsity of SNNs to identify highly efficient, application-specific accelerator designs. We develop a high-level, cycle-accurate simulation framework for this hardware and demonstrate the framework’s benefits in enabling detailed and fine-grained exploration of SNN design choices, such as the layer-wise logical-to-hardware ratio (LHR). Our experimental results show that our design can (i) achieve up to 76% reduction in hardware resources and (ii) deliver a speed increase of up to $31.25\times $ , while requiring 27% fewer hardware resources compared to sparsity-oblivious designs. We further showcase the robustness of our framework by varying spike train lengths with different neuron population sizes to find the optimal trade-off points between accuracy and hardware latency.
LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator

The majority of road accidents occur because of human errors, including distraction, recklessness, and drunken driving. One of the effective ways to overcome this dangerous situation is by implementing self-driving technologies in vehicles. In this paper, we focus on building an efficient deep-learning model for self-driving cars. We propose a new and effective convolutional neural network model called `LaksNet' consisting of four convolutional layers and two fully connected layers. We conduct extensive experiments using our LaksNet model with the training data generated from the Udacity simulator. Our model outperforms many existing pre-trained ImageNet and NVIDIA models in terms of the duration of the car for which it drives without going off the track on the simulator.
VD-GR: Boosting Visual Dialog with Cascaded Spatial-Temporal Multi-Modal GRaphs

We propose $\mathbb{VD}$-$\mathbb{GR}$ - a novel visual dialog model that combines pre-trained language models (LMs) with graph neural networks (GNNs). Prior works mainly focused on one class of models at the expense of the other, thus missing out on the opportunity of combining their respective benefits. At the core of $\mathbb{VD}$-$\mathbb{GR}$ is a novel integration mechanism that alternates between spatial-temporal multi-modal GNNs and BERT layers, and that covers three distinct contributions: First, we use multi-modal GNNs to process the features of each modality (image, question, and dialog history) and exploit their local structures before performing BERT global attention. Second, we propose hub-nodes that link to all other nodes within one modality graph, allowing the model to propagate information from one GNN (modality) to the other in a cascaded manner. Third, we augment the BERT hidden states with fine-grained multi-modal GNN features before passing them to the next $\mathbb{VD}$-$\mathbb{GR}$ layer. Evaluations on VisDial v1.0, VisDial v0.9, VisDialConv, and VisPro show that $\mathbb{VD}$-$\mathbb{GR}$ achieves new state-of-the-art results across all four datasets.
Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots

In this work, we address the problem of real-time dense depth estimation from monocular images for mobile underwater vehicles. We formulate a deep learning model that fuses sparse depth measurements from triangulated features to improve the depth predictions and solve the problem of scale ambiguity. To allow prior inputs of arbitrary sparsity, we apply a dense parameterization method. Our model extends recent state-of-the-art approaches to monocular image based depth estimation, using an efficient encoder-decoder backbone and modern lightweight transformer optimization stage to encode global context. The network is trained in a supervised fashion on the forward-looking underwater dataset, FLSea. Evaluation results on this dataset demonstrate significant improvement in depth prediction accuracy by the fusion of the sparse feature priors. In addition, without any retraining, our method achieves similar depth prediction accuracy on a downward looking dataset we collected with a diver operated camera rig, conducting a survey of a coral reef. The method achieves real-time performance, running at 160 FPS on a laptop GPU and 7 FPS on a single CPU core and is suitable for direct deployment on embedded systems. The implementation of this work is made publicly available at https://github.com/ebnerluca/uw_depth.
DSAM-GN: Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification

In recent years, vehicle re-identification (Re-ID) has gained increasing importance in various applications such as assisted driving systems, traffic flow management, and vehicle tracking, due to the growth of intelligent transportation systems. However, the presence of extraneous background information and occlusions can interfere with the learning of discriminative features, leading to significant variations in the same vehicle image across different scenarios. This paper proposes a method, named graph network based on dynamic similarity adjacency matrices (DSAM-GN), which incorporates a novel approach for constructing adjacency matrices to capture spatial relationships of local features and reduce background noise. Specifically, the proposed method divides the extracted vehicle features into different patches as nodes within the graph network. A spatial attention-based similarity adjacency matrix generation (SASAMG) module is employed to compute similarity matrices of nodes, and a dynamic erasure operation is applied to disconnect nodes with low similarity, resulting in similarity adjacency matrices. Finally, the nodes and similarity adjacency matrices are fed into graph networks to extract more discriminative features for vehicle Re-ID. Experimental results on public datasets VeRi-776 and VehicleID demonstrate the effectiveness of the proposed method compared with recent works.