Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of Planetary Systems within Star Clusters: Aspects of the Solar System’s Early Evolution

Most planetary systems—including our own—are born within stellar clusters, where interactions with neighboring stars can help shape the system architecture. This paper develops an orbit-averaged formalism to characterize the cluster’s mean-field effects, as well as the physics of long-period stellar encounters. Our secular approach allows for an analytic description of the dynamical consequences of the cluster environment on its constituent planetary systems. We analyze special cases of the resulting Hamiltonian, corresponding to eccentricity evolution driven by planar encounters, as well as hyperbolic perturbations upon dissipative disks. We subsequently apply our results to the early evolution of our solar system, where the cluster’s collective potential perturbs the solar system’s plane, and stellar encounters act to increase the velocity dispersion of the Kuiper Belt. Our results are twofold. First, we find that cluster effects can alter the mean plane of the solar system by ≲1° and are thus insufficient to explain the ψ ≈ 6° obliquity of the Sun. Second, we delineate the extent to which stellar flybys excite the orbital dispersion of the cold classical Kuiper Belt and show that while stellar flybys may grow the cold belt’s inclination by the observed amount, the resulting distribution is incompatible with the data. Correspondingly, our calculations place an upper limit on the product of the stellar number density and residence time of the Sun in its birth cluster, η τ ≲ 2 × 104 Myr pc−3.
Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Convolutional neural networks are vulnerable to small $\ell^p$ adversarial attacks, while the human visual system is not. Inspired by neural networks in the eye and the brain, we developed a novel artificial neural network model that recurrently collects data with a log-polar field of view that is controlled by attention. We demonstrate the effectiveness of this design as a defense against SPSA and PGD adversarial attacks. It also has beneficial properties observed in the animal visual system, such as reflex-like pathways for low-latency inference, fixed amount of computation independent of image size, and rotation and scale invariance. The code for experiments is available at this https URL.
Inverse Ising techniques to infer underlying mechanisms from data

As a problem in data science the inverse Ising (or Potts) problem is to infer the parameters of a Gibbs–Boltzmann distributions of an Ising (or Potts) model from samples drawn from that distribution. The algorithmic and computational interest stems from the fact that this inference task cannot be carried out efficiently by the maximum likelihood criterion, since the normalizing constant of the distribution (the partition function) cannot be calculated exactly and efficiently. The practical interest on the other hand flows from several outstanding applications, of which the most well known has been predicting spatial contacts in protein structures from tables of homologous protein sequences. Most applications to date have been to data that has been produced by a dynamical process which, as far as it is known, cannot be expected to satisfy detailed balance. There is therefore no a priori reason to expect the distribution to be of the Gibbs–Boltzmann type, and no a priori reason to expect that inverse Ising (or Potts) techniques should yield useful information. In this review we discuss two types of problems where progress nevertheless can be made. We find that depending on model parameters there are phases where, in fact, the distribution is close to Gibbs–Boltzmann distribution, a non-equilibrium nature of the under-lying dynamics notwithstanding. We also discuss the relation between inferred Ising model parameters and parameters of the underlying dynamics.
On The Secrecy Performance Over N-Wave With Diffuse Power Fading Channel

We investigate the realistic propagation conditions effects on wireless physical layer security, which are different from classical Rice and Rayleigh fading. Specifically, we study how the superposition of a number of dominant specular waves and diffusely propagating components impacts the achievable secrecy performance. We derive analytical expressions for the secrecy outage probability and the average secrecy capacity, which have similar complexity to other alternatives in the literature derived for simpler fading models. We provide very useful insights on the impact on physical layer security of <inline-formula><tex-math notation="LaTeX">$(i)$</tex-math></inline-formula> the number; <inline-formula><tex-math notation="LaTeX">$(ii)$</tex-math></inline-formula> the relative amplitudes and <inline-formula><tex-math notation="LaTeX">$(iii)$</tex-math></inline-formula> the overall power of the dominant specular components. We show that it is possible to obtain remarkable improvements on the system secrecy performance when: <inline-formula><tex-math notation="LaTeX">$(a)$</tex-math></inline-formula> the relative amplitudes of the dominant specular components for the legitimate channel are more unbalanced compared to those of the eavesdropper's channel, and <inline-formula><tex-math notation="LaTeX">$(b)$</tex-math></inline-formula> the power of the dominant components for the main channel is significantly larger than the power of the dominant components for the wiretap channel.
Understanding individual behaviour: from virtual to physical patterns

As "Big Data" has become pervasive, an increasing amount of research has connected the dots between human behaviour in the offline and online worlds. Consequently, researchers have exploited these new findings to create models that better predict different aspects of human life and recommend future behaviour. To date, however, we do not yet fully understand the similarities and differences of human behaviour in these virtual and physical worlds. Here, we analyse and discuss the mobility and application usage of 400,000 individuals over eight months. We find an astonishing similarity between people's mobility in the physical space and how they move from app to app in smartphones. Our data shows that individuals use and visit a finite number of apps and places, but they keep exploring over time. In particular, two distinct profiles of individuals emerge: those that keep changing places and services, and those that are stable over time, named as "explorers" and "keepers". We see these findings as crucial to enrich a discussion for the potentials and the challenges of building human-centric AI systems, which might leverage recent results in Computational Social Science.
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System

In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs. bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants of a recurrent neural network that models the temporal structure of video sequences of students solving math problems. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in mean F -score of 50 % over the state-of-the-art method on this new dataset.
Delay Effects on Amplitude Death, Oscillation Death, and Renewed Limit Cycle Behavior in Cyclically Coupled Oscillators

The effects of a distributed 'weak generic kernel' delay on cyclically coupled limit cycle and chaotic oscillators are considered. For coupled Van der Pol oscillators (and in fact, other oscillators as well) the delay can produce transitions from amplitude death(AD) or oscillation death (OD) to Hopf bifurcation-induced periodic behavior, with the delayed limit cycle shrinking or growing as the delay is varied towards or away from the bifurcation point respectively. The transition from AD to OD is mediated here via a pitchfork bifurcation, as seen earlier for other couplings as well. Also, the cyclically coupled undelayed van der Pol system here is already in a state of AD/OD, and introducing the delay allows both oscillations and AD/OD as the delay parameter is varied. This is in contrast to other limit cycle systems, where diffusive coupling alone does not result in the onset of AD/OD. For systems where the individual oscillators are chaotic, such as a Sprott oscillator system or a coupled van der Pol-Rayleigh system with parametric forcing, the delay may produce AD/OD (as in the Sprott case), with the AD to OD transition now occurring via a transcritical bifurcation instead. However, this may not be possible, and the delay might just vary the attractor shape. In either of these situations however, increased delay strength tends to cause the system to have simpler behavior, streamlining the shape of the attractor, or shrinking it in cases with oscillations.
Smart SDN Management of Fog Services

A Smart Service Manager is proposed to direct user requests (such as those coming from IoT devices) at the edge towards appropriate servers where the requested services can be satisfied. Services are housed at different Fog locations, and the system is subject to variations in workload. The approach is based on using a Software Defined Network (SDN) controller as the means to take decisions with measurement data based machine learning that uses Reinforcement Learning to make the best choices. The system we have developed is illustrated with experimental results on a test-bed with time-varying loads that confirm its ability to adapt to significant changes in system load and preserve the users’ Quality of Service.
Cloacal swabs are unreliable sources for estimating lower gastro-intestinal tract microbiota in chicken.

Background The gastrointestinal microbiota in chicken ( Gallus gallus domesticus ) has a central role in health and performance. The ceca are a vital site of functional activity, but assessing cecal microbiota in longitudinal studies remains a challenge. The cecal communities are broadly similar to large intestine samples. Cloacal sampling, due to their proximity to the large intestine, is an alternative, non-invasive method used for assaying and monitoring disease-causing agents, and maybe a viable option for longitudinal studies. Results We collected paired cecal content, and cloacal swab samples from twenty randomly selected commercial broilers raised on two dietary treatments. The microbiota of each sample was assessed using 16S rRNA V4 hypervariable region sequencing on an Illumina MiSeq platform and analyzed using the MOTHUR pipeline. Analysis of fourteen paired samples resulted in 1603 OTU’s assigned to 82 Families. Eleven families were shared between the cecal and cloacal samples, with seven and eleven families unique to cecal content and cloacal swabs, respectively. Paired t-test and Wilcoxon Signed-Rank test showed significant differences in the Chao1 index between the cecal content and cloacal swabs (p-value = 0.000845 and p-value = 0.001397, respectively). However, the Inverse Simpson species diversity estimator was not different using the Wilcoxon Signed-Rank test (p-value = 0.3258) and a paired t-test (p-value = 0.3864). β-diversity between the cloacal swabs and cecal microbiota also showed significant differences based on PERMANOVA (p-value = <0.001), HOMOVA (p-value <0.001), and Weighted Unifrac (WSig = <0.001) testing. Conclusions Cloacal swabs do not approximate either the α or β diversity of cecal samples, based on a paired sample analysis. The high variability of cloacal microbiota has been reported previously, and this study provides additional evidence of the randomness of cloacal microbiota in contrast to cecal microbiota. Our findings indicate that cloacal samples are not suitable for longitudinal studies of gut microbiota patterns. High inter-individual variation of cloacal swab data warrants further assessment of their reliability as a targeted diagnostic method.
Humic Acid Reduces the Available Cadmium, Copper, Lead, and Zinc in Soil and Their Uptake by Tobacco

Tobacco (Nicotiana tabacum L.) is a crop that is able to accumulate metals. In this study, humic acid was selected as a Cd, Cu, Pb, and Zn passivator, and added to calcareous field soil in amounts of 6.4, 10.3, and 14.8 kg·ha−1. Its impact on the soil fractions of the metals in the soil was extracted by the Community Bureau of Reference (BCR) sequential extraction method, and their accumulation of the metals in tobacco leaves was investigated. Application of 14.8 kg·ha−1 humic acid decreased the DTPA-extracted concentrations of Pb, Cd, Zn, and Cu by 39%, 37%, 29%, and 18%, respectively, as compared with untreated soil. The fractions of Cd, Pb, Cu, and Zn in soil were extracted by the BCR sequential extraction method, and the relationship between the difference metal fractions in the soil and the metal contents in the plant materials were analyzed. The exchangeable fractions of Cd, Pb, Cu, and Zn and the reducible fractions of Pb and Cu are the main bioavailable fractions. Additionally, the reducible fractions of Cd and Zn, the oxidizable fractions of Pb, Cu, and Zn, and all residual fractions of metals were nonbioavailable fractions in the soil. The soils were treated with humic acid (HA) to shift bioavailable metals to stable phases that were less bioavailable. The available Cd, Pb, Cu, and Zn were strongly retained in the soil after the application of humic acid, which decreased the uptake in tobacco in the upper, middle, and lower leaves.
A Scan-to-BIM Methodology Applied to Heritage Buildings

Heritage buildings usually have complex (non-parametric) geometries that turn their digitization through conventional methods in inaccurate and time-consuming processes. When it comes to the survey and representation of historical assets, remote sensing technologies have been playing key roles in the last few years: 3D laser scanning and photogrammetry surveys save time in the field, while proving to be extremely accurate at registering non-regular geometries of buildings. However, the efficient transformation of remote-sensing data into as-built parametric smart models is currently an unsolved challenge. A pragmatic and organized Historic Building Information Modeling (HBIM) methodology is essential in order to obtain a consistent model that can bring benefits and integrate conservation and restoration work. This article addresses the creation of an HBIM model of heritage assets using 3D laser scanning and photogrammetry. Our findings are illustrated in one case study: The Engine House Paços Reais in Lisbon. The paper first describes how and what measures should be taken to plan a careful scan-to-HBIM process. Second, the description of the remote-sensing survey campaign is conducted accordingly and is aimed at a BIM output, including the process of data alignment, cleaning, and merging. Finally, the HBIM modeling phase is described, based on point cloud data.
A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). It is shown that the obtained model is able to predict the state of charge of the battery with acceptable precision in the case of a failure of the current sensor.
Follow-up Telephone Contact following Discharge from Long-Term Acute Care Hospitals

Readmissions to acute care hospitals within 30 days post discharge are a burden to healthcare economy. Medicare Payment Advisory Commission (2007) estimated a cost of $12 billion dollars is spent each year on Medicare patients who are readmitted to an acute care hospital within 30 days post discharge. MedPAC (2007) estimated that 75% of those readmissions are avoidable. A review of the literature was conducted. Thirteen studies were reviewed and analyzed and the results of the literature review indicated there is evidence supportive of implementing telephone follow-up contact with patients discharged from the LTACH to assess for additional needs and to intervene early in the event of health deterioration. The purpose of this project was to implement the post discharge telephone follow up contact for patients discharged from an LTACH that is part of a larger health system. One of the interventions in Project RED was used to implement the post discharge telephone contact. Tools used to collect data were those contained in the Project RED toolkit (Boston University Medical School, 2014). Statistical analyses included frequency distribution tables for demographic data and readmission rate outcome data; and independent t tests to analyze the differences between the baseline group and the intervention group. Results indicated that there was no difference in the mean age or LOS between the Baseline and Intervention groups. The results indicated a lower readmission rate in the intervention group compared to the baseline group. FOLLOW-UP TELEPHONE CONTACT 3 Follow-up Telephone Contact following Discharge from Long-Term Acute Care Hospitals
The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion

With the profound understanding of the world, modeling and simulation has been used to solve the problems of complex systems. Generally, mechanism-models are often used to model the engineering systems following the Newton laws, and this kind of modeling approach is called white-box modeling; however, when the internal structure and characteristics of some systems are hard to understand, the black-box modeling based on statistic and data-modeling is often used. For most complex real systems, a single modeling approach can hardly describe the target system accurately. In this paper, we firstly discuss and compare the white-box and black-box modeling approaches. Then, to mitigate the limitations of these two modeling methods in mechanism-partially-observed systems, the gray-box based modeling approach integrating both a mechanism model and data model is proposed. In order to explain the idea of gray-box based modeling, the atmosphere dispersion modeling is studied in practical cases from two symmetric aspects. Specifically, the framework of data assimilation is used to illustrate the modeling from white-box to gray-box, while the Gauss features based Support Vector Regression (SVR) models are used to illustrate the modeling from black-box to gray-box. To verify the feasibility of the gray-box modeling method, we conducted both simulation experiments and real dataset symmetry experiments. The experiment results show the enhanced performance of the gray-box based modeling approach. In the end, we expect that this gray-box based modeling approach will be an alternative modeling approach for different existing systems.
Development of an inducible T7 expression system in Bacillus subtilis ATCC 6051a for production of α-L-arabinofuranosidase

Background: Xylan is the second most abundant polysaccharide biomass on the earth, the polymers have a backbone of β-1,4-linked xylose residues with various side-chain substitutions, such as arabinose, acetic acid, glucuronic acid, and other esterified groups, thus, the removal of arabinose side groups by α-L-arabinofuranosidases is helpful in various industrial processes involving xylan treatment. Bacillus subtilis ATCC 6051a is known for its excellent capacity of secretory production of recombinant peptides, however, poor experience in genetic manipulation and lack of universal expression elements impede this strain for wider application.Results: Xylose inducible comK was integrated into the genome of B. subtilis ATCC 6051a, and the transformation efficiency of the engineered strain B. subtilis 164S was increased by more than 1000 folds. B. subtilis 164S was further modified to generate B. subtilis 164T7P which incorporates a D-xylose inducible T7 RNA polymerase. The recombinant GFP expressed by 164T7P is more than thirteen times that achieved by P43 promoter, representing the most efficient expression system that had been ever reported in B. subtilis . Subsequently, abfA1 , encoding a glycoside hydrolase (GH) family 51 enzyme was cloned and overexpressed in 164T7P. The activity of recombinant α-L-arabinofuranosidase (AbfA1) reached 90.6±2.0 U mL -1 in the fermentation broth. Using p NPA as a substrate, kinetic parameters of the crude enzyme were Km of 1.4±0.1 mM and kcat of 139.4 s −1 . The optimum temperature and pH of the recombinant AbfA1 towards p NPA were observed to be 45 °C and pH 6.5, respectively.Conclusion: With enhanced cellular competence and introduction of T7 RNA polymerase, B. subtilis ATCC 6051a was engineered as a versatile cell tool for recombinant production of heterologous peptides employing T7 promoter. The novel expression kit demonstrated a very low level of leaky expression of target genes. The efficiency and applicability of the system were demonstrated by high-level production of a bacterial type α-L-arabinofuranosidase.
Effect of Climate and Roasting on Polyphenols and Tocopherols in the Kernels and Skin of Six Hazelnut Cultivars (Corylus avellana L.)

Poland is one of the largest producers of hazelnuts in Europe; however, information about the contents of biologically active compounds in hazelnuts grown in Poland is limited. The aim of this study was to analyze and compare the concentrations of phenolic acids, flavonols, and tocopherols in raw and roasted hazelnut kernels and their skin, in addition to determining the effect of the cultivar and year of cultivation. The experiment was carried out in 2016–2018, and the nuts of six hazelnut cultivars were used for research purposes. Among the detected phenolic acids, p -coumaric acid was the most abundant in roasted and unroasted kernels, whereas the apigenin content represented 43%–45% of the total flavonols. The harvest year had a significant effect on the polyphenol content of hazelnuts, whereas roasting had a minor impact. The hazelnut skin contained 168- to 378- fold higher contents of total phenolic compounds than the raw and roasted nuts. Gallic acid was the most abundant phenolic acid in hazelnut skin, contributing 95.0% of the total polyphenols on average. α-Tocopherol was the most abundant tocopherol in the hazelnut cultivars, followed by β + ƴ-tocopherol and δ-tocopherol in hazelnut cultivars. The statistical analysis showed that roasting and year of cultivation did not affect the tocopherol contents of the investigated samples.