Miracle-dz/UniFAR-FTUs huggingface dataset:
Open quantum system dynamics and the mean force Gibbs state

The dynamical convergence of a system to the thermal distribution, or Gibbs state, is a standard assumption across all of the physical sciences. The Gibbs state is determined just by temperature and energies of the system. However, at decreasing system sizes, i.e., for nanoscale and quantum systems, the interaction with their environments is not negligible. The question then arises: Is the system's steady state still the Gibbs state? If not, how may the steady state depend on the interaction details? Here, we provide an overview of recent progress on answering these questions. We expand on the state of the art along two general avenues: First, we take the static point-of-view, which postulates the so-called mean force Gibbs state. This view is commonly adopted in the field of strong coupling thermodynamics, where modified laws of thermodynamics and nonequilibrium fluctuation relations are established on the basis of this modified state. Second, we take the dynamical point of view, originating from the field of open quantum systems, which examines the time-asymptotic steady state within two paradigms. We describe the mathematical paradigm, which proves return to equilibrium, i.e., convergence to the mean force Gibbs state, and then discuss a number of microscopic physical methods, particularly master equations. We conclude with a summary of established links between statics and equilibration dynamics and provide an extensive list of open problems. This comprehensive overview will be of interest to researchers in the wider fields of quantum thermodynamics, open quantum systems, mesoscopic physics, statistical physics, and quantum optics and will find applications whenever energy is exchanged on the nanoscale, from quantum chemistry and biology to magnetism and nanoscale heat management.
The Impact of Data Volume on Performance of Depp Learning Based Building Rooftop Extraction Using Very High Spatial Resolution Aerial Images

Building rooftop data are of importance in several urban applications and in natural disaster management. In contrast to traditional surveying and mapping, by using high spatial resolution aerial images, deep learning-based building rooftops extraction methods are efficient and accurate. Although more training data is preferred in deep learning-based tasks, the effect of data volume on building extraction models is underexplored. Therefore, the paper explores the impact of data volume on the performance of building rooftop extraction from very-high-spatial-resolution (VHSR) images using deep learning-based methods. To do so, we manually labelled 0.12m spatial resolution aerial images and perform a comparative analysis of models trained on datasets of different sizes using popular deep learning architectures for segmentation tasks, including Fully Convolutional Networks (FCN)-8s, U-Net and DeepLabv3+. The experiments showed that with more training data, algorithms converged faster and achieved higher accuracy, while better algorithms were able to better mitigate the lack of training data.
A Biologically Plausible Learning Rule for Perceptual Systems of organisms that Maximize Mutual Information

It is widely believed that the perceptual system of an organism is optimized for the properties of the environment to which it is exposed. A specific instance of this principle known as the Infomax principle holds that the purpose of early perceptual processing is to maximize the mutual information between the neural coding and the incoming sensory signal. In this article, we present a method to implement this principle accurately with a local, spike-based, and continuous-time learning rule.
Epidemics from the Eye of the Pathogen

While a common trend in disease modeling is to develop models of increasing complexity, it was recently pointed out that outbreaks appear remarkably simple when viewed in the incidence vs. cumulative cases (ICC) plane. This article details the theory behind this phenomenon by analyzing the stochastic SIR (Susceptible, Infected, Recovered) model in the cumulative cases domain. We prove that the Markov chain associated with this model reduces, in the ICC plane, to a pure birth chain for the cumulative number of cases, whose limit leads to an independent increments Gaussian process that fluctuates about a deterministic ICC curve. We calculate the associated variance and quantify the additional variability due to estimating incidence over a finite period of time. We also illustrate the universality brought forth by the ICC concept on real-world data for Influenza A and for the COVID-19 outbreak in Arizona.
Threshold behavior in spin lasers: Spontaneous emission and nonlinear gain

A hallmark of spin-lasers, injected with spin-polarized carriers, is their threshold behavior with the onset of stimulated emission. Unlike the single threshold in conventional lasers with unpolarized carriers, two thresholds are expected in spin lasers. With the progress in scaled-down lasers and the use of novel two-dimensional materials it is unclear if the common description of spin lasers assuming a negligible spontaneous emission and linear optical gain remains relevant or even how to identify the lasing thresholds. Our rate-equation description addresses these questions by considering a large spontaneous emission and a nonlinear optical gain. We provide a transparent approach and analytical results to explore the resulting threshold behavior, its deviation from the prior studies, as well as guide future spin-lasers as a versatile platform for spintronics beyond magnetoresistance.
Polarized muons and the origin of biological homochirality

While biologists have not yet reached a consensus on the definition of life, homochirality - the specific molecular handedness of biomolecules - is a phenomenon only produced by life. The unraveling of its origin requires interdisciplinary research, by exploring fundamental physics, chemistry, astrophysics and biology. Here, we consider the origin of biological homochirality in the context of astrophysics and particle physics. The weak force, one of the fundamental forces operating in nature, is parity-violating. On Earth, at ground level, most of our cosmic radiation dose comes from polarized muons formed in a decay involving the weak force. We discuss how the magnetic polarization is transmitted in cosmic showers within several different environments which are prime targets in the search for the origin of life. We consider how this polarization could have induced a biological preference for one type of chirality over the other, and discuss the implications for the search of life in other worlds.
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation

We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is given by the success probability of a query, which corresponds to the probability that the query succeeds in a randomly sampled program. It is well-defined when each random sample uniquely determines the truth values of all logical atoms. Argumentation problems, however, represent an interesting practical application where this is not always the case. SMProbLog generalizes the semantics of ProbLog to the setting where multiple truth assignments are possible for a randomly sampled program, and implements the corresponding algorithms for both inference and learning tasks. We then show how this novel framework can be used to reason about probabilistic argumentation problems. Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning, and a novel approach to probabilistic argumentation problems based on such framework.
Neural Network Adversarial Attack Method Based on Improved Genetic Algorithm

: Deep learning algorithms are widely used in fields such as computer vision and natural language processing, but they are vulnerable to security threats from adversarial attacks because of their internal presence of a large number of nonlinear functions and parameters leading to their uninterpretability. In this paper, we propose a neural network adversarial attack method based on an improved genetic algorithm. The improved genetic algorithm improves the variation and crossover links based on the original genetic optimization algorithm, which greatly improves the iteration efficiency and shortens the running time. The method does not need the internal structure and parameter information of the neural network model, and it can obtain the adversarial samples with high confidence in a short time by the classification and confidence information of the neural network. The experimental results show that the method in this paper has a wide range of applicability and high efficiency for the model, and provides a new idea for the adversarial attack.
Attaining Interpretability in Reinforcement Learning via Hierarchical Primitive Composition

—Deep reinforcement learning has shown its effectiveness in various applications and provides a promising direction for solving tasks with high complexity. In most reinforcement learning algorithms, however, two major issues need to be dealt with - the sample inefﬁciency and the interpretability of a policy. The former happens when the environment is sparsely rewarded and/or has a long-term credit assignment problem, while the latter becomes a problem when the learned policies are deployed at the customer side product. In this paper, we propose a novel hierarchical reinforcement learning algorithm that mitigates the aforementioned issues by decomposing the original task in a hierarchy and by compounding pretrained primitives with intents. We show how the proposed scheme can be employed in practice by solving a pick and place task with a 6 DoF manipulator.
Temporal shift reinforcement learning

The function approximators employed by traditional image-based Deep Reinforcement Learning (DRL) algorithms usually lack a temporal learning component and instead focus on learning the spatial component. We propose a technique, Temporal Shift Reinforcement Learning (TSRL), wherein both temporal, as well as spatial components are jointly learned. Moreover, TSRL does not require additional parameters to perform temporal learning. We show that TSRL outperforms the commonly used frame stacking heuristic on all of the Atari environments we test on while beating the SOTA for all except one of them. This investigation has implications in the robotics as well as sequential decision-making domains. Our code is available at - https://github.com/Deepakgthomas/TSM_RL
Sample-constrained partial identification with application to selection bias

Summary Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure, and conclude with a substantive motivating example on the causal effect of education on income in the highly selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\texttt{R}$\end{document} package \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\texttt{selectioninterval}$\end{document}.
Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification

Interpreting the brain commands is now easier using brain-computer interface (BCI) technologies. Motor imagery (MI) signal detection is one of the BCI applications, where the movements of the hand and feet can be recognized via brain commands that can be further used to handle emergency situations. Design of BCI techniques encountered challenges of BCI illiteracy, poor signal to noise ratio, intersubject variability, complexity, and performance. The automated models designed for emergency should have lesser complexity and higher performance. To deal with the challenges related to the complexity performance tradeoff, the frequency features of brain signal are utilized in this study. Feature matrix is created from the power of brain frequencies, and newly proposed relative power features are used. Analysis of the relative power of alpha sub-band to beta, gamma, and theta sub-band has been done. These proposed relative features are evaluated with the help of different classifiers. For motor imagery classification, the proposed approach resulted in a maximum accuracy of 93.51% compared to other existing approaches. To check the significance of newly added features, feature ranking approaches, namely, mutual information, chi-square, and correlation, are used. The ranking of features shows that the relative power features are significant for MI task classification. The chi-square provides the best tradeoff between accuracy and feature space. We found that the addition of relative power features improves the overall performance. The proposed models could also provide quick response having reduced complexity.
Interaction of climate and socio-ecological environment drives the dengue outbreak in epidemic region of China

Transmission of dengue virus is a complex process with interactions between virus, mosquitoes and humans, influenced by multiple factors simultaneously. Studies have examined the impact of climate or socio-ecological factors on dengue, or only analyzed the individual effects of each single factor on dengue transmission. However, little research has addressed the interactive effects by multiple factors on dengue incidence. This study uses the geographical detector method to investigate the interactive effect of climate and socio-ecological factors on dengue incidence from two perspectives: over a long-time series and during outbreak periods; and surmised on the possibility of dengue outbreaks in the future. Results suggest that the temperature plays a dominant role in the long-time series of dengue transmission, while socio-ecological factors have great explanatory power for dengue outbreaks. The interactive effect of any two factors is greater than the impact of single factor on dengue transmission, and the interactions of pairs of climate and socio-ecological factors have more significant impact on dengue. Increasing temperature and surge in travel could cause dengue outbreaks in the future. Based on these results, three recommendations are offered regarding the prevention of dengue outbreaks: mitigating the urban heat island effect, adjusting the time and frequency of vector control intervention, and providing targeted health education to travelers at the border points. This study hopes to provide meaningful clues and a scientific basis for policymakers regarding effective interventions against dengue transmission, even during outbreaks.
A Comparative Study on the Effect of Exosomes Secreted by Mesenchymal Stem Cells Derived from Adipose and Bone Marrow Tissues in the Treatment of Osteoarthritis-Induced Mouse Model

Background Exosomes as extracellular vesicles (EVs) are nanoscale intercellular messengers secreted from cells to deliver biological signals. Today, exosomes have become a new field of research in regenerative medicine and are considered as potential therapies to control inflammation and wound healing and enhance and improve healing in many diseases. Given the global burden of osteoarthritis (OA) as the fastest-growing health condition and one of the major causes of physical disability in the aging population, research to establish EVs as therapeutic products can meet the basic clinical needs in the management of osteoarthritis and provide a therapeutic solution. Objectives The present study is aimed at evaluating the regenerative potentials of the exosomes secreted from adipose and bone marrow tissue-derived mesenchymal stem cells (AD- and BM-MSCs) in ameliorating the symptoms of OA. Method In this experimental study, AD- and BM-MSCs were isolated and cultured in the laboratory until passage 3. Finally, these cells' secreted exosomes were isolated from their conditioned medium. Ciprofloxacin-induced OA mouse models underwent intra-articular injection of exosomes from AD-MSCs and BM-MSCs. Finally, the expression levels of collagen I and II, sox9, and aggrecan genes using real-time PCR, histological analysis, and immunohistochemical (IHC) studies were performed. Results Real-time PCR data showed that although the expression level of collagen type II was lower in both exosome-treated groups than the normal, but it was significantly increased in comparison with the sham and OA, with higher expression in BM-Exo rather than AD-Exo group. Similarly, the histological staining and IHC results have provided almost identical data, emphasizing on better therapeutic effect of BM-MSCs-exosome than AD-MSCs-exosome. Conclusion BM-MSCs secreted exosomes in comparison with AD-MSCs could be considered as a better therapeutic option to improve osteoarthritis and exhibit potential as a disease-modifying osteoarthritis cell-free product.
Mortality, functional outcomes and quality of life after hip fractures complicated by infection: a case control study

Abstract Introduction: Infection is a detrimental complication of operatively treated hip fractures. The objective of this retrospective case-control study was to evaluate the mortality, the physical function and the quality of life of hip fractures complicated with infection and determine risk factors for deep infection in hip fractures. Patients and methods: All patients with hip fractures (31A and 31B OTA/AO) that were operatively managed over a 10-year period that subsequently developed deep infection were included in the study. Thirty-nine patients met the inclusion criteria. These patients were compared with a matched control group of 198 patients without infection. Minimum follow-up was 1 year. Mortality, Barthel index score, EQ-5D-5L, Parker mobility score and visual analogue scale (VAS) pain score were compared between groups. Results: Mortality at 1 month was 20.5 % and 43 % at 1 year. Half of the infections were acute and 28 % were polymicrobial. Mortality was greater in the infection group (43 % vs. 16.5 %, p<0.0014), and Barthel index was inferior in the infection group (14 vs. 18, p<0.0017) compared to control group. Logistic regression analysis revealed that time from admission to surgery was a negative factor that predisposed to infection. Conclusions: Patients complicated with infection after a hip fracture have higher mortality and inferior functional results. Delay from admission to surgery predisposes to infection.
Supramolecular hydrogels based on bile acids and their derivatives

Molecular hydrogels are soft materials formed by the self-assembly of small molecules in aqueous solutions via supramolecular interactions. Although much effort has been made in the past several decades in the study of these hydrogels, the mechanism of their formation remains to be understood and the prediction of their formation is a challenge. The main purpose of this thesis is to develop novel molecular hydrogels derived from bile acids, which are naturally occurring biocompounds, and to find the relationship between the gelator structure and the gelation ability. Two new molecular gelation systems based on bile acids and their derivatives have been developed, which may be useful in biomedical applications. The marginal solubility of the solute in water has been found to be a prerequisite for the formation of such molecular hydrogels. The alignment of the nanofibers in the gels leads to the formation of nematic hydrogels. The first gelation system is based on a cholic acid dimer as a gelator, which has two cholic acid molecules covalently linked by a diethylenetriamine spacer. This dimer is insoluble in water, but it forms hydrogels with 3-D fibrous networks in the presence of selected carboxylic acids. The carboxylic acids protonate the dimer, making it marginally soluble in water to yield hydrogels. Only weak and hydrophilic carboxylic acids were capable of inducing the gelation of the dimer and the mechanical strength of the hydrogels could be varied by judicious choice of the acids. Hydrophobic interactions and hydrogen bonding between the side chains of carboxylic acids improve the mechanical properties of hydrogels. The marginal solubility of the acid-dimer complex is regarded to be the critical factor for the formation of hydrogels. Another hydrogelation system was developed by purging to the aqueous solutions of a series of bile salts with carbon dioxide (CO2), yielding hydrogels made of entire natural biological molecules and providing a convenient storage reservoir of CO2 in water. Bile salts are well dissolved in water, while the solubility of bile acids is limited. The carboxylate group of bile salts may be partially protonated in aqueous solutions by bubbling CO2, making them only marginally soluble in water. This forms fibrous