Miracle-dz/UniFAR-FTUs huggingface dataset:
Schema theory for gene expression programming

This thesis studied a new variant of Evolutionary Algorithms called Gene Expression Programming. The evolution process of Gene Expression Programming was investigated from the practice to the theory. As a practice level, the original version of Gene Expression Programming was applied to a classification problem and an enhanced version of the algorithm was consequently developed. This allowed the development of a general understanding of each component of the genotype and phenotype separated representation system of the solution employed by the algorithm. Based on such an understanding, a version of the schema theory was developed for Gene Expression Programming. The genetic modifications provided by each genetic operator employed by this algorithm were analysed and a set of theorems predicting the propagation of the schema from one generation to another was developed. Also a set of experiments were performed to test the validity of the developed schema theory obtaining good agreement between the experimental results and the theoretical predictions.
Disassembly model for the production of astrophysical strangelets

Determination of the baryon number (or mass) distribution of the strangelets, that may fragment out of the warm and excited strange quark matter (SQM) ejected in the merger of strange stars (SSs) in compact binary stellar systems of the Galaxy, is attempted by using a statistical disassembly model (SMM). Finite mass of strange quarks is taken into account in the analysis. Strangelet charge and the resulting Coulomb correction are included to get a plausible mass distribution of galactic strangelets at their source.
HSPA1A, HSPA2, and HSPA8 Are Potential Molecular Biomarkers for Prognosis among HSP70 Family in Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease, which leads to impairment of cognition and memory. The heat shock protein 70 (HSP70) family plays an important role in the pathogenesis of AD. It is known to regulate protein misfolding in a variety of diseases, including inhibition of Aβ aggregation and NFT formation in AD. As yet, the diagnostic molecular markers of AD remain unclear. Herein, we sought to investigate molecular markers of HSP70 family that can affect diagnosis and treatment in AD through computational analysis. In this study, the intersection between HSP70 family members and immune molecules was taken to screen immune-related HSP70 family genes. Based on the datasets from the NCBI-Gene Expression Omnibus (GEO) database, we found that the expression levels of HSPA1A and HSPA2 were significantly increased in AD samples, while HSPA8 significantly decreased. Surprisingly, the combination of the 3 hub genes had a good diagnosis of AD via receiver operating characteristic curve (ROC). Moreover, the clinical value of the 3 hub genes was further assessed by the Spearman correlation analysis with AD-related genes, β-secretase activity, and γ-secretase activity. In terms of immune cell infiltration, we showed that the distribution of seven immune cell types (macrophages M2, neutrophils, T cells CD4 memory activated, macrophages M0, NK cells activated, plasma cells, and T cells follicular helper) was associated with the occurrence of AD by CIBERSORT. Furthermore, our data suggested that EP300, MYC, TP53, JUN, CREBBP, and ESR1 might be key transcription factors (TFs) for the 3 hub genes. In general, these findings suggest that HSPA1A, HSPA2, and HSPA8 are potential molecular biomarkers for prognosis among HSP70 family in AD, and it provides a new perspective on diagnostic and therapeutic targets for AD.
The Visual Segmentation of Scene Information and Applications in Predictive Haptics

We as humans take our ability to digest a scene and extract its context knowledge to be for granted. There are several senses involved including but not limited to sight, hearing, and touch. This also includes our ability to combine information from the different senses to enrich our understanding. In the healthcare robotics space, a lot of success has been met with emulating or attempting to emulate these abilities of humans in everyday processes. To be specific, one process, context inference, is a very subconscious but powerful process. When someone picks up a glass cup, they can feel that the cup is made of glass where their hand meets the cup but are still able to infer that the rest of the cup is also glass. This is a powerful example of how humans take local information and use it to derive global context. In this paper, we attempt to emulate this process through a PR2 robot by developing a way to segment a scene for the various objects in the scene. The PR2 can then estimate the material of the different objects using a pre-trained neural network that takes in spectroscopy measurements and pictures of a small patch of each object. This would thereby allow the PR2 to emulate the same ability to abstract its local information to a global context as the material of each object would be determined by a small measurement. We set up a table with an arrangement of objects and test different approaches to segmenting this scene to provide points of interest and measurement to the PR2. Some objects that were used include pots, glasses, mugs, sweaters, and scissors. After testing 3 different approaches to segmentation, a 3D analysis based one was able to sufficiently segment the scenes and provided the PR2 with enough information to make proper measurements and make reasonable estimates. Finally, we demonstrate how a PR2 robot can do all of this and leverage this system to estimate the materials of everyday objects so that it can infer interactions with these objects. From this work, we find that we are one step closer to providing robots with the same advantage that we have to mix partial contextual understandings to make better globally informed decisions.
Quelling the concerns of EPR and Bell

We begin with a review of the famous thought experiment that was proposed by Einstein, Podolsky and Rosen (EPR) and mathematically formulated by Bell; the outcomes of which challenge the completeness of quantum mechanics and the locality of Nature. We then suggest a reinterpretation of the EPR experiment that utilizes observer complementarity; a concept from quantum gravity which allows spatially separated observers to have their own, independent reference frames. The resulting picture provides a self-consistent resolution of the situation that does not jeopardize causality nor unitarity, nor does it resort to ''spooky'' (non-local) interactions. Our conclusion is that EPR and Bell rely on an overly strong definition of locality that is in conflict with fundamental physics.
Elements of the theory of numbers

Part I The Fundamentals Introduction: The Primes The Fundamental Theorem of Arithmetic and Its Consequences An Introduction to Congruences Polynomial Congruences Primitive Roots Residues Multiplicative Functions Part II Special Topics Representation Problems An Introduction to Number Fields Partitions Recurrence Relations Appendix I: Notation Appendix II: Mathematical Tables Appendix III: Sample Final Examinations Appendix IV: Hints and Answers to selected problems
Rigorous coherent-structure theory for falling liquid films: Viscous dispersion effects on bound-state formation and self-organization

We examine the interaction of two-dimensional solitary pulses on falling liquid films. We make use of the second-order model derived by Ruyer-Quil and Manneville [Eur. Phys. J. B 6, 277 (1998); Eur. Phys. J. B 15, 357 (2000); Phys. Fluids 14, 170 (2002)] by combining the long-wave approximation with a weighted residual technique. The model includes (second-order) viscous dispersion effects which originate from the streamwise momentum equation and tangential stress balance. These effects play a dispersive role that primarily influences the shape of the capillary ripples in front of the solitary pulses. We show that different physical parameters, such as surface tension and viscosity, play a crucial role in the interaction between solitary pulses giving rise eventually to the formation of bound states consisting of two or more pulses separated by well-defined distances and traveling at the same velocity. By developing a rigorous coherent-structure theory, we are able to theoretically predict the pulse-separ.
A Task Offloading Method for Smart Instruments Based on Edge Computing

The development of edge computing technology has enabled the promotion of traditional industrial instruments to smart instruments with collaborative working capabilities. Smart instruments are widely distributed in industrial production sites and can monitor working conditions in real time. Implementing multi-instruments collaborative computing on the edge side will help improve the responsiveness of industrial systems and reduce instrument maintenance costs. This paper proposes a task offloading method for smart instruments based on edge computing. Firstly, the instrument capability is evaluated based on the characteristics of the smart instrument. Then, the task scheduling of smart instruments is optimized based on Lyapunov algorithm. Finally the simulation verification is carried out. The experimental results show that the algorithm in this paper has a significant effect on improving computing power and reducing resource occupancy.
Cross-Plane DDoS Attack Defense Architecture Based on Flow Table Features in SDN

Software-Defined Networking (SDN) actualizes the separation of control and forwarding and innovates network functionalities with a logically centralized controller. Contemporary SDN infrastructure exposes the potential bottlenecks which are prone to engage in distributed denial of service attack (DDoS) thus posing an ever-increasing threat. This paper adopts the idea of “cross-plane collaboration” accomplishing DDoS attack defense and incorporates a two-phase project deploying the lightweight detection mechanism in data layer and the fine-grained filtering model in control layer. The coadjutant detection mechanism introduces a novel three-dimensional entropy consisting of five flow table features performing real-time feature detection; the defense strategy schedules an attack classification algorithm based on neural network by means of extracting four flow rule features designed to locate compromised interfaces occupied by malicious traffic. Extensive experiments are implemented to demonstrate the method we proposed brings excellent superiority. The detection rate of the classification filtering model is 99.4%, and it is real-time, with a detection time of 0.51s. In addition, the method of cross-layer defense reduces the CPU utilization of the controller.
Is Untrusted Randomness Helpful?

Randomized algorithms and protocols assume the availability of a perfect source of randomness. In real life, however, perfect randomness is rare and is almost never guaranteed. The gap between these two facts motivated much of the work on randomness and derandomization in theoretical computer science. In this work, we deﬁne a new type of randomized algorithms (and protocols), that we call doubly-randomized algorithms (protocols). Such algorithms have access to two separate (read-once) random strings. The ﬁrst string is trusted to be perfectly random, but its length is bounded by some parameter k = k ( n ) (where n is the length of the input). We think of k as relatively small, say sub-linear or poly-logarithmic in n . The second string is of unbounded length and is assumed to be random, but its randomness is not trusted. The output of the algorithm is either an output in the set of possible outputs of the problem, or a special symbol, interpreted as do not know and denoted by ⊥ . On every input for the algorithm, the output of the algorithm must satisfy the following two requirements: We discuss relations of this new deﬁnition to several previously studied notions in randomness and derandomization. For example, when considering polynomial-time algorithms, if k is we get the complexity class ZPP , while if k we get the complexity class BPP , and a general k , the viewed an interactive with a probabilistic polynomial-time and a probabilistic polynomial-time veriﬁer, random number of trusted random bits. We also give query complexity separations that show cases where the untrusted random string is provenly helpful. Speciﬁcally, we show that there are promise problems that can be solved by doubly-randomized protocols with only one query and just a logarithmic number of trusted random bits, whereas any randomized protocol requires either a linear number of random bits or an exponential number of queries, and any zero-error randomized protocol requires a polynomial number of queries.
Translation and Expression of Professional Terms of Public Emergencies in External Reports

In order to better grasp the expression and translation methods of professional terms in the process of external reporting of public emergencies and grasp the characteristics and elements of the translation of publicity events, this study takes public emergencies as the research object and focuses on the application of English and Chinese professional terms and translation strategies in the process of external reporting. Therefore, based on the in-depth analysis of the news reporting framework of public health emergencies, this paper deeply summarizes the application skills of professional terms in publicity texts, so as to provide a certain reference for further improving the translator's ability to control the means of discourse cohesion and the level of discourse control of Chinese-English professional terms in the translation of public health events.
Use of Dioscorea dumetorum Pax as feed ingredient and natural antioxidant on growth, carcass characteristics and blood metabolites of broiler chickens

This experiment was conducted to evaluate the effects of Dioscorea dumetorum chips (DDC) as a feed ingredient and natural antioxidant on growth performance, carcass characteristics, and blood metabolites in broiler chickens. A total of 240 seven-day-old male broiler chickens were randomly assigned to 5 diets: maize and soya bean meal as the basal diet without antioxidant or negative control diet (D0-A), basal diet with synthetic antioxidant or positive control diet (D0+A) and the basal diet containing 4, 8 or 12% of DDC without synthetic antioxidant corresponding to diets D4-A, D8-A and D12-A. Each experimental unit contains 12 birds reared on litter. The results indicated that feed intake was higher (P<0.05) for diet D8-A. Dietary inclusion of DDC had no effect on live weight gain during starting period. Average feed conversion ratio varied from 2.07 to 2.36. This was better for D12-A, over all rearing period. At day 56 of age, the higher carcass yield and most of others part (P<0.05) were obtained on the diets D12-A and D8-A. The abdominal fat weight percentage has reduced with increasing the level of DDC in the diet. Haematological indices of broiler finishers were not affected by the diets (P>0.05). However, the concentration of haematocrit increased significantly with inclusion levels of DDC in the diet compare to the control diets. Also, high uric acid concentration was recorded (P<0.05) in broiler chickens fed diet D12-A. So, 12% and 8% of DDC in diets improved feed efficiency and can replace synthetic antioxidant and part of maize as a feed ingredient to enhance broiler chicken’s growth and overall health.
Cluster Merging Using Enhanced Density based Fuzzy C-Means Clustering Algorithm

The fuzzy set theory has been wide used in clustering of machine learning with data mining since fuzzy theory has been introduced in 1960s. In particular, fuzzy C-means algorithm is a popular fuzzy clustering algorithm up to date. An element is assigned to any cluster with each membership value using fuzzy C-means algorithm. This algorithm is affected from the location of initial cluster center and the proper cluster size like a general clustering algorithm as K-means algorithm. This setting up for initial clustering is subjective. So, we get improper results according to circumstances. In this paper, we propose a cluster merging using enhanced density based fuzzy C-means clustering algorithm for solving this problem. Our algorithm determines initial cluster size and center using the properties of training data. Proposed algorithm uses grid for deciding initial cluster center and size. For experiments, objective machine learning data are used for performance comparison between our algorithm and others.
Infrared Optical Conductivity of Bulk Bi 2 Te 2 Se

: Mid- and near-infrared measurements reveal that the optical conductivity of the three-dimensional topological insulator, Bi 2 Te 2 Se, is dominated by bulk carriers and shows a linear-in-frequency increase at 0.5 to 0.8 eV. This linearity might be interpreted as a signature of three-dimensional (bulk) Dirac bands; however, band-structure calculations show that transitions between bands with complex dispersion contribute instead to the inter-band optical conductivity at these frequencies and, hence, the observed linearity is accidental. These results warn against the oversimpliﬁed interpretations of optical-conductivity measurements in di ﬀ erent Dirac materials.
An Adaptively Constructed Algebraic Multigrid Preconditioner for Irreducible Markov Chains

The computation of stationary distributions of Markov chains is an important task in the simulation of stochastic models. The linear systems arising in such applications involve non-symmetric M-matrices, making algebraic multigrid methods a natural choice for solving these systems. In this paper we investigate extensions and improvements of the bootstrap algebraic multigrid framework for solving these systems. This is achieved by reworking the bootstrap setup process to use singular vectors instead of eigenvectors in constructing interpolation and restriction. We formulate a result concerning the convergence speed of GMRES for singular systems and experimentally justify why rapid convergence of the proposed method can be expected. We demonstrate its fast convergence and the favorable scaling behavior for various test problems.
Psychophysiological Adaptations to Pilates Training in Overweight and Obese Individuals: A Topical Review

The prevalence of overweightness and obesity has been documented as a major public health issue since it has increased at an alarming rate worldwide. Structured physical exercise programs have been reported as an essential strategy for preventing, managing, and treating obesity, inducing critical improvements in various physiological and psychological markers. However, it is unclear whether Pilates training can elicit positive changes in body composition, physical fitness, cardiometabolic health, and well-being among overweight and obese populations. The purpose of this topical review was to catalog studies investigating the physiological and psychological adaptations to Pilates training in order to identify what outcomes have been assessed, the research methods used, and the results. The inclusion/exclusion criteria were met by 14 published articles involving 582 participants (83% female) who were overweight or obese. The present topical review on Pilates training-induced adaptations shows that this widely used exercise type can significantly improve the majority of the selected indicators. These beneficial changes are frequently focused on anthropometric parameters, body composition, glucose, and lipid metabolism, as well as blood pressure in sedentary overweight or obese women. Specialized equipment-based Pilates interventions and trials investigating various mental health indices were limited. Further research is warranted in this area, emphasizing the Pilates training configuration and potential mechanisms behind positive alterations in several psychophysiological markers through large-scale randomized controlled trials with superior methodological quality, implementing long-term interventions in various populations that are overweight and obese.
JET LUMINOSITY FROM NEUTRINO-DOMINATED ACCRETION FLOWS IN GAMMA-RAY BURSTS

A hyperaccretion disk formed around a stellar-mass black hole is a plausible model for the central engine that powers gamma-ray bursts (GRBs). If the central black hole rotates and a poloidal magnetic field threads its horizon, a powerful relativistic jet may be driven by a process resembling the Blandford–Znajek (BZ) mechanism. We estimate the luminosity of such a jet as a function of mass accretion rate and other accretion parameters assuming that the poloidal magnetic field strength is comparable to the inner accretion disk pressure. We show that the jet efficiency attains its maximal value when the accretion flow is cooled via optically thin neutrino emission. The jet luminosity is much larger than the energy deposition through neutrino–antineutrino annihilation () provided that the black hole is spinning rapidly enough. When the accretion rate onto a rapidly spinning black hole is larger than 0.003–0.01 M☉ s−1, the disk becomes optically thin to neutrinos, its pressure increases and the jet luminosity is sufficient to drive a GRB. The transition of the accretion rate above and below this limiting value may cause the large variability observed in GRB.
A 3D view of the Hydra I cluster core- II. Stellar populations

Abstract Several observations of the central region of the Hydra I galaxy cluster point to a multi-epoch assembly history. Using our novel FORS2/VLT spectroscopic data set, we were able to map the luminosity-weighted age, [Fe/H] and [α/Fe] distributions for the stellar populations around the cD galaxy NGC 3311. Our results indicate that the stellar populations follow the trends of the photometric substructures, with distinct properties that may aid to constrain the evolutionary scenarios for the formation of the cluster core.
Nonadiabatic tunneling in circularly polarized laser fields. II. Derivation of formulas

We provide detailed analysis of strong field ionization of degenerate valence p orbitals by circularly polarized fields. Our analytical approach is conceptually equivalent to the Perelomov, Popov, and Terent'ev (PPT) theory and is virtually exact for short range potentials. After benchmarking our results against the PPT theory for s orbitals, we obtain the results for p orbitals. We also show that, as long as the dipole approximation is valid, both the PPT method and our results are gauge invariant, in contrast with widely used strong field approximation (SFA). Our main result, which has already been briefly outlined in [I. Barth and O. Smirnova, Phys. Rev. A 84, 063415 (2011)], is that strong field ionization preferentially removes electrons counter-rotating to the circularly polarized laser field. The result is illustrated using the example of Kr atom. Strong, up to one order of magnitude, sensitivity of strong field ionization to the sense of electron rotation in the initial state is one of the key signatures of non-adiabatic regime of strong field ionization.
Strain-controlled spin splitting in the conduction band of monolayer WS 2

Spin splitting bands that arise in the conduction band minimum (CBM) of the ${\text{WS}}_{2}$ monolayer (ML) play an important role in the spin-orbit phenomena such as spin-valley coupled electronics. However, application of strain strongly modifies electronic properties of the ${\text{WS}}_{2}$ ML, which is expected to significantly affect the properties of the spin splitting bands. Here, by using fully-relativistic first-principles calculations based on density-functional theory, we show that a substantial spin splitting band observed in the CBM is effectively controlled and tuned by applying the biaxial strain. We also find that these spin splitting bands induce spin textures exhibiting fully out-of-plane spin polarization in the opposite direction between the $K$ and $Q$ points and their time reversals in the first Brillouin zone. Our study clarifies that the strain plays a significant role in the spin-orbit coupling of the ${\text{WS}}_{2}$ ML, which has very important implications in designing future spintronics devices.