Miracle-dz/UniFAR-FTUs huggingface dataset:
Relation between Photogrammetry and Spinal Mouse for Sagittal Imbalance Assessment in Adolescents with Thoracic Kyphosis

The evaluation of postural alignment in childhood and adolescence is fundamental for sports, health, and daily life activities. Spinal Mouse (SM) and photogrammetry (PG) are two of the most debated tools in postural evaluation because choosing the proper instrument is also important to avoid false or misleading data. This research aims to find out the best linear regression models that could relate the analytic kyphosis measurements of the SM with one or more PG parameters of body posture in adolescents with kyphotic posture. Thirty-four adolescents with structural and non-structural kyphosis were analyzed (13.1 ± 1.8 years; 1.59 ± 0.13 m; 47.0 ± 12.2 kg) using SM and PG on the sagittal plane in a standing and forward-bending position, allowing us to measure body vertical inclination, trunk flexion, and sacral inclination and hip position during bending. The stepwise backward procedure was assessed to estimate the variability of the grade of inclination of the spine and thoracic spine curvature with fixed upper and lower limits, evaluated with SM during flexion. In both models, the PG angle between the horizontal line and a line connecting the sacral endplate–C7 spinous process and the PG hip position were the best regressors (adjusted-R2 SM bend = 0.804, p < 0.001; adjusted-R2 SM fixed bending = 0.488, p < 0.001). Several Spinal Mouse and photogrammetry parameters showed significant correlations, especially when the Spinal Mouse measurements were taken when the adolescents were in the forward-bending position. Physicians and kinesiologists may consider photogrammetry as a good method for spinal curve prediction.
Modified Approach to Inductance Calculation of Variable Reluctance Resolver Based on Segmented Winding Function Method

The overall level of high-end manufacturing industry has greatly improved in recent years, and the position detection required by robots and other servo products has been playing one of the most important roles. Among the common sensors, Variable Reluctance Resolvers (VRRs) have become the first choice under harsh working conditions due to their advantages in high temperature resistance, strong vibration resistance and small size, etc. In this article, the internal VRR of a hybrid double stator motor is analyzed. Based on the Modified Winding Function Approach (MWFA), the Segmented Winding Function Method (SWFM) is proposed by modifying the air gap function and the coil turn distribution function in order to draw more precise analytical approach. With the proposed method, the air gap flux density and the self-inductance of the excitation winding are calculated, and compared to the Finite Element Analysis (FEA) to verify the consistency and accuracy of the proposed method.
Extracting nonlinear spatiotemporal dynamics in active dendrites using data-driven statistical approach

We propose a data-driven statistical method for extracting nonlinear spatiotemporal membrane dynamics of active dendrites. We employ a framework of probabilistic information processing to extract the nonlinear spatiotemporal dynamics obeying the reaction-diffusion equation from partially observable data. By employing sequential Monte-Carlo method and other statistical methods, membrane dynamics and their underlying electrical properties are simultaneously estimated in the proposed method. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendrites can be extracted from partially observable data.
HELMA: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

Large language models (LLMs), such as Chat-GPT, are prone to generate hallucinations, i.e., content that conﬂicts with the source or cannot be veriﬁed by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the H allucination E valuation for Large L anguage M odels ( HELMA ) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing and alleviating hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-ﬁltering . Speciﬁcally, we ﬁrst adopt two different sampling methods to generate hallucinated samples based on instructions, and then use an example-enhanced ﬁltering method to select the best one. Furthermore, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT has some probabilities to generate hallucinations and existing LLMs face great challenges in recognizing the hallucinations in text. In addition, the performance can be improved by providing external knowledge or adding reasoning steps. Our benchmark can be accessed at https://github.com/ RUCAIBox/HELMA .
Metal Halide Thermoelectrics: Prediction of High-Performance 
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Thermoelectric devices can directly convert waste heat into electricity, which makes them an important clean energy technology. The underlying materials performance can be evaluated by the dimensionless figure of merit ZT. Metal halides are attractive candidates due to their chemical flexibility and ease of processing; however, the maximum ZT realized (ZT = 0.15) falls far below the level needed for commercialization (ZT>1). Using a first-principles procedure we assess the thermoelectric potential of copper halide CsCu2I3, which features 1D Cu-I connectivity. The n-type crystal is predicted to exhibit a maximum ZT of 2.2 at 600 K along the b-axis. The strong phonon anharmonicity of this system is shown by locally stable non-centrosymmetric Amm2 structures that are averaged to form the observed centrosymmetric Cmcm space group. Our work provides insights into the structure-property relations in metal halide thermoelectrics and suggests a path forward to engineer higher-performance heat-to-electricity conversion.
Fermionic matter-wave quantum optics with cold-atom impurity models

Motivated by recent cold-atom realisations of matter-wave waveguide QED, we study simple fermionic impurity models and discuss fermionic analogues of several paradigmatic phenomena in quantum optics, including formation of non-trivial bound states, (matter-wave) emission dynamics, and collective dissipation. For a single impurity, we highlight interesting ground-state features, focusing in particular on real-space signatures of an emergent length scale associated with an impurity screening cloud. We also present novel non-Markovian many-body effects in the quench dynamics of single- and multiple-impurity systems, including fractional decay around the Fermi level and multi-excitation population trapping due to bound states in the continuum.
Adaptive identification of SISO linear infinite-dimensional systems

We propose an adaptive algorithm for identifying the unknown parameter in a linear exponentially stable single-input single-output infinite-dimensional system. We assume that the transfer function of the infinite-dimensional system can be expressed as a ratio of two infinite series in s (the Laplace variable). We also assume that certain identifiability conditions, which include a persistency of excitation condition, hold. For a fixed integer n, we propose an update law driven by real-time input-output data for estimating the first n+1 coefficients in the numerator and the denominator of the transfer function. We show that the estimates for the transfer function coefficients generated by the update law are close to the true values at large times provided n is sufficiently large (the estimates converge to the true values as time and n tend to infinity). The unknown parameter can be reconstructed using the transfer function coefficient estimates obtained with n large and the algebraic expressions relating the transfer function coefficients to the unknown parameter. We also provide a numerical scheme for verifying the identifiability conditions and for choosing n sufficiently large so that the value of the reconstructed parameter is close to the true value. The class of systems to which our approach is applicable includes many partial differential equations with constant/spatially-varying coefficients and distributed/boundary input and output. We illustrate the efficacy of our approach using three examples: a delay system with four unknown scalars, a 1D heat equation with two unknown scalars and a 1D wave equation with an unknown spatially-varying coefficient.
Enhancing Transformer Backbone for Egocentric Video Action Segmentation

Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robotics. Although recent research has utilized advanced visual-language frameworks, transformers remain the backbone of action segmentation models. Therefore, it is necessary to improve transformers to enhance the robustness of action segmentation models. In this work, we propose two novel ideas to enhance the state-of-the-art transformer for action segmentation. First, we introduce a dual dilated attention mechanism to adaptively capture hierarchical representations in both local-to-global and global-to-local contexts. Second, we incorporate cross-connections between the encoder and decoder blocks to prevent the loss of local context by the decoder. We also utilize state-of-the-art visual-language representation learning techniques to extract richer and more compact features for our transformer. Our proposed approach outperforms other state-of-the-art methods on the Georgia Tech Egocentric Activities (GTEA) and HOI4D Office Tools datasets, and we validate our introduced components with ablation studies. The source code and supplementary materials are publicly available on https://www.sail-nu.com/dxformer.
Milestones in Autonomous Driving and Intelligent Vehicles—Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into three independent articles and the first part is a survey of surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, high-definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this article is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.
Visualization for Recommendation Explainability: A Survey and New Perspectives

Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, centered around Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, an area not extensively covered in general LLMs, making it well-suited for evaluating methods aiming to enhance LLMs' domain-specific capabilities. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our method outperforms the commonly used LLM with retrieval methods. We make our source code and sample data available at: https://aka.ms/Microsoft_QA.
TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding

Prompt tuning has achieved great success in transferring the knowledge from large pretrained vision-language models into downstream tasks, and has dominated the performance on visual grounding (VG). However, almost all existing prompt tuning paradigms suffer from poor interpretability. In this paper, we argue that their poor interpretability is attributed to the holistic prompt generation and inference process. By"holistic", we mean that they usually directly learn a set of vectors as the prompt (i.e., prompt generation), and use the learned global prompt to augment the textual input for the VG model (i.e., prompt inference). To this end, we propose a new prompt construction paradigm with explicit explainable ability, named TreePrompt. Specifically, we first deconstruct a complex sentence into a tree, that is consistent with human reasoning. Then, following the syntax tree, we compose a structured prompt in a bottom-up manner. Thanks to this step-by-step prompt construction process, each intermediate prompt (i.e., tree node) permits us to understand the reasoning process. Extensive ablations on various backbones and benchmarks consistently demonstrate the effectiveness and interpretability of our TreePrompt.
ViDaS Video Depth-aware Saliency Network

We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware Saliency network to address the problem of attention modeling ``in-the-wild", via saliency prediction in videos. Contrary to existing visual saliency approaches using only RGB frames as input, our network employs also depth as an additional modality. The network consists of two visual streams, one for the RGB frames, and one for the depth frames. Both streams follow an encoder-decoder approach and are fused to obtain a final saliency map. The network is trained end-to-end and is evaluated in a variety of different databases with eye-tracking data, containing a wide range of video content. Although the publicly available datasets do not contain depth, we estimate it using three different state-of-the-art methods, to enable comparisons and a deeper insight. Our method outperforms in most cases state-of-the-art models and our RGB-only variant, which indicates that depth can be beneficial to accurately estimating saliency in videos displayed on a 2D screen. Depth has been widely used to assist salient object detection problems, where it has been proven to be very beneficial. Our problem though differs significantly from salient object detection, since it is not restricted to specific salient objects, but predicts human attention in a more general aspect. These two problems not only have different objectives, but also different ground truth data and evaluation metrics. To our best knowledge, this is the first competitive deep learning video saliency estimation approach that combines both RGB and Depth features to address the general problem of saliency estimation ``in-the-wild". The code will be publicly released.
Log-linear models for capture-recapture

SUMMARY Log-linear models are developed for capture-recapture experiments, and their advantages and disadvantages discussed. Ways in which they can be extended, sometimes with only partial success, to open populations, subpopulations, trap dependence, and long chains of recapture periods are presented. The use of residual patterns, and analysis of subsets of data, to identify behavioural patterns and acceptable models is emphasised and illustrated with two examples. 1. Capture-Recapture The biological problem with which we are concerned is that of estimating the parameters of a free-living population of unknown size, from a sequence of s samples, in each of which any individual either is or is not observed. The models in this paper require that each animal be marked, naturally or artificially, in such a way that we can observe the numbers of animals which have any particular pattern of being or not being observed through the sequence of samples. There are t = (2s - 1) different observable patterns, which we shall refer to as capture histories, although there is no requirement for physical capture, and denote each pattern by a sequence of l's and 2's representing, respectively, seen or not seen in a particular sample. A general pattern of capture history will be denoted a, with ra, the number of individuals observed to have this pattern. Thus, for example, with s = 3 samples and a = 121, r,2, is the number of animals seen in the first and third samples but not in the second. The pattern with all 2's is by definition unobservable. In general, the population will be subject to demographic change by birth or immigration and by death or emigration. The simplest assumption for the response of an animal to the observer, one which underlies the classical Petersen, Schnabel, or Jolly-Seber estimators, is that every animal alive in the population at a sampling time has the same probability of being observed. This probability may vary from one sample to another or be constant over all, or a number of, samples. If this assumption fails it may do so for either or both of two reasons. The first is that of trap dependence, the probability of an individual being observed in a sample depending on its past history of capture. The second is that of heterogeneity between individuals who, because of differences in their behaviour or in where they live, inherently have different probabilities of being observed in any sample. The basic idea of heterogeneity refers to properties of individuals which remain constant over time, but individual animals may also have time-varying behaviour, the aspect most commonly considered being temporary departure of an individual from the population. Various excellent texts describe theoretical and practical aspects of capture-recapture, notably the comprehensive one by Seber (1982), the introduction by Begon (1979), the
Towards Intersectional Moderation: An Alternative Model of Moderation Built on Care and Power

Shortcomings of current models of moderation have driven policy makers, scholars, and technologists to speculate about alternative models of content moderation. While alternative models provide hope for the future of online spaces, they can fail without proper scaffolding. Community moderators are routinely confronted with similar issues and have therefore found creative ways to navigate these challenges. Learning more about the decisions these moderators make, the challenges they face, and where they are successful can provide valuable insight into how to ensure alternative moderation models are successful. In this study, I perform a collaborative ethnography with moderators of r/AskHistorians, a community that uses an alternative moderation model, highlighting the importance of accounting for power in moderation. Drawing from Black feminist theory, I call this "intersectional moderation. " I focus on three controversies emblematic of r/AskHistorians' alternative model of moderation: a disagreement over a moderation decision; a collaboration to fight racism on Reddit; and a period of intense turmoil and its impact on policy. Through this evidence I show how volunteer moderators navigated multiple layers of power through care work. To ensure the successful implementation of intersectional moderation, I argue that designers should support decision-making processes and policy makers should account for the impact of the sociotechnical systems in which moderators work.
NeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs

While Knowledge Graph Completion (KGC) on static facts is a matured field, Temporal Knowledge Graph Completion (TKGC), that incorporates validity time into static facts is still in its nascent stage. The KGC methods fall into multiple categories including embedding-based, rule-based, GNN-based, pretrained Language Model based approaches. However, such dimensions have not been explored in TKG. To that end, we propose a novel temporal neuro-symbolic model, NeuSTIP, that performs link prediction and time interval prediction in a TKG. NeuSTIP learns temporal rules in the presence of the Allen predicates that ensure the temporal consistency between neighboring predicates in a given rule. We further design a unique scoring function that evaluates the confidence of the candidate answers while performing link prediction and time interval prediction by utilizing the learned rules. Our empirical evaluation on two time interval based TKGC datasets suggests that our model outperforms state-of-the-art models for both link prediction and the time interval prediction task.
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution

Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics. We propose a new architecture for geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to generate a list of candidate entries from the geospatial ontology.Then it reranks the candidate entries using a transformer-based neural network that incorporates information from the ontology such as the entry’s population. This generate-and-rerank process is applied twice: first to resolve the less ambiguous countries, states, and counties, and second to resolve the remaining location mentions, using the identified countries, states, and counties as context. Our proposed toponym resolution framework achieves state-of-the-art performance on multiple datasets.Code and models are available at \url{https://github.com/clulab/geonorm}.