Past Information Research Articles

Comparative Performance of Arima and Garch Models in Modelling and Forecasting Volatility of Kuala Lumpur Composite Index

Time series is a set of observations in sequence over time. Time series modelling is used to create an applicable model that defines the necessary arrangement of the series by study the previous information of a time series. The past information of a time series is used to generate forecast value for the series. It is well acknowledged that a time series are regularly affected with outliers. Outliers may impact the forecasting where the tendency in parameter estimates created by extreme observation will reduce its effectiveness because the optimum predictor for an Autoregressive Integrated Moving Average (ARIMA) model is determined by its parameters. Thus, the occurrence of extreme observations might have a huge effect on predictions value. Therefore, Generalized Autoregressive Conditional Heteroskedastic (GARCH) model has been used to compare the result obtain from ARIMA model. This study used ARIMA and GARCH to compare the best model for forecasting Kuala Lumpur Composite Index (KLCI) when the outlier exists. The best models of ARIMA and GARCH were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). It can be concluded that GARCH model performed better compared to Box-Jenkins ARIMA in forecasting KLCI.

A machine-learning-based event-triggered model predictive control for building energy management

Model predictive control (MPC) for building energy management has exhibited a huge potential for largely cutting down energy use and improving human comfort. However, the high demand for computational power for solving the optimization is challenging the widespread deployment of MPC in real buildings. Typical MPC employs a time-triggered mechanism (TTM) that runs optimization recurrently at each time step without considering the necessity, which could cause excessive computation resource usage. This paper proposes an event-triggered mechanism (ETM) that only runs optimization when triggering events occur. Contrary to conventional ETMs that only considers the current information (e.g., room conditions), the ETM proposed is based on a cost function that covers the past, current, and future information. Based on the proposed ETM, a machine-learning-based event-triggered model predictive control (ETMPC) system that optimizes both building energy efficiency and thermal comfort is developed. The developed ETMPC system is then employed for air-conditioning control for performance evaluation through simulations. The control performance of the proposed ETMPC is compared to an MPC employing TTM and a common thermostat. Compared to the MPC employing TTM, the proposed ETMPC reduced the computational load in terms of the number of optimization runs by 77.6%–88.2%, meanwhile, achieving similar energy savings (more than 9.3% energy saving over the thermostat) as the MPC employing TTM does (9% energy saving over the thermostat). With the significant reduction of computational load, the ETMPC achieves similar thermal comfort performance as the MPC employing TTM does, and it is significantly better over the thermostat.

Finite-Horizon URTSS-Based Position Estimation for Urban Vehicle Localization

Vehicles in urban areas are facing the problem of lacking Global Positioning System (GPS) signals in the urban canyon environments. In this article, we present a finite-horizon Unscented Rauch-Tung-Striebel Smoother (URTSS)-based position estimation method for urban vehicle localization, which uses information from past, present, and near-future. To estimate vehicle pose, a nonlinear constant velocity state-space model is established. Sensors can only obtain immediate information, and we describe the near-future information obtained via predetermined paths using “pseudomeasurements.” The noise characteristics of pseudomeasurements are subsequently modeled and analyzed based on the fact that the uncertainty of pseudomeasurements increases over time. Using the URTSS, we design a finite-horizon estimator to achieve the fusion of past, present, and near-future information, which can perfectly yield a vehicle position estimation. Numerical simulation and road experiments are conducted to show that the proposed scheme is able to provide accurate position estimation in finite horizon when GPS signals are temporarily lost.

Estimating and pricing commodity futures with time‐delay stochastic processes

In commodity futures pricing models, the commodity present price is generally considered to reflect all information in the markets and past information is not regarded important. However, there is some empirical evidence that shows that this fact is unrealistic. In this paper, we consider some stochastic models with delay for pricing commodity futures. The functions of the commodity price stochastic process under the risk‐neutral measure are necessary for pricing derivatives. However, the observations in the market have risk. Then, we use a technique that allows us to estimate the functions of the risk‐neutral commodity spot price stochastic process, directly from futures prices traded in the market, and show how to price the commodity futures. Finally, we make an empirical application of this methodology with gold futures traded in the COMEX. Furthermore, we make clear the supremacy of the delay models in pricing gold futures.

The influence of own historical information and environmental historical information on the evolution of cooperation

This article has studied the influence of own historical information and environmental historical information on the evolution of cooperation in the three common social dilemma games. The results show that only the past two-step information can effectively promote cooperation. In the prisoner's dilemma game, cooperation can be improved by using only the individual own information. In the stag hunt game, using only environmental information can promote cooperation. In the snowdrift game, when the parameter values are different, the results are different. The cooperation ratio can be maximized by using either its own information or environmental information only. This article provides a new way to promote and understand cooperation.

Conditioning competitive behaviour in experimental Bertrand markets through contextual frames

Explaining framing effects is one of the main challenges faced by decision theories. This research experimentally examines how different contextual frames influence competitive behaviour in a Bertrand duopoly game (repeatedly played under a stranger matching), unexplored so far. The design comprises four frames: one abstract (a beauty-contest framing), two meaningful (the standard Bertrand framing and an access-to-river framing) and one evocative (a take-from-fund framing). Our findings show that, at first, the evocative frame differentiates from the rest mostly in market prices. While the evocative frame induces subjects to behave closer to the theoretical predictions initially, the others need some repetitions until convergence is achieved. Differences across frames eventually vanish at the end. During the transition, in the Bertrand frame, a quicker decay in prices is observed due to the behavioural reactions to historical market prices. Lastly, irrespectively of frames, behavioural reactions to immediate past information allow to explain strategic interaction in the long-run: a force-balance situation which is consistent with the related literature on price floors in Bertrand games.

Automotive Radar-Based Hitch Angle Tracking Technique for Trailer Backup Assistant Systems

We propose a technique for trailer angle detection (TAD) for use in advanced trailer backup assistance system (TBAS) for semi-autonomous or full-autonomous backup maneuver. TBAS incorporates a combined trailer-tow-vehicle kinematic model, which is requires an estimate of the hitch angle. The proposed radar-based TAD model, for the estimate of the hitch angle, processes reflections acquired from the mmWave radars situated at the rear side of the vehicle, to detect the trailer and track its orientation in relation to the tow-vehicle. This technique is based on the tracking of individual points in the merged radars point-cloud. Each tracked point is considered as a hitch angle estimator. Using the current and past position information of a point, the model estimates the current hitch angle. To offer an accurate and reliable estimation for the hitch angle, the model fuses the estimated hitch angle by all estimators, as well as the yaw rate of the vehicle. To track each individual radar point, the model employs the extended Kalman filter, which is robust to the noisy radar measurements. In the presence of strong and persistence reflections from the trailer, the model can track the trailer successfully and return the hitch angle with a reliability measure equal to 90%.

Memory-Anticipation Strategy to Compensate for Communication and Actuation Delays for Strings-Stable Platooning

Cooperative Adaptive Cruise Control (CACC) has the potential to significantly improve traffic throughput by reducing inter-vehicle following gap. However, communication and actuation delays in CACC systems are detrimental to string stability. A much larger time gap than the lump sum of the two delays is often required for CACC to guarantee string stability, which compromises their benefits on traffic throughput. It remains as a challenge to design string-stable CACC controllers that operate at small time gaps under delays. In this paper, we design a memory-anticipation CACC to explicitly handle communication and actuation delays to fulfill string stability specifications. The proposed controller employs a novel memory-anticipation spacing policy to address communication and actuation delays and is applicable to heterogeneous platoons. Unlike existing CACC controllers, our proposal utilizes past information of the preceding vehicle (i.e. the memory) instead of its current state/control information, which makes the controller robust to communication delay. Furthermore, a Smith predictor (i.e. the anticipation) is designed to compensate for the actuation delay. Both individual vehicle stability and string stability can be guaranteed with the minimum time gap as small as the summation of communication delay and actuation delay. Controller analysis and simulation verify the enhancement of the stability performance with respect to existing studies.

Motivated Memory in Economics—A Review

Motivated reasoning refers to the idea that people hold certain beliefs about themselves or the world due to their desire to do so, rather than striving for accuracy. This type of belief formation can lead to overconfidence and polarization, as well as facilitate immoral behavior at both the individual and collective levels. One of the supply-side mechanisms for motivated reasoning is motivated memory, or the selective retrieval of past experiences or information based on self-serving criteria. In this article, we review the still young economics literature on motivated memory. Summarizing both theoretical and empirical work, we highlight the key results this literature has produced. We also discuss open questions and potentially exciting avenues for future research in this area.

Versatile recurrent neural network for wide types of video restoration

Video shooting of natural scenes often suffers from various serious degradation, such as motion blur, impact of atmospheric turbulence, random noise and resolution reduction, etc. Different from the maturity of image restoration research, video restoration is much more complicated so that it lacks effective general method. Here, we present a versatile recurrent neural network (VRNN) to handle wide types of video degradation and generate stable videos with ideal clarity. We complete the design of VRNN through deducing a general video restoration paradigm that reveals the importance of simultaneously utilizing past and future information for restoring current frame. Specifically, we propose a novel RNN cell in which hidden state flows in bidirections, enriching temporal information contained in the extracted features. Furthermore, a feature fusion module involves temporal and spatial attention processing is designed to refine features of neighbouring frames and help reconstruct current frame. Extensive experiments on well-known public datasets (including four different kinds of video restoration tasks, with a total of 35,666 videos and 515,774 frames) show that the proposed VRNN achieves 1–4 dB of PSNR increasing or several times less of computational complexity in all tasks against state-of-the-art methods, manifesting the versatile and efficient ability of proposed VRNN in wide types of video restoration.

Geophysical Surveys for Archaeological Research in Urban Areas: The Case of the Roman Theatre in Padua

The Roman theatre of Padua stood in the area now occupied by Prato della Valle, a huge elliptical-shaped square with a central green part (Isola Memmia) surrounded by a canal, built in the second half of the 18th century and part of the modern urban city center of Padua. Some still-preserved remains of the Roman theatre stand at the bottom of this canal. Recently, in 2017, emptying and cleaning the canal and excavating these remains, which had been known in the past, new geometrical and archaeological information has been collected. To date, however, there are no specific indications about additional preserved buried parts of the Roman theatre and its overall extent between the central and the outer part of Prato della Valle. Therefore, several electrical resistivity tomographies (ERT) and ground penetrating radar (GPR) sections were collected in 2017 to gather new information. The results of geophysical prospecting with recent archaeological evidence and historical documents, even the complexity of the urban environment, provide further details on the possible extent and location of additional buried remains of the Roman theatre, opening new archaeological considerations and issues related to the use of ERT and GPR in urban contexts.

Orbital‐Insolation Controlled Porites Coral δ13C Seasonality Variations Since the Mid‐Holocene in the Northern South China Sea

AbstractCoral skeleton δ13C is a routinely measured indicator in modern insolation change research, but the knowledge of environmental and climatic signals recorded in its seasonality during geological time is sparse. In this study, we present eight Porites coral δ13C records from the mid‐late Holocene to the present from the northern South China Sea (NSCS). Compared with the main control factors for modern δ13C changes, coral δ13C seasonality in the NSCS since the mid‐Holocene shows a long‐term decreasing trend, which is consistent with the change trend as orbital‐induced (precession) insolation seasonal amplitude. By excluding other influencing factors, we speculate that the stronger coral δ13C seasonality (18.8%) over the mid‐Holocene than modern period is attributable to the metabolic effect, which predicts the stronger coral δ13C seasonality under stronger insolation seasonality. Our study has implications for coral δ13C seasonality as a potential indicator to record past insolation information under different climatic backgrounds.

Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion

Autonomous driving is gradually moving from single-vehicle intelligence to internet of vehicles, where traffic participants can share the traffic flow information perceived by each other. When the sensing technology is combined with the internet of vehicles, a sensor network all over the road can provide a large-scale of traffic flow data, thus providing a basis for building a traffic digital twin model. The digital twin can enable the traffic system not only to use past and present information, but also to predict traffic conditions, providing more effective optimization for autonomous driving and intelligent transportation, so as to make long-term rational planning of the overall traffic state and enhance the level of traffic intelligence. The current mainstream traffic sensors, namely radar and camera, have their own advantages, and the fusion of these two sensors can provide more accurate traffic flow data for the generation of digital twin model. In this paper, an end-to-end digital twin system implementation approach is proposed for highway scenarios. Starting from a paired radar-camera sensing system, a single-site radar-camera fusion framework is proposed, and then using the definition of a unified coordinate system, the traffic flow data between multiple sites is combined to form a dynamic real-time traffic flow digital twin model. The effectiveness of the digital twin building is verified based on the real-world traffic data.

History, Memory, and Place in the Popular Revival of Confucian Educational Traditions

In the first decade of the twenty-first century, traditional Confucian education re-emerged in China in the context of so-called study halls and academies. The goal of the parents, teachers, and headmasters associated with them is to cultivate modern virtuous persons through an approach called “classics-reading education.” Even though they allude to deep historical roots, these contemporary facilities are novel (re)creations, developed in response to current needs. This article examines the classics-reading movement under the theoretical lens of infrastructures of memory to illustrate the roles of memory and the perception of the past in the current making of “traditional” education and educational sites. Memories of and references to the past inform people's visions of a better future that is to be achieved through their tradition-related educational practices. Making a connection with the past through memory aims at stability in the face of future uncertainty.

Cardiovascular Toxicities of Ibrutinib: A Pharmacovigilance Study Based on the United States Food and Drug Administration Adverse Event Reporting System Database.

Although ibrutinib has been widely used to treat haematological malignancies, many studies have reported associated cardiovascular events. These studies were primarily animal experiments and clinical trials. For more rational clinical drug use, a study based on post-marketing data is necessary. Based on post-marketing data, we investigated the clinical features, time to onset, and outcomes of potential cardiovascular toxicities of ibrutinib. This disproportionality study utilised data from the 2014-2021 United States Food and Drug Administration Adverse Event Reporting System (FAERS) database. We used two disproportionality methods information component (IC) and reporting odds ratio (ROR)) to detect the potential cardiovascular toxicities of ibrutinib. Positive signals were defined as IC025 > 0 and ROR025 > 1. A total of 10 cardiovascular events showed positive signals: supraventricular tachyarrhythmias, haemorrhagic central nervous system vascular conditions, ventricular tachyarrhythmias, cardiac failure, ischaemic central nervous system vascular conditions, cardiomyopathy, conduction defects, myocardial infarction, myocardial infarction disorders of sinus node function, and torsade de pointes/QT prolongation. Cardiomyopathy and supraventricular tachyarrhythmias were the two most common signals. Disorders of sinus node function were observed for the first time, which may be a new adverse effect of ibrutinib. This pharmacovigilance study systematically explored the adverse cardiovascular events of ibrutinib and provided new safety signals based on past safety information. Attention should be paid to some high-risk signals.

Hybrid blockchain-based spectrum sharing algorithm for dynamic channel selection in cognitive radio

Currently, the available CR spectrum is not being used to their full potential. Certainly, the CR system is capable of meeting this challenge. Whenever a licensed user has to start another transmission on that channel, spectrum handoff allows an unlicensed user to leave its current channel. The SU then switches to a different channel to finish the incomplete transmission. This technique uses the handshaking protocol to transmit and receive an acknowledgment before data transmission to obtain the user's wait time.This paper describes the Convolution Neural Network automatically extracts the features without any human supervision. For classification, a two-layer hidden neural-network is utilized once features are extracted. The CNN performs convolution operation to extract the complex features of the data that is significant for doing regression. First, the preprocessed data is delivered to the CNN's input layer. The CNN conducts a convolution operation on the data to extract the complex features that are important for regression. . The developed model consists of a sequence layer, Long Short Term Memory (LSTM) layer, fully connected layer, and a regression layer. LSTM memory cells were used in the LSTM layer to provide extended memory support. LSTM units save the essential past state information to increase performance by accounting for dependencies, and they erase the unnecessary information to save memory an [1]d energy. The fully linked layer collects the processed output from all of the LSTM hidden units. The regression layer receives a single output as a result of this. The regression layer computes an output for which the loss value is computed and propagated backwards during the training phase. This is done for all of the training iterations until the loss is zero. The anticipated values of waiting time are next tested using a fully trained regression model. The observed waiting time as a function of the number of rounds played. The loss and RMSE (Root mean square error) keep decreasing for increasing number of iterations of training.

Asset prices with investor protection and past information

<p style='text-indent:20px;'>In this paper, we consider a dynamic asset pricing model in an approximate fractional economy to address empirical regularities related to both investor protection and past information. Our newly developed model features not only a controlling shareholder who diverts a fraction of the output, but also good (or bad) memory in his budget dynamics which can be well-calibrated by a pathwise way from the historical data. We find that poorer investor protection leads to higher stock holdings of the controlling shareholder, lower gross stock returns, lower interest rates, and lower modified stock volatilities if the ownership concentration is sufficiently high. More importantly, by establishing an approximation scheme for good (bad) memory of investors on the historical market information, we conclude that good (bad) memory would increase (decrease) aforementioned dynamics and reveal that good (bad) memory strengthens (weakens) investor protection for the minority shareholder when the ownership concentration is sufficiently high, while good (bad) memory inversely weakens (strengthens) investor protection for the minority shareholder when the ownership concentration is sufficiently low. Our model's implications are consistent with a number of interesting facts documented in the recent literature.</p>

Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s

The image caption generation algorithm necessitates the expression of image content using accurate natural language. Given the existing encoder-decoder algorithm structure, the decoder solely generates words one by one in a front-to-back order and is unable to analyze integral contextual information. This paper employs a Bi-LSTM (Bi-directional Long Short-Term Memory) structure, which not only draws on past information but also captures subsequent information, resulting in the prediction of image content subject to the context clues. The visual information is respectively fed into the F-LSTM decoder (forward LSTM decoder) and B-LSTM decoder (backward LSTM decoder) to extract semantic information, along with complementing semantic output. Specifically, the subsidiary attention mechanism S-Att acts between F-LSTM and B-LSTM, while the semantic information of B-LSTM and F-LSTM is extracted using the attention mechanism. Meanwhile, the semantic interaction is extracted pursuant to the similarity while aligning the hidden states, resulting in the output of the fused semantic information. We adopt a Bi-LSTM-s model capable of extracting contextual information and realizing finer-grained image captioning effectively. In the end, our model improved by 9.7% on the basis of the original LSTM. In addition, our model effectively solves the problem of inconsistent semantic information in the forward and backward direction of the simultaneous order, and gets a score of 37.5 on BLEU-4. The superiority of this approach is experimentally demonstrated on the MSCOCO dataset.

Cloud-mediated self-triggered synchronization of linear agents subject to disturbances

This paper deals with a cloud-mediated self-triggered state synchronization control method of a linear multi-agent system subject to disturbances. In the cloud-mediated self-triggered control approach, each agent asynchronously accesses the cloud repository to get past information on its neighboring agents. Then, the agent predicts the future behavior of its neighbors as well as of its own, and locally determines its next access time to the cloud repository. In this paper, we propose a cloud-mediated state synchronizing controller design method in the presence of disturbances. The uncertainty caused by the disturbances is appropriately handled in the self-triggering rule. It is also proved that the closed-loop system does not exhibit any Zeno behaviors.

Volumetric memory network for interactive medical image segmentation.

Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet clinically acceptable accuracy, thus typically require further refinement. To this end, we propose a novel Volumetric Memory Network, dubbed as VMN, to enable segmentation of 3D medical images in an interactive manner. Provided by user hints on an arbitrary slice, a 2D interaction network is firstly employed to produce an initial 2D segmentation for the chosen slice. Then, the VMN propagates the initial segmentation mask bidirectionally to all slices of the entire volume. Subsequent refinement based on additional user guidance on other slices can be incorporated in the same manner. To facilitate smooth human-in-the-loop segmentation, a quality assessment module is introduced to suggest the next slice for interaction based on the segmentation quality of each slice produced in the previous round. Our VMN demonstrates two distinctive features: First, the memory-augmented network design offers our model the ability to quickly encode past segmentation information, which will be retrieved later for the segmentation of other slices; Second, the quality assessment module enables the model to directly estimate the quality of each segmentation prediction, which allows for an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement. The proposed network leads to a robust interactive segmentation engine, which can generalize well to various types of user annotations (e.g., scribble, bounding box, extreme clicking). Extensive experiments have been conducted on three public medical image segmentation datasets (i.e., MSD, KiTS19, CVC-ClinicDB), and the results clearly confirm the superiority of our approach in comparison with state-of-the-art segmentation models. The code is made publicly available at https://github.com/0liliulei/Mem3D.