Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple but Quantifiable Approach to Dynamic Price Prediction in Ride-on-demand Services Leveraging Multi-source Urban Data

Ride-on-demand (RoD) services such as Uber and Didi are becoming increasingly popular, and in these services dynamic prices play an important role in balancing the supply and demand to benefit both drivers and passengers. However, dynamic prices also create concerns. For passengers, the "unpredictable" prices sometimes prevent them from making quick decisions: one may wonder if it is possible to get a lower price if s/he chooses to wait a while. It is necessary to provide more information to them, and predicting the dynamic prices is a possible solution. For the transportation industry and policy makers, there are also concerns about the relationship between RoD services and their more traditional counterparts such as metro, bus, and taxi: whether they affect each other and how. In this paper we tackle these two concerns by predicting the dynamic prices using multi-source urban data. Price prediction could help passengers understand whether they could get a lower price in neighboring locations or within a short time, thus alleviating their concerns. The prediction is based on urban data from multiple sources, including the RoD service itself, taxi service, public transportation, weather, the map of a city, etc. We train a simple linear regression model with high-dimensional composite features to perform the prediction. By combining simple basic features into composite features, we compensate for the loss of expressiveness in a linear model due to the lack of non-linearity. Additionally, the use of multi-source data and a linear model enables us to quantify and explain the relationship between multiple means of transportation by examining the weights of different features in the model. Our hope is that the study not only serves as an accurate prediction to make passengers more satisfied, but also sheds light on the concern about the relationship between different means of transportation for either the industry or policy makers.
Multi-Core Processor Scheduling Algorithm under The Influence Of Process Variation

Due to process variation, different cores of multi-core processors will generate frequency heterogeneity, and the difference in frequency will cause difference in the speed of executing instructions. The existing multi-core processor scheduling algorithms do not take the process variation into account, and ignore the deviation in the manufacturing process. This paper proposes a frequency-heterogeneous scheduling algorithm MFF (Max-Fast-First), which allocates long time-consuming tasks to cores with high frequency and assigns short time-consuming tasks to cores with low frequency, thus shortening the total operation time. The experimental results show that the MFF algorithm reduces the running time by 17.87% compared to the dynamic Min-Min algorithm, and it reduces the running time by 9.10% compared to the CATS(Criticality-Aware Task Scheduler) algorithm. At the same time, the running time of the MFF algorithm is less affected by the process variation, which is better than dynamic Min-Min and CATS to process variation.
Amycomicin is a potent and specific antibiotic discovered with a targeted interaction screen

Significance Bacteria, especially actinomycetes, produce the majority of our clinically useful small-molecule antibiotics. Genomic analyses of antibiotic-producing strains indicate that earlier discovery efforts revealed only a fraction of the likely antibiotic candidates. In an effort to uncover these previously missed candidates, we developed an approach that utilizes the ability of microbial communities to produce antibiotics that are not produced by any single member in isolation. Successful communities were established and deconvoluted to identify both producers and inducers of antibiotic activity. One inducer–producer pair made amycomicin, a potent and specific antibiotic against Staphylococcus aureus, an important human pathogen. Amycomicin targets fatty acid biosynthesis and exhibits in vivo efficacy against antibiotic-resistant skin infections in a mouse model. The rapid emergence of antibiotic-resistant pathogenic bacteria has accelerated the search for new antibiotics. Many clinically used antibacterials were discovered through culturing a single microbial species under nutrient-rich conditions, but in the environment, bacteria constantly encounter poor nutrient conditions and interact with neighboring microbial species. In an effort to recapitulate this environment, we generated a nine-strain actinomycete community and used 16S rDNA sequencing to deconvolute the stochastic production of antimicrobial activity that was not observed from any of the axenic cultures. We subsequently simplified the community to just two strains and identified Amycolatopsis sp. AA4 as the producing strain and Streptomyces coelicolor M145 as an inducing strain. Bioassay-guided isolation identified amycomicin (AMY), a highly modified fatty acid containing an epoxide isonitrile warhead as a potent and specific inhibitor of Staphylococcus aureus. Amycomicin targets an essential enzyme (FabH) in fatty acid biosynthesis and reduces S. aureus infection in a mouse skin-infection model. The discovery of AMY demonstrates the utility of screening complex communities against specific targets to discover small-molecule antibiotics.
SenseGAN

Recent proliferation of Internet of Things (IoT) devices with enhanced computing and sensing capabilities has revolutionized our everyday life. The massive data from these ubiquitous devices motivate the creation of intelligent IoT systems that can collectively learn. However, labelling data for learning purposes is extremely time-consuming, which greatly hinders deployment. In this paper, we describe a semi-supervised deep learning framework, called SenseGAN, that can leverage abundant unlabelled sensing data thereby minimizing the need for labelling effort. SenseGAN jointly trains three components with an adversarial game: (i) a classifier for predicting labels of input sensing data; (ii) a generator for generating sensing data samples based on the input labels; and (iii) a discriminator for differentiating the joint data/label distribution between real samples and partially generated samples from either the classifier or the generator. The classifier and the generator try to generate fake data/labels that can fool the discriminator. The adversarial game among the three components can mutually boost their performance, which helps the classifier learn to predict correct labels with unlabelled data in return. SenseGAN can effectively handle multimodal sensing inputs and easily stabilize the adversarial training process, which helps improve the performance of the classifier. Experiments on three IoT applications demonstrate the substantial improvements in accuracy and F1 score under SenseGAN, compared with supervised counterparts trained only on the labelled portion of the data, as well as other supervised and semi-supervised baselines. For these three applications, SenseGAN requires only 10% of the originally labelled data, to attain nearly the same accuracy as a deep learning classifier trained on the fully labelled dataset.
Extracellular Interactions between Fibulins and Transforming Growth Factor (TGF)-β in Physiological and Pathological Conditions

Transforming growth factor (TGF)-β is a multifunctional peptide growth factor that has a vital role in the regulation of cell growth, differentiation, inflammation, and repair in a variety of tissues, and its dysregulation mediates a number of pathological conditions including fibrotic disorders, chronic inflammation, cardiovascular diseases, and cancer progression. Regulation of TGF-β signaling is multifold, but one critical site of regulation is via interaction with certain extracellular matrix (ECM) microenvironments, as TGF-β is primarily secreted as a biologically inactive form sequestrated into ECM. Several ECM proteins are known to modulate TGF-β signaling via cell–matrix interactions, including thrombospondins, SPARC (Secreted Protein Acidic and Rich in Cystein), tenascins, osteopontin, periostin, and fibulins. Fibulin family members consist of eight ECM glycoproteins characterized by a tandem array of calcium-binding epidermal growth factor-like modules and a common C-terminal domain. Fibulins not only participate in structural integrity of basement membrane and elastic fibers, but also serve as mediators for cellular processes and tissue remodeling as they are highly upregulated during embryonic development and certain disease processes, especially at the sites of epithelial–mesenchymal transition (EMT). Emerging studies have indicated a close relationship between fibulins and TGF-β signaling, but each fibulin plays a different role in a context-dependent manner. In this review, regulatory interactions between fibulins and TGF-β signaling are discussed. Understanding biological roles of fibulins in TGF-β regulation may introduce new insights into the pathogenesis of some human diseases.
Global Marine N2 Fixation Estimates: From Observations to Models

Fixed nitrogen (N) limits productivity across much of the low-latitude ocean. The magnitude of its inventory results from the balance of N input and N loss, the latter largely occurring in regionally well-defined low-oxygen waters and sediments (denitrification and anammox). The rate and distribution of N input by biotic N2 fixation, the dominant N source, is not well known. Here we compile N2 fixation estimates from experimental measurements, tracer-based geochemical and modeling approaches, and discuss their limitations and uncertainties. The lack of adequate experimental data coverage and the insufficient understanding of the controls of marine N2 fixation result in high uncertainties, which make the assessment of the current N-balance a challenge. We suggest that a more comprehensive understanding of the environmental and ecological interaction of marine N2 fixers is required to advance the field toward robust N2 fixation rates estimates and predictions.
Image splicing detection based on Markov features in discrete octonion cosine transform domain

To improve the poor robustness and low accuracy of the existing algorithms of image splicing detection, a novel passive image forgery detection method is proposed in this study, which is based on DOCT (discrete octonion cosine transform) and Markov. By introducing the octonion and DOCT, the colour information of six image channels (the RGB model and the HSI model) can be exhaustively extracted, which enhances the robustness of the algorithm. On the issue of improving the detection accuracy, the standard deviation is used to characterise the relationship of the colour information between the parts of DOCT coefficient matrix, and the K -fold cross-validation is introduced to improve the identification performance of the classifier. The steps of the algorithm are as follows: Firstly, the 8 × 8 block DOCT transform is used to the original image to obtain parts of block DOCT coefficient. Secondly, the standard deviation is used to process the corresponding parts of all blocks of the image. Finally, the Markov feature vector of the DOCT coefficient is extracted and feds to the LIBSVM (a library for support vector machines). When using LIBSVM for classification, K -fold cross-validation is executed to select the best parameter pairs. The experiment results demonstrate that the algorithm is superior to the other state-of-the-art splicing detection methods.
Is Multimedia Multisensorial? - A Review of Mulsemedia Systems

Mulsemedia—multiple sensorial media—makes possible the inclusion of layered sensory stimulation and interaction through multiple sensory channels. The recent upsurge in technology and wearables provides mulsemedia researchers a vehicle for potentially boundless choice. However, in order to build systems that integrate various senses, there are still some issues that need to be addressed. This review deals with mulsemedia topics that remain insufficiently explored by previous work, with a focus on the multi-multi (multiple media-multiple senses) perspective, where multiple types of media engage multiple senses. Moreover, it addresses the evolution of previously identified challenges in this area and formulates new exploration directions.
Joint User Association and Resource Allocation Optimization for Ultra Reliable Low Latency HetNets

Ensuring ultra-reliable and low latency communications (URLLC) is necessary for enabling delay critical applications in 5G HetNets. We propose a joint user to BS association and resource optimization method that is attractive for URLLC in HetNets with Cellular Base Stations (CBSs) and Small Cell Base Stations (SBSs), while also reducing energy and bandwidth consumption. In our scheme, CBSs share portions of the available spectrum with SBSs, and they in exchange, provide data service to the users in their coverage area. We first show that the CBSs optimal resource allocation (ORA) problem is NP-hard and computationally intractable for large number of users. Then, to reduce its time complexity, we propose a relaxed heuristic method (RHM) which breaks down the original ORA problem into a heuristic user association (HUA) algorithm and a convex resource allocation (CRA) optimization problem. Simulation results show that the proposed heuristic method decreases the time complexity of finding the optimal solution for CBS's significantly, thereby benefiting URLLC. It also helps the CBSs to save energy by offloading users to SBSs. In our simulations, the spectrum access delay for cellular users is reduced by 93\% and the energy consumption is reduced by 33\%, while maintaining the full service rate.
Watching and Safeguarding Your 3D Printer

The increasing adoption of 3D printing in many safety and mission critical applications exposes 3D printers to a variety of cyber attacks that may result in catastrophic consequences if the printing process is compromised. For example, the mechanical properties (e.g., physical strength, thermal resistance, dimensional stability) of 3D printed objects could be significantly affected and degraded if a simple printing setting is maliciously changed. To address this challenge, this study proposes a model-free real-time online process monitoring approach that is capable of detecting and defending against the cyber-physical attacks on the firmwares of 3D printers. Specifically, we explore the potential attacks and consequences of four key printing attributes (including infill path, printing speed, layer thickness, and fan speed) and then formulate the attack models. Based on the intrinsic relation between the printing attributes and the physical observations, our defense model is established by systematically analyzing the multi-faceted, real-time measurement collected from the accelerometer, magnetometer and camera. The Kalman filter and Canny filter are used to map and estimate three aforementioned critical toolpath information that might affect the printing quality. Mel-frequency Cepstrum Coefficients are used to extract features for fan speed estimation. Experimental results show that, for a complex 3D printed design, our method can achieve 4% Hausdorff distance compared with the model dimension for infill path estimate, 6.07% Mean Absolute Percentage Error (MAPE) for speed estimate, 9.57% MAPE for layer thickness estimate, and 96.8% accuracy for fan speed identification. Our study demonstrates that, this new approach can effectively defend against the cyber-physical attacks on 3D printers and 3D printing process.
Bacterial Extracellular Polymeric Substances Amplify Water Content Variability at the Pore Scale

The function of microbial communities in soil is inextricably linked with the complex physical, chemical and biological structure of the soil itself. Pore-scale water content controls the hydraulic connectivity of microbial communities and microbes’ access to aqueous and gaseous substrates. In turn, soil bacteria directly influence local moisture conditions through the secretion of extracellular polymeric substances (EPS). However, the effect of a soil’s physical geometry on EPS-mediated water retention is not well understood. In this study, we systematically measured the rate and extent of water evaporation from pore structures as a function of both EPS concentration and pore size. Three different chamber types were employed: (i) glass capillary tubes (1.2 mm pore diameter) to represent a uniform macropore geometry; (ii) emulated soil micromodels (pore widths ~ 10 µm to > 300 µm) to represent an aggregated sandy loam pore geometry; and (iii) microfluidic capillary arrays (uniform channels 20 µm wide) to represent a uniform micropore geometry. All chambers were initially saturated with dilute EPS solutions collected from stationary-phase Sinorhizobium meliloti cultures and then the infiltration of air was tracked over time. In the largest chambers, EPS concentration had no effect on the extent of evaporation or on the magnitude or variability of the evaporation rate. However, in the chambers with micropore-sized physical features, EPS concentration strongly influenced rate, extent, and variability of pore water evaporation. In micropores, higher EPS concentrations enhanced water retention and led to greater variability in pore-scale water distributions. In real soil, these phenomena could act together to promote the intermediate water contents associated with productive soil systems, and more variable pore-scale water distributions could increase microbial community diversity and the resiliency of soil systems.
Fear as a biofeedback game mechanic in virtual reality: effects on engagement and perceived usability

Virtual Reality (VR) is now an affordable technology that is starting to penetrate the mass market. Providing accessible solutions to enhance VR experiences is crucial. In this paper, we consider a wearable solution as a mean of interaction in VR, to add a biofeedback mechanic. We hypothesized that the use of a biofeedback loop in a VR experience can enhance user engagement. We created a physiologically enhanced horror game coupled with a heart rate monitor smart wristband. We evaluated the players' engagement with and without biofeedback. We observed a high interest of the participants for biofeedback and highlighted higher engagement when the biofeedback mechanic was fully integrated in the experience.
Don't forget about neuromuscular disorders!

Respiratory medicine ostensibly targets diseases of the airways and the lungs, while respiratory problems arising from other entities are suggested to only play a minor role in this branch of medicine. However, diseases of the respiratory pump, particularly those associated with neuromuscular disorders (NMD), bear significant relevance to respiratory physicians, both in clinical practice and in terms of the scientific evolution of respiratory medicine and mechanical ventilation [1, 2]. During the 1952 Copenhagen polio epidemic, polio victims with severe respiratory acidosis secondary to respiratory paralysis were the first group of patients in whom mortality could be dramatically reduced by applying positive pressure ventilation in combination with careful airway management [3]. As things developed, the protagonist of this accomplishment and hero of the Copenhagen polio epidemic, Bjørn Ibsen, became known to many as the founding father of modern intensive care medicine [4]. In patients with NMD, ineffective coughing, recurrent aspiration and airway infections, respiratory failure and home mechanical ventilation require the expertise of respiratory physicians. Therefore, we should not forget about neuromuscular disorders! http://ow.ly/UeEA30lFMxn
Interactions of bioactive molecules with thin dendritic glycopolymer layers.

The authors report on highly swellable, stable layers of spherical dendritic glycopolymers, composed of hyperbranched poly(ethylene imine) (PEI) as core and two different maltose shells (A = dense shell and B = open shell). These glycopolymers are cross-linked and attached with poly(ethylene-alt-maleic anhydride) (PEMA) or citric acid on SiOx substrates. The swelling and adsorption of biomolecules were analyzed by spectroscopic ellipsometry and quartz crystal microbalance with dissipation. The swelling degree and complexation with the drug molecule adenosine triphosphate (ATP) were found to be up to 10 times higher for dendritic glycopolymer layers cross-linked with PEMA than for layers cross-linked with citric acid. ATP complexation by electrostatic interaction with the PEI cores was confirmed by x-ray photoelectron spectroscopy analysis. Complexation led to partial collapsing, stiffening, and increase of polymer layer viscosity of the PEMA cross-linked layers. From modeling of ellipsometric data, it was deduced that ATP complexation preferably takes place at the polymer layer-solution interface. The size effect of the adsorbates was investigated by comparing ATP complexation with the adsorption of larger vitamin B12 and human serum albumin (HSA) protein. PEI-Mal A cross-linked with PEMA was found to be resistant toward B12 and HSA adsorption due to the diffusion barrier of the polymer layer. Thus, the authors present potentially biocompatible polymer surfaces for drug loading and their surface supported release.
Architectural challenges on the analysis of human behaviour in synthetic environments

In this paper we review how human interaction models are designed for its use in synthetic environments, being these any kind of computer-generated environment, as in virtual reality, augmented reality or others. We present how human interaction techniques are modelled in synthetic environments relating to the self-representation of the user's body, the avatar, and the virtual objects present in the world, and we also propose alternatives on the design methods of that interaction models. We review the differences between in-lab and out-of-lab deployments of such synthetic environments and the problems that arise when we move those interaction models to actual applications with requirements such as mobility, massive use over the Internet, and implementations based on third party VR/AR platforms. By using the architecture of a reference framework for synthetic environments interaction models design, we present how the problems impact the overall design of the framework and propose some changes to adapt it to the new use cases presented. We also propose new lines of work to have a more industrial, reusable, cost-effective approach when designing new synthetic environments' interaction models from a domain specific point of view.