Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating and forecasting the operational performance of road intersections

Road intersections represent one of the most complex configurations encountered when traversing road networks. It is therefore of vital importance to improve their operational performance, as that can significantly contribute towards the efficiency of the whole transport network. Traditional approaches to improve the efficiency of intersections are based on analysis of static data or expert opinions. However, due to the advancements on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies, it is possible to enhance safety and improve road intersection efficiency by continuously monitoring traffic conditions and enabling situational awareness of vehicle drivers. Towards this end, we design, develop and evaluate a system for evaluating and forecasting the operational performance of road intersections by mining streams of V2I data. Our system makes use of graph mining and trajectory data mining methods to continuously evaluate a set of well-defined measures of effectiveness (MOEs) for traffic operations at different levels of road network abstraction. In addition, the system enables interactive analysis and exploration of the various MOEs. The system architecture and methods are general and can be used in various settings requiring continuous monitoring and/or forecasting of the road network state.
HOT SPOTS OF BUS STOP CRIME: THE IMPORTANCE OF ENVIRONMENTAL ATTRIBUTES

This study focuses on bus stop crime and seeks to identify the environmental attributes that can affect the bus rider's security while at the bus stop. Using crime data for the years 1994 and 1995 made available by the transit division of the Los Angeles Metropolitan Transportation Authority, the study discusses some general characteristics of bus stop crime. Following the argument of criminologists that certain place characteristics can affect the incidence of crime, the study employs qualitative research methodology (observation, mapping, interviews and surveys) to examine in detail the physical and social environment around the ten most dangerous bus stops in Los Angeles. It finds an abundance of "negative" environmental attributes and a general lack of "defensible space" elements. It also finds that different types of crime tend to occur under different environmental conditions. The paper discusses design responses as an approach to crime prevention at bus stops.
A Survey Of Mac Protocol For Broadcast Problem In Wireless Sensor Network

One of the most active research areas for computer science and telecommunication researcher is the wireless sensor networks. Usually after the deployment of sensor nodes in the hazardous, hostile or remote areas, these nodes work on their own. These sensors nodes will have to perform their function with scare and none replenish able power resources. One of the main design objectives for these networks is Energy efficiency. This paper presented the obstacle and challenges in the design and development of power aware medium access control protocol for wireless sensor networks.
Robots Conquering the Homeland of the Vikings:: Making Sense of Robot Vacuum Cleaning in Danish Public Eldercare

The movement of robots from the production line to the service sector provides a protein solution to innovate and transform public service delivery. However, although robots increasingly are adopted in public service delivery (e.g., in healthcare and eldercare) as an alternative to traditional labor intensive services, little is known about their impact on organizations work processes, and how key stakeholders react toward robots. On this backdrop, this single case study investigates implementation and use of robot vacuum cleaners in Danish eldercare at the local government level. Using an extended version of the technological frame concept, this paper illustrates how technologist, managers, frontline staff and clients have different perceptions towards robot vacuum cleaning. The technologist and managers praise the new innovation for facilitating savings on the current accounts. By contrast, the frontline staff and clients find that robots do not sufficiently clean the floors. With the domestication of the robots, this paper argues that public administration literature needs to rethink whether this is only jet another generation of technologies or something radically new is surfacing to energize changes in public service delivery.
Ensembles and Cocktails: Robust Finetuning for Natural Language Generation

When ﬁnetuning a pretrained language model for natural language generation tasks, one is currently faced with a tradeoff. Lightweight ﬁnetuning (e.g., preﬁx-tuning, adapters), which freezes all or most of the parameters of the pretrained model, has been shown to achieve stronger out-of-distribution (OOD) performance than full ﬁnetuning , which tunes all of the parameters. However, lightweight ﬁnetuning can underperform full ﬁnetuning in-distribution (ID). In this work, we present methods to combine the beneﬁts of full and lightweight ﬁnetuning, achieving strong performance both ID and OOD. First, we show that an ensemble of the lightweight and full ﬁnetuning models achieves the best of both worlds: performance matching the better of full and lightweight ﬁnetuning, both ID and OOD. Second, we show that we can achieve similar improvements using a single model instead of two with our proposed cocktail ﬁnetuning , which augments full ﬁnetuning via distillation from a lightweight model. Finally, we provide some explanatory theory in a multiclass logistic regression setting with a large number of classes, describing how distillation on ID data can transfer the OOD behavior of one model to another.
Assessing the impact of COVID-19 on STEM (science, technology, engineering, mathematics) researchers in India

Background: The coronavirus disease 2019 (COVID-19) pandemic and the nationally mandated lockdown has resulted in facility closures, decreased laboratory activities, and shifting to remote working. The effects of the pandemic have spread across all professions, including academia. Hence, the present study aims to understand the extent of the impact of the COVID-19 pandemic on STEM (science, technology, engineering, mathematics) researchers and stakeholders in India. Methods: The study employed a mixed method design. Both quantitative (survey) and qualitative (interview) methods were used to gain a comprehensive understanding on the impact of the COVID-19 pandemic on STEM (science, technology, engineering, mathematics) early career researchers (ECRs), graduate students, Heads of Institutes, suppliers of scientific equipment, funders, and other stakeholders in India. Results: A total of 618 researchers completed the survey, and 24 stakeholders were interviewed for this study. Our findings highlight the importance of institutional and social support for mental well-being and scientific productivity among researchers, especially during the pandemic. It also shows the impact of the disruptions in grant disbursals on research activities of scientists. Further, the gendered impact between these relationships was also noted, all of which hint at a need for structured reform within STEM. Conclusions: The study highlights the various challenges faced by early career researchers, and STEM scientists at various positions in their careers during the COVID-19 restrictions in India.
Gaze-Hand Alignment

Gaze and freehand gestures suit Augmented Reality as users can interact with objects at a distance without need for a separate input device. We propose Gaze-Hand Alignment as a novel multimodal selection principle, defined by concurrent use of both gaze and hand for pointing and alignment of their input on an object as selection trigger. Gaze naturally precedes manual action and is leveraged for pre-selection, and manual crossing of a pre-selected target completes the selection. We demonstrate the principle in two novel techniques, Gaze&Finger for input by direct alignment of hand and finger raised into the line of sight, and Gaze&Hand for input by indirect alignment of a cursor with relative hand movement. In a menu selection experiment, we evaluate the techniques in comparison with Gaze&Pinch and a hands-only baseline. The study showed the gaze-assisted techniques to outperform hands-only input, and gives insight into trade-offs in combining gaze with direct or indirect, and spatial or semantic freehand gestures.
We Are What We Watch: Movie Plots Predict the Personalities of Their Fans

How do We study this question by employing advanced analytical tools to a new rich dataset that combines detailed characterizations of movies and their plots with personality measures of social-media users who “liked” them. We identify novel associations between movie features such as quality, revenue and genre, and the personality dimensions of their fans. We then use machine-learning to show that movie plots—captured via text—predict the personalities of fans beyond all other variables studied. We further use text analytical methods to quantify how different psychological themes (e.g., leisure) and unique concepts that organically emerge from the data (e.g., adultery) relate to fans’ personalities, and show that movie plots align with the characteristic ways in which their fans think, feel, and behave. For example, films with anxiety have neurotic fans, where social films attract extraverted fans. Our findings provide fine-grained mappings between dimensions of personality and movie preferences, facilitate scalable automated assessment of audience psychographics, and showcase a text-analytic framework for studying how features of multidimensional cultural products relate to psychological characteristics of their consumers.
Contributions to neural speech synthesis using limited data enhanced with lexical features

Building single or multi-speaker neural network-based text-to-speech synthesis systems commonly relies on the availability of large amounts of high quality recordings from each speaker and conditioning the training process on the speaker’s identity or on a learned representation of it. However, when little data is available from each speaker, or the number of speakers is limited, the speech synthesis system can be hard to train and will result in poor speaker similarity and naturalness. In order to address this issue we explore several directions by engaging speaker adaptation, additional loss terms, data augmentation, speaker selection methods and different types of textual representations to improve the quality of the synthetic output speech. Our experiments are focused on the Romanian language that is considered a low-resource language. Objective and subjective measures are used to evaluate the effectiveness of the proposed methods.
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A second optimization also proposed Decomposition The proposed methods are validated through the simulation of the upper hemisphere of the scattered ﬁeld of a wide with a computer screen. Results for the root-mean-square error between reference simulations and scattered pressure synthesized from the sector-based encoded scattering matrix, with SVD compression, indicate minimal reconstruction error. Through the compression method, a scattering matrix is compressed to 15% of its original memory size, while maintaining 99% of its singular values.
Unsupervised Crowdsourcing with Accuracy and Cost Guarantees

We consider the problem of cost-optimal utilization of a crowdsourcing platform for binary, unsupervised classification of a collection of items, given a prescribed error threshold. Workers on the crowdsourcing platform are assumed to be divided into multiple classes, based on their skill, experience, and/or past performance. We model each worker class via an unknown confusion matrix, and a (known) price to be paid per label prediction. For this setting, we propose algorithms for acquiring label predictions from workers, and for inferring the true labels of items. We prove that (i) our algorithms satisfy the prescribed error threshold, and (ii) if the number of (unlabeled) items available is large enough, the algorithms incur a cost that is near-optimal. Finally, we validate our algorithms, and some heuristics inspired by them, through an extensive case study.
Prolonged Learning and Hasty Stopping: The Wald Problem with Ambiguity

This paper studies sequential information acquisition by an ambiguity-averse decision-maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM’s stopping rule is nonmonotonic in beliefs and features randomized stopping. (JEL C61, D81, D83, D91)
Cooperative Multi-Agent Trajectory Generation with Modular Bayesian Optimization

We present a modular Bayesian optimization framework that efficiently generates time-optimal trajectories for a cooperative multi-agent system, such as a team of UAVs. Existing methods for multi-agent trajectory generation often rely on overly conservative constraints to reduce the complexity of this high-dimensional planning problem, leading to suboptimal solutions. We propose a novel modular structure for the Bayesian optimization model that consists of multiple Gaussian process surrogate models that represent the dynamic feasibility and collision avoidance constraints. This modular structure alleviates the stark increase in computational cost with problem dimensionality and enables the use of minimal constraints in the joint optimization of the multi-agent trajectories. The efficiency of the algorithm is further improved by introducing a scheme for simultaneous evaluation of the Bayesian optimization acquisition function and random sampling. The modular BayesOpt algorithm was applied to optimize multi-agent trajectories through six unique environments using multi-fidelity evaluations from various data sources. It was found that the resulting trajectories are faster than those obtained from two baseline methods. The optimized trajectories were validated in real-world experiments using four quadcopters that fly within centimeters of each other at speeds up to 7.4 m/s.
Differentiable signed distance function rendering

Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also poses severe challenges: shapes are intertwined with visibility, whose discontinuous nature introduces severe bias in computed derivatives unless costly precautions are taken. Shape representations like triangle meshes suffer from additional difficulties, since the continuous optimization of mesh parameters cannot introduce topological changes. One common solution to these difficulties entails representing shapes using signed distance functions (SDFs) and gradually adapting their zero level set during optimization. Previous differentiable rendering of SDFs did not fully account for visibility gradients and required the use of mask or silhouette supervision, or discretization into a triangle mesh. In this article, we show how to extend the commonly used sphere tracing algorithm so that it additionally outputs a reparameterization that provides the means to compute accurate shape parameter derivatives. At a high level, this resembles techniques for differentiable mesh rendering, though we show that the SDF representation admits a particularly efficient reparameterization that outperforms prior work. Our experiments demonstrate the reconstruction of (synthetic) objects without complex regularization or priors, using only a per-pixel RGB loss.
Auditing Algorithms: Determining Ethical Parameters of Artificial Intelligence Systems in Healthcare

The hypothesis we intend to test is the identification of potential sources of bias and/or ethical concerns in healthcare data and to propose methods for reducing or eliminating the bias. To test this hypothesis we have selected an open source dataset focused on the impact of HbA1c Measurement on Hospital Readmission Rates. A detailed analysis will be made of dataset parameters in order to determine whether a given feature or set of features is affecting or biasing the algorithm or results. By testing this hypothesis we hope to identify potential sources of bias and ethical concerns in the data or algorithm and we will identify methods that may beneficially minimize unintended bias in algorithm and/or analysis.
Meta Value Learning for Fast Policy-Centric Optimal Motion Planning

—This paper considers policy-centric optimal motion planning with limited reaction time. The motion planning queries are determined by their goal regions and cost functionals, and are generated over time from a distribution. Once a new query is requested, the robot needs to quickly generate a motion planner which can steer the robot to the goal region while minimizing a cost functional. We develop a meta-learning-based algorithm to compute a meta value function, which can be fast adapted using a small number of samples of a new query. Simulations on a unicycle are conducted to evaluate the developed algorithm and show the anytime property of the proposed algorithm.
LRH: Land-use effects on beta diversity

: Previous studies have found mixed results regarding the relationship between 37 beta diversity and latitude. In addition, by influencing local environmental 38 heterogeneity, land use may modify spatial taxonomic and functional variability among 39 communities causing biotic differentiation or homogenization. We tested 1) whether 40 taxonomic and functional beta diversities among streams within watersheds differ 41 between subtropical and boreal regions and 2) whether land use is related to taxonomic 42 and functional beta diversities in both regions. We sampled aquatic insects in 100 43 subtropical (Brazil) and 100 boreal (Finland) streams across a wide gradient of land use, 44 including agriculture and exotic planted, secondary, and native forests. We calculated 45 beta diversity at the watershed scale (among 5 streams in each watershed). We found 46 higher taxonomic beta diversity among subtropical than among boreal streams, whereas 47 functional beta diversity was similar between the 2 regions. Total land use was 48 positively correlated with taxonomic and functional beta diversity among subtropical 49 streams, while local environmental heterogeneity was positively correlated with beta 50 diversity among boreal streams. We suggest that different types and intensities of land 51 use may increase among-stream heterogeneity, promoting distinct insect assemblage 52 compositions among streams. Our findings also suggest that beta diversity patterns and 53 their underlying determinants are highly context dependent. 54
Vertical Integration with Incomplete Information ∗

Using an independent private values model, we analyze the eﬀects of and incentives for vertical integration. Each ﬁrm is characterized by an amount of internally held resources and a maximum demand for these resources as inputs, which determine the ﬁrm’s extent of vertical integration, and a distribution for the ﬁrm’s private marginal value for resources. After private information is realized, ﬁrms trade resources and payoﬀs are realized. Depending on type realizations, a vertically integrated ﬁrm may sell resources to others, buy resources from others, and/or consume resources held internally. A certain extent of vertical integration is necessary and suﬃcient for the ﬁrst-best to be possible. With two ﬁrms, equilibrium vertical integration is socially optimal but with more ﬁrms, vertical integration is typically excessive because of externalities from bilateral transactions. The model provides both rationale and guidance for divestiture after a vertical merger.
A NOVEL SEQUENTIAL MONTE CARLO FILTER WITH GAUSSIAN MIXTURE MODELS AND KERNEL DENSITY ESTIMATION

In this paper, a modiﬁed kernel-based ensemble Gaussian mixture ﬁltering (EnGMF) is introduced to produce fast and consistent orbit determination capabilities in a sparse measurement environment. The EnGMF is based on kernel density estimation (KDE) to com- bine particle ﬁlters and Gaussian sum ﬁlters. This work proposes using Silverman’s rule of thumb to reduce the computational burden of KDE. Equinoctial orbital elements are used to improve the accuracy of the KDE bandwidth parameter in the modiﬁed EnGMF. Through numerical simulation, the proposed implementation is compared to state-of-the- art approaches in terms of accuracy, consistency, and computational speed.
An Experimental Study of Triggered Multi-User Uplink Access with Real Application Traffic

The 802.11ax amendment introduced Triggered Uplink Access (TUA) to Wi-Fi to support uplink Multi-User (MU) MIMO. TUA coordinates simultaneous transmission of uplink users via an AP-transmitted trigger that gives an AP-selected group of users permission to transmit simultaneously for an AP-selected duration of time. Thus, TUA promises performance gains by enabling multi-user transmission and reducing contention overhead for access. In this paper, for the first time, we experimentally study the role of real application traffic on the performance of TUA. In particular, while TUA gains for fully backlogged traffic are well established, we show that bursty closed-loop traffic radically transforms performance. Using a real-time emulator, we experimentally evaluate the empirical limits of triggered uplink multi-user access with traffic from a real file transfer application and different uplink triggering strategies. Our results show that TUA significantly reduces file transfer latency compared to legacy single-user uplink, but unfortunately the standardized method for low-overhead backlog reporting leaves substantial benefits unrealized. Moreover, we show that unlike a single-user uplink, TUA has non-monotonic performance with respect to the frame aggregation limit.
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis

Nonlinear independent component analysis (nICA) aims at recovering statistically independent latent components that are mixed by unknown nonlinear functions. Central to nICA is the identifiability of the latent components, which had been elusive until very recently. Specifically, Hyv\"arinen et al. have shown that the nonlinearly mixed latent components are identifiable (up to often inconsequential ambiguities) under a generalized contrastive learning (GCL) formulation, given that the latent components are independent conditioned on a certain auxiliary variable. The GCL-based identifiability of nICA is elegant, and establishes interesting connections between nICA and popular unsupervised/self-supervised learning paradigms in representation learning, causal learning, and factor disentanglement. However, existing identifiability analyses of nICA all build upon an unlimited sample assumption and the use of ideal universal function learners -- which creates a non-negligible gap between theory and practice. Closing the gap is a nontrivial challenge, as there is a lack of established ``textbook'' routine for finite sample analysis of such unsupervised problems. This work puts forth a finite-sample identifiability analysis of GCL-based nICA. Our analytical framework judiciously combines the properties of the GCL loss function, statistical generalization analysis, and numerical differentiation. Our framework also takes the learning function's approximation error into consideration, and reveals an intuitive trade-off between the complexity and expressiveness of the employed function learner. Numerical experiments are used to validate the theorems.