Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid modelling of transportation systems by means of Petri nets

A novel hybrid model for transportation systems is proposed, in which the functioning is represented via a continuous-time model whenever no changes in the traffic flow occur, whereas the changes in the system behaviour are modelled through a discrete event system coupled with the continuous-time system. Via the discrete-event part of the hybrid model, its continuous-time part is adapted according to the variation occurred in the system behaviour. The hybrid modelling methodology proposed is based on the use of a special class of Petri nets, the hybrid stochastic Petri nets (HSPN). These nets are composed of some discrete parts, and some continuous parts, so that a hybrid Petri net can be viewed as a combination of an usual (discrete) Petri net and a continuous Petri net, the 'fluid' version of usual timed Petri nets. The discrete part of the HSPN 'regulates' the continuous PN, which models the functioning of the transportation system. This results in the model reacting to any change to the current system behaviour by adapting the traffic parameters to the new state.
Acceleration of neural network model execution on embedded systems

Deep learning has made various breakthroughs in on cognitions tasks. However, deep learning neural networks, by nature, is not only computation intensive but also memory intensive. Therefore, most of the existing deep learning applications are cloud-based, which then raise the concern of latency, privacy violation, and bandwidth consumption. Due to the trend of exponential growth of IOT (internet of things) devices, deploying deep learning applications to the edge devices is, or soon will be, required for the revolution. It is critical to tune the neural network models to accelerate the execution of deep learning applications on embedded systems. Even with the breakthroughs in the FPGAs or ASICs tailored for deep learning, it is proven necessary to adjust the neural network models for efficient execution. Many studies have been presented in the past few years; however, unfortunately, many approaches are not good fits for industrial productions. For example, while an accuracy loss from 99.9% to 99.0% is a mere less than 1% loss, but from the industrial point of view, it means the error rate grows by at least ten times, which might fail the applications to become mature products for the market. This paper presents recent advancements in neural network models. The compression techniques are categorized into four based on their principles. The first category focuses on reducing the model size. The approaches are mostly known by pruning, quantization, and compression. The second category focuses on speeding up the matrix multiplications. The attempts are made on matrix factorization and filtering. The third category takes advantage of repurposing the models based on domain knowledge. The representative works include knowledge distillation and transfer learning. The last category is of hybrid approaches. We conclude this paper with the discussion of the foreseeable challenges in the industrial applications.
A silicon Brillouin laser

Making silicon shine bright Silicon is the workhorse of the semiconductor electronics industry, but its lack of optical functionality is a barrier to developing a truly integrated silicon-based optoelectronics platform. Although there are several ways of exploiting nonlinear light-matter interactions to coax silicon into optical functionality, the effects tend to be weak. Otterstrom et al. used a suspended silicon waveguide racetrack structure to stimulate the stronger nonlinear effect of Brillouin scattering and achieve lasing from silicon. The ability to engineer the nonlinearity and tune the optical response through the design of the suspended cavity provides a powerful and flexible route for developing silicon-based optoelectronic circuits and devices. Science, this issue p. 1113 Brillouin scattering is exploited to develop a silicon laser. Brillouin laser oscillators offer powerful and flexible dynamics as the basis for mode-locked lasers, microwave oscillators, and optical gyroscopes in a variety of optical systems. However, Brillouin interactions are markedly weak in conventional silicon photonic waveguides, stifling progress toward silicon-based Brillouin lasers. The recent advent of hybrid photonic-phononic waveguides has revealed Brillouin interactions to be one of the strongest and most tailorable nonlinearities in silicon. In this study, we have harnessed these engineered nonlinearities to demonstrate Brillouin lasing in silicon. Moreover, we show that this silicon-based Brillouin laser enters a regime of dynamics in which optical self-oscillation produces phonon linewidth narrowing. Our results provide a platform to develop a range of applications for monolithic integration within silicon photonic circuits.
How Traders' Appearances and Moral Descriptions Influence Receivers' Choices in the Ultimatum Game

This work reports on a series of experiments involving 960 participants (aged between 20-30 years and equally balanced by gender), asked to play the receiver role in a modified version of the Ultimatum Game, where together with information on the offer's fairness (e.g. 40 (fair) vs 10 (unfair) of 100 euros), a photo depicted the trader's appearance (trustworthy vs. untrustworthy) and a text provided his moral description (honest vs. dishonest). Receivers were asked to motivate their decision in connection with the appearance, moral judgment, and fairness of the offer, and report on how these variables affected their emotional feelings. Data analysis shows that, in all conditions containing a fair offer, the trader's appearance plays a significant role in the receivers' decisions in terms of acceptance rate. Moral descriptions play a significant role only in conditions containing an unfair offer. However, when asked to motivate their choices, subjects do not feel the interference of the social appearance, rather they provide more or less equal number of motivations with reference to the amount of offers and moral judgments. As for the emotions driving their decisions, non-converging feelings are observed both at intra and inter group level.
The Emerging Role of Biomarkers in the Diagnosis of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a common complication of pregnancy; its rising incidence is a result of increased maternal obesity and older maternal age together with altered diagnostic criteria identifying a greater proportion of pregnant women with GDM. Its consequences are far-reaching, associated with poorer maternal and neonatal outcomes compared to non-GDM pregnancies, and GDM has implications for metabolic health in both mother and offspring. Objective markers to identify women at high risk for the development of GDM are useful to target therapy and potentially prevent its development. Established clinical risk factors for GDM include overweight/obesity, age, ethnicity, and family history of diabetes, though they lack specificity for its development. The addition of biomarkers to predictive models of GDM may improve the ability to identify women at risk of GDM prior to its development. These biomarkers reflect the pathophysiologic mechanisms of GDM involving insulin resistance, chronic inflammation, and altered placental function. In addition, the role of epigenetic changes in GDM pathogenesis highlights the complex interplay between genetic and environmental factors, potentially offering further refinement of the prediction of GDM risk. In this review, we will discuss the clinical challenges associated with the diagnosis of GDM and its current pathophysiologic basis, giving rise to potential biomarkers that may aid in its identification. While not yet validated for clinical use, we explore the possible clinical role of biomarkers in the future. We also explore novel diagnostic tools, including high throughput methodologies, that may have potential future application in the identification of women with GDM.
Redundant robot localization system based in wireless sensor network

Localization and Mapping of autonomous robots in an harsh and unstable environment such as a steep slope vineyard is a challenging research topic. Dead Reckoning systems can fail due to the harsh conditions of the terrain, and the Global Position System can be affected by noise or even be unavailable. Agriculture is moving towards precision agriculture, with advanced monitoring systems and wireless sensor networks. These systems and wireless sensors are installed in the crop field and can be considered relevant landmarks for robot localization. In this paper the distance accuracy provided by bluetooth based sensors is deeply studied and characterized. It is considered a multi antenna receiver bluetooth system and obtained the transfer functions (from Received Signal Strength Indication (RSSI) to distance estimation) for each set of antenna and sensors. The performance of this technology is compared against Time-of-flight based technologies (Pozyx). The obtained results show that the agricultural wireless sensors can be used as redundant artificial landmarks for localization purposes. Besides, the RSSI characterization allowed to improve the previous results of our Beacon Mapping Procedure (BMP) required for accurate and reliable localization systems.
A New Non-Blocking Approach on GPU Dynamical Memory Management

Dynamic memory allocation is a very important and basic technique implemented on modern computer architecture. In the massively parallel processor (MPP) architecture such as Graphics Processing Units (GPUs), many threads try to send allocation or deallocation requests to system in the same time, which could cause the issue of synchronization or race condition. In this paper, we design a new signal model with signal queue to handle the interaction of threads. Based on the signal model, we involve the concept of buddy memory to construct a non-blocking parallel buddy system. Our design have no synchronization problem and adopt a simpler structure implemented than before. Finally, we implement our model in real hardware and experimental results show that the model have better performance than other methods.
Anemia and acute coronary syndrome: current perspectives

Reference hemoglobin (Hb) values for the definition of anemia are still largely based on the 1968 WHO Scientific Group report, which established a cutoff value of <13 g/dL for adult men and <12 g/dL for adult nonpregnant women. Subsequent studies identified different normal values according to race and age. Estimated prevalence of anemia on admission in the setting of an acute coronary syndrome (ACS) is between 10% and 43% of the patients depending upon the specific population under investigation. Furthermore, up to 57% of ACS patients may develop hospital-acquired anemia (HAA). Both anemia on admission and HAA are associated with worse short- and long-term mortality, even if different mechanisms contribute to their prognostic impact. Baseline anemia can usually be traced back to preexisting disease that should be specifically investigated and corrected whenever possible. HAA is associated with clinical characteristics, medical therapy and interventional procedures, all eliciting cardiovascular adaptive response that can potentially worsen myocardial ischemia. The intrinsic fragility of anemic patients may limit aggressive medical and interventional therapy due to an increased risk of bleeding, and could independently contribute to worse outcome. However, primary angioplasty for ST elevation ACS should not be delayed because of preexisting (and often not diagnosed) anemia; delaying revascularization to allow fast-track anemia diagnosis is usually feasible and justified in non-ST-elevation ACS. Besides identification and treatment of the underlying causes of anemia, the only readily available means to reverse anemia is red blood cell transfusion. The adequate transfusion threshold is still being debated, although solid evidence suggests reserving red blood cell transfusions for patients with Hb level <8 g/dL and considering it in selected cases with Hb levels of between 8 and 10 g/dL. No evidence supports the use of iron supplements and erythropoiesis-stimulating agents in the setting of ACS.
A new syntax-based aspect detection approach for sentiment analysis in Arabic reviews

With the explosive growth of different form of social media on the Web, individuals and organizations are increasingly trying to use the content in these media for decision making. Thanks to the automatic and accurate measurement of customer opinions, the organization can integrate, understand and analyze the needs of its targets and thus adapt its marketing strategy. In this paper, we describe a new syntax-based aspect detection approach for sentiment analysis in Arabic reviews. Many characteristics of the Arabic subjective text were studied and used in order to elaborate syntactic rules and disambiguation techniques that have been integrated in our approach. The proposed approach is based on a five-step process, namely: pre-processing, lexical entities disambiguation, separation between aspects, extraction of aspects and grouping of aspects. To demonstrate the effectiveness of our proposal, we used two datasets of Arabic social content related to hotel and product reviews. The former is based on content extracted from TripAdvisor while the latter is from the Souq website. The Experimental results showed that the syntax-based approach obtains an F-measure of 69,56% (resp. 68.29%) for the hotels dataset (resp. the products dataset).
Restoration of Arthropod Assemblages in a Spartina Salt Marsh following Removal of the Invasive Plant Phragmites australis

Invasive plants are one of the most serious threats to native species assemblages and have been responsible for the degradation of natural habitats worldwide. As a result, removal of invasive species and reestablishment of natural vegetation have been attempted in order to restore biodiversity and ecosystem function. This study examined how native arthropod assemblages, an abundant and functionally important group of organisms in many ecosystems, are affected by the incursion of the invasive wetland plant Phragmites australis and if the restoration of the native vegetation in brackish Spartina alterniflora marshes results in the reestablishment of the arthropod community. The invasion of Phragmites into a coastal Spartina marsh in southern New Jersey seriously altered arthropod assemblages and trophic structure by changing the abundance of trophic groups (detritivores, herbivores, carnivores) and their taxonomic composition. Herbivore assemblages shifted from the dominance of external free‐living specialists (e.g., planthoppers) in Spartina to concealed feeders in Phragmites (stem‐feeding cecidomyiids). Moreover, free‐living arthropods in Phragmites became dominated by detritivores such as Collembola and chironomids. The dominant marsh spiders, web‐building linyphiids, were significantly reduced in Phragmites habitats, likely caused by differences in the physical environment of the invaded habitats (e.g., lower stem densities). Thus, trophic structure of arthropod assemblages in Phragmites, as seen in the large shifts in feeding guilds, was significantly different from that in Spartina. Removal of Phragmites with the herbicide glyphosate resulted in the rapid return of Spartina (≤5 yrs). Moreover, return of the dominant vegetation was accompanied by the recovery of most original habitat characteristics (e.g., live and dead plant biomass, water flow rate). The arthropod assemblage associated with Spartina also quickly returned to its preinvasion state and was not distinguishable from that in uninvaded Spartina reference sites. This study provides evidence that the reestablishment of native vegetation in areas previously altered by an invasive plant can result in the rapid recovery of the native arthropod assemblage associated with the restored habitat.
Local Storage-Based Consolidation With Resource Demand Prediction and Live Migration in Clouds

Server consolidation is a useful solution aiming at cost-efficiency and high resource utilization of data centers and clusters. Nowadays, as the data-intensive and I/O intensive applications are widely used, more attention is paid to the local storage-based clouds which can offer much better I/O performance at relatively low price compared with the shared storage. However, it will obviously increase the migration cost (e.g., energy and time). Meanwhile, there is few suitable resource demand estimation method for local storage-based clouds at present, which plays an important role in system’s migration efficiency. And we find out that in this specific storage architecture, almost all the existing server consolidation algorithms do not have a suitable resource demand estimation method and a live migration scheme. To solve this problems, this paper designs and implements Combining Three (C3), a cloud architecture for local storage, C3 significant modules: prediction, consolidation, and migration. It was proved in statistical analysis that ARIMA may be the most suitable prediction model for the server workload, which motivates us to propose the resource estimation predictor. Also, we improve the existing consolidation method by adjusting the sorting index and fit degree during migration. Then, we propose a live migration scheme for local storage environment as the third module of C3. We conduct extensive experiments using real-world traces from Google to validate the effectiveness and superiority of our proposed algorithm.
User Interest Propagation and Its Application in Recommender System

User interest prediction plays an important role in online services, such as electronic commerce, social network, and online advertising. This information is not always explicitly available for online systems. To identify the interests, existing studies merely focused on modeling the relationships between user generated contents and user interests. However, the interests of a user should not only be inferred by user generated contents, but also the relationships between users which might imply the user interests. In this paper, our goal is to unveil the true interests of users based on user generated contents as well as relationships between users. We built a probabilistic user interests model and proposed a user interests propagation algorithm (UIP) to tackle this problem. A factor graph-based approach is utilized to estimate the distribution of the interests of users. We conducted experiments on real-world datasets to validate the effectiveness of the model. Furthermore, we integrated our UIP algorithm with the classical matrix factorization algorithm to deal with the rating prediction task. Experimental studies confirm the superiority of the proposed approach.
Relational Knowledge Prediction via Dynamic Bi-Mode Embedding

Knowledge graphs are a crucial concept in artificial intelligence with a wide spectrum of real-life applications. Nonetheless, they are currently suffering from the incompleteness issue, i.e., relational knowledge in the graphs may not yet meet the practical needs. To address this issue, mainstream solutions propose to predict links by using compositional models or translation models. However, the prediction accuracy is still of particular concern. In this paper, we propose a new method, namely, Bi-Mult, which combines the advantages of compositional models and translation models. Bi-Mult is based on the compositional model, such that an entity (resp. relation) embedding is decomposed into two parts, one is to represent intra-entity (resp. relation) state and the other is for inter-entity (resp. relation) state, and we call such embedding as bi-mode embedding. In addition, the bi-mode relation embedding enhances relation’s interaction with entities, resulting its improvement on handling antisymmetric relations. Moreover, we incorporate mapping matrices in translation models through bi-mode entity embedding to construct dynamic embeddings for expressing complex relations, such as “1-to-N”, “N-to-1,” and “N-to-N” relations. In experiments, we evaluate our method on the benchmark data sets and the task of link prediction, and our method is demonstrated to outperform state-of-the-art methods consistently and significantly.
Gazecode: open-source software for manual mapping of mobile eye-tracking data

Purpose: Eye movements recorded with mobile eye trackers generally have to be mapped to the visual stimulus manually. Manufacturer software usually has sub-optimal user interfaces. Here, we compare our in-house developed open-source alternative to the manufacturer software, called GazeCode. Method: 330 seconds of eye movements were recorded with the Tobii Pro Glasses 2. Eight coders subsequently categorized fixations using both Tobii Pro Lab and GazeCode. Results: Average manual mapping speed was more than two times faster when using GazeCode (0.649 events/s) compared with Tobii Pro Lab (0.292 events/s). Inter-rater reliability (Cohen's Kappa) was similar and satisfactory; 0.886 for Tobii Pro Lab and 0.871 for GazeCode. Conclusion: GazeCode is a faster alternative to Tobii Pro Lab for mapping eye movements to the visual stimulus. Moreover, it accepts eye-tracking data from manufacturers SMI, Positive Science, Tobii, and Pupil Labs.
Biomodulation of Osteoarthritis

Osteoarthritis (OA) is a chronic, disabling condition that affects synovial joints. Its pathogenesis involves multiple etiologies, including mechanical, genetic, and biochemical factors. OA is generally described as “noninflammatory” arthritis in contrast to rheumatoid arthritis, but this is increasingly recognized as a misnomer, since inflammation does indeed contribute to both the symptoms and the progression of OA. Morning or inactivity stiffness is a common symptom in OA, but acute inflammatory flares with all the clinical signs (redness, warmth, swelling, and further loss of function) are also common in OA patients. From the homotoxicological perspective, OA falls into the degeneration phase on the Disease Evolution Table and shares many of the characteristics of degenerative disease processes, namely, chronic inflammation accompanied by the release of dangerous free radicals such as peroxynitrite, disturbance of the normal cycle of degeneration and repair, and disturbance of angiogenic balance in the direction of inappropriate vascularization.
PULSED LASER ATOM PROBE CHARACTERIZATION OF SILICON CARBIDE

Field-jon microscopy and field evaporation of a-SiC and /3-SiC materials have been successfully demonstrated. Since field-ion micrographs could be taken immediately after pulsed field evaporation, the effects of preferential field evaporation of silicon that occurred under ramped field evaporation were eliminated. However, due to the high electrical resistivity of silicon carbide, conventional voltage pulsed field evaporation was not practical and pulsed laser field evaporation was required. No significant variations in performance were found as a function of laser wavelength but the full-width-at-half maximum mass resolution of 0.4 amu was inferior to voltage pulsed field evaporation of metals. In addition, the background noise level was considerably higher. Within the statistical accuracy of the analyses, the compositions were found to be slightly deficient in silicon. These experiments also revealed that the laser power required to field evaporate SiC was greater than for other semiconducting materials.