Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondrial Dysfunction in Friedreich Ataxia Mitochondrial Dysfunction in Friedreich Ataxia

Friedreich’s Ataxia (FRDA) is the commonest hereditary form of ataxia affecting the Western European population. FRDA is an autosomal recessive neurodegenerative disorder caused by an intronic GAA repeat expansion within the FXN gene; the 96% of the patients are homozygous, while the remaining 4% are compound heterozygous carrying the GAA repeat mutation on one allele and point mutations on the other one.Usually FRDA first symptoms appear at young age during the firsts two decades of life. The clinical features include progressive gait and limb ataxia, dysarthria, muscle weakness, peripheral sensory neuropathy, pes cavus, and scoliosis. FRDA is a multi-systemic disorder; therefore, patients develop non-neurological signs, such as hypertrophic cardiomyopathy, diabetes, and urological problems. human. Mitochondrial dysfunction in FRDA is strictly linked to frataxin functional role in the iron biogenesis. Frataxin deficiency leads to impairment ISCs formation which in turn affects the ISC-containing proteins (including complex I, II, and III of the mitochondrial electron transport chain and aconitase). The pathogenic mechanism triggered by the reduced production of frataxin leads to the generation of oxidative stress, mitochondrial energy imbalance and an increase in lipid peroxidation, as shown in cerebellar granule neurons (CGNs), and mouse fibroblasts. Lipid peroxidation has been proven to be one of the causes inducing neuronal death, as by pre-treating the cells with poly-unsaturated fatty acids (dPUFAs), the phenotype was rescued. In this chapter we review the current knowledge on the mitochondrial dysfunction in FRDA.
Analysis of Physical Test Indexes of College Students Based on Data Mining Model

This paper takes the physical ﬁtness test data and the physical health self-assessment data as the research objects. The decision tree algorithm is used to construct a decision tree model for students who fail to meet the physical test. Thus, the classiﬁcation of students with diﬀerent physical qualities is realized. The association rule Apriori algorithm is used to mine the association of physical ﬁtness test indexes so as to judge the hidden law between students’ physical ﬁtness and behavior habits and get the correlation information of various physical health indexes. The back propagation (BP) neural network algorithm is used to establish the physical ﬁtness test prediction model. By using these data mining models, this paper explores the hidden association information in college students’ physical test data, which can provide more scientiﬁc and eﬀective guidance for students’ physical tests.
PHILOSOPHY AND COGNITION ON HIDDEN SECRETS OF HUMAN BODY

Here, new ideas are presented regarding the biophysical arrangement of the human body and other living beings. For their argumentation, an extraordinary comprehensive approach was applied, based on the philosophical concept of the high perfection of living organisms and covering also physical logic and principles, as well as well-known everyday truths. Ultimately, the topic concerns human health, moral principles and worldview.
Research on Consumers' Buying Behavior in the Context of "Helping Farmers Live”: Based on the Dual Role of Economic Rationality and Normative Rationality

Abstract: As the digital economy has become an important driving force for a new round of global industrial change, live ecommerce has become a new outlet, and the "agriculture assisted live broadcasting" has emerged. This paper combs the existing literature and combines the characteristics of "agriculture assisted live broadcasting" to get a theoretical research model of consumers' purchasing behavior in the context of "assisted farmer direct broadcast". Using questionnaire survey method to collect data for empirical analysis, the research found that consumers’ perceived risk negatively affects purchase intention; efficacy positively affects purchase intention; consumer purchase intention positively affects purchase behavior; consumer perceived value and social norms have no significant effect on purchase intention. Finally, this article makes suggestions for businesses and relevant departments of the live broadcast industry.
The Retrospective on Atypical Brucella Species Leads to Novel Definitions

The genus Brucella currently comprises twelve species of facultative intracellular bacteria with variable zoonotic potential. Six of them have been considered as classical, causing brucellosis in terrestrial mammalian hosts, with two species originated from marine mammals. In the past fifteen years, field research as well as improved pathogen detection and typing have allowed the identification of four new species, namely Brucella microti, Brucella inopinata, Brucella papionis, Brucella vulpis, and of numerous strains, isolated from a wide range of hosts, including for the first time cold-blooded animals. While their genome sequences are still highly similar to those of classical strains, some of them are characterized by atypical phenotypes such as higher growth rate, increased resistance to acid stress, motility, and lethality in the murine infection model. In our review, we provide an overview of state-of-the-art knowledge about these novel Brucella sp. , with emphasis on their phylogenetic positions in the genus, their metabolic characteristics, acid stress resistance mechanisms, and their behavior in well-established in cellulo and in vivo infection models. Comparison of phylogenetic classification and phenotypical properties between classical and novel Brucella species and strains finally lead us to propose a more adapted terminology, distinguishing between core and non-core, and typical versus atypical brucellae, respectively.
for Obtaining Energy Matrix Scaling Factor

As discussed in S1 Text, in order to convert an energy matrix into kBT units one must infer an appropriate scaling factor α. In the main text we primarily use Bayesian parameter estimation by MCMC to infer this factor, but other methods can be used as well. Here we discuss two alternative methods: least squares regression to measured binding energy values, and calibrating to a theoretical mutation parameter. In this Text we will discuss the strengths and weaknesses of each method and compare predictions using these methods to predictions using MCMC.
Interval Estimation for the Two-Parameter Exponential Distribution under Progressive Type II Censoring on the Bayesian Approach

: Under progressive type II censoring, the credible interval estimation and the credible region for parameters of two-parameter exponential distribution based on the Bayesian approach are presented in this paper. Two methods of Bayesian credible region are proposed under a given conﬁdence level. We also presented the predictive interval of the future observation under this type of censoring. In order to compare the performance of our proposed Bayesian credible interval and region with the existing non-Bayesian methods, we conduct a simulation study by the Monte Carlo method to ﬁnd the corresponding coverage probabilities. This research is related to the topic of asymmetrical probability distributions and applications across disciplines. Finally, one engineering example is used to demonstrate the Bayesian credible interval estimation methods proposed in this paper.
Maneuvering Capability Simulation and Analysis for Twin Waterjet-propelled Vessel

Planning craft adopts two pumps as underwater power to push the system, which is placed in the stern of the ship body both sides respectively. The thrust and torque of the pump and the split duct deflector were analyzed under different working conditions, and on the basis of MMG, the mathematical model of waterjet maneuver motion of twin waterjet-propulsion units was built and the performance was simulated. The result show that the steering device can be applied as a controller aboard to maneuver ship, to propel the ship both in ahead and astern conditions, and to put the ship turn in its position and move transversely, and this propulsion device has excellent maneuvering performance.
Modification of Start-wave Model at Signalized Intersection

This paper studies the start-wave model,finds the traditional model disagreeing with the practice,establishes a new start-wave model and validates the new model by field data.It is Greenshields model,which is used in the deduction of the traditional start-wave model,that results in the difference of the model fromhe practice.The state where a queue begins to discharge is with high density,which Greenshields model isn't suitable to describe.Instead Greenberg model is fit for this state.Then the paper uses Greenberg model to modify the traditional start-wave model and sets up a new start-wave model.To collect field data,a special traffic survey scheme is designed.Using the field data,the paper calibrates the new model and verifies it.The start-wave velocity calculated by the new model is very close to the practice and all errors are less than 5%. The new start-wave model can be used to analyze the starting process of a queue and to evaluate the effect of the signal timing schemes at signalized intersection.
Aplikasi Deteksi Nilai Uang pada Mata Uang Indonesia dengan Metode Feature Matching

Banknote can't be detected and recognized by blind people.Application for banknote recognition, which can run on Androidsmartphone with camera, is made in this paper to help blind people.Indonesian Rupiah(IDR) is used as a working example. This doesnot require any communication with remote server, and all thenecessary computations take place on the phone itself. In thisapplication, user can tak picture of the banknote with double tapon the screen, and the results will be announced by voice. Thesystem relies on computer vision algorithm, such as featuredetection, feature description, and matching. Each application ofORB, SURF, and SIFT is applied in matching captured banknoteimages to template images. To improve the confidence of thebanknote recognition, homography is used to filter the featurematching results.The systems evaluate the performance on 700 images and report anaccuracy of 93.14% using SURF, 92.57% using SIFT, and 89.17%using ORB
Magas a munkahelyi és a családi szerepek közötti konﬂ iktus prevalenciája az orvosnők körében: a társas támogatás hiánya mint lehetséges rizikótényező work-family conﬂ PÁLYÁZATI FELHÍVÁS

A szerint a munkahelyi és munkahelyen kívüli támogatás (az úgynevezett társas támogatás) befolyásolhatja a munka- helyi és családi szerepek közötti konﬂ iktus szintjét. Annak ellére, hogy a munkahely-család konﬂ iktus – a stressz egyik forrása – nagyon gyakori orvosnők körében, nem áll rendelkezésre empirikus adat a munkahely-család konﬂ iktus és a társas támogatás közötti kapcsolatról olyan tradicionális és családcentrikus társadalomban, ahol a női szerepekkel szembeni elvárások magasak. A szerző fel-tételezte, hogy az orvosnők gyakori munkahely-család konﬂ iktusának hátterében a társas támogatás hiánya húzódik meg. Célkitűzés: A társas támogatás pszichoszociális jellemzőinek és gyakoriságának, valamint a munkahely-család konﬂ iktussal való kapcsolatának feltárása orvosnők körében. Módszer: Kvantitatív és kvalitatív vizsgálat önkitöltős role high prevalence of work-family conﬂ ict the lack of social support among female Aims: To investigate the prevalence and psychosocial characteristics of social support and its relations to work-family conﬂ ict among female physicians. Methods: Quantitative and qualitative study using ques-tionnaires ( n = 420) and in-depth interviews ( n = 123) among female and male physicians. Results: Female physicians reported sig- niﬁ cantly higher mean level and prevalence of work-family conﬂ ict compared to men. The predominant form of work-family was work-to-family conﬂ ict among physicians; however, signiﬁ cantly more female physicians experienced family-to-work conﬂ ict and strain-based work-family conﬂ ict compared to men (39% vs. 18% and 68% vs. 20%, respectively). Signiﬁ cantly more male physicians experienced time-based work-family conﬂ ict compared to women. Content analyses of interview data revealed that provision of support to physicians manifested itself in parental support in career selection, spousal support with household duties, peer support with enabling access to professional role models-mentors, peer support to ensure gender equity, and organizational support with family-centric policies. Female physicians reported signiﬁ cantly less parental, spousal, and peer support compared to men. Female physicians lacking parental, peer, or organizational support experienced signiﬁ cantly higher level of work-family conﬂ ict compared to appropriate control. In regression analyses, high job demands, job strain, high workload and number of children, younger age, and lack of support in the workplace predicted work-family conﬂ ict best (adjusted R 2 0.59). Conclusions: Lack of social – particu-larly parental, peer, and organizational – support may play an important role in the pathogenesis of work-family conﬂ ict experienced by female physicians.
GLAD: Neural Predicate Synthesis to Repair Omission Faults

Existing template and learning-based Automated Program Repair (APR) tools have successfully found patches for many benchmark faults. However, our analysis of existing results shows that omission faults pose a significant challenge. For template based approaches, omission faults provide no location to apply templates to; for learning based approaches that formulate repair as Neural Machine Translation (NMT), omission faults similarly do not provide faulty code to translate. To address these issues, we propose GLAD, a novel learning-based repair technique that targets if-clause synthesis. GLAD does not require a concrete faulty line as it is based on generative Language Models (LMs) instead of machine translation; consequently, it can repair omission faults. To provide the LM with project-specific information critical to synthesis, we incorporate two components: a type-based grammar that constrains the model, and a dynamic ranking system that evaluates candidate patches using a debugger. Our evaluation shows GLAD is highly orthogonal to existing techniques, correctly fixing 26 Defects4J v1.2 faults that previous NMT-based techniques could not, while maintaining a small runtime cost, underscoring its potential as a lightweight tool to complement existing tools in practice. An inspection of the bugs that GLAD fixes reveals that GLAD can quickly generate expressions that would be challenging for other techniques.
A formally certified end-to-end implementation of Shor’s factorization algorithm

Significance While hardware errors have garnered significant attention as the major obstacle to quantum computing, the error due to human factors in the implementation is less recognized. This paper identifies human programming errors as another important source of errors, whereas most existing techniques from the classical domain fail to transfer at scale to quantum programming. The adaptation of formal methods to quantum programming is proposed as a solution that circumvents quantum unique challenges and leads to the high-assurance implementation of large-scale quantum applications in a principle way. As a demonstration of the feasibility, this paper presents a formally certified end-to-end implementation of Shor’s prime factorization algorithm due to its complexity and importance.
Optimal Myopic Attacks on Nonlinear Estimation

Prior works have analyzed the security of estimation and control (E&C) for linear, time-invariant systems; however, there are few analyses of nonlinear systems despite their broad safety-critical use. We define two attack objectives on nonlinear E&C and illustrate that realizing the optimal attacks against the widely-adopted extended Kalman filter with industry-standard χ2 anomaly detection is equivalent to solving convex quadratically-constrained quadratic programs. Although these require access to the true state of the system, we provide practical relaxations on the optimal attacks to allow for execution at runtime given a specified amount of attacker knowledge. We show that the difference between the optimal and relaxed attacks is bounded by the attacker knowledge.
Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy

modulus of rigidity, and shear modulus. To better understand the benefit to model accuracy, these additional features are injected into various single and multiple DNN layers, and trained with four different dataset sizes. When these physics-enhanced models are evaluated against independent data of the same materials and similar dimensions to the training sets, supplementation with simplified physics-based parameters provides little reduction in prediction error over the baseline for models trained with dataset sizes of 60 and greater, although small improvement from 19.3% to 16.1% occurs when trained with a sparse size of 30. Conversely, notable accuracy gains occur when the independent test data is of material and dimensions not conforming to the training set. Specifically, when physics-enhanced data is injected into multiple DNN layers, reductions in error from 33.2% to 19.6%, 34.9% to 19.9%, 35.8% to 22.4%, and 43.0% to 28.4% are for training dataset sizes of 261, 117, 60, and 30, respectively, demonstrating attainment of a degree of generalizability.
A Level Set Theory for Neural Implicit Evolution under Explicit Flows

Coordinate-based neural networks parameterizing implicit surfaces have emerged as efficient representations of geometry. They effectively act as parametric level sets with the zero-level set defining the surface of interest. We present a framework that allows applying deformation operations defined for triangle meshes onto such implicit surfaces. Several of these operations can be viewed as energy-minimization problems that induce an instantaneous flow field on the explicit surface. Our method uses the flow field to deform parametric implicit surfaces by extending the classical theory of level sets. We also derive a consolidated view for existing methods on differentiable surface extraction and rendering, by formalizing connections to the level-set theory. We show that these methods drift from the theory and that our approach exhibits improvements for applications like surface smoothing, mean-curvature flow, inverse rendering and user-defined editing on implicit geometry.
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation

Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance. These tasks require extensive computational resources while only suggesting marginal improvements. Here, we demonstrate that by fusing graph-aware elements into existing pre-trained language models, we are able to outperform state-of-the-art models and close the gap imposed by additional pre-training tasks. We do so by proposing a mask structure to capture neighborhood information and a novel type encoder that adds a bias to the graph-attention weights depending on the connection type. Experiments on two KG-to-text benchmark datasets show our models are competitive while involving fewer parameters and no additional pre-training tasks. By formulating the problem as a framework, we can interchange the various proposed components and begin interpreting KG-to-text generative models based on the topological and type information found in a graph.
Modeling Operational Variability for Robust Multidisciplinay Design Optimization

The aim of this paper is to model and propagate operational uncertainties in view of its integration in a multidisciplinary optimization methodology for aircraft robust design. From databases relative to one speci c type of long-range airplane, we analyze the variations of four ight parameters (altitude, speed, temperature and range), and build the associated statistical distributions. Then, using an uncertainty propagation methodology, we identify the distribution of operational costs.
Orbital memory from individual Fe atoms on black phosphorus

Bistable valency in individual atoms presents a new approach toward single-atom memory, as well as a building block to create tunable and stochastic multi-well energy landscapes. Yet, this concept of orbital memory has thus far only been observed for cobalt atoms on the surface of black phosphorus, which are switched using tip-induced ionization. Here, we show that individual iron atoms on the surface of black phosphorus exhibit orbital memory using a combination of scanning tunneling microscopy and spectroscopy with ab initio calculations based on density functional theory. Unlike cobalt, the iron orbital memory can be switched in its non-ionized ground state. Based on calculations, we confirm that each iron valency has a distinct magnetic moment that is characterized by a distinguishable charge distribution due to the different orbital population. By studying the stochastic switching of the valency with varying tunneling conditions, we propose that the switching mechanism is based on a two-electron tunneling process.
Kraus-Like Decompositions

We introduce a new decomposition of quantum channels acting on group algebras, which we term Kraus-like (operator) decompositions. We motivate this decomposition with a general nonexistence result for Kraus operator decompositions in this setting. Given a length function which is a class function on a finite group, we construct a corresponding Kraus-like decomposition. We prove that this Kraus-like decomposition is \textit{convex} (meaning its coefficients are nonnegative and satisfy a sum rule) if and only if the length is conditionally negative definite. For a general finite group, we prove a stability condition which shows that the existence of a convex Kraus-like decomposition for all $t>0$ small enough necessarily implies existence for all time $t>0$. Using the stability condition, we show that for a general finite group, conditional negativity of the length function is equivalent to a set of semidefinite linear constraints on the length function. Our result implies that in the group algebra setting, a semigroup $P_t$ induced by a length function which is a class function is a quantum channel for all $t\geq 0$ if and only if it possesses a convex Kraus-like decomposition for all $t>0$.
Network state Estimation using Raw Video Analysis: vQoS-GAN based non-intrusive Deep Learning Approach

Content based providers transmits real time complex signal such as video data from one region to another. During this transmission process, the signals usually end up distorted or degraded where the actual information present in the video is lost. This normally happens in the streaming video services applications. Hence there is a need to know the level of degradation that happened in the receiver side. This video degradation can be estimated by network state parameters like data rate and packet loss values. Our proposed solution vQoS-GAN (video Quality of Service – Generative Adversarial Network) can estimate the network state parameters from the degraded received video data using a deep learning approach of semi supervised generative adversarial network algorithm. A robust and unique design of deep learning network model has been trained with the video data along with data rate and packet loss class labels and achieves over 95 percent of training accuracy. The proposed semi supervised generative adversarial network can additionally reconstruct the degraded video data to its original form for a better end user experience.