Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct Visual SLAM Using Sparse Depth for Camera-LiDAR System

This paper describes a framework for direct visual simultaneous localization and mapping (SLAM) combining a monocular camera with sparse depth information from Light Detection and Ranging (LiDAR). To ensure realtime performance while maintaining high accuracy in motion estimation, we present (i) a sliding window-based tracking method, (ii) strict pose marginalization for accurate pose-graph SLAM and (iii) depth-integrated frame matching for large-scale mapping. Unlike conventional feature-based visual and LiDAR mapping, the proposed approach is direct, eliminating the visual feature in the objective function. We evaluated results using our portable camera-LiDAR system as well as KITTI odometry benchmark datasets. The experimental results prove that the characteristics of two complementary sensors are very effective in improving real-time performance and accuracy. Via validation, we achieved low drift error of 0.98 % in the KITTI benchmark including various environments such as a highway and residential areas.
Low-Dose Radiotherapy Ameliorates Advanced Arthritis in hTNF-α tg Mice by Particularly Positively Impacting on Bone Metabolism

Inflammation and bone erosion are central in rheumatoid arthritis (RA). Even though effective medications for control and treatment of RA are available, remission is only seen in a subset of patients. Treatment with low-dose radiotherapy (LD-RT) which has been already successfully used for amelioration of symptoms in benign diseases should be a promising approach to reduce pain, inflammation, and particularly bone erosion in patients with RA. Even though anti-inflammatory effects of LD-RT are already described with non-linear dose response relationships, and pain-reducing effects have been clinically observed, the underlying mechanisms are widely unknown. Besides immune cells many other cell types, such as fibroblast-like synoviocytes (FLS), osteoclasts, and osteoblast are present in the affected joint and might be modulated by LD-RT. For this study, these cell types were obtained from human tumor necrosis factor-α transgenic (hTNF-α tg) mice and were consecutively exposed to different doses of ionizing radiation (0.1, 0.5, 1.0, and 2.0 Gy, respectively) in vitro. In order to study the in vivo effects of LD-RT within the arthritic joint, hind paws of arthritic hTNF-α tg mice were locally irradiated with 0.5 Gy, a single dose per fraction that is known for good clinical responses. Starting at a dose of 0.5 Gy, proliferation of FLS was reduced and apoptosis significantly enhanced with no changes in necrosis. Further, expression of RANK-L was slightly reduced following irradiation with particularly 0.5 Gy. Starting from 0.5 Gy, the numbers of differentiated osteoclasts were significantly reduced, and a lower bone resorbing activity of treated osteoclasts was also observed, as monitored via pit formation and Cross Laps presence. LD-RT had further a positive effect on osteoblast-induced mineralization in a discontinuous dose response relationship with 0.5 Gy being most efficient. An increase of the gene expression ratio of OPG/RANK-L at 0.1 and 0.5 Gy and of production of OPG at 0.5 and 1.0 Gy was observed. In vivo, LD-RT resulted in less severe arthritis in arthritic hTNF-α tg mice and in significant reduction of inflammatory and erosive area with reduced osteoclasts and neutrophils. Locally applied LD-RT can, therefore, induce a beneficial micro-environment within arthritic joints by predominantly positively impacting on bone metabolism.
Multi Scale Feedback Connection for Noise Robust Acoustic Modeling

Simply feeding of a last hidden layer of the deep neural network (DNN) back to the input layer recently found to be effective for noise robust acoustic modeling. Such high level feature strengthens the robustness of DNN based acoustic model while paying approximately twice the computational cost. In this paper, we proposed to feed such high level feature iteratively back to lower layers, which is referred as multi-scale feedback connection. With this intention, we firstly extract the high level feature at the last hidden layer of DNN. Second, this high level feature feed back to a lower scale features, they then generates a subsequent prediction as well as a subsequent high level feature. This subsequent high level feature is further feed down to a lower layers. We evaluated the proposed approach on both TIMIT and a large scale internal dataset. The large scale internal dataset includes voice search and far field dataset. Our finding is two aspects. First, at equivalent computational costs, the multiscale feedback connection outperforms the DNN, the DNN with skip connection and the DNN with feedback connection. The improvement is larger on the far field dataset. Second, pair layers-wise pretraining helps the proposed approach to converge better.
Development of the Youth Emotion Picture Set

Several facial expression image sets have been developed. Nevertheless, there is a lack of facial expression sets comprising adolescents’ images depicting all basic emotions. This study aimed to fill this gap through the development of an image database of youth facial expressions, containing pictures of six basic emotions plus neutral. Posed and spontaneous expressions were collected from 31 youths, 12 to 20 years old; 2,279 frames were obtained, and an initial screening was conducted through the exclusion of similar frames, low intensity images, and ambiguous or blended expressions; 256 frames met criteria and were rechecked by two expert judges. Images were retained if they depicted all the prototypical features of the designated expression. A final selection was conducted to assure an image set that covered all age ranges, both sexes and an even number of images by expression, resulting in 42 frames (21 male, six of each emotion). Expert judges, independent adults, independent teenagers, and a software validation were used to assure database validity. Agreement across raters was high, and no differences were observed for posed and spontaneous images. The data set developed in this study can be a valid tool in studies of facial expressions, in particular, with adolescents’ samples.
Hi, Bcd! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution

Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation for HSR, which is a non-convex large-scale optimization problem. Our contributions contain both computational and theoretical aspects. On the computational side, we develop three inexact block coordinate descent (BCD) schemes that are empirically found to run many times faster than a state-of-the-art method, which uses exact BCD. We achieve this by applying concepts in the proximal gradient (PG) and Frank-Wolfe (FW) methods and by exploiting the HSR problem structures. On the theoretical side, we show that these inexact BCD schemes guarantee convergence to a stationary point. In particular, the convergence result for a hybrid PG- FW inexact BCD scheme is new.
Improving Performance of Job Scheduling with Maintaining Efficiency and Fairness in Cloud Environment

A fair scheduling strategy is, when there are enough resources for the execution of the highest priority job, the one that does not delay the job at the front more than the average waiting time (AWT) of the already finished jobs. Some have focused on reducing cost of utilization. These studies have mainly focused on optimizing one criterion. In the present paper, we propose a new model multi-objective multi-criterion(MOMC) that not only minimizes the makespan,cost but also inculcates the fairness by reducing average waiting time and starvation. In this paper we proposed a new scheduling algorithm which allocates jobs based on hierarchy(Long-Short)Jobs allocation process on (Fast-Slow) Virtual Machines known as Two-Party Model(TPM). In this paper we also compare result with other algorithms like FCFS,NHCP.
Joint Audio-Video Driven Facial Animation

Automatic facial animation is a research topic of broad and current interest with widespread impact on various applications. In this paper, we present a novel joint audio-video driven facial animation system. Unlike traditional methods, we incorporate a large vocabulary continuous speech recognition (LVCSR) system to obtain phoneme alignments. The use of LVCSR reduces the high error rate associated with the traditional phoneme recognizer. We also introduce a knowledge guided 3D blendshapes modeling for each phoneme to avoid collecting training data and introducing bias from computer vision generated targets. To further improve the quality, we adopt video tracking and jointly optimize the facial animation by combining both sources. In the evaluations, we present both objective study and several subjective studies on three settings: audio-driven, video-driven, and joint audio-video driven. We find that the quality of our proposed system's facial animation generation surpasses that of the recent state-of-the-art systems.
The Impact of Medical Services and Medicine Separation Policy on Competitive Pharmaceutical Supply Chain Performance

This paper takes the performance of pharmaceutical supply chain as the research object, and combines with the medical reform policy to investigates the existing problems of the reform policy. This paper constructs a function model for pharmaceutical companies, hospitals and patients from the perspective of supply chain, and analyzes the decision selection and equilibrium profit of them under different policies. We found that under certain conditions, increasing service prices can increase both patient utility and hospital profit. In the process of medical reform, proper service price can improve the performance of the pharmaceutical supply chain.
Control of Higher Order Modes and Their Radiation in Microstrip Antenna Using Extremely Compact Defected Ground Structure & Symmetric Stub

Suppression of the radiation from higher order modes requires the elimination of those modes. In this paper, the elimination has been implemented with a novel miniaturized low pass filter (LPF) with high filter selectivity and wide stop band. Here, the fundamental operating frequency of the antenna falls in the pass band of the LPF. The attenuation of other higher order mode occurs since their frequencies fall in the LPF stop band. In this paper suppression has been carried out by using this LPF on antenna feed line. A successful demonstration has been presented to control the occurrence of higher order modes up to the second harmonic of the fundamental resonant frequency in a microstrip antenna. A miniaturized hair pin type defected ground structure (DGS) has been integrated to obtain elimination of higher order modes in the antenna at its microstrip feed level. A rectangular spiral type stub has been added to the feed line in order to improve the rejection characteristics.
Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities

Place recognition is a key element of mobile robotics. It can assist with the “wake-up” and “kidnapped robot” problems, where the robot position needs to be estimated without prior information. Among the different sensors that can be used for the task (e.g., camera, GPS, LiDAR), LiDAR has the advantage of operating in the dark and in GPS-denied areas. We propose a new method that uses solely the LiDAR data and that can be performed without robot motion. In contrast to other methods, our system leverages intensity information (as opposed to only range information) which is encoded into a novel descriptor of LiDAR intensities as a group of histograms, named DELIGHT. The descriptor encodes the distributed histograms of intensity of the surroundings which are compared using chi-squared tests. Our pipeline is a two-stage solution consisting of an intensity-based prior estimation and a geometry-based verification. For a map of 220k square meters, the method achieves localisation in around 3s with a success rate of 97 %, illustrating the applicability of the method in real environments.
Characterization of Missing Data in Clinical Registry Studies

Patterns of missing data are seldom well-characterized in observational research. This study examined the magnitude of, and factors associated with, missing data across multiple observational studies. Missingness was evaluated for demographic, clinical, and patient-reported outcome (PRO) data from a procedure registry (TOPS), a rare disease (cystic fibrosis) registry (Port-CF), and a comparative effectiveness registry (glaucoma, RiGOR). Generalized linear mixed effects models were fit to assess whether patient characteristics or follow-up methods predicted missingness. Data from 156,707 surgical procedures, 32,118 cystic fibrosis patients, and 2373 glaucoma patients were analyzed. Data were rarely missing for demographics, treatments, and outcomes. Missingness for clinical variables varied by registry and measure and depended on whether a variable was required. Within RiGOR, PRO forms were missing more often when collected by e-mail compared with office-based paper data collection. In Port-CF, missingness varied based on insurance status and sex. Strategic consideration of operational approaches affecting missing data should be performed prior to data collection and assessed periodically during study conduct.
A resonance-assisted intramolecular hydrogen bond in compounds containing 2-hydroxy-3,5-dinitrobenzoic acid and its various deprotonated forms: redetermination of several related structures

A large number of structural determinations of compounds containing 2-hydroxy-3,5-dinitrobenzoic acid and its various deprotonated forms, 2-hydroxy-3,5-dinitrobenzoate or 2-carboxy-4,6-dinitrophenolate, are biased. The reason for the bias follows from incorrectly applied constraints or restraints on the bridging hydrogen, which is involved in the intramolecular hydrogen bond between the neighbouring carboxylic/carboxylate and oxo/hydroxy groups. The present article examines the problem of the location and refinement of such a bridging hydrogen in a number of reported compounds. The analysis of the intramolecular hydrogen bonding is also discussed.
Point Attention Network for Gesture Recognition Using Point Cloud Data

In this work, we propose a neural network with novelty attention modules to perform human gesture recognition from point cloud data. Our network directly takes point clouds as input, and the attention modules learn to pay attention to only points that contribute to more accurate gesture recognition. The input point cloud consists of both spatial and temporal dimensions since the point cloud frames are concatenated as one input sample. This enables the network to learn the spatio-temporal features of the data without explicit modeling of gesture dynamics. The gather and scatter operations are proposed for points downsampling and upsampling in the feature space. We evaluate the performance of our network using a dataset of common Japanese gestures. The proposed network achieves state-of-the-art performance on this dataset. The analysis of architecture design and parameter choices are also discussed.
Fragmented ambulatory care and subsequent healthcare utilization among Medicare beneficiaries.

OBJECTIVES We sought to determine the associations between fragmented ambulatory care and subsequent emergency department (ED) visits and hospital admissions, while considering possible interactions between fragmentation and number of chronic conditions. STUDY DESIGN We conducted a cohort study over 3 years among 117,977 fee-for-service Medicare beneficiaries who were attributed to primary care physicians in a 7-county region of New York and had 4 or more ambulatory visits in the baseline year. METHODS We calculated fragmentation scores using a modified Bice-Boxerman Index and, because scores were skewed, divided them into quintiles. We used Cox regression models to determine associations between fragmentation and ED visits and, separately, hospital admissions, stratifying by number of chronic conditions and adjusting for age, gender, number of ambulatory visits, and case mix. RESULTS Among those with 1 to 2 or 3 to 4 chronic conditions, having the most (vs the least) fragmented care significantly increased the hazard of an ED visit and, separately, increased the hazard of an admission (adjusted P <.05 for each comparison). Among those with 5 or more chronic conditions, having the most fragmented care significantly increased the hazard of an ED visit but decreased the hazard of an admission (adjusted P <.05 for each comparison). Among those with 0 chronic conditions, having fragmented care was not associated with either outcome. CONCLUSIONS The relationship between fragmented ambulatory care and subsequent utilization varies with the number of chronic conditions. Beneficiaries with a moderate burden of chronic conditions (1-2 or 3-4) appear to be at highest risk of excess ED visits and admissions due to fragmented care.
The Effects of the Addiction Programme of Probation on Treatment Motivation, Abstinence and Quality of Life: a Comparative Study with Motivational Interviewing and Individual Intervention.

Introduction The aim of this study is to evaluate the effectiveness of Addiction Programme of Probation (APP) created by the Substance Abuse Treatment Commission of Ministry of Health. Methods The sample was consisted of probationers who were referred to Ege University by probation offices in February 2010. Literate probationers aged above 18, who fulfilled the SCID-I criteria for substance dependence, or had positive urine sample for illegal drugs were included. The probationers were allocated respectively to three treatment groups: APP (n=28); Motivational Interviewing (MI; n=30); Individual Intervention (II; n=30). The effectiveness of the treatment modalities was assessed by the State Anxiety Inventory; the WHO Quality of Life-Brief form; the Treatment Motivation Questionnaire. Visual analogue and Likert-type scales were used to assess the sense of the importance and the readiness of quitting drugs also the levels of craving. Urine toxicological analyses were performed fortnightly. Results The APP increased the readiness of quitting drugs. The MI increased both the sense of importance and readiness of quitting drugs, decreased the frequency and severity of craving. The II improved the mental and physical health, increased the confidence in treatment also readiness of quitting drugs. Additionally, II decreased the level of anxiety, the severity, frequency and duration of craving. There was a significant decrease in positive urine samples of all groups. Conclusion Because of changing only "the level of being ready to quit substance abuse", APP is considered as an inadequate program compared with MI and II.
Extending a Dynamic Friction Model with Nonlinear Viscous and Thermal Dependency for a Motor and Harmonic Drive Gear

In robotic actuation a well identified and modeled friction behavior of the actuator components helps to significantly improve friction compensation, output torque estimation, and dynamic simulations. The friction of two components, i.e. a brush-less DC motor and a harmonic drive gear (HD) is investigated in order to build an accurate dynamic model of the main actuator of the arms of the humanoid David namely the DLR Floating Spring Joint (FSJ). A dedicated testbed is built to precisely identify input and output torques, temperatures, positions, and elasticities of the investigated components at a controlled environment temperature. Extensive test series are performed in the full velocity operating range in a temperature interval from 24 to 50 °C. The nonlinear influences of velocity and temperature are identified to be dominant effects. It is proposed how to include these nonlinear velocity and temperature dependencies into a static and a dynamic friction model, e.g. LuGre. Dynamic models of the motor and HD are built with the proposed method and experimentally evaluated. The new models are compared to friction models with linear dependencies and show a significant improvement of correspondence with reality.
Balancing Testability and Security by Configurable Partial Scan Design

Scan chain design facilitates chip testing by providing an interface for the test engineers to access and control the internal states of the circuit. This feature has also been exploited to break systems such as the cryptographic chips by the attack known as scan chain side channel analysis. From the perspective of information access, test engineers and scan chain attackers have the same goal – observe and control the scan chain side channel information. Consequently, all the existing countermeasures have to make the tradeoff between scan chain security and the testability it can provide. In this paper, we propose a novel public-private partial scan chain design which can deliver both full testability and security. The key idea is to partition the flip flops into a public partial chain and a set of parallel private partial chains. The private partial chains are protected by means of a hardware implemented finite state machine and an obfuscation mechanism based on configurable physical unclonable function. We demonstrate how full testability can be achieved by the proposed public-private partial chains. We conduct security and performance analysis to show that our approach is robust against all the known scan chain based attacks and can improve testing time and power consumption with negligible hardware overhead.
Implementation of Auditory Filter Bank for Denoising Speech Signal using Simulink and MATLAB

Audio and speech processing technologies are emerging with increasing demand for speech quality. Audio is corrupted by different types of noise during acquisition of audio. Speech enhancement methodology should be able to remove noise content present in the original signal without modifying it. Recently, various speech processing technologies were incorporating features of human auditory system model to achieve high performance. Therefore consideration of the human auditory response and response of the basilar membrane will emulate the feature of signal processing performed at human auditory periphery into the speech processing technology. The proposed work deals with the implementation of the auditory filter bank in MATLAB and Simulink which is a bank of the filter where each filter uses the human ear response as the basis function. The proposed work also deals with the quality measurement of the enhanced signal over noisy signal. The significant application would be in the area of area of speech and music synthesis, speech feature extraction, audio coding, hearing aid, etc.