Miracle-dz/UniFAR-FTUs huggingface dataset:
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing

Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to improve the modeling performance in target domains (e.g., new printings). Current applications use all accessible source data directly in TL with no regard to the similarity between source and target data. This paper proposes a systematic method to find appropriate subsets of source data based on similarities between the source and target datasets for a given set of limited target domain data. Such similarity is characterized by the spatial and model distance metrics. A Pareto frontier-based source data selection method is developed, where the source data located on the Pareto frontier defined by two similarity distance metrics are selected iteratively. The method is integrated into an instance-based TL method (decision tree regression model) and a model-based TL method (fine-tuned artificial neural network). Both models are then tested on several regression tasks in metal AM. Comparison results demonstrate that 1) the source data selection method is general and supports integration with various TL methods and distance metrics, 2) compared with using all source data, the proposed method can find a small subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and 3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains.
CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder

Symmetries of input and latent vectors have provided valuable insights for disentanglement learning in VAEs.However, only a few works were proposed as an unsupervised method, and even these works require known factor information in training data. We propose a novel method, Composite Factor-Aligned Symmetry Learning (CFASL), which is integrated into VAEs for learning symmetry-based disentanglement in unsupervised learning without any knowledge of the dataset factor information.CFASL incorporates three novel features for learning symmetry-based disentanglement: 1) Injecting inductive bias to align latent vector dimensions to factor-aligned symmetries within an explicit learnable symmetry codebook 2) Learning a composite symmetry to express unknown factors change between two random samples by learning factor-aligned symmetries within the codebook 3) Inducing group equivariant encoder and decoder in training VAEs with the two conditions. In addition, we propose an extended evaluation metric for multi-factor changes in comparison to disentanglement evaluation in VAEs. In quantitative and in-depth qualitative analysis, CFASL demonstrates a significant improvement of disentanglement in single-factor change, and multi-factor change conditions compared to state-of-the-art methods.
Orbital Stability and Secular Dynamics of the Proxima Centauri Planetary System

The two innermost planets of the Proxima Centauri system are separated by just 0.02 au, inducing strong gravitational interactions between them. We assess this interaction by leveraging fast orbital stability indicators and find that orbital stability is very likely if the initial eccentricities of planets b and d are less than ∼0.2, but cannot confirm stability at larger values. We find that stability is not strongly affected by the true masses of the planets or by the distant planet c. However, mutual inclinations between 95° and 142° often result in unstable motion. We further explore the long-term evolution of the orbits in these stable regions of parameter space and find that circularization can take over 5 Gyr. This tidal evolution could support surface energy fluxes in excess of 1 W m−2 for over 1 Gyr, possibly affecting planet b’s habitability.
On the Instance Dependence of Optimal Parameters for the Quantum Approximate Optimisation Algorithm: Insights via Instance Space Analysis

The performance of the Quantum Approximate Optimisation Algorithm (QAOA) relies on the setting of optimal parameters in each layer of the circuit. This is no trivial task, and much literature has focused on the challenge of finding optimal parameters when the landscape is plagued with problems such as"barren plateaus". There are many choices of optimisation heuristics that can be used to search for optimal parameters, each with its own parameters and initialisation choices that affect performance. More recently, the question of whether such optimal parameter search is even necessary has been posed, with some studies showing that optimal parameters tend to be concentrated on certain values for specific types of problem instances. However, these existing studies have only examined specific instance classes of MaxCut, so it is uncertain if the claims of instance independence apply to a diverse range of instances. In this paper, we use Instance Space Analysis to study the dependence of instance characteristics on the performance of QAOA. Focusing on the MaxCut problem, we assess the effectiveness of parameter initialisation strategies and introduce a new initialisation approach based on instance characteristics called Quantum Instance-Based Parameter Initialisation (QIBPI). This study reveals that using insights about instance characteristics in choosing initialisation parameters can improve QAOA performance. We also show that, within certain instance classes, parameters from smaller instances can be transferred to larger ones. This research provides a foundation for further instance space analysis for quantum algorithms and encourages a broader class of instances to be considered to ensure conclusions are not limited to particular well-studied test problems or classes.
Enhancing Campus Mobility: Achievements and Challenges of Autonomous Shuttle "Snow Lion"

The rapid evolution of autonomous vehicles (AVs) has significantly influenced global transportation systems. In this context, we present ``Snow Lion'', an autonomous shuttle meticulously designed to revolutionize on-campus transportation, offering a safer and more efficient mobility solution for students, faculty, and visitors. The primary objective of this research is to enhance campus mobility by providing a reliable, efficient, and eco-friendly transportation solution that seamlessly integrates with existing infrastructure and meets the diverse needs of a university setting. To achieve this goal, we delve into the intricacies of the system design, encompassing sensing, perception, localization, planning, and control aspects. We evaluate the autonomous shuttle's performance in real-world scenarios, involving a 1146-kilometer road haul and the transportation of 442 passengers over a two-month period. These experiments demonstrate the effectiveness of our system and offer valuable insights into the intricate process of integrating an autonomous vehicle within campus shuttle operations. Furthermore, a thorough analysis of the lessons derived from this experience furnishes a valuable real-world case study, accompanied by recommendations for future research and development in the field of autonomous driving.
A Dynamic Capacitance Matching (DCM)-based Current Response Algorithm for Signal Line RC Network

This paper proposes a dynamic capacitance matching (DCM)-based RC current response algorithm for calculating the current waveform of a signal line without performing SPICE simulation. Specifically, unlike previous method such as CCS model, driver linear representation, waveform functional fitting or equivalent load capacitance, our algorithm does not rely on fixed reduced model of both standard cell driver and RC load. Instead, our algorithm approaches the current waveform dynamically by computing current responses of the target driver for various load scenarios. Besides, we creatively use symbolic expression to combine the y-parameter of RC network with the pre-characterized driver library in order to perform capacitance matching by considering over/under-shoot effect. Our algorithm is experimentally verified on 40nm CMOS technology and has been partially adopted by latest commercial tool for other nodes. Experimental results show that our algorithm has excellent resolution and promising efficiency compared with traditional methods and SPICE golden result, especially for application in computing delay, power and signal line electromigration.
On Optimization of Next-Generation Microservice-Based Core Networks

Next-generation mobile core networks are required to be scalable and capable of efficiently utilizing heterogeneous bare metal resources that may include edge servers. To this end, microservice-based solutions where control plane procedures are deconstructed in their fundamental building blocks are gaining momentum. This letter proposes an optimization framework delivering the partitioning and mapping of large-scale microservice graphs onto heterogeneous bare metal deployments while minimizing the total network traffic among servers. An efficient heuristic strategy for solving the optimization problem is also provided. Simulation results show that, with the proposed framework, a microservice-based core can consistently support the requested load in heterogeneous bare metal deployments even when alternative architecture fails. Besides, our framework ensures an overall reduction in the control plane-related network traffic if compared to current core architectures.
Observation of $\psi(3686) \to \Omega^- K^+ \bar{\Xi}^0 $+c.c

Using $(27.12 \pm 0.14) \times 10^{8}$ $\psi(3686)$ events collected with the BESIII detector at BEPCII, the decay of $\psi(3686) \to \Omega^- K^+ \bar{\Xi}^0 +c.c. $ is observed for the first time. The branching fraction of this decay is measured to be $\mathcal{B}_{\psi(3686) \to \Omega^- K^+ \bar{\Xi}^0 +c.c. }=(2.78 \pm 0.40 \pm 0.18 ) \times 10^{-6}$, where the first uncertainty is statistical and the second is systematic. Possible baryon excited states are searched for in this decay, but no evident intermediate state is observed with the current sample size.
Ranking Heterogeneous Search Result Pages using the Interactive Probability Ranking Principle

The Probability Ranking Principle (PRP) ranks search results based on their expected utility derived solely from document contents, often overlooking the nuances of presentation and user interaction. However, with the evolution of Search Engine Result Pages (SERPs), now comprising a variety of result cards, the manner in which these results are presented is pivotal in influencing user engagement and satisfaction. This shift prompts the question: How does the PRP and its user-centric counterpart, the Interactive Probability Ranking Principle (iPRP), compare in the context of these heterogeneous SERPs? Our study draws a comparison between the PRP and the iPRP, revealing significant differences in their output. The iPRP, accounting for item-specific costs and interaction probabilities to determine the ``Expected Perceived Utility"(EPU), yields different result orderings compared to the PRP. We evaluate the effect of the EPU on the ordering of results by observing changes in the ranking within a heterogeneous SERP compared to the traditional ``ten blue links''. We find that changing the presentation affects the ranking of items according to the (iPRP) by up to 48\% (with respect to DCG, TBG and RBO) in ad-hoc search tasks on the TREC WaPo Collection. This work suggests that the iPRP should be employed when ranking heterogeneous SERPs to provide a user-centric ranking that adapts the ordering based on the presentation and user engagement.
A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming

Generalized linear regressions, such as logistic regressions or Poisson regressions, are long-studied regression analysis approaches, and their applications are widely employed in various classification problems. Our study considers a stochastic generalized linear regression model as a stochastic problem with chance constraints and tackles it using nonconvex programming techniques. Clustering techniques and quantile estimation are also used to estimate random data's mean and variance-covariance matrix. Metrics for measuring the performance of logistic regression are used to assess the model's efficacy, including the F1 score, precision score, and recall score. The results of the proposed algorithm were over 1 to 2 percent better than the ordinary logistic regression model on the same dataset with the above assessment criteria.
POE: Acoustic Soft Robotic Proprioception for Omnidirectional End-effectors

Soft robotic shape estimation and proprioception are challenging because of soft robot's complex deformation behaviors and infinite degrees of freedom. A soft robot's continuously deforming body makes it difficult to integrate rigid sensors and to reliably estimate its shape. In this work, we present Proprioceptive Omnidirectional End-effector (POE), which has six embedded microphones across the tendon-driven soft robot's surface. We first introduce novel applications of previously proposed 3D reconstruction methods to acoustic signals from the microphones for soft robot shape proprioception. To improve the proprioception pipeline's training efficiency and model prediction consistency, we present POE-M. POE-M first predicts key point positions from the acoustic signal observations with the embedded microphone array. Then we utilize an energy-minimization method to reconstruct a physically admissible high-resolution mesh of POE given the estimated key points. We evaluate the mesh reconstruction module with simulated data and the full POE-M pipeline with real-world experiments. We demonstrate that POE-M's explicit guidance of the key points during the mesh reconstruction process provides robustness and stability to the pipeline with ablation studies. POE-M reduced the maximum Chamfer distance error by 23.10 % compared to the state-of-the-art end-to-end soft robot proprioception models and achieved 4.91 mm average Chamfer distance error during evaluation.
Autonomous Multiple-Trolley Collection System with Nonholonomic Robots: Design, Control, and Implementation

The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research has primarily focused on handling a single trolley or individual functional components, creating a gap in providing cost-effective and efficient solutions for practical scenarios. In this paper, we propose a mobile manipulation robot incorporated with an autonomy framework for the collection and transportation of multiple trolleys that can significantly enhance operational efficiency. We address the key challenges in the trolley collection problem through the novel design of the mechanical system and the vision-based control strategy. We design a lightweight manipulator and docking mechanism, optimized for the sequential stacking and transportation of multiple trolleys. Additionally, based on the Control Lyapunov Function and Control Barrier Function, we propose a novel vision-based control with the online Quadratic Programming which significantly improves the accuracy and efficiency of the collection process. The practical application of our system is demonstrated in real world scenarios, where it successfully executes multiple-trolley collection tasks.
Cosmological first-order phase transitions without bubbles

In the traditional view a cosmic first-order phase transition cannot occur without nucleating handful of bubbles in the entire Hubble volume. The presence of domain walls during the transition may, however, significantly alter the dynamics of the phase transitions. Using lattice simulation, we demonstrate that vacuum fluctuations induce the destabilization of the domain walls that will classically transform into the domain trenches of the true vacuum, resulting in successful phase transitions without bubbles. After providing an analytical method to estimate the temperature at which the domain trenches are produced, we take the Z2-odd singlet model as an example and conclude that the bubble-free mechanism developed in this Letter constitutes a competing means of completing the phase transition against with quantum tunneling, opening up the new viable parameter region.
OPTIMAL CONVERGENCE RATE FOR MIRROR DESCENT METHODS WITH TIME-VARYING STEP SIZES RULES

In this paper, the optimal convergence rate O (cid:16) N − 1 / 2 (cid:17) (where N is the total number of iterations performed by the algorithm), without the presence of a logarithmic factor, is proved for mirror descent algorithms with time-varying step sizes, for solving both classical constrained non-smooth problems and problems with the composite model. The proven result is an improvement on the well-known rate O (cid:16) log( N ) N − 1 / 2 (cid:17) for the mirror descent algorithms with time-varying step sizes.
Node Compass: Multilevel Tracing and Debugging of Request Executions in JavaScript-Based Web-Servers

Adequate consideration is crucial to ensure that services in a distributed application context are running satisfactorily with the resources available. Due to the asynchronous nature of tasks and the need to work with multiple layers that deliver coordinated results in a single-threaded context, analysing performance is a challenging task in event-loop-based systems. The existing performance analysis methods for environments such as Node.js rely on higher-level instrumentation but lack precision, as they cannot capture the relevant underlying application flow. As a solution, we propose a streamlined method for recovering the asynchronous execution path of requests called the Nested Bounded Context Algorithm. The proposed technique tracks the application execution flow through multiple layers and showcases it on an interactive interface for further assessment. Furthermore, we introduce the vertical span concept. This representation of a span as a multidimensional object (horizontal and vertical) with a start and end of execution, along with its sub-layers and triggered operations, enables the granular identification and diagnosis of performance issues. We proposed a new technique called the Bounded Context Tracking Algorithm for event matching and request reassembling in a multi-layer trace . The two techniques allow aligning the executions of the request in a tree-based data structure for developed visualisations. These visualisations permit performance debugging of complex performance issues in Node.js.
Understanding and Facilitating Mental Health Help-Seeking of Young Adults: A Socio-technical Ecosystem Framework

Prior research on young adults' mental health help-seeking mostly focuses on one particular resource such as a mobile app or digital platform, paying less attention to their lived experiences interacting with the ecosystem of resources. We conducted in-depth interviews with 18 participants about their help-seeking and non-help-seeking experiences. Guided by Social Ecological Theory, we proposed a Socio-technical Ecosystem Framework for mental health care, consisting of four levels of resources, including technological-, interpersonal-, community-, and societal level resources. Using this framework, we identified two types of support systems for help-seeking, single-resource support system and multi-resource support system. These resources support young adults' help-seeking via three mechanisms, \textit{care-giving}, \textit{care-mediating}, and \textit{care-outreaching}, forming various pathways to care. We then pointed out the barriers to resource use at each level and the general challenges in finding a support system. Our findings contributed to a conceptual framework to categorize mental health care. It also serves as a practical framework to identify challenges in the pathways to care and discover design implications.
Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference

In large language models like the Generative Pre-trained Transformer, the Mixture of Experts paradigm has emerged as a powerful technique for enhancing model expressiveness and accuracy. However, deploying GPT MoE models for parallel inference on distributed systems presents significant challenges, primarily due to the extensive Alltoall communication required for expert routing and aggregation. This communication bottleneck exacerbates the already complex computational landscape, hindering the efficient utilization of high-performance computing resources. In this paper, we propose a lightweight optimization technique called ExFlow, to largely accelerate the inference of these MoE models. We take a new perspective on alleviating the communication overhead by exploiting the inter-layer expert affinity. Unlike previous methods, our solution can be directly applied to pre-trained MoE models without any fine-tuning or accuracy degradation. By proposing a context-coherent expert parallelism on distributed systems, our design only uses one Alltoall communication to deliver the same functionality while previous methods all require two Alltoalls. By carefully examining the conditional probability in tokens' routing across multiple layers, we proved that pre-trained GPT MoE models implicitly exhibit a strong inter-layer expert affinity. We then design an efficient integer programming model to capture such features and show that by properly placing the experts on corresponding GPUs, we can reduce up to 67% cross-GPU routing latency. Our solution beats the cutting-edge MoE implementations with experts from 8 to 64, with up to 2.2x improvement in inference throughput. We further provide a detailed study of how the model implicitly acquires this expert affinity at the very early training stage and how this affinity evolves and stabilizes during training.
Resíduo de acerola em dietas para codornas

RESUMO Objetivou-se avaliar a inclusão do resíduo da acerola em dietas de codornas de corte sobre o desempenho produtivo, rendimento de carcaça, biometria intestinal e viabilidade econômica das aves, aos 42 dias de idade. Foram utilizadas 400 codornas europeias, não sexadas, de um dia de idade, distribuídas em delineamento experimental inteiramente ao acaso, com cinco níveis de inclusão (0,0; 3,0; 6,0; 9,0 e 12,0%) de resíduo da acerola, com oito repetições e 10 aves por unidade experimental. Não foram observadas diferenças significativas (P>0,05) para o ganho de peso, o consumo de ração, a conversão alimentar, os pesos absolutos e os rendimentos de carcaça e os cortes nobres (peito, coxa e sobrecoxa). Houve efeito (P<0,05) linear para os pesos absolutos e relativos de fígado e moela. A biometria intestinal das aves não foi (P>0,05) influenciada pelos níveis de inclusão do resíduo de acerola. O resíduo de acerola pode ser utilizado como ingrediente alternativo nas dietas de codornas europeias, até o nível de 12% de inclusão, sem comprometer o desempenho produtivo, o rendimento de carcaça das aves e a viabilidade econômica.