Can ChatGPT reduce human financial analysts’ optimistic biases?

This paper examines the potential of ChatGPT, a large language model, as a financial advisor for listed firm performance forecasts. We focus on the constituent stocks of the China Securities Index 300 and compare ChatGPT’s forecasts for major financial performance measures with human analysts’ forecasts and the realised values. Our findings suggest that ChatGPT can correct the optimistic biases of human analysts. This study contributes to the literature by exploring the potential of ChatGPT as a financial advisor and demonstrating its role in reducing human biases in financial decision-making

Emergent Energy Dissipation in Quantum Limit

Energy dissipation is of fundamental interest and crucial importance in quantum systems. However, whether energy dissipation can emerge inside topological systems remains a question, especially when charge transport is topologically protected and quantized. As a hallmark, we propose a microscopic picture that illustrates energy dissipation in the quantum Hall (QH) plateau regime of graphene. Despite the quantization of Hall, longitudinal, and two-probe resistances (dubbed as the quantum limit), we find that the energy dissipation emerges in the form of Joule heat. By analyzing the energy distribution of electrons, it is found that electrons can evolve between equilibrium and non-equilibrium without inducing extra two-probe resistance. The relaxation of non-equilibrium electrons results in the dissipation of energy along the QH edge states. Eventually, we suggest probing the phenomenon by measuring local temperature increases in experiments and reconsidering the dissipation typically ignored in realistic topological circuits.Comment: 7 pages, 4 figures

Transport theory in non-Hermitian systems

Non-Hermitian systems have garnered significant attention due to the emergence of novel topology of complex spectra and skin modes. However, investigating transport phenomena in such systems faces obstacles stemming from the non-unitary nature of time evolution. Here, we establish the continuity equation for a general non-Hermitian Hamiltonian in the Schr\"odinger picture. It attributes the universal non-conservativity to the anti-commutation relationship between particle number and non-Hermitian terms. Our work derives a comprehensive current formula for non-Hermitian systems using Green's function, applicable to both time-dependent and steady-state responses. To demonstrate the validity of our approach, we calculate the local current in models with one-dimensional and two-dimensional settings, incorporating scattering potentials. The spatial distribution of local current highlights the widespread non-Hermitian phenomena, including skin modes, non-reciprocal quantum dots, and corner states. Our findings offer valuable insights for advancing theoretical and experimental research in the transport of non-Hermitian systems

Channel prediction method based on the data-driving for distribution automation main station

A data-driven channel prediction method for distribution automation master is proposed to address the poor quality of communication network and communication system transmission problems in distribution network communication. In this paper, an adaptive broad learning network (ABLN) consisting of a standard broad learning network and a hybrid learning network is introduced to predict the channel state information of the communication system. Among them, the hybrid learning network is used to solve the ill-conditioned solution problem when estimating the output weight matrix of the standard broad learning network. Therefore, the ABLN produces sparse output weight matrices and provides excellent prediction performance. In the simulation analysis, the outdoor and indoor scenes are considered based on OFDM system. The prediction performance of ABLN is subsequently evaluated in one step prediction and multistep prediction. The results show that for the prediction performance is concerned, the maximum improvement of ABLN is about 96.49% as compared to other evaluation models, indicating that the CSI is effectively predicted by the ABLN to support the adaptive transmission of the main station of the distribution automation and to satisfy the quality of the communication network of the distribution network

Modelling the impact of social network on energy savings

It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout programmes

GaVe: A webcam-based gaze vending interface using one-point calibration

Gaze input, i.e., information input via eye of users, represents a promising method for contact-free interaction in human-machine systems. In this paper, we present the GazeVending interface (GaVe), which lets users control actions on a display with their eyes. The interface works on a regular webcam, available on most of today's laptops, and only requires a short one-point calibration before use. GaVe is designed in a hierarchical structure, presenting broad item cluster to users first and subsequently guiding them through another selection round, which allows the presentation of a large number of items. Cluster/item selection in GaVe is based on the dwell time, i.e., the time duration that users look at a given Cluster/item. A user study (N=22) was conducted to test optimal dwell time thresholds and comfortable human-to-display distances. Users' perception of the system, as well as error rates and task completion time were registered. We found that all participants were able to quickly understand and know how to interact with the interface, and showed good performance, selecting a target item within a group of 12 items in 6.76 seconds on average. We provide design guidelines for GaVe and discuss the potentials of the system

Study on bearing deformation characteristics and lateral pressure distribution law of caved gangue in gob

Under the load of overlying strata, lateral pressure from the caved gangue in gob will be exerted on the support body of gob-side entry retaining or coal pillar. As time goes on, lateral pressure may lead to instability of support body of gob-side entry retaining or coal pillar, and then induce surface collapse. In order to study the bearing deformation characteristics and lateral pressure distribution law of caved gangue in gob, a combined test device for bearing deformation of caving gangue that can measure lateral pressure is developed. The caved gangue with uniform particle size distribution of 5 − 30 mm is taken as an example. By setting the same total loading time (16 h), the same target load (10 MPa) and the different number of loading levels (1, 2, 4), the bearing deformation characteristics, lateral pressure distribution law and particle size change of caving gangue before and after test are studied. The test results indicated that: Along with the increase of axial load, the axial deformation of caved gangue increases gradually, the residual bulking coefficient and porosity decrease gradually, which are more obvious in loading stage than in constant loading stage. In the early constant loading stage, the axial deformation of caved gangue grows rapidly, and then tends to be slow and steady gradually, if no strain surge occurs, the relationship between strain and time meets the logarithmic relationship. With the same target load and total loading time, as the number of loading levels increases, the total strain generated in constant loading stage increases significantly, and is 3.02%, 9.07%, 17.72% respectively, which indicates that the total energy input of caved gangue decreases with the increase of loading level number, but it plays a significant role in promoting the sliding filling and structural adjustment of caved gangue. The lateral pressure coefficient of caved gangue increases obviously with the increase of load. Caved gangue body shows strong reduction effect of load transfer and the value of load acting on caved gangue body decreases progressively from top to bottom. In the process of bearing deformation of caved gangue, the total amount of caved gangue with 5～10 mm particle size is in dynamic equilibrium. The research results have certain guiding significance for mining subsidence control

How to Choose Interesting Points for Template Attacks?

Template attacks are widely accepted to be the most powerful side-channel attacks from an information theoretic point of view. For template attacks, many papers suggested a guideline for choosing interesting points which is still not proven. The guideline is that one should only choose one point as the interesting point per clock cycle. Up to now, many different methods of choosing interesting points were introduced. However, it is still unclear that which approach will lead to the best classification performance for template attacks. In this paper, we comprehensively evaluate and compare the classification performance of template attacks when using different methods of choosing interesting points. Evaluation results show that the classification performance of template attacks has obvious difference when different methods of choosing interesting points are used. The CPA based method and the SOST based method will lead to the best classification performance. Moreover, we find that some methods of choosing interesting points provide the same results in the same circumstance. Finally, we verify the guideline for choosing interesting points for template attacks is correct by presenting a new way of conducting template attacks