Miracle-dz/UniFAR-FTUs huggingface dataset:
Gravitational collapse in dust lanes and the appearance of spiral structure in galaxies

We consider the possibility that there are two fundamental types of spirals in each spiral galaxy: one results from a spiral density wave, and the other is a stochastic spiral produced by the shear of self-propagating star formation events. It is proposed that there is a physical and causal connection between these two spirals, with the role of intermediary played by the dust lanes, which we assume to be shocked interstellar matter. We show that dust lanes should continuously collapse and fragment into massive cloud complexes by the action of their self-gravitational forces. The origin of the molecular ring at 5--6 kpc from the galactic center seems to be the result of a sharp minimum in the opposition to dust lane collapse by galactic tidal forces at these radii. When the resulting clouds begin to form stars (probably by an independent process), the pressures generated by their own OB associations will push them away from their star clusters and cause them to be shuffled around randomly in the region of the galaxy that lies ahead of the dust lane. Massive star formation may occur in the same clouds on numerous occasions, but they will have a different location each time.more » This shuffling will not only produce the appearance of a stochastic arm of young, star-forming sites (superposed on the underlying spiral), but it will also give the required stability to the numerical calculations of Gerola and Seiden, when combined with the assumption of a steady state cloud population for the whole galaxy. Numerous observational examples of these processes are cited for our own Galaxy and for other galaxies. « less
Damping of Propagating Kink Waves in the Solar Corona

Alfvénic waves have gained renewed interest since the existence of ubiquitous propagating kink waves were discovered in the corona. It has long been suggested that Alfvénic waves play an important role in coronal heating and the acceleration of the solar wind. To this effect, it is imperative to understand the mechanisms that enable their energy to be transferred to the plasma. Mode conversion via resonant absorption is believed to be one of the main mechanisms for kink wave damping and it is considered to play a key role in the process of energy transfer. This study examines the damping of propagating kink waves in quiescent coronal loops using the Coronal Multi-channel Polarimeter. A coherence-based method is used to track the Doppler velocity signal of the waves, which enables us to investigate the spatial evolution of velocity perturbations. The power ratio of outward to inward propagating waves is used to estimate the associated damping lengths and quality factors. To enable accurate estimates of these quantities, we provide the first derivation of a likelihood function suitable for fitting models to the ratio of two power spectra obtained from discrete Fourier transforms. Maximum likelihood estimation is used to fit an exponential damping model to the observed variation in power ratio as a function of frequency. We confirm earlier indications that propagating kink waves are undergoing frequency-dependent damping. Additionally, we find that the rate of damping decreases, or equivalently the damping length increases, for longer coronal loops that reach higher in the corona.
Neumann system, spherical pendulum and magnetic fields

In this paper we study a certain magnetic-like perturbation of the Neumann system. We prove the integrability of this system and show how its solutions are related to the solutions of a charged spherical pendulum influenced by the topologically nontrivial magnetic field Bd(q) = q/q3 of the Dirac monopole. In the case when the quadratic potential of the Neumann system has a suitable axial symmetry, our system describes the motion of a charged particle under the influence of the potential and the homogeneous magnetic field Bh(q) = (1, 0, 0).
Mixed mechanism of conformational selection and induced fit as a molecular recognition process in the calreticulin family of proteins

The fundamental question on the mechanism of molecular recognition during ligand binding has attracted a lot of scientific scrutiny. The two competing theories of ligand binding–“induced fit” and “conformational selection” have been proposed to explain biomolecular recognition. Since exploring a family of proteins with similar structural architectures and conserved functional roles can provide valuable insight into the significance of molecular structure and function, we performed molecular dynamics simulations on the calreticulin family of proteins, which specifically recognize monoglucosylated N-glycan during the protein folding process. Atomistic simulations of lectins in free and bound forms demonstrated that they exist in several conformations spanning from favorable to unfavorable for glycan binding. Our analysis was confined to the carbohydrate recognition domain (CRD) of these lectins to demonstrate the degree of conservation in protein sequence and structure and relate them with their function. Furthermore, we computed the lectin-glycan binding affinity using the mmPBSA approach to identify the most favorable lectin conformation for glycan binding and compared the molecular interaction fields in terms of noncovalent bond interactions. We also demonstrated the involvement of Tyr and Trp residues in the CRD with the non-reducing end glucose and central mannose residues, which contribute to some of the specific interactions. Furthermore, we analyzed the conformational changes in the CRD through SASA, RMSFs and protein surface topography mapping of electrostatic and hydrophobic potentials. Our findings demonstrate a hybrid mechanism of molecular recognition, initially driven by conformational selection followed by glycan-induced fluctuations in the key residues to strengthen the glycan binding interactions.
Bethe ansatz for two-deviation states in quantum spin chains of arbitrary S with anisotropic Heisenberg exchange

The Bethe ansatz is used to calculate the eigenstates of Heisenberg spin chains of arbitrary S with two deviations from the completely aligned state. The states are analysed into bound (class A/B) and 'free' (class C). States with a high probability of having two deviations on the same site are found to be class C. The authors also obtain asymptotic expressions for the magnetisation and the autocorrelation function in the spin-flop phase as the field approaches the spin-flop critical field.
Temporal control mechanism of repetitive tapping with simple rhythmic patterns

In previous studies, it was shown that the temporal control in equal-interval tapping is governed by a memory mechanism, which preserves the information of the preceding 20 intervals to determine the interval of the present tap. In the first stage of the present study, an equal-interval tapping experiment was carried out. The results of the experiment confirmed that the 20-interval memory mechanism governs the temporal control of single-finger equal-interval tapping for various tempi. In the following stages of the present study, simple rhythmic patterns were constructed with long and short time values with a 2:1 ratio. The temporal fluctuation in repetitive tapping of these rhythmic patterns was analyzed using Fourier analysis and autoregressive models. The results showed that the 20-interval memory mechanism also governs the temporal control of the tapping for the simple rhythmic patterns: In the case of a rapid tempo, the 20-interval memory mechanism is active for the long time value, whereas in the case of a slow tempo, it is active for the short time value. The point at which the memory mechanism switches between the long and short time values is located around a tempo in which the short time value corresponds to 350 ms.
Computed tomography assessment of body composition in patients with nonmetastatic breast cancer: what are the best prognostic markers?

Objective To correlate body composition measures, based on computed tomography (CT) analysis of muscle mass and adipose tissue, with disease-free survival in breast cancer patients. Materials and Methods This single-center retrospective study included 262 female patients with nonmetastatic breast cancer. Body composition was assessed on a pretreatment CT scan (at the L3 level). The analysis included quantification of the areas of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle mass, as well as of the mean skeletal muscle density. The VAT/SAT ratio, skeletal mass index (SMI), and skeletal muscle gauge (SMG) were calculated. Results Of the 262 patients evaluated, 175 (66.8%) were classified as overweight or obese on the basis of their body mass index. We observed low SMI in 35 patients (13.4%) and elevated VAT in 123 (46.9%). Disease-free survival was significantly shorter in the patients who underwent neoadjuvant chemotherapy (p = 0.044), in those with a low SMI (p = 0.006), in those with low SMG (p = 0.013), and in those with a low VAT/SAT ratio (p = 0.050). In a multivariate analysis, only SMG, the VAT/SAT ratio, and having undergone neoadjuvant chemotherapy retained their statistical significance. Conclusion Our results confirm that low SMG and the VAT/SAT ratio can be used as imaging biomarkers to assess prognosis in patients with nonmetastatic breast cancer.
Robust Gyro‐free Attitude Estimation for a Small Fixed‐wing Unmanned Aerial Vehicle

This paper proposes a backup attitude estimation scheme for small fixed‐wing unmanned aerial vehicles (UAVs) in the event of gyroscopic failure. The attitude is propagated in terms of 3 degrees‐of‐freedom (DoF) aircraft dynamics. The errors in attitude propagation are updated using indirect attitude information obtained from accelerations as sensed by onboard accelerometers and a global positioning system (GPS) receiver. In the event of gyroscopic failure, large uncertainties are introduced into the attitude propagation model. Such uncertainties in states and parameters are modeled as norm‐bound uncertainties and a discrete‐time robust extended Kalman filter (REKF) is implemented to estimate the attitude of the UAV.
Seismic and ultrasonic data analysis for characterizing people and animals

In this paper we analyze the seismic signals generated by people and animals walking. It is known that when a person walks, the heel strikes first and then the front of the foot; whereas animals walk on their hoofs. This difference in the walking patterns result in significant changes in the seismic signatures for both people and animals. Similarly, men walk differently than women and they also have different weight distributions resulting in different signatures for men and women. They also have different cadence or gait patterns. We distinguish the significant features in seismic signatures to distinguish people and animals. Ultrasonic Doppler returns capture the variations in the gait. The Doppler returns will be analyzed to distinguish people and animals. Algorithms to classify the signatures will be provided. The algorithms will be tested on the data collected at a horse farm with women, men and people walking. The results will be discussed along with possible future research directions to reduce the number of false alarms.
Stress, Genetics and Mood: Impact of COVID-19 on a College Freshman Sample

Objectives: Use a longitudinal approach to study the impact of the Covid-19 pandemic on the emergence of symptoms of depression and/or anxiety in college freshmen. Define the interplay between genetic risk and psychosocial factors in shaping vulnerability or resilience to pandemic stress. Methods: University of Michigan freshmen were characterized at baseline using multiple psychological instruments. They were genotyped and polygenic risk score for depression (MDD-PRS) was calculated. Daily physical activity was captured. They were sampled at multiple time points throughout the freshman year on clinical rating scales, including GAD-7 and PHQ-9 for anxiety and depression, respectively. The 2019-2020 cohort (N=122) was compared to an earlier cohort (N=106) to assess the impact of the pandemic. Results: Across cohorts, 25%-57% of freshmen developed significant symptoms of anxiety or depression. In the 2019-2020 cohort, measures of anxiety and depression increased significantly after the onset of COVID-19. Physical activity was dramatically reduced by the pandemic and was associated with the emergence of mood symptoms. Low MDD-PRS subjects exhibited lower relative risk for depression/anxiety during a typical freshman year, but they were more negatively impacted by the pandemic than High MDD-PRS subjects. Conversely, a cluster of psychological indices at baseline predicted resilience in High MDD-PRS subjects who did not develop a mood disorder post-stress. Conclusions: The pandemic had a profound impact on college freshmen triggering depression and anxiety symptoms. Pandemic stress overrode the advantage conferred by "genetic resilience". By contrast, "psychosocial resilience" was protective even in the face of high genetic risk and pandemic stress.
Rough wall turbulent boundary layers

This paper describes a detailed experimental study of turbulent boundary-layer development over rough walls in both zero and adverse pressure gradients. In contrast to previous work on this problem the skin friction was determined by pressure tapping the roughness elements and measuring their form drag. Two wall roughness geometries were chosen each giving a different law of behaviour; they were selected on the basis of their reported behaviour in pipe flow experiments. One type gives a Clauser type roughness function which depends on a Reynolds number based on the shear velocity and on a length associated with the size of the roughness. The other type of roughness (typified by a smooth wall containing a pattern of narrow cavities) has been tested in pipes and it is shown here that these pipe results indicate that the corresponding roughness function does not depend on roughness scale but depends instead on the pipe diameter. In boundary-layer flow the first type of roughness gives a roughness function identical to pipe flow as given by Clauser and verified by Hama and Perry & Joubert. The emphasis of this work is on the second type of roughness in boundary-layer flow. No external length scale associated with the boundary layer that is analogous to pipe diameter has been found, except perhaps for the zero pressure gradient case. However, it has been found that results for both types of roughness correlate with a Reynolds number based on the wall shear velocity and on the distance below the crests of the elements from where the logarithmic distribution of velocity is measured. One important implication of this is that a zero pressure gradient boundary layer with a cavity type rough wall conforms to Rotta's condition of precise self preserving flow. Some other implications of this are also discussed.
Stochastic Modeling of Biofilm Formation with Bacterial Quorum Sensing

Bacteria generally live in complicated structures called biofilms, consisting of communicating bacterial colonies and extracellular polymeric substance (EPS). Since biofilms are related to detrimental effects such as infection or antibiotic resistance in different settings, it is essential to model their formation. In this paper, a stochastic model is proposed for biofilm formation, using bacterial quorum sensing (QS). In this model, the biological processes in the biofilm formation are modeled as a chemical reaction network which includes bacterial reproduction, productions of autoinducer and EPS, and their diffusion. The modified explicit tau-leap simulation algorithm is adapted based on the two-state QS mechanism. Our approach is validated by using the experimental results of Pseudomonas putida IsoF bacteria for autoinducer and bacteria concentration. It is also shown that the percentage of EPS in the biofilm increases significantly after the state change in QS, while it decreases before QS is activated. The presented work shows how the biofilm growth can be modeled realistically by using the QS mechanism in stochastic simulations of chemical reactions.
How to select an objective function using information theory

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? Information theory gives one answer: To maximize the information in the model, select the most likely objective function or whichever represents the error in the fewest bits. To evaluate different objectives, transform them into likelihood functions. As likelihoods, their relative magnitudes represent how much we should prefer one objective versus another, and the log of their magnitude represents the expected uncertainty of the model.
Single Cell Training on Architecture Search for Image Denoising

Neural Architecture Search (NAS) for automatically finding the optimal network architecture has shown some success with competitive performances in various computer vision tasks. However, NAS in general requires a tremendous amount of computations. Thus reducing computational cost has emerged as an important issue. Most of the attempts so far has been based on manual approaches, and often the architectures developed from such efforts dwell in the balance of the network optimality and the search cost. Additionally, recent NAS methods for image restoration generally do not consider dynamic operations that may transform dimensions of feature maps because of the dimensionality mismatch in tensor calculations. This can greatly limit NAS in its search for optimal network structure. To address these issues, we re-frame the optimal search problem by focusing at component block level. From previous work, it's been shown that an effective denoising block can be connected in series to further improve the network performance. By focusing at block level, the search space of reinforcement learning becomes significantly smaller and evaluation process can be conducted more rapidly. In addition, we integrate an innovative dimension matching modules for dealing with spatial and channel-wise mismatch that may occur in the optimal design search. This allows much flexibility in optimal network search within the cell block. With these modules, then we employ reinforcement learning in search of an optimal image denoising network at a module level. Computational efficiency of our proposed Denoising Prior Neural Architecture Search (DPNAS) was demonstrated by having it complete an optimal architecture search for an image restoration task by just one day with a single GPU.
Towards a general purpose machine translation system for Sranantongo

Machine translation for Sranantongo (Sranan, srn), a low-resource Creole language spoken predominantly in Surinam, is virgin territory. In this study we create a general purpose machine translation system for srn. In order to facilitate this research, we introduce the SRNcorpus, a collection of parallel Dutch (nl) to srn and monolingual srn data. We experiment with a wide range of proven machine translation methods. Our results demonstrate a strong baseline machine translation system for srn.
Privacy-Preserving Collaborative Learning Through Feature Extraction

We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor, and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use three different datasets for evaluations.
Incorporating Ethics in Computing Courses: Barriers, Support, and Perspectives from Educators

Incorporating ethics into computing education has become a priority for the SIGCSE community. Many computing departments and educators have contributed to this endeavor by creating standalone computing ethics courses or integrating ethics modules and discussions into preexisting curricula. In this study, we hope to support this effort by reporting on computing educators' attitudes toward including ethics in their computing classroom, with a special focus on the structures that hinder or help this endeavor. We surveyed 138 higher education computing instructors to understand their attitudes toward including ethics in their classes, what barriers might be preventing them from doing so, and which structures best support them. We found that even though instructors were generally positive about ethics as a component of computing education, there are specific barriers preventing ethics from being included in some computing courses. In this work, we explore how to alleviate these barriers and outline support structures that could encourage further integration of ethics and computing in higher education.
Forecasting formation of a Tropical Cyclone Using Reanalysis Data

The tropical cyclone formation process is one of the most complex natural phenomena which is governed by various atmospheric, oceanographic, and geographic factors that varies with time and space. Despite several years of research, accurately predicting tropical cyclone formation remains a challenging task. While the existing numerical models have inherent limitations, the machine learning models fail to capture the spatial and temporal dimensions of the causal factors behind TC formation. In this study, a deep learning model has been proposed that can forecast the formation of a tropical cyclone with a lead time of up to 60 hours with high accuracy. The model uses the high-resolution reanalysis data ERA5 (ECMWF reanalysis 5 th generation), and best track data IBTrACS (International Best Track Archive for Climate Stewardship) to forecast tropical cyclone formation in six ocean basins of the world. For 60 hours lead time the models achieve an accuracy in the range of 86 . 9% − 92 . 9% across the six ocean basins. The model takes about 5-15 minutes of training time depending on the ocean basin, and the amount of data used and can predict within seconds, thereby making it suitable for real-life usage.