Predicting the price trends of stocks based on deep learning and high-frequency data has been studied intensively in recent years. Especially, limit order book which describes supply-demand balance a market is used as feature neural network; however these methods do not utilize properties orders. On other hand, order-encoding method our prior work can take advantage properties. In this paper, we apply some types convolutional network architectures to order-based features predict direction...

Abstract Although deep neural networks are excellent for text sentiment analysis, their applications in real-world practice occasionally limited owing to black-box property. In this study, we propose a novel network model called contextual (CSNN) that can explain the process of its analysis prediction way humans find natural and agreeable catch up summary contents. The CSNN has following interpretable layers: word-level original layer, shift global importance concept-level layer. Because...

We investigated the distribution and antimicrobial resistance of 120 Staphylococcus felis isolates from feline patients in Japan, mainly urinary tract (28.3%), abscesses (23.3%), ears (22.5%), nasal cavity (10.8%). The S. differed those previous studies Japan other countries. Antimicrobial susceptibility testing revealed a relatively high to penicillin (PEN, 33.3%), followed by erythromycin (ERY, 15.8%), clindamycin (CLI, 13.3%), levofloxacin (5.0%). However, oxacillin was not detected....

Abstract The growth of social media recently has made individual investors more reliant on online for information. This trend significantly affects investor behavior and information disparity. For instance, can lead to the phenomenon “meme stocks," in which stock prices rapidly rise fall. Despite increasing interest meme stocks, few studies have focused In this study, we model stocks focusing who are influenced by network information, leading spread. We combine...

In AI-assisted decision-making, it is crucial but challenging for humans to appropriately rely on AI, especially in high-stakes domains such as finance and healthcare. This paper addresses this problem from a human-centered perspective by presenting an intervention self-confidence shaping, designed calibrate at targeted level. We first demonstrate the impact of shaping quantifying upper-bound improvement human-AI team performance. Our behavioral experiments with 121 participants show that...

With maturation of ubiquitous computing technology, it has become feasible to design new systems improve our urban life. In this paper, we introduce a application for car navigation in city. Every system operation today the current position vehicle, destination, and currently chosen route destination. If vehicles city could share information, they use traffic information globally plan semi-optimal routes each vehicle. Thus, propose cooperative with sharing (RIS). RIS system, vehicle...

Summary We built an artificial market model and investigated the impact of large erroneous orders on financial price formations. Comparing case consented in short term with that continuous small long term, if amounts are same, we found induced almost same fall range. also analysed effects variation limits for employ a limitation shorter than time exist effectively prevent fluctuations. up‐tick rules, adopting trigger method Japan Financial Services Agency adopted November 2013. whether dark...

Abstract The general personality traits, notably the Big-Five have been increasingly integrated into recommendation systems. personality-aware recommendations, which incorporate human systems, shown promising results in areas including music, movie, and e-commerce recommendations. On other hand, number of research delving applicability recommendations specialized domains such as finance education remains limited. In addition, these unique challenges incorporating domain-specific...

Prediction of financial market data with deep learning models has achieved some level recent success. However, historical suffer from an unknowable state space, limited observations, and the inability to model impact your own actions on can often be prohibitive when trying find investment strategies using reinforcement learning. One way overcome these limitations is augment real agent based artificial simulation. Artificial simulations designed reproduce realistic features may used create...

In this study, we propose an artificial market approach, which is a new agent-based approach to foreign exchange studies. Using emergent phenomena of markets such as the peaked and fat-tailed distribution rate changes were explained. First, collected field data through interviews questionnaires with dealers found that features dealer interaction in learning similar genetic operations biology. Second, constructed model using algorithm. Our was multiagent system agents having internal...

Observational evidence is sought that the long-term (104 yr) action of a mean motion resonance with Jupiter can produce structure in meteoroid stream, concentrating meteoroids dense swarm. More specifically, predictions tabulated by Asher & Clube enhanced meteor and fireball activity from Taurid Complex swarm 7:2 are compared observational data collected Japan over several decades. The model was proposed for reasons independent observations analysed here, these newly considered shown to be...

This research is intended to increase drivers' utility by reducing traffic congestion. To attain our purpose, we propose a simple route guidance mechanism based on information sharing (RIS). Drivers using the RIS give planned details server, which then sends accumulated those routes drivers. Multiagent simulation in lattice network and radial ring confirmed that: i) as drivers increased, travel time of both other decreased; ii) times were substantially shorter than mechanisms.

What would happen if temperatures were subdued and result in a cool summer? One can easily imagine that air conditioner, ice cream or beer sales be suppressed as of this. Less obvious is agricultural shipments might delayed, sound proofing material decrease. The ability to extract such causal knowledge important, but it also important distinguish between cause-effect pairs are known those likely unknown, rare. Therefore, this paper, we propose method for extracting rare from Japanese...

In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers' reactions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated impacts decisions, focusing both amateur and expert audiences. Our findings indicate that GPT-4 can generate persuasive analyses affecting decisions of amateurs professionals. Furthermore, we evaluate aspects grammar, convincingness, logical coherence,...