The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees' grasping gestures and executing anthropomorphic grasp processes. Although prosthetic hands equipped with vision systems enables the objects' feature recognition, they lack perception of human grasping intention. Therefore, this paper explores the estimation of grasping gestures solely through visual data to accomplish anthropopathic grasping control and the determination of grasping intention within a multi-object environment. To address this, we propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. It's subsequently implemented on the prosthetic hand. Furthermore, we propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. This algorithm predicts pre-grasping object utilizing regression prediction and prior spatial segmentation estimation derived from the prosthetic hand's position and trajectory. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc. The experimental results presented a similarity coefficient $R^{2}$ of grasping process of 0.911, a Root Mean Squared Error ($RMSE$) of 2.47\degree, a success rate of grasping of 95.43$\%$, and an average duration of grasping process of 3.07$\pm$0.41 s. Furthermore, grasping experiments in a multi-object environment were conducted. The average accuracy of intent estimation reached 94.35$\%$. Our methodologies offer a groundbreaking approach to enhance the prosthetic hand's functionality and provides valuable insights for future research.

Model library is an effective tool for improving the performance of single-model Out-of-Distribution (OoD) detector, mainly through model selection and detector fusion. However, existing methods in the literature do not provide uncertainty quantification for model selection results. Additionally, the model ensemble process primarily focuses on controlling the True Positive Rate (TPR) while neglecting the False Positive Rate (FPR). In this paper, we emphasize the significance of the proportion of models in the library that identify the test sample as an OoD sample. This proportion holds crucial information and directly influences the error rate of OoD detection.To address this, we propose inverting the commonly-used sequential p-value strategies. We define the rejection region initially and then estimate the error rate. Furthermore, we introduce a novel perspective from change-point detection and propose an approach for proportion estimation with automatic hyperparameter selection. We name the proposed approach as DOS-Storey-based Detector Ensemble (DSDE). Experimental results on CIFAR10 and CIFAR100 demonstrate the effectiveness of our approach in tackling OoD detection challenges. Specifically, the CIFAR10 experiments show that DSDE reduces the FPR from 11.07% to 3.31% compared to the top-performing single-model detector.

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm called edge model caching. In this paper, we develop a novel model placement scheme, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To overcome this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with $\left(1-\epsilon\right)/2$-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

In the rapid development of artificial intelligence, solving complex AI tasks is a crucial technology in intelligent mobile networks. Despite the good performance of specialized AI models in intelligent mobile networks, they are unable to handle complicated AI tasks. To address this challenge, we propose Systematic Artificial Intelligence (SAI), which is a framework designed to solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format intent-based input to connect self-designed model library and database. Specifically, we first design a multi-input component, which simultaneously integrates Large Language Models (LLMs) and JSON-format intent-based inputs to fulfill the diverse intent requirements of different users. In addition, we introduce a model library module based on model cards which employ model cards to pairwise match between different modules for model composition. Model cards contain the corresponding model's name and the required performance metrics. Then when receiving user network requirements, we execute each subtask for multiple selected model combinations and provide output based on the execution results and LLM feedback. By leveraging the language capabilities of LLMs and the abundant AI models in the model library, SAI can complete numerous complex AI tasks in the communication network, achieving impressive results in network optimization, resource allocation, and other challenging tasks.

The introduction of the transformer architecture and the self-attention mechanism has led to an explosive production of language models trained on specific downstream tasks and data domains. With over 200, 000 models in the Hugging Face ecosystem, users grapple with selecting and optimizing models to suit multifaceted workflows and data domains while addressing computational, security, and recency concerns. There is an urgent need for machine learning frameworks that can eliminate the burden of model selection and customization and unleash the incredible power of the vast emerging model library for end users. Here, we propose a context-aware routing system, Tryage, that leverages a language model router for optimal selection of expert models from a model library based on analysis of individual input prompts. Inspired by the thalamic router in the brain, Tryage employs a perceptive router to predict down-stream model performance on prompts and, then, makes a routing decision using an objective function that integrates performance predictions with user goals and constraints that are incorporated through flags (e.g., model size, model recency). Tryage allows users to explore a Pareto front and automatically trade-off between task accuracy and secondary goals including minimization of model size, recency, security, verbosity, and readability. Across heterogeneous data sets that include code, text, clinical data, and patents, the Tryage framework surpasses Gorilla and GPT3.5 turbo in dynamic model selection identifying the optimal model with an accuracy of 50.9% , compared to 23.6% by GPT 3.5 Turbo and 10.8% by Gorilla. Conceptually, Tryage demonstrates how routing models can be applied to program and control the behavior of multi-model LLM systems to maximize efficient use of the expanding and evolving language model ecosystem.

One hundred seventy-four new packages made it to CRAN in October. Here are my “Top 40” selections in sixteen categories: Astronomy, Biology, Business, Computational Methods, Data, Ecology, Finance, Genomics, Mathematics, Machine Learning, Medicine, Pharma, Statistics, Time Series, Utilities, Visualization. Astronomy skylight v1.1: Provides a function to calculate sky illuminance values (in lux) for both the sun and moon. The model is a verbatim translation of the code by Janiczek and DeYoung (1987). There are vignettes for Use and Advanced Use. Biology palaeoverse v1.0.0: Provides tools to support data preparation and exploration for palaeobiological analyses including functions for data cleaning, binning (time and space), summarisation and visualisation with the goals of improving code reproducibility and accessibility and establishing standards for the palaeobiological community. See Jones et al. for details, and the contribution guide to get involved. pirouette v1.6.5: Implements a method to create a Bayesian posterior from a phylogeny that depicts the true evolutionary relationships. See Richèl et al. (2020) for background. There are several vignettes including a Tutorial, a demo, and a guide showing how to use the package in a scientific experiment. Business bupaverse v0.1.0: Facilitates loading the packages comprising the bupaverse, an integrated suite of R packages for handling and analysing business process data, developed by the Business Informatics research group at Hasselt University, Belgium. See the Getting Started Guide. Computational Methods fastWavelets v1.0.1: Provides an Rcpp implementation of the Maximal Overlap Discrete Wavelet Transform (MODWT) and the À Trous Discrete Wavelet Transform. See Quilty & Adamowski (2018) for background and README for examples. gips v1.0.0: Employs the methods described in Graczyk et al. (2022) to find the permutation symmetry group under which the covariance matrix of the data is invariant. See the vignettes Optimizers, Theory, and gips. HomomorphicEncryption v0.1.0: Implements the Brakerski-Fan-Vercauteren (2012), Brakerski-Gentry-Vaikuntanathan (2014), and Cheon-Kim-Kim-Song (2016) schema for fully homomorphic encryption. There are seven short vignettes including BFV, BGV, and CKKS. rxode2random v2.0.9: Implements parallel random number generation. See Wang et al. (2016) and Fidler et al (2019) for background and README for an example.. Data airnow v0.1.0: Provides functions to retrieve U.S. Government AirNow air quality data. See README to get started. amazonadsR v0.1.0: Provides functions to collect data on digital marketing campaigns using the Windsor.ai API. See the tutorial for an example and also look at the related new packages: bingadsR, facebookadsR, googleadsR, instagramadsR, linkedinadsR, pinterestadsR, redditadsR, snapchatadsR, ticktokadsR, twitteradsR. Pablo Sanchez was on a roll in October. congress v0.0.1: Provides functions to download and read data on United States congressional proceedings through the Congress.gov API of the Library of Congress. See README for an example. Ecology canaper v1.0.0: Provides functions to analyze the spatial distribution of biodiversity especially useful in the categorical analysis of neo- and paleo-endemism (CANAPE) as described in Mishler et al. (2014) and for statistical tests to determine the types of endemism that occur in a study area while accounting for the evolutionary relationships of species. There are vignettes on CANAPE, randomization, and parallel computing. EcoEnsemble v1.0.1: Provides functions to fit and sample from the ensemble model described in Spence et al (2018). There is an Introduction and there are two additional vignettes: ExploringPriors and SyntheticData. rTRIPLEXCWFlux v0.2.0: Encodes the carbon uptake submodule and evapotranspiration submodule of the TRIPLEX-CW-Flux model to run the simulation of carbon-water coupling. See Zhou et al. (2008) Monteith (1965) for background and the vignette for examples. stopdetection v0.1.1: Enables stop detection in time stamped trajectory by implementing the Stay Point detection algorithm originally described in Ye (2009) that uses time and distance thresholds to characterize spatial regions as stops. See the vignette for examples. Finance highOrderPortfolios v0.1.0: Implements methods to select portfolios using high order moments to characterize return distributions. See Zhou & Palomar (2021) and Wang et al. (2022) for the theory and the vignette to get started. MSTest v0.1.0: Implements hypothesis testing procedures described in Hansen (1992), Carrasco, Hu, & Ploberger (2014) and Dufour & Luger (2017) that can be used to identify the number of regimes in Markov switching models. See README for an example. Genomics metevalue v0.1.13: Implements the e-value method to correct p-values in omics data association studies. See Hebestreit & Klein (2022) and Akalin et.al (2012) for background and the vignette for an example. SCpubr v1.0.4: Implements a system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data. Look here for an online reference manual. Mathematics Boov v1.0.0: Provides functions to perform the Boolean operations union, difference and intersection on volumes. Computations are done by the C++ library CGAL. See README for some examples. Also, have a look at the package MinkowskiSum. fitode v0.1.1: Provides methods and functions for fitting ordinary differential equations that use sensitivity equations to compute gradients of ODE trajectories with respect to underlying parameters. See the vignette for details. manifold v0.1.1: Implements operations for Riemannian manifolds including geodesic distance, Riemannian metric, and exponential and logarithm maps, and also incorporates a random object generator on the manifolds. See Dai, Lin, and Müller (2021) for details. Machine Learning SoftBart v1.0.1: Implements the SoftBart model of described by Linero and Yang (2018) with the optional use of a sparsity-inducing prior to allow for variable selection. The vignette contains theory and examples. tidyfit v0.5.1: Extends the tidy data environment with functions to fit and cross validate linear regression and classification algorithms on grouped data. There are several vignettes including Predicting Boston House Prices, Multinomial Classification, and Rolling Window Time Series Regression. Medicine cities v0.1.0: Provides functions to simulate clinical trials and summarize causal effects and treatment policy estimands in the presence of intercurrent events. Have a look at the demo. RCT2 v0.0.1: Implements various statistical methods for designing and analyzing two-stage randomized controlled trials using the methods developed by Imai, Jiang, and Malani (2021) and Imai, Jiang, and Malani (2022). There are vignettes on Interference and Causal Inference. Pharma DTSEA v0.0.3: Implements a novel tool to identify candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. See Gómez-Carballa et al. (2022) and Feng et al. (2022) for disease expression profiles, Wishart et al. (2018) and Gaulton et al. (2017) for drug target information, and Kanehisa et al. (2021) for the details of KEGG database. There is a vignette. nlmixr2lib v0.1.0: Provides tools to create model libraries for nlmixr2. Models include pharmacokinetic, pharmacodynamic, and disease models used in pharmacometrics. See the vignette Creating a model library. Statistics aIc v1.0: Implements set of tests for compositional pathologies including for coherence of correlations as suggested by Erb et al. (2020), compositional dominance of distance, compositional perturbation invariance as suggested by (Aitchison (1992) and singularity of the covariation matrix. See the vignette for details and examples. ktweedie v1.0.1: Uses Reproducing Kernel Hilbert Space methods to implement Tweedie compound Poisson gamma models with high-dimensional predictors for the analyses of zero-inflated response variables. See the vignette for examples. missoNet v1.0.0: Implements efficient procedures for fitting conditional graphical lasso models linking predictor variables to response variables or tasks, when the response data may contain missing values. See the vignette for examples. ShalpeyOutlier v0.1.0: Provides methods to use Shapley values to detect, explain, and cell wise impute multivariate outliers. See Mayrhofer and Filzmoser (2022) for details and the vignette for examples. SpatialfdaR v1.0.0: Provides functions to that implement finite element analysis methods to spatial functional data analysis. See Sangalli et al. (2013) and Bernardi et al. (2018) for background and the vignette for an example. Time Series dfms v0..1.3: Provides a user friendly and computationally efficient approach to estimate linear Gaussian dynamic factor models using Kalman filter and EM algorithm methods. See Doz et al. (2011) and Banbura & Modugno (2014) for background and the vignette for examples. Utilities ExclusionTable v1.0.0: Provides functions for creating tables of excluded observations by reporting the number before and after each subset() call together with the number of observations that have been excluded. See the vignette. shiny.tailwind v0.2.2: Allows TailwindCSS to be used in Shiny apps with just-in-time compiling including custom CSS with @apply directive, and custom tailwind configurations. See README for examples. Visualization AlphaHull3D v1.1.0: Provides functions to compute the alpha hull of a set of points (informallly: the shape formed by these points) in 3D space. See README for some visualizations, and also have a look at the related packages MeshesTools, and PolygonSoup. bangladesh v1.0.0: Provides sf objects, shape files, and functions to draw regional chorpleth maps for Bangladesh. See the vignette. ggstats v0.1.0: Provides functions to create forest plots of regression model coefficients along with new statistics to compute proportions, weighted mean and cross-tabulation statistics, as well as new geometries to add alternative background color to a plot. There are vignettes on plotting coefficients and on computing cross-tabulation, custom proportions, and weighted means. jagshelper v0.1.11: Provides tools to streamline Bayesian analyses in JAGSincluding functions for extracting output, streamlining assessment of convergence, and producing summary plots. See the vignette for examples. roughsf v1.0.0: Provides functions to draw maps, including “sketchy”, hand-drawn-like maps using the Javascript library Roughjs. See README for examples.

We extend the WSINDy (Weak SINDy) method of sparse recovery introduced previously by the authors (arXiv:2005.04339) to the setting of partial differential equations (PDEs). As in the case of ODE discovery, the weak form replaces pointwise approximation of derivatives with local integrations against test functions and achieves effective machine-precision recovery of weights from noise-free data (i.e. below the tolerance of the simulation scheme) as well as natural robustness to noise without the use of noise filtering. The resulting WSINDy_PDE algorithm uses separable test functions implemented efficiently via convolutions for discovery of PDE models with computational complexity $O(NM)$ from data points with $M = N^{D+1}$ points, or $N$ points in each of $D+1$ dimensions. We demonstrate on several notoriously challenging PDEs the speed and accuracy with which WSINDy_PDE recovers the correct models from datasets with surprisingly large levels noise (often with levels of noise much greater than 10%).

Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for helping middle-school students learn to become modelers. We describe Vmodel, a system we have created that uses visual representations and that enables middle-school students to create qualitative models. Software coaches use simple analyses of model structure plus qualitative simulation to provide feedback and explanations. This system has been used in several studies in Chicago public school classrooms, using curricula developed in collaboration with teachers.

Leasing is a popular channel to market new cars. Pricing a leasing contract is complicated because the leasing rate embodies an expectation of the residual value of the car after contract expiration. To aid lessors in their pricing decisions, the paper develops resale price forecasting models. A peculiarity of the leasing business is that forecast errors entail different costs. Identifying effective ways to address this characteristic is the main objective of the paper. More specifically, the paper contributes to the literature through i) consolidating and integrating previous work in forecasting with asymmetric cost of error functions, ii) systematically evaluating previous approaches and comparing them to a new approach, and iii) demonstrating that forecasting with asymmetric cost of error functions enhances the quality of decision support in car leasing. For example, under the assumption that the costs of overestimating resale prices is twice that of the opposite error, incorporating corresponding cost asymmetry into forecast model development reduces decision costs by about eight percent, compared to a standard forecasting model. Higher asymmetry produces even larger improvements.