Miracle-dz/UniFAR-FTUs huggingface dataset:
High-accuracy vehicle color recognition using hierarchical fine-tuning strategy for urban surveillance videos

Abstract. Vehicle color recognition is easily affected by subtle environmental changes. The existing recognition methods cannot achieve an accurate result. A high-accuracy vehicle color recognition method using a hierarchical fine-tuning strategy for urban surveillance videos is proposed. Different from the conventional convolutional neural networks-based methods, which usually obtain a single classification model, the proposed method combines pretraining and hierarchical fine-tunings to obtain different classification models that can adapt to the change of illumination conditions. First, the GoogLeNet is pretrained using the ILSVRC-2012 dataset to obtain the initial weight parameters of the network. During the first stage of fine-tuning, the whole vehicle color dataset is used to fine-tune the pretrained results to get the initial classification model. Then, an image quality assessment method is proposed to evaluate the illumination conditions of the image. The whole vehicle color dataset is divided into some subdatasets according to the evaluation results. The second stage of fine-tuning is performed on the initial classification model using each subdataset. Thus, the final classification models for the subdatasets are obtained. The experimental results on different databases demonstrate that the recognition accuracy of the proposed method can achieve superior performance over the state-of-the-art methods.
Sleep staging with deep learning: a convolutional model

Sleep staging is a crucial task in the context of sleep studies that involves the analysis of multiple signals, thus being a very tedious and complex task. Even for a trained expert, it can take several hours to annotate the signals recorded from a patient’s sleep during a single night. To solve this problem several automatic methods have been developed, although most of them rely on hand engineered features. To address the inner problems of this approach, in this work we explore the possibility of solving this problem with a deep learning network that can self-learn the relevant features from the signals. Particularly, we propose a convolutional network, obtaining higher performance than in previous methods, achieving an average precision of 0 . 91, recall of 0 . 90, and F-1 score of 0 . 90.
On Enabling Attribute-Based Encryption to Be Traceable Against Traitors

Attribute-based encryption (ABE) is a versatile one-to-many encryption primitive, which enables fine-grained access control over encrypted data. Due to its promising applications in practice, ABE schemes with high efficiency, security and expressivity have been continuously emerging. On the other hand, due to the nature of ABE, a malicious user may abuse its decryption privilege. Therefore, being able to identify such a malicious user is crucial towards the practicality of ABE. Although some specific ABE schemes in the literature enjoys the tracing function, they are only proceeded case by case. Most of the ABE schemes do not support traceability. It is thus meaningful and important to have a generic way of equipping any ABE scheme with traceability. In this work, we partially solve the aforementioned problem. Namely, we propose a way of transforming (non-traceable) ABE schemes satisfying certain requirements to fully collusion-resistant black-box traceable ABE schemes, which adds only $O(\sqrt{\mathcal{K}})$ elements to the ciphertext where ${\mathcal{K}}$ is the number of users in the system. And to demonstrate the practicability of our transformation, we show how to convert a couple of existing non-traceable ABE schemes to support traceability.
Can social news websites pay for content and curation? The SteemIt cryptocurrency model

SteemIt is a Reddit-like social news site that pays members for posting and curating content. It uses micropayments backed by a tradeable currency, exploiting the Bitcoin cryptocurrency generation model to finance content provision in conjunction with advertising. If successful, this paradigm might change the way in which volunteer-based sites operate. This article investigates 925,092 new members’ first posts for insights into what drives financial success in the site. Initial blog posts on average received US$0.01, although the maximum accrued was US$20,680.83. Longer, more sentiment-rich or more positive comments with personal information received the greatest financial reward in contrast to more informational or topical content. Thus, there is a clear financial value in starting with a friendly introduction rather than immediately attempting to provide useful content, despite the latter being the ultimate site goal. Follow-up posts also tended to be more successful when more personal, suggesting that interpersonal communication rather than quality content provision has driven the site so far. It remains to be seen whether the model of small typical rewards and the possibility that a post might generate substantially more are enough to incentivise long-term participation or a greater focus on informational posts in the long term.
Preliminary investigation on quantitative evaluation method of scientific papers based on text analysis

Recently, there are many resources of scientific research on the Web. Because the gap between the amount of academic information available on the Web and human processing abilities becomes large, several problems have arisen: (1) losing opportunities of research presentation, (2) loosing opportunities of gathering research information, (3) increasing burden of peer review, (4) difficulty in selecting papers to read. In order to solve these problems, quantitative evaluation index of a paper as a selection criterion is needed. This paper proposes quantitative evaluation methods of scientific papers on the basis of text analysis. The journal similarity of a target journal to an authoritative journal is defined with using distributed representations of papers. When the similarity of a target journal is high, its quality in terms of writing and organization is expected to be high. This paper also proposes an evaluation method using ROUGE (Recall-Oriented Understudy for Gisting Evaluation). Proposed evaluation methods are evaluated by experiments. Experiments results show that the journal similarity has rough correspondence to Scimago Journal Rank (SJR). The result also implies the possibility of evaluating journals that have not yet been indexed in some authoritative journal indices using the proposed methods. The evaluation method using ROUGE is shown to have the possibility of evaluating the consistency of papers.
Hierarchical fiducial marker design for pose estimation in large‐scale scenarios

In this paper, a hierarchical fiducial marker, called HArCo, is designed to guarantee a smooth pose estimation for large‐scale applications. HArCo markers have a visually identifiable structure on multiple scales, so they can be used for consistent pose estimation across a range of altitudes. Experimental results are presented to validate the performance of the proposed methodologies; oscillating platform landing experiments were conducted to show the ability of HArCo to be used in real landing tasks on the deck of a ship.
Load Sharing and Wayside Battery Storage for Improving AC Railway Network Performance, With Generic Model for Capacity Estimation, Part 1

Load sharing between feeder stations has the ability to significantly improve the performance of ac electric railways by reducing the number of traction power network grid connections and minimizing losses and plant size. In this paper, the existing rail power conditioner technology, traditionally used for load balancing, is investigated from the new perspective of providing reactive voltage support and active power sharing between stations. The concept is validated through a feasibility study conducted for a heavy haul ac railway network.
Covariances, Robustness, and Variational Bayes

Variational Bayes (VB) is an approximate Bayesian posterior inference technique that is increasingly popular due to its fast runtimes on large-scale datasets. However, even when VB provides accurate posterior means for certain parameters, it often mis-estimates variances and covariances. Furthermore, prior robustness measures have remained undeveloped for VB. By deriving a simple formula for the effect of infinitesimal model perturbations on VB posterior means, we provide both improved covariance estimates and local robustness measures for VB, thus greatly expanding the practical usefulness of VB posterior approximations. The estimates for VB posterior covariances rely on a result from the classical Bayesian robustness literature relating derivatives of posterior expectations to posterior covariances. Our key assumption is that the VB approximation provides good estimates of a select subset of posterior means -- an assumption that has been shown to hold in many practical settings. In our experiments, we demonstrate that our methods are simple, general, and fast, providing accurate posterior uncertainty estimates and robustness measures with runtimes that can be an order of magnitude smaller than MCMC.
A Wiener-Process-Model-Based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability

Remaining useful life (RUL) prediction has attracted more and more attention in recent years because of its significance in predictive maintenance. The degradation processes of systems from the same population are generally different from one another due to their various operational conditions and health states. This behavior is defined as unit-to-unit variability (UtUV), which brings difficulty to RUL prediction. To handle this problem, this paper develops a Wiener-process-model (WPM)-based method for RUL prediction with the consideration of the UtUV. In this method, an age- and state-dependent WPM is specially designed to describe the various degradation processes of different units. A unit maximum likelihood estimation (UMLE) algorithm is proposed to estimate the UtUV parameter according to the measurements of training units, without any restriction to the distribution pattern of the parameter. The UtUV parameter is further updated via particle filtering (PF) according to the measurements of the testing unit. In the particle updating process, a fuzzy resampling algorithm is developed to handle the sample impoverishment problem of PF. With the updated parameter, the RUL is predicted through a degradation process simulation algorithm. The effectiveness of the proposed method is verified through a simulation study and a turbofan engine degradation dataset.
Navigating double marginalisation: migrant Chinese sexual and gender minority young people’s views on mental health challenges and supports

Abstract Sexual and/or gender minority young people who are also members of an ethnic minority can experience unique challenges. Limited research draws directly on the mental health experiences of these ‘double minority’ youth. This study focused on Chinese sexual/gender minority youth in New Zealand. It sought to explore features they found challenging for, or supportive of, their mental health and wellbeing. Semi-structured interviews were conducted with 11 Chinese sexual/gender minority participants aged between 19 and 29 years old and residing in Auckland, New Zealand. An inductive approach to qualitative data analysis was used. Two major domains of findings emerged. Firstly, participants described mental health challenges linked to racism, sexism, cis-heteronormativity and challenges in relation to intersecting identities. Secondly, Chinese culture and community connections, family and peer support and role models seemed to facilitate resiliency. However, the fear of ‘losing face’, unwillingness to disclose distress when unwell and mental health service providers’ lack of cultural and linguistic competency were described as barriers to effective mental health support. In conclusion, Chinese and sexual/gender minority identities were integral parts of participants’ sense of self, and this was associated with their mental health and wellbeing. Further research is required to explore ways to reduce barriers and promote resiliency.
A special superior cortex compressive fracture of femoral neck

Rationale: Garden type I femoral neck fractures are incomplete stable fractures with impaction in valgus fractures that the question of whether there exists Garden type I femoral neck fracture is currently uncertain. There is still disagreement on the existence of the Garden type I fractures. Patient concerns: Herein we report 2 cases with the Garden type I femoral neck fractures. Both of them had a history of simple fall. Diagnoses: The X-ray showed incomplete fractures of the femoral neck and the Garden classification is type I. Further computed tomography and magnetic resonance imaging were used to confirm the diagnosis of this type fracture, which showed that the superior cortex of femoral neck was discontinuous and the inferior cortex remains intact. Interventions: The conservative treatment schedule including immobilization of the affected lower limb, strict bed rest, bed-to-wheelchair transfer training, and half-to-full weight bearing mobilization were chosen to treat the patients. Outcomes: Both of them achieved fracture union with conservative treatment at 3-month follow-up. No secondary displacement, signal of osteonecrosis of the femoral head or other complications occurred during 24-month follow-up in 2 cases. Lessons: Although the Garden type I femoral neck fractures are rare, our report is in line with the real existence of it.
Registration of three‐dimensional scanning LiDAR sensors: An evaluation of model‐based and model‐free methods

Registration, also know as extrinsic calibration, is the process of determining the position and orientation of a sensor relative to a known frame of reference. For ranging sensors such as light detection and ranging (LiDAR) used in field robotic applications, the quality of the registration determines the utility of the range measurements. This paper makes two contributions. The first is the introduction of a new method, termed maximum sum of evidence (MSoE) for registering three‐dimensional LiDAR sensors to moving platforms. This method is shown to produce more accurate registration solutions than two leading methods for these sensors, the adaptive structure registration filter (ASRF) and Rényi quadratic entropy (RQE). The second contribution of the paper is to study the accuracy of the MSoE registration against these two other approaches. One of these, like the MSoE, requires a truth model of the environment. The other, a model‐free method, seeks the registration that minimizes the RQE of a compound point cloud. The main finding of this investigation is that while the model‐based methods prove more accurate than the model‐free approach, the results of all three methods are fit for their intended field robotic applications. This leads us to conclude that registration based on RQE is preferable in many, if not all, field robotic applications for reasons of convenience, since a truth model of the environment is not required.
Variability in antimicrobial use in pediatric ventilator-associated events

Abstract Objective To assess variability in antimicrobial use and associations with infection testing in pediatric ventilator-associated events (VAEs). Design Descriptive retrospective cohort with nested case-control study. Setting Pediatric intensive care units (PICUs), cardiac intensive care units (CICUs), and neonatal intensive care units (NICUs) in 6 US hospitals. Patients Children≤18 years ventilated for≥1 calendar day. Methods We identified patients with pediatric ventilator-associated conditions (VACs), pediatric VACs with antimicrobial use for≥4 days (AVACs), and possible ventilator-associated pneumonia (PVAP, defined as pediatric AVAC with a positive respiratory diagnostic test) according to previously proposed criteria. Results Among 9,025 ventilated children, we identified 192 VAC cases, 43 in CICUs, 70 in PICUs, and 79 in NICUs. AVAC criteria were met in 79 VAC cases (41%) (58% CICU; 51% PICU; and 23% NICU), and varied by hospital (CICU, 20–67%; PICU, 0–70%; and NICU, 0–43%). Type and duration of AVAC antimicrobials varied by ICU type. AVAC cases in CICUs and PICUs received broad-spectrum antimicrobials more often than those in NICUs. Among AVAC cases, 39% had respiratory infection diagnostic testing performed; PVAP was identified in 15 VAC cases. Also, among AVAC cases, 73% had no associated positive respiratory or nonrespiratory diagnostic test. Conclusions Antimicrobial use is common in pediatric VAC, with variability in spectrum and duration of antimicrobials within hospitals and across ICU types, while PVAP is uncommon. Prolonged antimicrobial use despite low rates of PVAP or positive laboratory testing for infection suggests that AVAC may provide a lever for antimicrobial stewardship programs to improve utilization.
An HF Passive RFID Tag IC With Low Modulation Index ASK Demodulator

This paper presents a fully integrated high-frequency (HF) radio-frequency identification (RFID) tag chip based on the ISO/IEC 15693 and discusses a series of design techniques, including analog and digital control circuits. In order to get a more coupled power supply, an amplitude shift keying demodulator with very low modulation index is proposed. Moreover, modulator, limiter, and electrostatic discharge circuit are mentioned to make the tag IC work effectively and reliably. Besides, digital control circuit with 2-kbit EEPROM memory is designed to help the tag operate correctly under a very low magnetic field. The proposed RFID tag IC was fabricated using a 0.18-μm 2P6M CMOS technology with an area of 926 μm by 855 μm and a power consumption of less than 120 μA. Data rates of 6.62–52.97 kb/s are supported. The tag can operate under a minimum magnetic field of 0.12 A/m and a minimum modulation index of 2.33% which is far less than 10% required by the RFID standard. Compared with the prior references, the proposed tag IC has better performances especially in the aspect of low modulation index.
The nucleosome remodeling and deacetylase complex protein CHD4 regulates neural differentiation of mouse embryonic stem cells by down-regulating p53

Lineage specification of the three germ layers occurs during early embryogenesis and is critical for normal development. The nucleosome remodeling and deacetylase (NuRD) complex is a repressive chromatin modifier that plays a role in lineage commitment. However, the role of chromodomain helicase DNA-binding protein 4 (CHD4), one of the core subunits of the NuRD complex, in neural lineage commitment is poorly understood. Here, we report that the CHD4/NuRD complex plays a critical role in neural differentiation of mouse embryonic stem cells (ESCs). We found that RNAi-mediated Chd4 knockdown suppresses neural differentiation, as did knockdown of methyl-CpG–binding domain protein Mbd3, another NuRD subunit. Chd4 and Mbd3 knockdowns similarly affected changes in global gene expression during neural differentiation and up-regulated several mesendodermal genes. However, inhibition of mesendodermal genes by knocking out the master regulators of mesendodermal lineages, Brachyury and Eomes, through a CRISPR/Cas9 approach could not restore the impaired neural differentiation caused by the Chd4 knockdown, suggesting that CHD4 controls neural differentiation by not repressing other lineage differentiation processes. Notably, Chd4 knockdown increased the acetylation levels of p53, resulting in increased protein levels of p53. Double knockdown of Chd4 and p53 restored the neural differentiation rate. Furthermore, overexpression of BCL2, a downstream factor of p53, partially rescued the impaired neural differentiation caused by the Chd4 knockdown. Our findings reveal that the CHD4/NuRD complex regulates neural differentiation of ESCs by down-regulating p53.
Implication of obesity on motion, posture and internal stress of the foot: an experimental and finite element analysis

Abstract Obesity causes increased loading on the foot which can damage the soft tissue and bone ultimately leading to foot problems. Experimental and computational methods were used to analyse the chain of biomechanical changes in the lower limb due to obesity. The experimental study shows some changes in foot posture and gait where obese subjects were more likely to have pronated feet, smaller joint angles in the sagittal and frontal planes, smaller cadence, and smaller stride length. Anatomically correct finite element models generated on obese subjects showed increased and altered internal and plantar stress. Altered foot posture was identified as a key indicator of increased internal stress indicating the importance of foot posture correction.
Interactive spatial analytics for human-aware building design

We present a computational spatial analytics tool for designing environments that better support human-related factors. Our system performs both static and dynamic analyses: the first relates to the building geometry and organization, while the second additionally considers the crowd movement in the space. The results are presented to the designers in the form of numerical values, traces and heat maps displayed on top of the floor plan. We demonstrate our approach with a user study whereby novice architects have tested the proposed approach to iteratively improve a building accessibility in real-time with respect to a selected number of static and dynamic metrics. The results indicate that the users were able to successfully improve their design solutions and thus generate more human-aware environments. The usability and effectiveness of the tool where also measured, yielding positive scores. The modular and flexible nature of the tool enables further extension to incorporate additional static and dynamic spatial metrics.
Extracellular Vesicles from Wharton's Jelly Mesenchymal Stem Cells Suppress CD4 Expressing T Cells Through Transforming Growth Factor Beta and Adenosine Signaling in a Canine Model.

Mesenchymal stem cells (MSCs) are widely investigated as potential therapeutic agents due to their potent immunomodulatory capacity. Although specific mechanisms by which MSC acts on immune cells are emerging, many questions remain, including the potential of extracellular vesicles (EVs) to mediate biological activities. Canine MSCs are of interest for both veterinary and comparative models of disease and have been shown to suppress CD4pos T cell proliferation. The aim of this study was to determine whether EV isolated from canine Wharton's jelly-derived MSC (WJ-MSC EV) suppresses CD4pos T cell proliferation using biochemical mechanisms previously ascribed to soluble mediators [transforming growth factor beta (TGF-β) and adenosine]. WJ-MSC EV exhibited mode of 125 nm diameter, low buoyant density (1.1 g/mL), and expression of EV proteins Alix and TSG101. Functionally, EVs inhibited CD4pos T cell proliferation in a dose-dependent manner, which was absent in EV-depleted samples and EVs from non-MSC fibroblasts. EV suppression of CD4pos T cell proliferation was inhibited by a TGF-βRI antagonist, neutralizing antibodies to TGF-β, or A2A adenosine receptor blockade. TGF-β was present on EVs as latent complexes most likely tethered to EV membrane by betaglycan. These data demonstrate that canine WJ-MSC EV utilizes TGF-β and adenosine signaling to suppress proliferation of CD4pos T cell and will enable further investigation into mechanisms of immune cell modulation, as well as refinement of WJ-MSC and their EVs for therapeutic application.