Miracle-dz/UniFAR-FTUs huggingface dataset:
A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences

Some exciting new approaches to neural architectures for the analysis of conversation have been introduced over the past couple of years. These include neural architectures for detecting emotion, dialogue acts, and sentiment polarity. They take advantage of some of the key attributes of contemporary machine learning, such as recurrent neural networks with attention mechanisms and transformer-based approaches. However, while the architectures themselves are extremely promising, the phenomena they have been applied to to date are but a small part of what makes conversation engaging. In this paper we survey these neural architectures and what they have been applied to. On the basis of the social science literature, we then describe what we believe to be the most fundamental and definitional feature of conversation, which is its co-construction over time by two or more interlocutors. We discuss how neural architectures of the sort surveyed could profitably be applied to these more fundamental aspects of conversation, and what this buys us in terms of a better analysis of conversation and even, in the longer term, a better way of generating conversation for a conversational system.
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?

The practical success of much of NLP depends on the availability of training data. However, in real-world scenarios, training data is often scarce, not least because many application domains are restricted and specific. In this work, we compare different methods to handle this problem and provide guidelines for building NLP applications when there is only a small amount of labeled training data available for a specific domain. While transfer learning with pre-trained language models outperforms other methods across tasks, alternatives do not perform much worse while requiring much less computational effort, thus significantly reducing monetary and environmental cost. We examine the performance tradeoffs of several such alternatives, including models that can be trained up to 175K times faster and do not require a single GPU.
Task-driven Modular Co-design of Vehicle Control Systems

When designing autonomous systems, we need to consider multiple trade-offs at various abstraction levels, and the choices of single (hardware and software) components need to be studied jointly. In this work we consider the problem of designing the control algorithm as well as the platform on which it is executed. In particular, we focus on vehicle control systems, and formalize state-of-the-art control schemes as monotone feasibility relations. We then show how, leveraging a monotone theory of co-design, we can study the embedding of control synthesis problems into the task-driven co-design problem of a robotic platform. The properties of the proposed approach are illustrated by considering urban driving scenarios. We show how, given a particular task, we can efficiently compute Pareto optimal design solutions.
Error estimates for the numerical approximation of a non-smooth quasilinear elliptic control problem

In this paper, we carry out the numerical analysis of a non-smooth quasilinear elliptic optimal control problem, where the coecient in the divergence term of the corresponding state equation is a nitely 𝑃𝐶 2 (continuous and 𝐶 2 apart from nitely many points) function in the state variable. Although the nonlinearity of the quasilinear elliptic equation is non-smooth, the corresponding control-to-state operator is of class 𝐶 1 but not of class 𝐶 2 . Analogously, the discrete control-to-state operators associated with the approximated control problems are proven to be of class 𝐶 1 only. An explicit formula of a second-order generalized derivative of the cost functional is also established. We then exploit a second-order sucient optimality condition to prove a priori error estimates for a variational and a piecewise constant approximation of the continuous optimal control problem.
Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings

This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize ``who spoke what'' with low latency even when multiple people are speaking simultaneously. Our model is based on token-level serialized output training (t-SOT) which was recently proposed to transcribe multi-talker speech in a streaming fashion. To further recognize speaker identities, we propose an encoder-decoder based speaker embedding extractor that can estimate a speaker representation for each recognized token not only from non-overlapping speech but also from overlapping speech. The proposed speaker embedding, named t-vector, is extracted synchronously with the t-SOT ASR model, enabling joint execution of speaker identification (SID) or speaker diarization (SD) with the multi-talker transcription with low latency. We evaluate the proposed model for a joint task of ASR and SID/SD by using LibriSpeechMix and LibriCSS corpora. The proposed model achieves substantially better accuracy than a prior streaming model and shows comparable or sometimes even superior results to the state-of-the-art offline SA-ASR model.
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions

We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of “templates”, rendered images of the CAD models for the new objects. In contrast with the state-of-the-art methods, the new objects on which our method is applied can be very different from the training objects. As a result, we are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous template-based approaches further confirms the benefits of local features for template matching. We outperform the state-of-the-art template matching methods on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data are publicly available at https://github.com/nv-nguyen/template-pose.
Current Challenges in Infinite-Domain Constraint Satisfaction: Dilemmas of the Infinite Sheep

A Constraint Satisfaction Problem (CSP) is a computational problem where we are given variables and constraints about them; the question is whether the variables can be assigned values such that all constraints are satisfied. We give an overview of the current state of research on CSPs where values for the variables and constraints are taken from a finitely bounded homogeneous structure which is fixed beforehand. We explain the main mathematical ideas so far, the three dilemmas they brought upon us, and what could be done to overcome them in order to obtain a satisfactory understanding of the computational complexity of such CSPs.
SDSS-IV MaNGA: Exploring the Local Scaling Relations for N/O

We present, for the first time, the relationship between local stellar mass surface density, Σ*, and N/O derived from SDSS-IV MaNGA data, using a sample of 792,765 high signal-to-noise ratio star-forming spaxels. Using a combination of phenomenological modeling and partial correlation analysis, we find that Σ* alone is insufficient to predict the N/O in MaNGA spaxels and that there is an additional dependence on the local star formation rate surface density, ΣSFR. This effect is a factor of 3 stronger than the dependence of 12+log(O/H) on ΣSFR. Surprisingly, we find that the local N/O scaling relations also depend on the total galaxy stellar mass at fixed Σ* and the galaxy size at fixed stellar mass. We find that more compact galaxies are more nitrogen rich, even when Σ* and ΣSFR are controlled for. We show that ∼50% of the variance of N/O is explained by the total stellar mass and size. Thus, the evolution of nitrogen in galaxies is set by more than just local effects and does not simply track the buildup of oxygen in galaxies. The precise form of the N/O–O/H relation is therefore sensitive to the sample of galaxies from which it is derived. This result casts doubt on the universal applicability of nitrogen-based strong-line metallicity indicators derived in the local universe.
Dual metrics on the boundary of strictly polyhedral hyperbolic 3-manifolds

Let $M$ be a compact oriented 3-manifold with non-empty boundary consisting of surfaces of genii $>1$ such that the interior of $M$ is hyperbolizable. We show that for each spherical cone-metric $d$ on $\partial M$ such that all cone-angles are greater than $2\pi$ and the lengths of all closed geodesics that are contractible in $M$ are greater than $2\pi$ there exists a unique strictly polyhedral hyperbolic metric on $M$ such that $d$ is the induced dual metric on $\partial M$.
Commodity WiFi Sensing in Ten Years: Status, Challenges, and Opportunities

The prevalence of WiFi devices and ubiquitous coverage of WiFi networks provide us the opportunity to extend WiFi capabilities beyond communication, particularly in sensing the physical environment. In this article, we survey the evolution of WiFi sensing systems utilizing commodity devices over the past decade. It groups WiFi sensing systems into three main categories: 1) activity recognition (large scale and small scale); 2) object sensing; and 3) localization. We highlight the milestone work in each category and the underline techniques they adopted. Next, this work presents the challenges faced by existing WiFi sensing systems. Finally, we comprehensively discuss the future trending of commodity WiFi sensing.
The aetiology and clinical characteristics of cryptococcal infections in Far North Queensland, tropical Australia

Cryptococcal infections are an important cause of morbidity and mortality in tropical Australia. This retrospective audit was conducted to characterise the aetiology, temporospatial epidemiology, and clinical course of 49 cryptococcal infections in Far North Queensland between 1 January 1999 and 31 December 2019. Cryptococcus gattii was identified in 15/32 (47%) in whom it was possible to speciate the organism. Among these 15 patients, 13 (87%) had a rural residential address, 10 (67%) were Indigenous Australians and 11 (73%) presented during the May-November dry season. When compared to the 17 patients with Cryptococcus neoformans infection, patients with C. gattii were less likely to be immunocompromised (0/15 versus 8/17 (47%), p = 0.003). Neurosurgery was necessary in 5/15 C. gattii cases and 3/17 (18%) C. neoformans cases (p = 0.42). Outcomes were generally good with 42/49 (86%) cases—and 14/15 (93%) with C. gattii infection—surviving to hospital discharge. These positive outcomes are likely to be explained by the development of standardised treatment guidelines during the study period, low rates of comorbidity in the patients with C. gattii infection and access to liposomal amphotericin and neurosurgical support in the well-resourced Australian healthcare system.
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this work, we present D2ADA, a general active domain adaptation framework for semantic segmentation. To adapt the model to the target domain with minimum queried labels, we propose acquiring labels of the samples with high probability density in the target domain yet with low probability density in the source domain, complementary to the existing source domain labeled data. To further facilitate labeling efficiency, we design a dynamic scheduling policy to adjust the labeling budgets between domain exploration and model uncertainty over time. Extensive experiments show that our method outperforms existing active learning and domain adaptation baselines on two benchmarks, GTA5 ->Cityscapes and SYNTHIA ->Cityscapes. With less than 5% target domain annotations, our method reaches comparable results with that of full supervision. Our code is publicly available at https://github.com/tsunghan-wu/D2ADA.
Comparison of Qualifications-Based Selection and Best-Value Procurement for Construction Manager–General Contractor Highway Construction

Faster project delivery and the infusion of contractor knowledge into design are the primary drivers for choosing construction manager–general contractor (CM-GC) project delivery. This study focuses on the use of qualifications-based selection (QBS) and best-value (BV) procurement approaches, how and why agencies use each approach, and their associated opportunities and obstacles. Data for this study were obtained from a majority of federally funded CM-GC projects completed between 2004 and 2015. It was found that the use of BV procurement versus QBS procurement had no statistically significant difference in project characteristics or in performance of the projects in the data set. The choice of BV or QBS procurement coincided with an agency’s CM-GC stage of organizational development and influences of nonagency stakeholders on the CM-GC process. When agencies and the local industry were new to CM-GC, they were found to use BV procurement because it is closer to the traditional procurement culture and is perceived to result in a fair market project price. Alternatively, agencies and local industry partners with an established history of using CM-GC projects were found to choose QBS procurement. Because of the low level of design at the time of procurement, assumptions relating to risk, production rates, materials sources, and other aspects may be too preliminary to secure a reliable price. The use of BV procurement was found to pose a risk to innovation and to increase negotiation efforts. Qualitative trends from the project data, interviews, and the literature point to agencies using QBS procurement for the majority of CM-GC projects and BV procurement for CM-GC projects with lesser complexity or more highly developed designs at the time of selection.
Proposed Model for Inventory Review Policy through ABC Analysis in an Automotive Manufacturing Industry

A global market inventory is one of the largest and most significant resources of a manufacturing business. ABC analysis is one of the methods used extensively in manufacturing industries for inventory classification. The familiar ABC inventory classification approach categorizes inventory items as A, B and C classes according to their annual consumption value. To direct and control the inventory items more proficiently the inventories managers regularly classify and group all the inventory items. The objective of this paper is to create a periodic review policy based on the ABC classification in order to control the raw materials more efficiently. Based on the data collected through original equipment manufacturer, the necessary analysis was carried out. From the observed results, it was found that the developed periodic review policy improved the inventory turnover ratio from 3.15 to 2.13 by optimizing the inventory level for one year.
Verification Of FAA's Emissions And Dispersion Modeling System (EDMS)

Air quality has been a major environmental concern for many years. Recently the issue of airport emissions has presented growing concerns and is being studied in much more depth. Airport emissions come from a variety of point, line and area sources, making emissions modeling for airports very complex and more involved. Accurate air quality models, specific to airport needs, are required to properly analyze this complex array of air pollution sources created by airports. Accurate air quality models are needed to plan for increased growth of current airports and address concerns over proposed new ones. The Federal Aviation Administration’s (FAA) Emissions and Dispersion Modeling System (EDMS) is a program that is the required model for assessing emissions from airport sources. This research used EDMS Version 4.21, which incorporates the EPA dispersion model AERMOD, to model detailed airport data and compare the model’s predicted values to the actual measured carbon monoxide concentrations at 25 locations at a major U.S. airport. Statistics relating the model characteristics as well as trends are presented. In this way, a thorough investigation of the accuracy of the EDMS modeled values of carbon monoxide was possible. EDMS modeling included two scenarios, the first scenario referred to as practice detail included general airport information that a modeler could find from the airport being studied and the second scenario referred to as research detail utilized very detailed information from observer logs during a three day observation period. Each of the modeling scenarios was compared to the field measured data and to each other. These comparisons are important to insure the model is adequately describing emissions sources at airports. Data analysis of this study was ii disappointing since measured levels of CO were generally higher than modeled values. Since EDMS is continually changing and improving perhaps these results can help enhance future models.
Perspectives of Rural Primary Care Clinicians on Pediatric Attention-Deficit/Hyperactivity Disorder Care

This article has supplementary material on the web site: www.jdbp. org. ABSTRACT: Objective: Despite efficacious treatments, evidence-based guidelines, and increased availability of integrated behavioral health care, youth coping with attention-deficit/hyperactivity disorder (ADHD) receive suboptimal care. More research is needed to understand and address care gaps, particularly within rural health systems that face unique challenges. We conducted a qualitative study within a predominantly rural health system with a pediatric-integrated behavioral health care program to address research gaps and prepare for quality improvement initiatives, including primary care clinician (PCC) trainings and clinical decision support tools in the electronic health record (EHR). Method: Semistructured interviews were conducted with 26 PCCs representing clinics within the health system. Interview guides were based on the Practical Robust Implementation and Sustainability Model to elicit PCC views regarding determinants of current practices and suggestions to guide quality improvement efforts. We used thematic analysis to identify patterns of responding that were common across participants. Results: PCCs identified several internal and external contextual factors as determinants of current practices. Of note, PCCs recommended increased access to continuing education trainings held in clinic over lunch and delivered in less than 30 minutes. Suggested improvements to the EHR included incorporating parent and teacher versions of the Vanderbilt Rating Scale into the EHR, documentation templates aligned with evidence-based guidelines, and alerts and suggestions to aid medication management during appointments. Conclusion: Future research to identify implementation strategies to help rural PCCs adopt innovations are needed given the increased responsibility for managing ADHD care and intractable gaps in access to behavioral health care in rural regions.
Deep Multi-Branch Two-Stage Regression Network for Accurate Energy Expenditure Estimation with ECG and IMU Data

Objective: Energy Expenditure (EE) estimation plays an important role in objectively evaluating physical activity and its impact on human health. EE during activity can be affected by many factors, including activity intensity, individual physical and physiological characteristics, environment, etc. However, current studies only use very limited information, such as heart rate and step count, to estimate EE, which leads to a low estimation accuracy. Methods: In this study, we proposed a deep multi-branch two-stage regression network (DMTRN) to effectively fuse a variety of related information including motion information, physiological characteristics, and human physical information, which significantly improved the EE estimation accuracy. The proposed DMTRN consists of two main modules: a multi-branch convolutional neural network module which is used to extract multi-scale context features from inertial measurement unit (IMU) data and electrocardiogram (ECG) data and a two-stage regression module which aggregated the extracted multi-scale context features containing the physiological and motion information and the anthropometric features to accurately estimate EE. Results: Experiments performed on 33 participants show that our proposed method is more accurate and the average root mean square error (RMSE) is reduced by 22.8% compared with previous works. Conclusion: The EE estimation accuracy was improved by the proposed DMTRN model with a well-designed network structure and new input signal ECG. Significance: This study verified that ECG was much more effective than HR for EE estimation and cast light on EE estimation using the deep learning method.
Knowledge regarding cancer-related fatigue: a survey of physical therapists and individuals diagnosed with cancer

ABSTRACT Background Cancer-related fatigue (CRF) is a common side effect and remains under-diagnosed. Screening of CRF by physical therapists (PTs) and patient perspectives of their experiences has not been comprehensively examined. Purpose To survey PTs to understand the frequency of CRF screening, and to assess the knowledge and experiences of survivors as it relates to CRF. Methods Two separate electronic surveys developed by the authors were distributed. One targeted oncology PTs, the other for adult survivors of cancer. Results Of the 199 PT respondents, 36% reported screening for CRF at every encounter. Screening included interviews (46%) and/or standardized questionnaires (37%). The most common barriers to receiving treatment for CRF was lack of physician referrals and time constraints. Of patient responses (n = 61), 84% reported CRF as an important ongoing issue; 77% reported that they initiated the discussion about CRF with their provider, and 23% reported being told there were treatment options for CRF. Conclusion CRF is common among cancer survivors. However, consistent screening by PTs is lacking. Patients with CRF frequently initiated the conversation with their providers because of symptoms and many patients were not told of treatment options. These findings represent a substantial gap in clinical practice regarding CRF screening and management.