Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial clustering in vaccination hesitancy: The role of social influence and social selection

The phenomenon of vaccine hesitancy behavior has gained ground over the last three decades, jeopardizing the maintenance of herd immunity. This behavior tends to cluster spatially, creating pockets of unprotected sub-populations that can be hotspots for outbreak emergence. What remains less understood are the social mechanisms that can give rise to spatial clustering in vaccination behavior, particularly at the landscape scale. We focus on the presence of spatial clustering, and aim to mechanistically understand how different social processes can give rise to this phenomenon. In particular, we propose two hypotheses to explain the presence of spatial clustering: (i) social selection, in which vaccine-hesitant individuals share socio-demographic traits, and clustering of these traits generates spatial clustering in vaccine hesitancy; and (ii) social influence, in which hesitant behavior is contagious and spreads through neighboring societies, leading to hesitant clusters. Adopting a theoretical spatial network approach, we explore the role of these two processes in generating patterns of spatial clustering in vaccination behaviors under a range of spatial structures. We find that both processes are independently capable of generating spatial clustering, and the more spatially structured the social dynamics in a society are, the higher spatial clustering in vaccine-hesitant behavior it realizes. Together, we demonstrate that these processes result in unique spatial configurations of hesitant clusters, and we validate our models on both processes with fine-grain empirical data on vaccine hesitancy, social determinants, and social connectivity in the US. Finally, we propose, and evaluate the effectiveness of, two novel intervention strategies to diminish hesitant behavior. Our generative modeling approach informed by unique empirical data provides insights on the role of complex social processes in driving spatial heterogeneity in vaccine hesitancy.
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees

In this work we present a preliminary investigation of a novel algorithm called Dyna-T. In reinforcement learning (RL) a planning agent has its own representation of the environment as a model. To discover an optimal policy to interact with the environment, the agent collects experience in a trial and error fashion. Experience can be used for learning a better model or improve directly the value function and policy. Typically separated, Dyna-Q is an hybrid approach which, at each iteration, exploits the real experience to update the model as well as the value function, while planning its action using simulated data from its model. However, the planning process is computationally expensive and strongly depends on the dimensionality of the state-action space. We propose to build a Upper Confidence Tree (UCT) on the simulated experience and search for the best action to be selected during the on-line learning process. We prove the effectiveness of our proposed method on a set of preliminary tests on three testbed environments from Open AI. In contrast to Dyna-Q, Dyna-T outperforms state-of-the-art RL agents in the stochastic environments by choosing a more robust action selection strategy.
Design of in-situ detector applied to aircraft radio altimeter

To realize the in-situ detection of the aircraft radio altimeter in out-field,the in-situ detector for the altimeter was developed by taking the industrial personal computer(IPC)PC104 and embedded processor STM32 as the system core,in which the design thoughts of the functional block diagram and each module,software functional partitioning and architecture are provided. The detector can implement the altimeter signal isolation and the voltage conversion,generate the altitude simulation signal and analog signal of the indicator,and provide the check signal of the altimeter to indicate the normal signal and alarm trip signal,and to measure the linear and nonlinear altitude voltage. The design has realized the simple and friendly human-computer interaction,and has been used and verified in the aviation test.
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution

In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device. However, this pipeline is redundant and inefficient for the independent processes, and some inner features could have been shared. Therefore, we present an efficient paradigm to perform Simultaneously Image Colorization and Super-resolution (SCS) and propose an end-to-end SCSNet to achieve this goal. The proposed method consists of two parts: colorization branch for learning color information that employs the proposed plug-and-play Pyramid Valve Cross Attention (PVCAttn) module to aggregate feature maps between source and reference images; and super-resolution branch for integrating color and texture information to predict target images, which uses the designed Continuous Pixel Mapping (CPM) module to predict high-resolution images at continuous magnification. Furthermore, our SCSNet supports both automatic and referential modes that is more flexible for practical application. Abundant experiments demonstrate the superiority of our method for generating authentic images over state-of-the-art methods, e.g., averagely decreasing FID by 1.8 and 5.1 compared with current best scores for automatic and referential modes, respectively, while owning fewer parameters (more than x2) and faster running speed (more than x3).
Accretion mode versus radio morphology in the LOFAR Deep Fields

Radio-loud active galaxies have two accretion modes [radiati vely inef ﬁcient (RI) and radiatively efﬁcient (RE)], with distinct optical and infrared signatures, and two jet dynamical behaviours, which in arcsec- to arcmin-resolution radio surv e ys manifest primarily as centre- or edge-brightened structures [F anaroff–Rile y (FR) class I and II]. The nature of the relationship between accretion mode and radio morphology (FR class) has been the subject of long debate. We present a comprehensive investigation of this relationship for a sample of 286 well-resolved radio galaxies in the LOFAR Two-metre Sk y Surv e y Deep Fields (LoTSS-Deep) ﬁrst data release, for which robust morphological and accretion mode classiﬁcations have been made. We ﬁnd that two-thirds of luminous FRII radio galaxies are RI, and identify no signiﬁcant differences in the visual appearance or source dynamic range (peak/mean surface brightness) of the RI and RE FRIIs, demonstrating that both RI and RE systems can produce FRII structures. We also ﬁnd a signiﬁcant population of low-luminosity FRIIs (predominantly RI), supporting our earlier conclusion that FRII radio structures can be produced at all radio luminosities. We demonstrate that in the luminosity range where both morphologies are present, the probability of producing FRI or FRII radio morphology is directly linked to stellar mass, while across all morphologies and luminosities, RE accretion occurs in systems with high speciﬁc star formation rate, presumably because this traces fuel availability . In summary , the relationship between accretion mode and radio morphology is very indirect, with host-galaxy environment controlling these two key parameters in different ways.
A characterization of approximability for biased CSPs

A µ-biased Max-CSP instance with predicate ψ:{0,1}r → {0,1} is an instance of Constraint Satisfaction Problem (CSP) where the objective is to find a labeling of relative weight at most µ which satisfies the maximum fraction of constraints. Biased CSPs are versatile and express several well studied problems such as Densest-k-Sub(Hyper)graph and SmallSetExpansion. In this work, we explore the role played by the bias parameter µ on the approximability of biased CSPs. We show that the approximability of such CSPs can be characterized (up to loss of factors in arity r) using the bias-approximation curve of Densest-k-SubHypergraph (DkSH). In particular, this gives a tight characterization of predicates which admit approximation guarantees that are independent of the bias parameter µ. Motivated by the above, we give new approximation and hardness results for . In particular, assuming the Small Set Expansion Hypothesis (SSEH), we show that with arity r and k = µ n is NP-hard to approximate to a factor of Ω(r3µr−1log(1/µ)) for every r ≥ 2 and µ < 2−r. We also give a O(µr−1log(1/µ))-approximation algorithm for the same setting. Our upper and lower bounds are tight up to constant factors, when the arity r is a constant, and in particular, imply the first tight approximation bounds for the Densest-k-Subgraph problem in the linear bias regime. Furthermore, using the above characterization, our results also imply matching algorithms and hardness for every biased CSP of constant arity.
Evaluation and Optimization of Walkability of Children’s School Travel Road for Accessibility and Safety Improvement

(1) Background: In the context of a children friendly city, accessibility and safety are the basic needs of children’s pedestrian school travel. This study proposes a comprehensive evaluation method of pedestrian accessibility and safety for children’s school travel. (2) Methods: Firstly, the school travel network was constructed by simulating the path of children walking to school. Secondly, from the meso and micro dimensions, the impact factors of pedestrian accessibility and safety were combed out, and an evaluation index system was constructed. Finally, pedestrian accessibility and safety were evaluated based on the Space Syntax analysis and ArcGIS spatial analysis, and the results were superimposed and spatially differentiated. The new evaluation method was tested in the Jintang Road area in Hedong District, Tianjin, China. (3) Results: The pedestrian accessibility and safety of children’s school travel road in the study area needed to be improved. It was found that the main impact factors were the effective walking width, the spatial connectivity, the visual integration, the obstruction of pedestrian safety, the completeness of crossing facilities and the influence of traffic flow and put forward optimization strategies. After optimized simulation verification, the overall improvement was achieved. (4) Conclusion: The evaluation method is helpful to calculate the pedestrian accessibility and safety of children’s school travel, and help decision makers determine the design and management strategies of child-friendly streets.
Comparative O-GlcNAc Proteomic Analysis Reveals a Role of O-GlcNAcylated SAM68 in Lung Cancer Aggressiveness

Simple Summary Lung cancer claims the most lives annually among cancers; to date, invasion and metastasis still pose challenges to effective treatment. O-GlcNAcylation, an enzymatic modification of proteins after biosynthesis, modulates the functions of many proteins. Aberrant O-GlcNAcylation is linked to pathogenic mechanisms of cancer, including invasion and metastasis. However, little is known about the profile of O-GlcNAcylated proteins involved in cancer aggressiveness. Here, by comparing profiles of O-GlcNAcylated proteins from two lung cancer cell lines different in their invasive potential, we identified candidates for O-GlcNAcylated proteins that may be involved in cancer aggressiveness. One of these candidates, SAM68, was further characterized. Results confirmed O-GlcNAcylation of SAM68; functional analyses on SAM68 with mutations at O-GlcNAcylation sites suggested a role of O-GlcNAcylated SAM68 in modulating lung cancer cell migration/invasion. Future elucidation of the functional significance of differential O-GlcNAcylation of proteins identified in this study may provide new insights into mechanisms of lung cancer progression. Abstract O-GlcNAcylation is a reversible and dynamic post-translational protein modification catalyzed by O-GlcNAc transferase (OGT). Despite the reported association of O-GlcNAcylation with cancer metastasis, the O-GlcNAc proteome profile for cancer aggressiveness remains largely uncharacterized. Here, we report our comparative O-GlcNAc proteome profiling of two differentially invasive lung adenocarcinoma cell lines, which identified 158 down-regulated and 106 up-regulated candidates in highly invasive cells. Among these differential proteins, a nuclear RNA-binding protein, SAM68 (SRC associated in mitosis of 68 kDa), was further investigated. Results showed that SAM68 is O-GlcNAcylated and may interact with OGT in the nucleus. Eleven O-GlcNAcylation sites were identified, and data from mutant analysis suggested that multiple serine residues in the N-terminal region are important for O-GlcNAcylation and the function of SAM68 in modulating cancer cell migration and invasion. Analysis of clinical specimens found that high SAM68 expression was associated with late cancer stages, and patients with high-OGT/high-SAM68 expression in their tumors had poorer overall survival compared to those with low-OGT/low-SAM68 expression. Our study revealed an invasiveness-associated O-GlcNAc proteome profile and connected O-GlcNAcylated SAM68 to lung cancer aggressiveness.
Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

This study aims to build a system for detecting a driver’s internal state using body-worn sensors. Our system is intended to detect inattentive driving that occurs during long-term driving on a monotonous road, such as a high-way road. The inattentive state of a driver in this study is an absent-minded state caused by a decrease in driver vigilance levels due to fatigue or drowsiness. However, it is difficult to clearly define these inattentive states because it is difficult for the driver to recognize when they fall into an absent-minded state. To address this problem and achieve our goal, we have proposed a detection algorithm for inattentive driving that not only uses a heart rate sensor, but also uses body-worn inertial sensors, which have the potential to detect driver behavior more accurately and at a much lower cost. The proposed method combines three detection models: body movement, drowsiness, and inattention detection, based on an anomaly detection algorithm. Furthermore, we have verified the accuracy of the algorithm with the experimental data for five participants that were measured in long-term and monotonous driving scenarios by using a driving simulator. The results indicate that our approach can detect both the inattentive and drowsiness states of drivers using signals from both the heart rate sensor and accelerometers placed on wrists.
Acceptability of extending HPV-based cervical screening intervals from 3 to 5 years: an interview study with women in England

Objectives The introduction of primary Human Papillomavirus (HPV) testing in the National Health Service (NHS) Cervical Screening Programme in England means the screening interval for 25–49 years can be extended from 3 to 5 years. We explored women’s responses to the proposed interval extension. Methods We conducted semi-structured phone/video interviews with 22 women aged 25–49 years. Participants were selected to vary in age, socioeconomics and screening history. We explored attitudes to the current 3-year interval, then acceptability of a 5-year interval. Interviews were transcribed verbatim and analysed using framework analysis. Results Attitudes to the current 3-year interval varied; some wanted more frequent screening, believing cancer develops quickly. Some participants worried about the proposed change; others trusted it was evidence based. Frequent questions concerned the rationale and safety of longer intervals, speed of cancer development, the possibility of HPV being missed or cell changes occurring between screens. Many participants felt reassured when the interval change was explained alongside the move to HPV primary screening, of which most had previously been unaware. Conclusions Communication of the interval change should be done in the context of broader information about HPV primary screening, emphasising that people who test negative for HPV are at lower risk of cell changes so can safely be screened every 5 years. The long time needed for HPV to develop into cervical cancer provides reassurance about safety, but it is important to be transparent that no screening test is perfect.
Which factors affect the performance of technology business incubators in China? An entrepreneurial ecosystem perspective

To examine which factors affect the performance of technology business incubators in China, the present study proposes an entrepreneurial ecosystem framework with four key areas, i.e., people, technology, capital, and infrastructure. We then assess this framework using a three-year panel data set of 857 national-level technology business incubators in 33 major cities from 28 provinces in China, from 2015 to 2017. We utilize factor analysis to downsize dozens of characteristics of these technology business incubators into seven factors related to the four proposed areas. Panel regression model results show that four of the seven factors related to three areas of the entrepreneurial ecosystem, namely people, technology, and capital areas, have statistically significant associations with an incubator’s performance when applied to the overall national data set. Further, seven factors related to all four areas have various statistically significant associations with an incubator’s performance in five major regional data set. In particular, a technology related factor has a consistently statistically significant association with the performance of the incubator in both national model and the five regional models, as we expected.
Rache: Radix-additive caching for homomorphic encryption

One of the biggest concerns for many applications in cloud computing lies in data privacy. A potential solution to this problem is homomorphic encryption (HE), which supports certain operations directly over the ciphertexts. Conventional HE schemes, however, exhibit significant performance overhead and are hardly applicable to real-world applications. This paper presents Rache, a caching optimization for accelerating the performance of HE schemes. The key insights of Rache include (i) caching some homomorphic ciphertexts before encrypting the large volume of plaintexts; (ii) expanding the plaintexts into a summation of powers of radixes; and (iii) constructing the ciphertexts with only homomorphic addition. The extensive evaluation shows that Rache exhibits almost linear scalability and outperforms Paillier by orders of magnitude.
Impact of Service Quality of Low-Cost Carriers on Airline Image and Consumers’ Satisfaction and Loyalty during the COVID-19 Outbreak

Low-cost carriers (LCCs) in Saudi Arabia operate in a competitive, highly demanding environment. Customer-related attributes may be influenced by the levels of service quality in a no-frills airline, which might impact satisfaction and loyalty. Given the unique traveler and market characteristics of the aviation sector in the kingdom, we sought to investigate the impact of service quality of LCCs on customer satisfaction and loyalty and the perceived airline image. A total of 299 passengers at two international airports were approached using a modified SERVQUAL scale. Results revealed that service quality was a significant predictor of customer satisfaction (β = 0.46, p < 0.0001), airline image (β = 0.55, p < 0.0001), and customer loyalty (β = 0.16, p = 0.006). The responsiveness dimension was the most important dimension of service quality, since it predicted all other constructs (satisfaction, loyalty, and brand image). Airline tangibles and reliability were independently associated with brand image and loyalty, respectively. Based on these results, LCCs should tailor future strategic plans that rely heavily on improving different service quality measures, particularly the responsiveness domain.
No Community Can Do Everything: Why People Participate in Similar Online Communities

Large-scale quantitative analyses have shown that individuals frequently talk to each other about similar things in different online spaces. Why do these overlapping communities exist? We provide an answer grounded in the analysis of 20 interviews with active participants in clusters of highly related subreddits. Within a broad topical area, there are a diversity of benefits an online community can confer. These include (a) specific information and discussion, (b) socialization with similar others, and (c) attention from the largest possible audience. A single community cannot meet all three needs. Our findings suggest that topical areas within an online community platform tend to become populated by groups of specialized communities with diverse sizes, topical boundaries, and rules. Compared with any single community, such systems of overlapping communities are able to provide a greater range of benefits.
Quantum Jump Metrology with Quantum Feedback in Cavity Networks

Quantum metrology enhances measurement precision by utilising the properties of quantum physics. In interferometry, this is typically achieved by evolving highly-entangled quantum states before performing single-shot measurements to reveal information about an unknown phase. While this is the theoretical optimum approach, implementation with all but the smallest states is still extremely challenging. An alternative approach is quantum jump metrology, which deduces information by continuously monitoring an open quantum system while inducing phase-dependent temporal correlations with the help of quantum feedback. Taking this approach here, we analyse measurements of a relative phase in an optical network of two cavities with quantum feedback in the form of laser pulses. It is shown that the proposed scheme can exceed the standard quantum limit without the need for complex quantum states.
Configuration Space Decomposition for Scalable Proxy Collision Checking in Robot Planning and Control

Real-time robot motion planning in complex high-dimensional environments remains an open problem. Motion planning algorithms, and their underlying collision checkers, are crucial to any robot control stack. Collision checking takes up a large portion of the computational time in robot motion planning. Existing collision checkers make trade-offs between speed and accuracy and scale poorly to high-dimensional, complex environments. We present a novel space decomposition method using K-Means clustering in the Forward Kinematics space to accelerate proxy collision checking. We train individual configuration space models using Fastron, a kernel perceptron algorithm, on these decomposed subspaces, yielding compact yet highly accurate models that can be queried rapidly and scale better to more complex environments. We demonstrate this new method, called Decomposed Fast Perceptron (D-Fastron), on the 7-DOF Baxter robot producing on average 29× faster collision checks and up to 9.8× faster motion planning compared to state-of-the-art geometric collision checkers.