Heterogeneous Value Evaluation for Large Language Models

The emergent capabilities of Large Language Models (LLMs) have made it crucial to align their values with those of humans. Current methodologies typically attempt alignment with a homogeneous human value and requires human verification, yet lack consensus on the desired aspect and depth of alignment and resulting human biases. In this paper, we propose A2EHV, an Automated Alignment Evaluation with a Heterogeneous Value system that (1) is automated to minimize individual human biases, and (2) allows assessments against various target values to foster heterogeneous agents. Our approach pivots on the concept of value rationality, which represents the ability for agents to execute behaviors that satisfy a target value the most. The quantification of value rationality is facilitated by the Social Value Orientation framework from social psychology, which partitions the value space into four categories to assess social preferences from agents' behaviors. We evaluate the value rationality of eight mainstream LLMs and observe that large models are more inclined to align neutral values compared to those with strong personal values. By examining the behavior of these LLMs, we contribute to a deeper understanding of value alignment within a heterogeneous value system.Comment: Our full prompts are released in the repo: https://github.com/zowiezhang/A2E

Recursive Algorithm for Generating Two-Staged Cutting Patterns of Punched Strips

The manufacture of parts made of metal sheet often includes two successive processes: the cutting process at which a guillotine shear cuts the sheet into strips, and the punching process at which a stamping press punches out the blanks from the strips. This paper presents an algorithm for generating optimal two-staged cutting patterns of strips for the cutting process. At the first stage the sheet is divided into segments with parallel cuts. Each segment contains strips with the same length and direction. The segments are cut into strips at the second stage. The algorithm calls a recursion function to determine the optimal strip layouts on segments of various lengths, and calls another recursion function to optimally arrange the segments in the sheet, so that the value of the pattern reaches maximum. The computational results indicate that the algorithm is efficient both in computation time and in material usage

Towards Domain Invariant Real-time Point Cloud Perception

In recent years, autonomous driving has witnessed substantial advancements, owing in part to the rapid advancement of 3D sensor technologies, particularly Light Detection and Ranging (LiDAR) sensors.Despite these advancements, the challenge of open-world autonomous driving remains a pressing issue that requires resolution. This encompasses the need for dependable and resilient 3D perception across a multitude of environments. Particularly, point cloud is irregular, unordered, and continuous with large data size, which presents unique challenges for its real-time processing. Another major challenge came from the perception system’s inability to adapt to different environments, known as the domain adaptation problem. This is due in part to a lack of diverse and representative datasets, and in part to existing models’ insufficient generalization ability. To tackle this challenge, this dissertation conducts a thorough investigation into the domain adaptation challenges associated with real-time point cloud perception. This dissertation addresses these challenges associated with the deployment and train- ing of point cloud perception systems in a self-contained manner.To ensure sufficient real- time capability during deployment, a novel approach called task-attentive 3D perception is proposed. It incorporates HD-map, vehicle states, and emergency breaking distance to dynamically remove task un-related point cloud for driving safety and computation effi- ciency. Furthermore, this thesis looks at the problem of driving in varied domains from both the data and the model standpoint. First, a novel method for creating the target real- world domains in the simulator using real-world prior is presented. Using this method, a noval large-scale multi-task dataset called Domains-3D is developed, which comprises both real-world and synthetic domains. Finally, using this dataset, a novel Plug-and-Play (PnP) domain adaptation algorithm for 3D point clouds that minimizes domain shifts is presented. This algorithm improves a model’s performance for both cross-scene domain adaptation and intra-domain 3D object detection

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

To achieve the anticipated performance of massive multiple input multiple output (MIMO) systems in wireless communication, it is imperative that the user equipment (UE) accurately feeds the channel state information (CSI) back to the base station (BS) along the uplink. To reduce the feedback overhead, an increasing number of deep learning (DL)-based networks have emerged, aimed at compressing and subsequently recovering CSI. Various novel structures are introduced, among which Transformer architecture has enabled a new level of precision in CSI feedback. In this paper, we propose a new method named TransNet+ built upon the Transformer-based TransNet by updating the multi-head attention layer and implementing an improved training scheme. The simulation results demonstrate that TransNet+ outperforms existing methods in terms of recovery accuracy and achieves state-of-the-art

A cutting-and-inventory control problem in the manufacturing industry of stainless steel wares

Circular pieces are often cut from stainless steel roll to make common commodities such as pots and cups. The related factories are often make-to-order ones. The stock rolls usually have the same width. Each working order requires pieces of the same size. Pieces of different sizes cannot appear in the same cutting pattern because the orders do not arrive simultaneously. The approach proposed in this paper assumes that the original roll can be slit into a strip and a partial roll. The strip is used to fulfill the current order. The partial roll will be used to fulfill future orders and cannot be slit further. The approach determines several standard widths for the partial rolls and uses a greedy procedure to select the roll (either the original roll or a partial roll) to fulfill the current order. The computational results indicate that the approach is efficient in improving material utilization.Cutting stock Two-dimensional cutting Circle cutting Stainless steel rolls

Simulation study on exhaust turbine power generation for waste heat recovery from exhaust of a diesel engine

Diesel engine has been used as the primary mover in vehicles for a long time. It is known that around 25%–30% of the fuel energy is wasted in the exhaust gas from diesel engines. In this study, a turbine power generation system including a 1.8 kW 60,000 r/min high-speed permanent magnet generator and a micro exhaust gas turbine, which is coupled to a diesel engine is designed and modeled to investigate its potential for recovering the wasted energy in the exhaust gas from a diesel engine. Computational models are set up using GT-POWER, MATLAB/SIMULINK and ANSOFT software. The performance and characteristics of the generator, the exhaust gas turbine and the engine are investigated. The simulation results showed that the exhaust turbine power generation system recovered the energy from the engine exhaust gas to generate electrical power. Simultaneously, the maximum power generated is 1.8 kW when the turbine speed is 60,000 rpm. The system efficiency reached its peak of 42.8% when the engine speed is 3000 rpm Last but not least, the electromagnetic characteristics of high-speed permanent magnet generator, which is coupled to an exhaust turbine, are also discussed and presented