Miracle-dz/UniFAR-FTUs huggingface dataset:
UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown

3D models are an essential part of many robotic applications. In applications where the environment is unknown a-priori, or where only a part of the environment is known, it is important that the 3D model can handle the unknown space efficiently. Path planning, exploration, and reconstruction all fall into this category. In this letter we present an extension to OctoMap which we call UFOMap. UFOMap uses an explicit representation of all three states in the map, i.e., unknown, free, and occupied. This gives, surprisingly, a more memory efficient representation. We provide methods that allow for significantly faster insertions into the octree. Furthermore, UFOMap supports fast queries based on occupancy state using so called indicators and based on location by exploiting the octree structure and bounding volumes. This enables real-time colored octree mapping at high resolution (below 1 cm). UFOMap is contributed as a C++ library that can be used standalone but is also integrated into ROS.
Slice Tuner: A Selective Data Collection Framework for Accurate and Fair Machine Learning Models

As machine learning becomes democratized in the era of Software 2.0, one of the most serious bottlenecks is collecting enough labeled data to ensure accurate and fair models. Recent techniques including crowdsourcing provide cost-effective ways to gather such data. However, simply collecting data as much as possible is not necessarily an effective strategy for optimizing accuracy and fairness. For example, if an online app store has enough training data for certain slices of data (say American customers), but not for others, collecting more American customer data will only bias the model training. Instead, we contend that one needs to selectively collect data and propose Slice Tuner, which collects possibly-different amounts of data per slice such that the model accuracy and fairness on all slices are optimized. At its core, Slice Tuner maintains learning curves of slices that estimate the model accuracies given more data and uses convex optimization to find the best data collection strategy. The key challenges of estimating learning curves are that they may be inaccurate if there is not enough data, and there may be dependencies among slices where collecting data for one slice influences the learning curves of others. We solve these issues by iteratively and efficiently updating the learning curves as more data is collected. We evaluate Slice Tuner on real datasets using crowdsourcing for data collection and show that Slice Tuner significantly outperforms baselines in terms of model accuracy and fairness, even for initially small slices. We believe Slice Tuner is a practical tool for suggesting concrete action items based on model analysis.
Measuring the matter density of the Galactic disc using stellar streams

We present a novel method for determining the total matter surface density of the Galactic disc by analysing the kinematics of a dynamically cold stellar stream that passes through or close to the Galactic plane. The method relies on the fact that the vertical component of energy for such stream stars is approximately constant, such that their vertical positions and vertical velocities are interrelated via the matter density of the Galactic disc. By testing our method on mock data stellar streams, with realistic phase-space dispersions and Gaia uncertainties, we demonstrate that it is applicable to small streams out to a distance of a few kilo-parsec, and that the surface density of the disc can be determined to a precision of $6{{\ \rm per\ cent}}$. This method is complementary to other mass measurements. In particular, it does not rely on any equilibrium assumption for stars in the Galactic disc, and also makes it possible to measure the surface density to good precision at large distances from the Sun. Such measurements would inform us of the matter composition of the Galactic disc and its spatial variation, place stronger constraints on dark disc substructure, and even diagnose possible non-equilibrium effects that bias other types of dynamical mass measurements.
The “good” Boussinesq equation : long-time
asymptotics

We consider the initial-value problem for the ``good'' Boussinesq equation on the line. Using inverse scattering techniques, the solution can be expressed in terms of the solution of a $3 \times 3$-matrix Riemann-Hilbert problem. We establish formulas for the long-time asymptotics of the solution by performing a Deift-Zhou steepest descent analysis of a regularized version of this Riemann-Hilbert problem.
Data Warehouse and Decision Support on Integrated Crop Big Data

In recent years, precision agriculture is becoming very popular. The introduction of modern information and communication technologies for collecting and processing Agricultural data revolutionise the agriculture practises. This has started a while ago (early 20th century) and it is driven by the low cost of collecting data about everything; from information on fields such as seed, soil, fertiliser, pest, to weather data, drones and satellites images. Specially, the agricultural data mining today is considered as Big Data application in terms of volume, variety, velocity and veracity. Hence it leads to challenges in processing vast amounts of complex and diverse information to extract useful knowledge for the farmer, agronomist, and other businesses. It is a key foundation to establishing a crop intelligence platform, which will enable efficient resource management and high quality agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse (ADW). ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility. We also evaluate the performance of ADW and present some complex queries to extract and return necessary knowledge about crop management.
Convergence Guarantees for Non-Convex Optimisation With Cauchy-Based Penalties

In this paper, we propose a proximal splitting methodology with a non-convex penalty function based on the heavy-tailed Cauchy distribution. We first suggest a closed-form expression for calculating the proximal operator of the Cauchy prior, which then makes it applicable in generic proximal splitting algorithms. We further derive the condition required for guaranteed convergence to the global minimum in optimisation problems involving the Cauchy based penalty function. Setting the system parameters by satisfying the proposed condition ensures convergence even though the overall cost function is non-convex, when minimisation is performed via a proximal splitting algorithm. The proposed method based on Cauchy regularisation is evaluated by solving generic signal processing examples, i.e. 1D signal denoising in the frequency domain, two image reconstruction tasks including de-blurring and denoising, and error recovery in a multiple-antenna communication system. We experimentally verify the proposed convergence conditions for various cases, and show the effectiveness of the proposed Cauchy based non-convex penalty function over state-of-the-art penalty functions such as <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> and total variation (<inline-formula><tex-math notation="LaTeX">$TV$</tex-math></inline-formula>) norms.
Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction

Surgical gesture recognition is important for surgical data science and computer-aided intervention. Even with robotic kinematic information, automatically segmenting surgical steps presents numerous challenges because surgical demonstrations are characterized by high variability in style, duration and order of actions. In order to extract discriminative features from the kinematic signals and boost recognition accuracy, we propose a multi-task recurrent neural network for simultaneous recognition of surgical gestures and estimation of a novel formulation of surgical task progress. To show the effectiveness of the presented approach, we evaluate its application on the JIGSAWS dataset, that is currently the only publicly available dataset for surgical gesture recognition featuring robot kinematic data. We demonstrate that recognition performance improves in multi-task frameworks with progress estimation without any additional manual labelling and training.
Speaker Identification using EEG

In this paper we explore speaker identification using electroencephalography (EEG) signals. The performance of speaker identification systems degrades in presence of background noise, this paper demonstrates that EEG features can be used to enhance the performance of speaker identification systems operating in presence and absence of background noise. The paper further demonstrates that in presence of high background noise, speaker identification system using only EEG features as input demonstrates better performance than the system using only acoustic features as input.
HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images

The majority of learning-based semantic segmentation methods are optimized for daytime scenarios and favorable lighting conditions. Real-world driving scenarios, however, entail adverse environmental conditions such as nighttime illumination or glare which remain a challenge for existing approaches. In this work, we propose a multimodal semantic segmentation model that can be applied during daytime and nighttime. To this end, besides RGB images, we leverage thermal images, making our network significantly more robust. We avoid the expensive annotation of nighttime images by leveraging an existing daytime RGB-dataset and propose a teacher-student training approach that transfers the dataset’s knowledge to the nighttime domain. We further adopt a domain adaptation method to align the learned feature spaces across the domains and propose a novel two-stage training scheme. Furthermore, due to a lack of thermal data for autonomous driving, we present a new dataset comprising over 20,000 time-synchronized and aligned RGB-thermal image pairs. In this context, we also present a novel target-less calibration method that allows for automatic robust extrinsic and intrinsic thermal camera calibration. Among others, we use our new dataset to show state-of-the-art results for nighttime semantic segmentation.
Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages

The contrast between the need for large amounts of data for current Natural Language Processing (NLP) techniques, and the lack thereof, is accentuated in the case of African languages, most of which are considered low-resource. To help circumvent this issue, we explore techniques exploiting the qualities of morphologically rich languages (MRLs), while leveraging pretrained word vectors in well-resourced languages. In our exploration, we show that a meta-embedding approach combining both pretrained and morphologically-informed word embeddings performs best in the downstream task of Xhosa-English translation.
On the Inclusion and Utilization of Pilot Tones in Unique Word OFDM

Unique word-orthogonal frequency division multiplexing (UW-OFDM) is known to provide various performance benefits over conventional OFDM using cyclic prefixes (CP). Most important, UW-OFDM features excellent spectral sidelobe suppression properties and an outstanding bit error ratio performance. Current research has mainly focused on principle performance bounds of UW-OFDM, with less attention on challenges aside from idealized communication scenarios, such as system parameter estimation tasks. In this work we present an approach for including frequency pilot tones into the UW-OFDM signaling scheme, which can then be utilized for these estimation tasks. Suitable optimization criteria are presented and interactions of pilots with data symbols are highlighted. Pilot tone based estimation of a carrier frequency offset (CFO) is conducted as an estimation example, revealing considerable differences to conventional OFDM. Simulation results in a multipath environment demonstrate a significantly increased estimation accuracy in UW-OFDM over CP-OFDM, which becomes even more dominant with an increasing CFO. This performance difference is due to the inherent redundancy present in an UW-OFDM signal.
Postural Control of Upright Stance with Robust Nonlinear Compensator in the Presence of Multiple Delayed Feedback

Regulation of postural balance and equilibrium is a complicated neurophysiological process that involves movement coordination for effective execution. The central nervous system (CNS) effectively regulates this task. The proprioceptors impart feedback of fascicle length and fascicle velocity from joint to CNS which then controls the movement. The muscle feedback by proprioceptors i.e. Muscle-Spindle (MS) and Golgi-Tendon-Organs (GTO) have neural transmission latencies that make control of CNS activity and computations an intricate issue to handle. This article discusses a robust nonlinear compensator design relying on vector filed method (feedback-linearization) in the presence of physiological delays for postural stability of a single link biomechanical model. We included small and medium delays in MS sensory feedback and GTO forced feedback. We designed and implemented H2dynamic controller with feedback linearization to measure the joint torque or output to maintain postural stability. The joint torque makes up for the latencies and settles the movement profiles inside anatomical imperatives and demonstrates the applicability of feedback linearization with H2compensator in the presence of physiological latencies.
A visualization monitoring technique in camera surveillance system

Surveillance camera system is gradually becoming familiar with the popular of cameras and camera installation services. Usually these systems only support separated camera screens without establishing a panoramic view to track and assess an interested area. Overcoming that, the paper presents an object locating technique for visual monitoring in surveillance camera system with a combination of detecting the object’s position on each specific camera and building the map to the physical space, then analyzing errors based on camera settings.
A comparison study on the effect of coaching as a nursing intervention on comfort levels and blood sugar levels in two groups of individuals with diabetes

DISSERTATION: A Comparison Study on the Effect of Coaching as a Nursing Intervention on Comfort Levels and Blood Sugar Levels in Two Groups of Individuals with Diabetes Student: Anita Cimino Kerrigan Degree: Doctorate of Education College: Ball State University Date: May 2011 Pages: 90 The purpose of this comparative study was to investigate the effect of coaching, as a nursing intervention, on comfort levels and blood sugar levels of individuals with diabetes. The hypothesis of the study was that individuals with diabetes who received coaching at specified intervals of time would have higher levels of comfort, as measured by the Diabetes Mellitus Comfort Questionnaire (DMCQ), and lower blood sugar levels, as recorded on the individual’s personal glucometer, than individuals with diabetes who did not receive coaching. Prior to conducting the study, approval of the university review board and hospitals was obtained. Participants were solicited from two accredited hospital-based diabetic education programs in a Midwestern city. Participation in the study was voluntary. The participants solicited from one hospital-based diabetic education program received coaching as a nursing intervention throughout the study while the participants from the other hospital-based diabetic education program did not. The participants were enrolled in the study the last day of the diabetic education program and completed a demographic data form, the DMCQ, and the Self Care InventoryRevised (SCI-R) to measure compliance. They also documented the average of their daily blood sugar levels from the past seven days as recorded on their personal glucometer. Two and four weeks after enrolling in the study participants from each group repeated the process of completing the DMCQ, the SCI-R, and documenting the average daily blood sugar levels over the past seven days as recorded on their glucometers. One hospital-based diabetic education group received coaching from the researcher via a telephone call two and four weeks after completing the formal diabetic education program and prior to completing the DMCQ, the SCI-R, and documenting their average daily blood sugar from the next seven days. Confidentiality of data collected from the participants was maintained. There was no risk of harm. Of the participants enrolled in the study, there were 30 participants who completed the study for one group and 35 participants who completed the study for the other group. Analysis of variance was used to analyze the data. Results showed no significant difference in comfort levels, compliance scores, or blood sugar levels between the two groups. The hypothesis of the study was not supported. Nonetheless, the information obtained from this study is valuable to nursing by contributing to the growing body of knowledge for developing cost-effective education and supportive strategies for individuals with diabetes to manage their condition.
X-ray CT reconstruction via $\ell_{0}$ gradient projection

Using a small number of sampling views during a CT (computed tomography) exam is a widely accepted technique for low-dose CT reconstruction, which reduces the risk of inducing cancer or other diseases in patients. In this scenario, total variation (TV) based compressed sensing (CS) methods, which uses a regularization term that penalizes the $\ell_{1}$ norm of the reconstructed image's gradient, outperform the traditional FBP (filtered back-projection) based algorithms in CT reconstruction. Furthermore, in order to reduce well-known artifacts (smoothed edges and texture details) favored by TV-based CS methods, several variants have been proposed, which, in a general context, can be understood as using a regularization term that approximates the $\ell_{0}$ norm of the reconstructed image's gradient. These type of methods yield state-of-the-art reconstruction results. In this paper we exploit a variant of the $\ell_{0}$ gradient minimization problem, which directly penalizes the number of non-zero gradients in the reconstructed image, and propose to solve the low-dose CT reconstruction problem. Extended experiments, based on the ASTRA toolbox, show that the propose method is faster (almost twice as fast) and delivers higher quality reconstructions than TV-based CS methods and alternatives that reduce smooth artifacts.
On the Co-Design of AV-Enabled Mobility Systems

The design of autonomous vehicles (AVs) and the design of AV-enabled mobility systems are closely coupled. Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management decisions. This calls for tools to study such a coupling and co-design AVs and AV-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making in the future.
Real-Time Software-Reconfigurable Hybrid In-House Access With OFDM-NOMA

In this paper, a real-time software-reconfigurable orthogonal frequency division multiplexing based non-orthogonal multiple access (OFDM-NOMA) scheme with dynamic power allocation is proposed and experimentally demonstrated for hybrid in-house access over single-mode fiber (SMF) and visible light communication (VLC). Such hybrid access can provide both wired and wireless transmission, which can accommodate the increasingly heterogeneous in-home network and realize seamless coverage. The fiber link is used to provide high-speed and stable transmission for a wired user whilst the VLC link can support flexible and secured wireless access. With the help of power-domain multiplexing based NOMA, the power ratio between both the fiber user and the VLC user can be adjusted in a software-reconfigurable manner, which can satisfy various user demands and also ensure user fairness. Experimental results validate that an aggregate data rate of 1.84 Gbit/s can be achieved in a 1-GSa/s hybrid access system. In addition, it is demonstrated that the power ratio between the fiber user and the VLC user can be adjusted in real-time, facilitating the optimal overall system bit error rate (BER) performance and hence the balanced user fairness. The proposed real-time software-reconfigurable dynamic power allocation scheme for OFDM-NOMA is a promising scheme for the future heterogeneous in-house network, as it allows the flexible power allocation according to users’ demands and enables better user fairness.
A Constant Insertion Loss Phase Shifter Used for Low Sidelobe Level Arrays

The parasitic amplitude modulation of phase shifters for a phased array will degrade the sidelobe level when the phased array is scanning. To solve this problem, a phase shifter with constant insertion loss is proposed in this paper. A 1 × 16 low sidelobe linear ideal array is designed to illustrate the influence of the parasitic amplitude modulation of the phase shifters. The proposed phase shifter is a type of reflection-type phase shifter, which consists of a 3-dB coupler and two reflective loads. In order to achieve a maximum phase shift range more than 360°, two tunable units are employed and connected by a quarter-wavelength transmission line. Each of the tunable unit is composed of a varactor, an inductor and an equalization resister. The proposed phase shifter has a maximum tunable range of more than 360°. A 1 × 16 dipole array with the proposed constant insertion loss is designed to verify the low sidelobe property of the array. The simulated results show that the sidelobe level of the designed array is lower than −33.2 dB, when the scanning angle is 45°.