Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuk, V. A. Goy, S. D. Liubimov, A. V. Molochkov
An Elementary Exposition of Pisier's Inequality

Pisier's inequality is central in the study of normed spaces and has important applications in geometry. We provide an elementary proof of this inequality, which avoids some non-constructive steps from previous proofs. Our goal is to make the inequality and its proof more accessible, because we think they will find additional applications. We demonstrate this with a new type of restriction on the Fourier spectrum of bounded functions on the discrete cube.
Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification

Standard deep learning models that employ the categorical cross-entropy loss are known to perform well at image classification tasks. However, many standard models thus obtained often exhibit issues like feature redundancy, low interpretability, and poor calibration. A body of recent work has emerged that has tried addressing some of these challenges by proposing the use of new regularization functions in addition to the cross-entropy loss. In this paper, we present some surprising findings that emerge from exploring the role of simple orthogonality constraints as a means of imposing physics-motivated constraints common in imaging. We propose an Orthogonal Sphere (OS) regularizer that emerges from physics-based latent-representations under simplifying assumptions. Under further simplifying assumptions, the OS constraint can be written in closed-form as a simple orthonormality term and be used along with the cross-entropy loss function. The findings indicate that orthonormality loss function results in a) rich and diverse feature representations, b) robustness to feature sub-selection, c) better semantic localization in the class activation maps, and d) reduction in model calibration error. We demonstrate the effectiveness of the proposed OS regularization by providing quantitative and qualitative results on four benchmark datasets - CIFAR10, CIFAR100, SVHN and tiny ImageNet.
Dual-SLAM: A framework for robust single camera navigation

SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position estimates. This makes SLAM fast but also makes it vulnerable to local pose estimation failures. As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle. This paper attempts to correct this problem. We note that while local pose estimation is ill-conditioned, pose estimation over longer sequences is well-conditioned. Thus, local pose estimation errors eventually manifest themselves as mapping inconsistencies. When this occurs, we save the current map and activate two new SLAM threads. One processes incoming frames to create a new map and the other, recovery thread, backtracks to link new and old maps together. This creates a Dual-SLAM framework that maintains real-time performance while being robust to local pose estimation failures. Evaluation on benchmark datasets shows Dual-SLAM can reduce failures by a dramatic 88%.
The Reaction to Phto period and Temperature of Fertility the Different of the Wheat Nucleus Substitutional Lines of D_2 Genus Aegilops Cytoplasm

The eleven wheat nucleus substitution Lines in D 2 genus( Aegilops crassa , Aegilops vavilovii and Aegilops juvenalis ) cytoplasm and three of cheir parents were sown in the five locations between latitude of China.The results were shown in the followings:(1) There were some differences in the DL sensitivity of wheat nucleus substitution lines strains of the three Aegilops types. According to the integral analysis Aegilops crassa response was one of the most sensitive in them,next was Aegilops vavilovii and then was Aegilops juvenalis . (2) There were different intensive effects of DL and AT on the fertility of D 2 genus Agilops cytoplasm wheat nucleus substitution lines DL was basic factor and AT effect was shown in the similar DL condition. (3) The nucleoplasm intereffect was the genetic way of the response of wheat nucleus substitution line fertility of D 2 genus Aegilops cytoplasm on light and temperature.
Emergence of effective temperatures in an out-of-equilibrium model of RNA folding

We investigate the possibility of extending the notion of temperature in a stochastic model for the RNA/protein folding driven out of equilibrium. We simulate the dynamics of a small RNA hairpin subject to an external pulling force, which is time-dependent. First, we consider a fluctuation-dissipation relation (FDR), that extends the fluctuation-dissipation theorem in nonequilibrium contexts. We verify that various effective temperatures values can be obtained in a range of parameters and for different observables, only when the slowest intrinsic relaxation timescale of the system regulates the dynamics of the system. Then, we introduce a different nonequilibrium temperature, which is defined from the rate of heat exchanged with a weakly-interacting thermal bath. Notably, this `kinetic' temperature can be defined for any frequency of the external switching force. We also discuss and compare the behavior of these two emerging parameters, by discriminating the time-delayed nature of the FDR temperature from the instantaneous character of the kinetic temperature.
EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PONTENSIAL PENCILAN The Effectiveness of Quantile Regression in Dealing with Potential Outliers

Quantile regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimates such as the presence of potential outliers in the data. The purpose of this study was to evaluate the effectiveness of quantile regression in dealing with potential outliers in multiple linear regression compared to ordinary least square (OLS). This study used simulation data in multiple regression model with the number of independent variables (p=3) for different sample sizes (n = 20, 40, 60, 100, 200) and β0 = 0 and β1 = β2 = β3 = 1 repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β parameters was measured by Mean square error (MSE) and the best model is chosen based on the smallest Akaike Information Criterion (AIC) value. The results showed that in contrast to OLS, quantile regression was able to deal with potential outliers and provided a better estimator with a smaller mean mean square error. Compared to OLS and other quantiles, this study also provides sufficient results that quantile 0.5 provides the best parameter estimate and the best model based on the smallest MSE and AIC values.
Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, progression, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis and models disease progression using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Our progression model accurately characterizes the severity level of each pathological trajectory and identifies significant changes in clinical variables and treatment actions during sepsis state transitions. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials, prevention, and therapeutic strategies for sepsis.
On the impact of the structural surface effect on global stellar properties and asteroseismic analyses

In a series of papers, we have recently demonstrated that it is possible to construct stellar structure models that robustly mimic the stratification of multidimensional radiative magnetohydrodynamic simulations at every time-step of the computed evolution. The resulting models offer a more realistic depiction of the near-surface layers of stars with convective envelopes than parametrizations, such as mixing length theory, do. In this paper, we explore how this model improvement impacts on seismic and non-seismic properties of stellar models across the Hertzsprung–Russell diagram. We show that the improved description of the outer boundary layers alters the predicted global stellar properties at different evolutionary stages. In a hare and hound exercise, we show that this plays a key role for asteroseismic analyses, as it, for instance, often shifts the inferred stellar age estimates by more than 10 per cent. Improper boundary conditions may thus introduce systematic errors that exceed the required accuracy of the PLATO space mission. Moreover, we discuss different approaches for computing stellar oscillation frequencies. We demonstrate that the so-called gas Γ1 approximation performs reasonably well for all main-sequence stars. Using a Monte Carlo approach, we show that the model frequencies of our hybrid solar models are consistent with observations within the uncertainties of the global solar parameters when using the so-called reduced Γ1 approximation.
Policy Reform Towards Waste Management in Pontianak Municipality, West Kalimantan Indonesia (A SWOT Analysis and Lessons Learned from Kyoto City)

This study aims to describe the current state of the implementation of waste management as part of public service provision in Pontianak Municipality, West Kalimantan, Indonesia. For this purpose, this study employed a qualitative descriptive approach by applying SWOT analysis accompanied by lessons learned from Japan. The findings indicated that there has been an increase in waste transportation services year by year and that the waste management carried out by Pontianak Municipality still primarily relies on landfill. Waste reduction efforts through 3R programs such as integrated waste treatment plant and waste bank have not shown significant results in reducing the generation of waste. Using SWOT analysis, several factors that affect the performance of waste management in Pontianak Municipality were identified. By considering those influencing factors and learning from best practices executed by Kyoto City, this study suggests that the government needs to develop an integrated waste management based on priority scale with measurable and realistic objectives, particularly those related to reduction and recycling activities as well as stakeholder‘s engagement.
From gene identification and functional characterization to genome editing approaches for inherited retinal disorders

The first steps in vision occur in the retina when rod and cone photoreceptors transform light into a biochemical signal, which gets processed by bipolar cells, ganglion cells and finally by the brain. Our group investigates genetic causes and mechanisms involved in inherited stationary and progressive retinal diseases as congenital stationary night blindness (CSNB), and rod-cone dystrophy (RCD), also called retinitis pigmentosa. This thesis gives several insights on the retinal physiology. On one hand, we identified GPR179, a new gene mutated in complete CSNB, studied the localization and the physiopathology of missense and splice-site mutations. We also delivered a new knock-out mouse model which we functionally characterized, and studied GPR179 partners to provide a better understanding of the first visual synapse between photoreceptors and ON-bipolar cells. On the other hand, we genotypically and phenotypically characterized one of the most popular RCD model, the P23H rat model. There is currently no treatment for RCD and different therapeutic strategies are under investigation. We wanted to deliver the basis for a genome editing approach for RHO mutations, acting as a dominant negative effect, which cannot be addressed by current gene replacement strategies. We opened the field by performing in vitro, ex vivo and in vivo genome editing experiments using meganucleases, TALEN (Transcription Activator-Like Effector Nuclease) and finally CRISPR/Cas9 system (clustered regularly interspaced short palindromic repeats/CRISPR associated protein 9) and revealed how challenging the setting of genome editing strategies was.
Generalized Langevin equations and fluctuation-dissipation theorem for particle-bath systems in electric and magnetic fields

The Brownian motion of a particle immersed in a medium of charged particles is considered when the system is placed in magnetic or electric fields. Coming from the Zwanzig-Caldeira-Legget particle-bath model, we modify it so that not only the charged Brownian particle (BP) but also the bath particles respond to the external fields. For stationary systems the generalized Langevin equations are derived. Arbitrarily time-dependent electric fields do not affect the memory functions, the thermal noise force, and the BP velocity correlation functions. In the case of a constant magnetic field two equations with different memory functions are obtained for the BP motion in the plane perpendicular to the field. As distinct from the previous theories, the random thermal force depends on the field magnitude. Its time correlation function is connected with one of the found memory functions through the familiar second fluctuation-dissipation theorem.
Bounds on the Spectral Sparsification of Symmetric and Off-Diagonal Nonnegative Real Matrices

We say that a square real matrix [Formula: see text] is off-diagonal nonnegative if and only if all entries outside its diagonal are nonnegative real numbers. In this paper, we show that for any off-diagonal nonnegative symmetric matrix [Formula: see text], there exists a nonnegative symmetric matrix [Formula: see text] which is sparse and close in spectrum to [Formula: see text].
Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition

Recently, inspired by Transformer, self-attention-based scene text recognition approaches have achieved outstanding performance. However, we find that the size of model expands rapidly with the lexicon increasing. Specifically, the number of parameters for softmax classification layer and output embedding layer are proportional to the vocabulary size. It hinders the development of a lightweight text recognition model especially applied for Chinese and multiple languages. Thus, we propose a lightweight scene text recognition model named Hamming OCR. In this model, a novel Hamming classifier, which adopts locality sensitive hashing (LSH) algorithm to encode each character, is proposed to replace the softmax regression and the generated LSH code is directly employed to replace the output embedding. We also present a simplified transformer decoder to reduce the number of parameters by removing the feed-forward network and using cross-layer parameter sharing technique. Compared with traditional methods, the number of parameters in both classification and embedding layers is independent on the size of vocabulary, which significantly reduces the storage requirement without loss of accuracy. Experimental results on several datasets, including four public benchmaks and a Chinese text dataset synthesized by SynthText with more than 20,000 characters, shows that Hamming OCR achieves competitive results.
A Privacy-Preserving and Scalable Authentication Protocol for the Internet of Vehicles

One of the most important and critical requirements for the Internet of Vehicles (IoV) is security under strict latency. Typically, authentication protocols for vehicular ad hoc networks need to authenticate themselves frequently. This results in reduced application traffic and increased overhead. Moreover, the mobile nature of vehicles makes them a prime target for physical, side channel, and cloning attacks. To address these issues, this article presents an efficient protocol for authentication in the IoV. The proposed protocol uses physical unclonable functions to provide the desired security characteristics. To reduce the overhead of authentication and improve the throughput of application layer packets, the proposed protocol uses a three-layered infrastructure architecture for IoVs, i.e., roadside units (RSUs), RSU gateways, and trusted authority. A vehicle needs to authenticate only once when it enters the area of an RSU gateway which may engulf multiple RSUs. A performance analysis of the protocol shows that the proposed strategy significantly reduces the number of authentication packets and MAC/PHY overhead while the security analysis demonstrates its robustness against various types of attacks.
Technology in Indian agriculture - a review

Optimization of agricultural practices for enhanced crop yield is considered to be essential phenomena for the countries like India. In order to strengthen the economy and also to meet the food demand for the exponentially growing population, optimizing the agricultural practices has become necessity. In India, weather and geographical conditions are highly variable and were thought to be the major bottleneck of agricultural practices to achieve improved crop yield. Agricultural practices in India are facing many challenges such as change in climatic conditions, different geographical environment, conventional agricultural practices; economic and political scenario. Economic loss due to the lack of information on crop yield productivity is another major concern in the country. These hurdles can be overcome by the implementation of advanced technology in agriculture. Some of the trends observed are smart farming, digital agriculture and Big Data Analytics which provide useful information regarding various crop yields influencing factors and predicting the accurate amounts of crop yield. The exact prediction of crop yield helps formers to develop a suitable cultivation plan, crop health monitoring system, management of crop yield efficiently and also to establish the business strategy in order to decrease economic losses. This also makes the agricultural practices as one of the highly profitable venture. This paper presents insights on the various applications of technology advancements in agriculture such as Digital Agriculture, smart farming or internet of agriculture technology (IoAT), Precision Agriculture, Crop Management, Weed and Pest control, Crop protection and Big data analytics.
Lifetime measurements in Ru II and f-values determination in Ru II--III

A new set of theoretical f -values is reported for 178 Ru II transitions of astrophysical interest, involving energy levels below 58 000 cm−1. The theoretical model, including core-polarization effects, has been tested by comparing theoretical lifetimes with new measurements performed by time-resolved laser-induced fluorescence spectroscopy for 23 4d6(5D)5p odd levels of Ru II and by comparing theoretical and experimental Landé g-factors. The first set of transition probabilities is proposed for 25 strong lines depopulating the 4d55p 5,7P◦ terms of Ru III. The new results will allow quantitative investigations of stellar spectra and allow the astrophysicists to refine the ruthenium abundance in stars.
Revisiting open data research through the Lens of the Data Value Chain

While the idea of open government data is not new, there appears to be a constant shift in leading research objectives guiding the field. This is because the reasons behind the increased research interest keep changing. The latest motivation stems from economic arguments, namely the reuse of public sector information) – which in turn creates a market for open-data based value added services. Although over the last decade many research topics have been identified and various research agendas have been proposed, most of them either focus on specific areas or are rooted in the popular approach of the time. Using the idea of the data value chain, this paper provides an integrated view of open government data research – which then allows a systematic and consistent identification of research topics and clarification of corresponding open questions in the area of open data quality. Research areas proposed are context/environment (policy and regulations), supply side (government organizations), consumer side (service providers and users), (societal or economic) impact, and technology (supporting the DSC, including the data). In addition – and what is regularly missing from most reviews – theory (providing definitions and a sound base) is considered as the sixth area.
Occupational Stress among Nurses

Nursing is considered as highly stressful profession. Occupational stress is regarded as a major occupational health problem for healthcare professionals especially nurses. Evidence shows that occupational stress decreases the quality of nursing care and increases the chances of making errors. This could have an adverse impact on patient care. Also research has shown that prolonged stress not only predisposes nurses to physical disorders of the musculoskeletal and cardiovascular system, but also increase the vulnerability to psychiatric illnesses such as depressive and anxiety disorders. Therefore, it can lead to poorer health in the nurses themselves. Apart from physical and psychological consequences occupational stress has been reported to affect productivity, job satisfaction and job performance of nurses. Studies also report that occupational stress results in considerable costs to organizations in terms of absenteeism, attrition rate, injury claims, infection rates, errors in treating patient, reduced productivity, and health care resources. Published research writings demonstrated numerous causes for occupational stress among nurses. These include shift duties, interpersonal conflicts with peers, seniors, patients, or doctors, performing tasks not related to nursing, dealing with life-threatening emergencies, unhealthy work environment, work overload, death of patient and so on. It is a well-known fact that management of problem becomes easy if it`s route cause is identified. Therefore this review was undertaken to determine the most common occupational stressors and stress reduction strategies for nurses. Review found some significant sources of occupational stress such as excessive workload, dealing with death and dying, conflicts with other health team members, and insufficient resources. Stress reduction strategies include leading healthy life to practicing relaxation techniques.