Modelling the Selection of Waiting Areas on Subway Platforms Based on the Bacterial Chemotaxis Algorithm

Based on the bacterial chemotaxis algorithm, a new waiting-area selection model (WASM) is proposed that predicts well the pedestrian distribution in subway waiting areas. WASM regards passengers waiting on a subway platform as two-dimensional points and adopts an essential rejection factor to determine the target waiting area. Based on WASM, three experiments were carried out to explore how passenger volume, waiting-area capacity, and staircase position affect the number and distribution of waiting passengers. The experimental results show the following. 1) Regardless of the passenger flow, passengers prefer waiting areas that are between the stairs. 2) Setting proper capacity limits on waiting areas can help to improve subway transportation efficiency when passenger flow is relatively high. 3) The experimental results show that the closer the staircases, the more passengers are left stranded on the platform

Learning to Skip for Language Modeling

Overparameterized large-scale language models have impressive generalization performance of in-context few-shot learning. However, most language models allocate the same amount of parameters or computation to each token, disregarding the complexity or importance of the input data. We argue that in language model pretraining, a variable amount of computation should be assigned to different tokens, and this can be efficiently achieved via a simple routing mechanism. Different from conventional early stopping techniques where tokens can early exit at only early layers, we propose a more general method that dynamically skips the execution of a layer (or module) for any input token with a binary router. In our extensive evaluation across 24 NLP tasks, we demonstrate that the proposed method can significantly improve the 1-shot performance compared to other competitive baselines only at mild extra cost for inference

The Potential of Using Brain Images for Authentication

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition

Damage Identification of Piles Based on Vibration Characteristics

A method of damage identification of piles was established by using vibration characteristics. The approach focused on the application of the element strain energy and sensitive modals. A damage identification equation of piles was deduced using the structural vibration equation. The equation contained three major factors: change rate of element modal strain energy, damage factor of pile, and sensitivity factor of modal damage. The sensitive modals of damage identification were selected by using sensitivity factor of modal damage firstly. Subsequently, the indexes for early-warning of pile damage were established by applying the change rate of strain energy. Then the technology of computational analysis of wavelet transform was used to damage identification for pile. The identification of small damage of pile was completely achieved, including the location of damage and the extent of damage. In the process of identifying the extent of damage of pile, the equation of damage identification was used in many times. Finally, a stadium project was used as an example to demonstrate the effectiveness of the proposed method of damage identification for piles. The correctness and practicability of the proposed method were verified by comparing the results of damage identification with that of low strain test. The research provided a new way for damage identification of piles

A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation

Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy

Top-N Recommendation Based on Mutual Trust and Influence

To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach

Distributions of surface sediments surrounding the Antarctic Peninsula and its environmental significance

We analyzed grain size composition to provide information on the types and distributions as well as depositional varieties of marine surface sediments from the area surrounding the Antarctic Peninsula. The samples retrieved from the study area contain gravel, sand, silt and clay. As suggested by bathymetry and morphology, the study area is characterized by neritic, hemipelagic and pelagic deposits. The glacial-marine sediments can be divided into two types, residual paratill and compound paratill, which are primarily transported by glaciers and as ice-rafted debris. Ocean current effects on deposition are more obvious, and the deposit types are distributed consistently with terrain variations