Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, Development, and Evaluation of a PV_Bio-Gen Range Extender for an Off-Road Electric Vehicle

-Transformation from fossil fuels to clean energy is necessary due to the stricter environmental protection policies. Drivetrain hybridization by green energy sources seems to be an appropriate solution in farm applications. However, some constraints are raised, e.g., the low energy density of renewable energy sources and the long recharging time of batteries. Hence, the objective of this work is to suggest an Extended-Range Solar Assist Plug-In Hybrid Electric Tractor (E-RSAPHT) via a combination of a photovoltaic (PV) system and a biogas fuelled engine generator set (Bio-Gen) with a battery pack. Due to multi-power sources, a heuristic control strategy is proposed to split the generated powers while considering the extend daily operation hours. Moreover, some field measurements are conducted to define typical working cycles for farm hybrid vehicle application. Considering these points, the modelling, simulation and development of the E-RSAPHT are presented in this paper. Experimental results showed that the proposed system could improve energy autonomy and fuel efficiency. For typically evaluated toolbars, the operation ranges of the trailer, boom-type sprayer, and water-pump system were extended up to 292, 255, and 320% compared to the base battery-electric tractor, respectively. The conducted investigations illustrate that even for a 2100kg electric farm vehicle, a downsized 4.4kW Bio-Gen allows the hybrid-electric system to supply the power demand compared to the conventional system by using the internal combustion engine.
Admission Control and Downlink Scheduling for Advanced Antennas in WCDMA

Transmissions to a mobile can be limited to the direction where the mobile is located using Advanced Antennas. Beside other advantages, this allows for efficient radio resource management algorithms. One of them is the admission control, which determines whether a user should be allowed to set up a call or data connection. If the locations of the mobiles are considered in this decision, only mobiles in overloaded areas have to be blocked. Another algorithm, which can be improved
Toward an Open Platform of Blind Navigation via Interactions with Autonomous Robots

Recent advances in autonomous robotics, such as technology developed for self-driving vehicles, have demonstrated their impact in facilitating travel from point A to B, and may potentially assist independent navigation for blind and visually impaired people. Our previous and ongoing work using wearable vision and guiding robots integrates assistive haptic feedback [5, 8, 13] with existing navigation aids, e.g. canes and guide dogs. However, reproducing and extending this early work for further development and safe testing remains a challenge. To this end, we introduce open and reproducible hardware and software development protocols, some of which have already been adopted in university courses as well as research or robotic competitions. To validate the safety and effectiveness of assistive technology solutions, we encourage the spread of reproducible materials and standards to the wider community, including university educators, researchers, governments, and, most importantly, users who are blind and visually impaired.
Let’s Gamble: Uncovering the Impact of Visualization on Risk Perception and Decision-Making

—Data visualizations are standard tools for assessing and communicating risks. However, it is not always clear which designs are optimal or how encoding choices might inﬂuence risk perception and decision-making. In this paper, we report the ﬁndings of a large-scale gambling game that immersed participants in an environment where their actions impacted their bonuses. Participants chose to either enter a lottery or receive guaranteed monetary gains based on ﬁve common visualization designs. By measuring risk perception and observing decision-making, we showed that icon arrays tended to elicit economically sound behavior. We also found that people were more likely to gamble when presented area proportioned triangle and circle designs. Using our results, we model risk perception and discuss how our
Health Professional Digital Capabilities Frameworks: A Scoping Review

Introduction The term “digital healthcare professional” alludes to a health professional with the additional digital capabilities such as information and technology. The assumption that attaining technical knowledge and skills to meet the available professional standards in digital healthcare, will engage and empower healthcare users, thus deliver person-centered digital healthcare (PCDHc), is flawed. Identifying where digital healthcare and technologies can genuinely support person-centered care may lead to future discourse and practical suggestions to build person-centered integrated digital healthcare environments. This review examines current digital health and informatics capability frameworks and identifies the opportunity to include additional or alternative principles. Methods A scoping review was conducted. Literature valuing person-centered digital healthcare requirements, digital health capabilities, and competencies were identified between 2000 and 2019 inclusive, then collated and considered. Using a PRISMA approach for eligibility screening, thirteen articles met the study inclusion criteria. Analysis used a thematic framework approach, which assisted in the data management, abstraction and description, and finally the explanations. Results Analysis indexed fifty-nine (59) capabilities, charted thirteen (n13) categories, mapped four (n4) themes, which were then interpreted as findings. Findings The four themes identified were Change Management; User Application; Data, Information, and Knowledge; and Innovation. The themes recognize the opportunity to align the application of technical skills towards the capabilities required to deliver authentic PCDHc. Discussion Holistic mindsets are imperative in maintaining the objective of PCDHc. The authors propose that debates regarding professional digital capability persist in being “siloed” and “paternalistic” in nature. They also recommend that the transition to authentic PCDHc requires refocusing (rather than rewriting) current capabilities. The realignment of capabilities towards individual healthcare outcomes, rather than professional obligation, can steer the perspective towards a genuine PCDHc system. Conclusion This scoping review confirms the assumption that digital skills will empower all healthcare stakeholders is incorrect. This review also draws attention to the need for more research to enable digital healthcare systems and services to be designed to realize complex human behaviors and multiple person-centered care requirements. Now more than ever, it is imperative to align healthcare capabilities with technologies to ensure that the practice of PCDHc is the empowering journey for the healthcare user that theory implies.
Exploring market power using deep reinforcement learning for intelligent bidding strategies

Decentralized electricity markets are often dominated by a small set of generator companies who control the majority of the capacity. In this paper, we explore the effect of the total controlled electricity capacity by a single, or group, of generator companies can have on the average electricity price. We demonstrate this through the use of ElecSim, a simulation of a country-wide energy market. We develop a strategic agent, representing a generation company, which uses a deep deterministic policy gradient reinforcement learning algorithm to bid in a uniform pricing electricity market. A uniform pricing market is one where all players are paid the highest accepted price. ElecSim is parameterized to the United Kingdom for the year 2018. This work can help inform policy on how to best regulate a market to ensure that the price of electricity remains competitive.We find that capacity has an impact on the average electricity price in a single year. If any single generator company, or a collaborating group of generator companies, control more than ~11% of generation capacity and bid strategically, prices begin to increase by ~25%. The value of ~25% and may vary between market structures and ~11% countries. For instance, different load profiles may favour a particular type of generator or a different distribution of generation capacity. Once the capacity controlled by a generator company, which bids strategically, is higher than ~35%, prices increase exponentially. We observe that the use of a market cap of approximately double the average market price has the effect of significantly decreasing this effect and maintaining a competitive market. A fair and competitive electricity market provides value to consumers and enables a more competitive economy through the utilisation of electricity by both industry and consumers.
Echo Chambers in Collaborative Filtering Based Recommendation Systems

Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to the predicted preferences of users. Recently, these systems have faced growing criticism with respect to their impact on content diversity, social polarization, and the health of public discourse. In this work we simulate the recommendations given by collaborative filtering algorithms on users in the MovieLens data set. We find that prolonged exposure to system-generated recommendations substantially decreases content diversity, moving individual users into "echo-chambers" characterized by a narrow range of content. Furthermore, our work suggests that once these echo-chambers have been established, it is difficult for an individual user to break out by manipulating solely their own rating vector.
Strawberry Detection Using a Heterogeneous Multi-Processor Platform

Over the last few years, the number of precision farming projects has increased specifically in harvesting robots and many of which have made continued progress from identifying crops to grasping the desired fruit or vegetable. One of the most common issues found in precision farming projects is that successful application is heavily dependent not just on identifying the fruit but also on ensuring that localisation allows for accurate navigation. These issues become significant factors when the robot is not operating in a prearranged environment, or when vegetation becomes too thick, thus covering crop. Moreover, running a state-of-the-art deep learning algorithm on an embedded platform is also very challenging, resulting most of the times in low frame rates. This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3\% over the test set comprised of 146 images.
Effect of Immune Checkpoint Inhibitors Plus Chemotherapy on Advanced Gastric Cancer Patients with Elevated Serum AFP or Hepatoid Adenocarcinoma

Purpose Alpha-fetoprotein-producing gastric cancer (AFPGC) and hepatoid adenocarcinoma of stomach (HAS) are rare types of gastric cancer, with specific clinical manifestations and poor prognosis. The standardized treatment process of such cancers remains elusive. We aim to investigate the efficacy of immunotherapy combined with chemotherapy on patients with AFPGC or HAS. Patients and Methods AFPGC and HAS patients who underwent immunotherapy and/or chemotherapy as the first-line treatment at our institute from June 2016 to December 2018 were enrolled in this observational study. Their clinicopathological characteristics, serum AFP level and treatment methods were collected. The progression-free survival (PFS) and overall survival (OS) were analyzed and compared between patients who received immunotherapy plus chemotherapy and those received chemotherapy. Results A total of 21 patients with advanced AFPGC or HAS were included in the study and the median follow-up time was 28.0 months. Of the 21 patients, 7 patients received immunotherapy of PD-1 antibody (nivolumab) plus chemotherapy and 14 patients as control received chemotherapy with or without Herceptin/Apatinib. The median progression-free survival (mPFS) time was 5.0 months (4.3 months in the control group and 22.0 months in the immunotherapy group). The median overall survival (mOS) time of the control group was 16.0 months (14.0 months in chemotherapy alone subgroup, 20.0 months in chemotherapy plus Apatinib or Herceptin subgroup), while the mOS of patients receiving immunotherapy was not reached. Conclusion This study suggested PD-1 checkpoint inhibitor plus chemotherapy could benefit AFPGC and HAS patients. Its mechanism of action warrants further investigation.
Relation between Charging Times and Storage Properties of Nanoporous Supercapacitors

An optimal combination of power and energy characteristics is beneficial for the further progress of supercapacitors-based technologies. We develop a nanoscale dynamic electrolyte model, which describes both static capacitance and the time-dependent charging process, including the initial square-root dependency and two subsequent exponential trends. The observed charging time corresponds to one of the relaxation times of the exponential regimes and significantly depends on the pore size. Additionally, we find analytical expressions providing relations of the time scales to the electrode’s parameters, applied potential, and the final state of the confined electrolyte. Our numerical results for the charging regimes agree with published computer simulations, and estimations of the charging times coincide with the experimental values.
Trapped Bose-Bose mixtures at finite temperature: A quantum Monte Carlo approach

We study thermal properties of a trapped Bose-Bose mixture in a dilute regime using quantum Monte Carlo methods. Our main aim is to investigate the dependence of the superfluid density and the condensate fraction on temperature, for the mixed and separated phases. To this end, we use the diffusion Monte Carlo method, in the zero-temperature limit, and the path-integral Monte Carlo method for finite temperatures. The results obtained are compared with solutions of the coupled Gross-Pitaevskii equations for the mixture at zero temperature. We notice the existence of an anisotropic superfluid density in some phase-separated mixtures. Our results also show that the temperature evolution of the superfluid density and condensate fraction is slightly different, showing noteworthy situations where the superfluid fraction is smaller than the condensate fraction.
Closing the Generalization Gap in One-Shot Object Detection

Despite substantial progress in object detection and few-shot learning, detecting objects based on a single example - one-shot object detection - remains a challenge: trained models exhibit a substantial generalization gap, where object categories used during training are detected much more reliably than novel ones. Here we show that this generalization gap can be nearly closed by increasing the number of object categories used during training. Our results show that the models switch from memorizing individual categories to learning object similarity over the category distribution, enabling strong generalization at test time. Importantly, in this regime standard methods to improve object detection models like stronger backbones or longer training schedules also benefit novel categories, which was not the case for smaller datasets like COCO. Our results suggest that the key to strong few-shot detection models may not lie in sophisticated metric learning approaches, but instead in scaling the number of categories. Future data annotation efforts should therefore focus on wider datasets and annotate a larger number of categories rather than gathering more images or instances per category.
CxGBERT: BERT meets Construction Grammar

While lexico-semantic elements no doubt capture a large amount of linguistic information, it has been argued that they do not capture all information contained in text. This assumption is central to constructionist approaches to language which argue that language consists of constructions, learned pairings of a form and a function or meaning that are either frequent or have a meaning that cannot be predicted from its component parts. BERT’s training objectives give it access to a tremendous amount of lexico-semantic information, and while BERTology has shown that BERT captures certain important linguistic dimensions, there have been no studies exploring the extent to which BERT might have access to constructional information. In this work we design several probes and conduct extensive experiments to answer this question. Our results allow us to conclude that BERT does indeed have access to a significant amount of information, much of which linguists typically call constructional information. The impact of this observation is potentially far-reaching as it provides insights into what deep learning methods learn from text, while also showing that information contained in constructions is redundantly encoded in lexico-semantics.
Bayesian bandwidth estimation for local linear fitting in nonparametric regression models

Abstract This paper presents a Bayesian sampling approach to bandwidth estimation for the local linear estimator of the regression function in a nonparametric regression model. In the Bayesian sampling approach, the error density is approximated by a location-mixture density of Gaussian densities with means the individual errors and variance a constant parameter. This mixture density has the form of a kernel density estimator of errors and is referred to as the kernel-form error density (c.f. Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34.). While (Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34) use the local constant (also known as the Nadaraya-Watson) estimator to estimate the regression function, we extend this to the local linear estimator, which produces more accurate estimation. The proposed investigation is motivated by the lack of data-driven methods for simultaneously choosing bandwidths in the local linear estimator of the regression function and kernel-form error density. Treating bandwidths as parameters, we derive an approximate (pseudo) likelihood and a posterior. A simulation study shows that the proposed bandwidth estimation outperforms the rule-of-thumb and cross-validation methods under the criterion of integrated squared errors. The proposed bandwidth estimation method is validated through a nonparametric regression model involving firm ownership concentration, and a model involving state-price density estimation.
Statistical query complexity of manifold estimation

This paper studies the statistical query (SQ) complexity of estimating d-dimensional submanifolds in ℝn. We propose a purely geometric algorithm called Manifold Propagation, that reduces the problem to three natural geometric routines: projection, tangent space estimation, and point detection. We then provide constructions of these geometric routines in the SQ framework. Given an adversarial STAT(τ) oracle and a target Hausdorff distance precision ε = Ω(τ2/(d+1)), the resulting SQ manifold reconstruction algorithm has query complexity O(n polylog(n) ε−d/2), which is proved to be nearly optimal. In the process, we establish low-rank matrix completion results for SQ’s and lower bounds for randomized SQ estimators in general metric spaces.
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection

Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance. While current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes, they show a significant drop in performance for underrepresented classes that share similar geometric features. We propose a novel Detection Aware 3D Semantic Segmentation (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task. By utilizing multitask training, the shared feature representation of the network is guided to be aware of per class detection features that aid tackling the differentiation of geometrically similar classes. We additionally provide a pipeline that uses DASS to generate high recall proposals for existing 2-stage detectors and demonstrate that the added supervisory signal can be used to improve 3D orientation estimation capabilities. Extensive experiments on both the SemanticKITTI and KITTI object datasets show that DASS can improve 3D semantic segmentation results of geometrically similar classes up to 37.8% IoU in image FOV while maintaining high precision bird’s-eye view (BEV) detection results.
EDNet: Efficient Disparity Estimation with Combination Volume and Spatial Attention based Residual Learning

Existing state-of-the-art disparity estimation works mostly leverage the 4D concatenation volume and construct a very deep 3D convolution neural network for disparity regression, which is inefficient considering the high memory consumption and slow inference speed. In this paper, we propose a network named EDNet for efficient disparity estimation. To be specific, we construct a combination volume which incorporates contextual information from the concatenation volume and feature similarity measurement from the correlation volume. The combination volume can be aggregated by 2D convolutions which require less running memory. We further propose a spatial attention based residual learning module to generate attention-aware residual features. Accurate disparity correction can be provided even in lowtexture regions as the residual learning process can specifically concentrate on inaccurate regions. Extensive experiments on Scene Flow and KITTI datasets show that our network outperforms previous 3D convolution based works and achieves state-of-the-art performance with significantly faster speed and less memory consumption, demonstrating the effectiveness of our proposed method.