Miracle-dz/UniFAR-FTUs huggingface dataset:
MODELING OF MOVING DEFORMABLE CONTINENTS BY ACTIVE TRACERS: CLOSING AND OPENING OF OCEANS, RECIRCULATION OF OCEANIC CRUST

The evolution of the ‘mantle – moving deformable continents’ system has been studied by numerical experiments. The continents move self-consistently with the mantle flows of thermo-compositional convection. Our model (two-dimensional mantle convection, non-Newtonian rheology, the presence of deformable continents) demonstrates the main features of global geodynamics: convergence and divergence of continents; appearance and disappearance of subduction zones; backrolling of subduction zones; restructuring of mantle flows; stretching, breakup and divergence of continents; opening and closing of oceans; oceanic crust recirculation in the mantle, and overriding of hot mantle plumes by continents. In our study, the continental crust is modeled by active markers which transfer additional viscosity and buoyancy, while the continental lithosphere is marked only by increased viscosity with neutral buoyancy. The oceanic crust, in its turn, is modeled by active markers that have only an additional buoyancy. The principal result of our modeling is a consistency between the numerical calculations and the bimodal dynamics of the real Earth: the oceanic crust, despite its positive buoyancy near the surface, submerges in subduction zones and sinks deep into the mantle. (Some part of the oceanic crust remains attached to the continental margins for a long time.) In contrast to the oceanic crust, the continental crust does not sink in subduction zones. The continental lithosphere, despite its neutral buoyancy, also remains on the surface due to its viscosity and coupling with the continental crust. It should be noted that when a continent overrides a subduction zone, the subduction zone disappears, and the flows in the mantle are locally reorganized. The effect of basalt-eclogite transition in the oceanic crust on the mantle flow pattern and on the motion of continents has been studied. Our numerical experiments show that the inclusion of this effect in the model considerably alters the pattern of mantle flows and leads to distinct changes in the evolution of continents. Moreover, a new effect arises – bulging of heavy material (eclogitized former oceanic crust) at the core-mantle boundary, wherefrom it arises with the mantle plumes on the surface of the Earth.
Design of green optical networks with signal quality guarantee

Energy consumption of communication networks is growing very fast due to the rapidly increasing traffic demand. Consequently, design of green communication networks gained a lot of attention. In this paper we focus on optical Wavelength Division Multiplexing (WDM) networks, able to support this growing traffic demand. Several energy-aware routing and wavelength assignment (EA-RWA) techniques have been proposed for WDM networks in order to minimize their operational cost. These techniques aim at minimizing the number of active links by packing the traffic as much as possible, thus avoiding the use of lightly loaded links. As a result, EA-RWA techniques may lead to longer routes and to a high utilization on some specific links. This has a detrimental effect on the signal quality of the optical connections, i.e., lightpaths. In this study we quantify the impact of power consumption minimization on the optical signal quality. and address this problem by proposing a combined impairment and energy-aware RWA (IEA-RWA) approach. Towards this goal we developed a complete mathematical model that incorporates both linear and non-linear physical impairments together with an energy efficiency objective. The IEA-RWA problem is formulized as a Mixed Integer Linear Programming (MILP) model where both energy efficiency and signal quality considerations are jointly optimized. By comparing the proposed IEA-RWA approach with existing RWA (IA-RWA and EA-RWA) schemes, we demonstrate that our solution allows for a reduction of energy consumption close to the one obtained by EA-RWA approaches, while still guaranteeing a sufficient level of the optical signal quality.
Batched multi triangulation

The multi triangulation framework (MT) is a very general approach for managing adaptive resolution in triangle meshes. The key idea is arranging mesh fragments at different resolution in a directed acyclic graph (DAG) which encodes the dependencies between fragments, thereby encompassing a wide class of multiresolution approaches that use hierarchies or DAGs with predefined topology. On current architectures, the classic MT is however unfit for real-time rendering, since DAG traversal costs vastly dominate raw rendering costs. In this paper, we redesign the MT framework in a GPU friendly fashion, moving its granularity from triangles to precomputed optimized triangle patches. The patches can be conveniently tri-stripped and stored in secondary memory to be loaded on demand, ready to be sent to the GPU using preferential paths. In this manner, central memory only contains the DAG structure and CPU workload becomes negligible. The major contributions of this work are: a new out-of-core multiresolution framework, that, just like the MT, encompasses a wide class of multiresolution structures; a robust and elegant way to build a well conditioned MT DAG by introducing the concept of V-partitions, that can encompass various state of the art multiresolution algorithms; an efficient multithreaded rendering engine and a general subsystem for the external memory processing and simplification of huge meshes.
Biopsychosocial Predictors of Postpartum Depression: Protocol for Systematic Review and Meta-Analysis

During the postpartum period, psychological disorders may emerge. Aims and objectives: With the current study, we aim to explore the biological determinants that act on women during labor and incur the risk for postpartum depression (PPD). To reach the aim, we will perform the following tasks: (i) identify biological peripartum risk factors and calculate pooled prevalence of PPD for each of them; (ii) explore the strength of the relationship between peripartum risk factors and PPD; (iii) rank the predictors by their prevalence and magnitude of association with PPD. The knowledge obtained will support the development and implementation of early diagnostic and preventive strategies. Methods and analysis: We will systematically go through peer-reviewed publications available in the PubMed search engine and online databases: Scopus, Web of Science, EMBASE. The scope of the review will include articles published any time in English, Arabic, or Polish. We will deduplicate literature sources with the Covidence software, evaluate heterogeneity between the study results, and critically assess credibility of selected articles with the Joanna Briggs Institute’s bias evaluation tool. The information to extract is the incidence rate, prevalence, and odds ratio between each risk factor and PPD. A comprehensive analysis of the extracted data will allow us to achieve the objectives. The study findings will contribute to risk stratification and more effective management of PPD in women.
ProbSparse Attention-Based Fault Diagnosis for Industrial Robots Under Different Working Conditions

Industrial robots are complex and critical equipment in intelligent manufacturing systems that require ensuring safe and stable operation. The in situ fault diagnosis of industrial robots faces serious industrial environment interference and usually operates in different working conditions. In this article, we propose a ProbSparse attention-based transformer (PSAT) for in situ fault diagnosis for industrial robots under different working conditions. The sparsity measurement in the ProbSparse attention module is specially designed to reduce industrial noise and improve the key general feature extraction. In addition, we explored the length of input signals and proposed a lightweight diagnostic framework based on shorter time series. It can not only reduce the complexity of the model with better real-time performance, but also reduce the storage cost of diagnostic samples, which is very valuable and particularly suitable for industrial robots. The superiority of the proposed method with 99.5% accuracy is validated on real industrial signals industrial robots, which is higher than advanced models.
How does harvest time affect the major fatty acids and bioactive compounds in hazelnut cultivars (Corylus avellana L.)?

This study was conducted to investigate the effects of harvest time on the protein, oil, fatty acids and bioactive compounds in hazelnut cultivars (Corylus avellana L. cvs. ‘Tombul’, ‘Palaz’, ‘Çakıldak’, ‘Okay 28’ and ‘Allahverdi’). The harvest was carried out at 7 different periods with weekly intervals from 20 July to 31 August. As the harvest time progressed, increases and decreases were detected in protein, oil, fatty acids and bioactive compounds. The highest oil content was measured in the H5 and H6 harvest periods. The highest content was determined in H3 for oleic acid. Higher total phenolics, total flavonoids and antioxidant activity were obtained in the first 3 harvest periods than in the other periods. The present findings revealed that the protein, fatty acids and bioactive compounds in hazelnut cultivars may differ according to the harvest time. The results obtained will provide clearer ideas to both the industry and the producers about the optimum harvest time for the intended use of these cultivars.
On Modeling Feedback Congestion Control Mechanism of TCP using Fluid Flow Approximation and Queueing Theory

The Internet uses a window-based congestion control mechanism in TCP (Transmission Control Protocol). In the literature, there have been a great number of analytical studies on TCP. Most of those studies have focused on the statistical behavior of TCP by assuming a constant packet loss probability in the network. However, the packet loss probability, in reality, changes according to packet transmission rates from TCP connections. In this paper, we explicitly model the interaction between the congestion control mechanism of TCP and the network as a feedback system. Namely, we model the congestion control mechanism of TCP as a dynamic system, where the input to the system is the packet loss probability and the output is the window size. Inversely, we model the network as a dynamic system, where the input is the window size and the output is the packet loss probability. The network is modeled by a M/M/1/m queueing system by assuming an existence of a single bottleneck link. Using our analytic model, the transient behavior of TCP connections is quantitatively evaluated with several numerical examples.
Human gesture recognition via bag of angles for 3D virtual city planning in CAVE environment

Cave Automatic Virtual Environment (CAVE) provides an immersive virtual environment for 3D city planning. However, the user in the cave has to wear wearable markers for the interaction with the 3D models. To develop a natural interaction, depth camera like Kinect has to be considered. In this paper, we propose a new skeleton joint representation called Bag of Angles (BoA) for human gesture recognition. We evaluated our proposed BoA representation on two dataset UTD-MHAD and UTD-MHAD-KinectV2. The evaluation results demonstrated that the proposed BoA representation can achieve a higher recognition accuracy compared to the other existing representation methods.
Tie2 in Tumor Endothelial Signaling and Survival: Implications for Antiangiogenic Therapy

Signaling through the Tie2 receptor on endothelial cells has been shown to play an important role in normal and pathologic vascular development. We generated K1735 murine melanoma tumor cells that inducibly express soluble Tie2 receptor (Tie2Ex) to study the effects of inhibiting Tie2 signaling on tumor vasculature. Tie2Ex induction rapidly decreased AKT activation but not extracellular signal-regulated kinase (ERK) activation in tumor endothelial cells as detected by immunostaining. This was accompanied by an increase in endothelial cell TUNEL staining but no change in Ki-67 expression. Together with a decrease in the percentage of perfused vessels, this suggested that tumor vessel regression and impaired vascular function rather than angiogenesis inhibition was responsible for the delay in tumor growth following Tie2Ex treatment. However, Tie2Ex failed to inhibit the growth of larger, more established K1735 tumors. These tumors were additionally treated with sorafenib, a multikinase inhibitor that inhibits tumor endothelial cell ERK activation but not AKT activation. Combining Tie2Ex and sorafenib decreased both endothelial cell AKT and ERK activation, decreased endothelial cell survival and proliferation, and significantly inhibited growth of the more established tumors. These studies indicate that activity of specific signaling pathways and prosurvival effects are brought about by Tie2 activation in tumor endothelial cells, and knowledge of the effects of Tie2 inhibition can lead to development of more effective therapeutic regimens for inhibiting tumor neovascularization. (Mol Cancer Res 2009;7(3):300–10)
Learning about Canonical Views from Internet Image Collections

Although human object recognition is supposedly robust to viewpoint, much research on human perception indicates that there is a preferred or "canonical" view of objects. This phenomenon was discovered more than 30 years ago but the canonical view of only a small number of categories has been validated experimentally. Moreover, the explanation for why humans prefer the canonical view over other views remains elusive. In this paper we ask: Can we use Internet image collections to learn more about canonical views? We start by manually finding the most common view in the results returned by Internet search engines when queried with the objects used in psychophysical experiments. Our results clearly show that the most likely view in the search engine corresponds to the same view preferred by human subjects in experiments. We also present a simple method to find the most likely view in an image collection and apply it to hundreds of categories. Using the new data we have collected we present strong evidence against the two most prominent formal theories of canonical views and provide novel constraints for new theories.
Tracking Beliefs and Intentions in the Werewolf Game

We propose a model of belief and intention change over the course of a dialogue, in the case where the decisions taken during the dialogue affect the possibly conflicting goals of the agents involved. We use Situation Calculus to model the evolution of the world and an observation model to analyze the evolution of intentions and beliefs. In our formalization, utterances, that only change the beliefs and intentions, are observations. We illustrate our formalization with the game of Werewolf.
A model for neural activity in the absence of external stimuli

We study a stochastic process describing the continuous time evolution of the membrane potentials of finite system of neurons in the absence of external stimuli. The values of the membrane potentials evolve under the effect of {\it chemical synapses}, {\it electrical synapses} and a \textit{leak current}. The evolution of the process can be informally described as follows. Each neuron spikes randomly following a point process with rate depending on its membrane potential. When a neuron spikes, its membrane potential is immediately reset to a resting value. Simultaneously, the membrane potential of the neurons which are influenced by it receive an additional positive value. Furthermore, between consecutive spikes, the system follows a deterministic motion due both to electrical synapses and the leak current. Electrical synapses push the system towards its average potential, while the leak current attracts the membrane potential of each neuron to the resting value. We show that in the absence leakage the process converges exponentially fast to an unique invariant measure, whenever the initial configuration is non null. More interesting, when leakage is present, we proved the system stops spiking after a finite amount of time almost surely. This implies that the unique invariant measure is supported only by the null configuration.
All They Know: A Study in Multi-Agent Autoepistemic Reasoning

With few exceptions the study of nonmonotonic reasoning has been confined to the single-agent case. However, it has been recognized that intelligent agents often need to reason about other agents and their ability to reason nonmonotonically. In this paper we present a formalization of multi-agent autoepistemic reasoning, which naturally extends earlier work by Levesque. In particular, we propose an n-agent modal belief logic, which allows us to express that a formula (or finite set of them) is all an agent knows, which may include beliefs about what other agents believe. The paper presents a formal semantics of the logic in the possible-world framework. We provide an axiomatization, which is complete for a large fragment of the logic and sufficient to characterize interesting forms of multi-agent autoepistemic reasoning. We also extend the stable set and stable expansion ideas of single-agent autoepistemic logic to the multi-agent case.
Moored Observations of the Surface Meteorology and Air–Sea Fluxes in the Northern Bay of Bengal in 2015

Time series of surface meteorology and air–sea fluxes from the northern Bay of Bengal are analyzed, quantifying annual and seasonal means, variability, and the potential for surface fluxes to contribute significantly to variability in surface temperature and salinity. Strong signals were associated with solar insolation and its modulation by cloud cover, and, in the 5- to 50-day range, with intraseasonal oscillations (ISOs). The northeast (NE) monsoon (DJF) was typically cloud free, with strong latent heat loss and several moderate wind events, and had the only seasonal mean ocean heat loss. The spring intermonsoon (MAM) was cloud free and had light winds and the strongest ocean heating. Strong ISOs and Tropical Cyclone Komen were seen in the southwest (SW) monsoon (JJA), when 65% of the 2.2-m total rain fell, and oceanic mean heating was small. The fall intermonsoon (SON) initially had moderate convective systems and mean ocean heating, with a transition to drier winds and mean ocean heat loss in the last month. Observed surface freshwater flux applied to a layer of the observed thickness produced drops in salinity with timing and magnitude similar to the initial drops in salinity in the summer monsoon, but did not reproduce the salinity variability of the fall intermonsoon. Observed surface heat flux has the potential to cause the temperature trends of the different seasons, but uncertainty in how shortwave radiation is absorbed in the upper ocean limits quantifying the role of surface forcing in the evolution of mixed layer temperature.
Sexual violence and neonatal outcomes: a Norwegian population-based cohort study

Objective The objective of this study was to explore the association between sexual violence and neonatal outcomes. Design National cohort study. Setting Women were recruited to the Norwegian Mother and Child Cohort Study (MoBa) while attending routine ultrasound examinations from 1999 to 2008. Population A total of 76 870 pregnant women. Methods Sexual violence and maternal characteristics were self-reported in postal questionnaires during pregnancy. Neonatal outcomes were retrieved from the Medical Birth Registry of Norway (MBRN). Risk estimations were performed with linear and logistic regression analysis. Outcome measures: gestational age at birth, birth weight, preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA). Results Of 76 870 women, 18.4% reported a history of sexual violence. A total of 4.7% delivered prematurely, 2.7% had children with a birth weight <2500 g and 8.1% children were small for their gestational age. Women reporting moderate or severe sexual violence (rape) had a significantly reduced gestational length (2 days) when the birth was provider-initiated in an analysis adjusted for age, parity, education, smoking, body mass index and mental distress. Those exposed to severe sexual violence had a significantly reduced gestational length of 0.51 days with a spontaneous start of birth. Crude estimates showed that severe sexual violence was associated with PTB, LBW and SGA. When controlling for the aforementioned sociodemographic and behavioural factors, the association was no longer significant. Conclusions Sexual violence was not associated with adverse neonatal outcomes. Moderate and severe violence had a small but significant effect on gestational age; however, the clinical influence of this finding is most likely limited. Women exposed to sexual violence in this study reported more of the sociodemographic and behavioural factors associated with PTB, LBW and SGA compared with non-abused women.
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures

This paper presents our approach used for cross-lingual textual entailment task (task 8) organized within SemEval 2013. Crosslingual textual entailment (CLTE) tries to detect the entailment relationship between two text fragments in different languages. We solved this problem in three steps. Firstly, we use a off-the-shelf machine translation (MT) tool to convert the two input texts into the same language. Then after performing a text preprocessing,we extract multiple feature types with respect to surface text and grammar. We also propose novel feature types regarding to sentence difference and semantic similarity based on our observations in the preliminary experiments. Finally, we adopt a multiclass SVM algorithm for classification. The results on the cross-lingual data collections providedby SemEval 2013 show that (1) we can build portable and effective systems across languages using MT and multiple effective features; (2) our systems achieve the best results among the participants on two test datasets, i.e., FRA-ENG and DEU-ENG.
A Probabilistic Transmission Dynamic Model to Assess Indoor Airborne Infection Risks

The purpose of this article is to quantify the public health risk associated with inhalation of indoor airborne infection based on a probabilistic transmission dynamic modeling approach. We used the Wells‐Riley mathematical model to estimate (1) the CO2 exposure concentrations in indoor environments where cases of inhalation airborne infection occurred based on reported epidemiological data and epidemic curves for influenza and severe acute respiratory syndrome (SARS), (2) the basic reproductive number, R0 (i.e., expected number of secondary cases on the introduction of a single infected individual in a completely susceptible population) and its variability in a shared indoor airspace, and (3) the risk for infection in various scenarios of exposure in a susceptible population for a range of R0. We also employ a standard susceptible‐infectious‐recovered (SIR) structure to relate Wells‐Riley model derived R0 to a transmission parameter to implicate the relationships between indoor carbon dioxide concentration and contact rate. We estimate that a single case of SARS will infect 2.6 secondary cases on average in a population from nosocomial transmission, whereas less than 1 secondary infection was generated per case among school children. We also obtained an estimate of the basic reproductive number for influenza in a commercial airliner: the median value is 10.4. We suggest that improving the building air cleaning rate to lower the critical rebreathed fraction of indoor air can decrease transmission rate. Here, we show that virulence of the organism factors, infectious quantum generation rates (quanta/s by an infected person), and host factors determine the risk for inhalation of indoor airborne infection.