Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting

Numerals that contain much information in financial documents are crucial for financial decision making. They play different roles in financial analysis processes. This paper is aimed at understanding the meanings of numerals in financial tweets for fine-grained crowd-based forecasting. We propose a taxonomy that classifies the numerals in financial tweets into 7 categories, and further extend some of these categories into several subcategories. Neural network-based models with word and character-level encoders are proposed for 7-way classification and 17-way classification. We perform backtest to confirm the effectiveness of the numeric opinions made by the crowd. This work is the first attempt to understand numerals in financial social media data, and we provide the first comparison of fine-grained opinion of individual investors and analysts based on their forecast price. The numeral corpus used in our experiments, called FinNum 1.0 , is available for research purposes.Comment: Accepted by the 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2018), Santiago, Chil

Effect of Na+ Flow on Cd2+ Block of Tetrodotoxin-resistant Na+ Channels

Tetrodotoxin-resistant (TTX-R) Na+ channels are 1,000-fold less sensitive to TTX than TTX-sensitive (TTX-S) Na+ channels. On the other hand, TTX-R channels are much more susceptible to external Cd2+ block than TTX-S channels. A cysteine (or serine) residue situated just next to the aspartate residue of the presumable selectivity filter “DEKA” ring of the TTX-R channel has been identified as the key ligand determining the binding affinity of both TTX and Cd2+. In this study we demonstrate that the binding affinity of Cd2+ to the TTX-R channels in neurons from dorsal root ganglia has little intrinsic voltage dependence, but is significantly influenced by the direction of Na+ current flow. In the presence of inward Na+ current, the apparent dissociation constant of Cd2+ (∼200 μM) is ∼9 times smaller than that in the presence of outward Na+ current. The Na+ flow–dependent binding affinity change of Cd2+ block is true no matter whether the direction of Na+ current is secured by asymmetrical chemical gradient (e.g., 150 mM Na+ vs. 150 mM Cs+ on different sides of the membrane, 0 mV) or by asymmetrical electrical gradient (e.g., 150 mM Na+ on both sides of the membrane, −20 mV vs. 20 mV). These findings suggest that Cd2+ is a pore blocker of TTX-R channels with its binding site located in a multiion, single-file region near the external pore mouth. Quantitative analysis of the flow dependence with the flux-coupling equation reveals that at least two Na+ ions coexist with the blocking Cd2+ ion in this pore region in the presence of 150 mM ambient Na+. Thus, the selectivity filter of the TTX-R Na+ channels in dorsal root ganglion neurons might be located in or close to a multiion single-file pore segment connected externally to a wide vestibule, a molecular feature probably shared by other voltage-gated cationic channels, such as some Ca2+ and K+ channels

Advancing Regular Language Reasoning in Linear Recurrent Neural Networks

In recent studies, linear recurrent neural networks (LRNNs) have achieved Transformer-level performance in natural language modeling and long-range modeling while offering rapid parallel training and constant inference costs. With the resurged interest in LRNNs, we study whether they can learn the hidden rules in training sequences, such as the grammatical structures of regular language. We theoretically analyze some existing LRNNs and discover their limitations on regular language. Motivated by the analysis, we propose a new LRNN equipped with a block-diagonal and input-dependent transition matrix. Experiments suggest that the proposed model is the only LRNN that can perform length extrapolation on regular language tasks such as Sum, Even Pair, and Modular Arithmetic.Comment: The first two authors contributed equally to this wor

Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation

An ideal length-extrapolatable Transformer language model can handle sequences longer than the training length without any fine-tuning. Such long-context utilization capability relies heavily on a flexible positional embedding design. Upon investigating the flexibility of existing large pre-trained Transformer language models, we find that the T5 family deserves a closer look, as its positional embeddings capture rich and flexible attention patterns. However, T5 suffers from the dispersed attention issue: the longer the input sequence, the flatter the attention distribution. To alleviate the issue, we propose two attention alignment strategies via temperature scaling. Our findings show improvement on the long-context utilization capability of T5 on language modeling, retrieval, multi-document question answering, and code completion tasks without any fine-tuning. This suggests that a flexible positional embedding design and attention alignment can go a long way toward Transformer length extrapolation

Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System

This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations

Debris Flow Risk Assessment and Land-Use Planning – A Case Study of Jhonglun Hot Spring Area

The Jhonglun Scenic Area in Chiayi County, is famous for its hot spring, the region was hit by debris flow with tremendous losses and resulted with dramatic change of the landscape during Typhoon Morakot in 2009. The most effective strategy for reducing natural hazard risks is through land-use planning. Following the concept of Risk=Hazard*Exposure*Vulnerability, this study conducted risk identification through the collection of landslide inventory and history debris flow hazard mapping of Chiayi DF051 potential debris flow torrent. Together with elements at risk information from field investigations, the risk analysis was conducted with several return periods debris flow simulation to recognize the possible economic losses and fatalities by debris flow. The identified high risk areas in Jhonglun Scenic Area were compared to the current special district planning to understand the spatial distribution of high risk areas. The result shows that some of the designated zones were among the areas with high debris flow risks, which further indicates that land-use planning should consider the consequences of natural hazards. The result of this study provides one of the first steps for land use planning restrictions within the potential debris flow region

Fostering equitable access to higher education in Hong Kong : a study of the tertiary financial assistance scheme

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio