Remote Antenna Unit Selection Assisted Seamless Handover for High-Speed Railway Communications with Distributed Antennas

To attain seamless handover and reduce the han- dover failure probability for high-speed railway (HSR) com- munication systems, this paper proposes a remote antenna unit (RAU) selection assisted handover scheme where two antennas are installed on high speed train (HST) and distributed antenna system (DAS) cell architecture on ground is adopted. The RAU selection is used to provide high quality received signals for trains moving in DAS cells and the two HST antennas are employed on trains to realize seamless handover. Moreover, to efficiently evaluate the system performance, a new met- ric termed as handover occurrence probability is defined for describing the relation between handover occurrence position and handover failure probability. We then analyze the received signal strength, the handover trigger probability, the handover occurrence probability, the handover failure probability and the communication interruption probability. Numerical results are provided to compare our proposed scheme with the current existing ones. It is shown that our proposed scheme achieves better performances in terms of handover failure probability and communication interruption probability.Comment: 7 figures, accepted by IEEE VTC-Spring, 201

Evaluating probability forecasts

Probability forecasts of events are routinely used in climate predictions, in forecasting default probabilities on bank loans or in estimating the probability of a patient's positive response to treatment. Scoring rules have long been used to assess the efficacy of the forecast probabilities after observing the occurrence, or nonoccurrence, of the predicted events. We develop herein a statistical theory for scoring rules and propose an alternative approach to the evaluation of probability forecasts. This approach uses loss functions relating the predicted to the actual probabilities of the events and applies martingale theory to exploit the temporal structure between the forecast and the subsequent occurrence or nonoccurrence of the event.Comment: Published in at http://dx.doi.org/10.1214/11-AOS902 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Extreme events and event size fluctuations in biased random walks on networks

Random walk on discrete lattice models is important to understand various types of transport processes. The extreme events, defined as exceedences of the flux of walkers above a prescribed threshold, have been studied recently in the context of complex networks. This was motivated by the occurrence of rare events such as traffic jams, floods, and power black-outs which take place on networks. In this work, we study extreme events in a generalized random walk model in which the walk is preferentially biased by the network topology. The walkers preferentially choose to hop toward the hubs or small degree nodes. In this setting, we show that extremely large fluctuations in event-sizes are possible on small degree nodes when the walkers are biased toward the hubs. In particular, we obtain the distribution of event-sizes on the network. Further, the probability for the occurrence of extreme events on any node in the network depends on its 'generalized strength', a measure of the ability of a node to attract walkers. The 'generalized strength' is a function of the degree of the node and that of its nearest neighbors. We obtain analytical and simulation results for the probability of occurrence of extreme events on the nodes of a network using a generalized random walk model. The result reveals that the nodes with a larger value of 'generalized strength', on average, display lower probability for the occurrence of extreme events compared to the nodes with lower values of 'generalized strength'

Deep Recurrent Survival Analysis

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at fine-grained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three real-world tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code: https://github.com/rk2900/drs

The Zipf law for random texts with unequal probabilities of occurrence of letters and the Pascal pyramid

We model the generation of words with independent unequal probabilities of occurrence of letters. We prove that the probability $p(r)$ of occurrence of words of rank $r$ has a power asymptotics. As distinct from the paper published earlier by B. Conrad and M. Mitzenmacher, we give a brief proof by elementary methods and obtain an explicit formula for the exponent of the power law.Comment: 4 page

Complex Collective Decisions and the Probability of Collective Inconsistencies

Many groups are required to make collective decisions over multiple interconnected propositions. The "doctrinal paradox" or "discursive dilemma" shows that propostionwise majority voting can lead to inconsistent collective outcomes, even when the judgments of individual group members are consistent. How likely is the occurence of this paradox? This paper develops a simple model for determining the probability of the paradox's occurrence, given various assumptions about the probability of different individual judgments. Several convergence results will be proved, identifying conditions under which the probability of the paradox's occurrence converges to certainty as the number of individuals increases, and conditions under which that probability vanishes. The present model will also be used for assessing the "truth-tracking" performance of two escape-routes from the paradox, the premise- and conclusion-based procedures. Finally, the results on the probability of the doctrinal paradox will be compared with existing results on the probability of Condorcet's paradox of cyclical preferences. It will be suggested that the doctrinal paradox is more likely to occur than Condorcet's paradox.

Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures

First-occurrence time of high-level crossings in a continuous random process

Statistical probability distribution of first occurrence and first recurrence times of given level crossing in continuous random proces