Miracle-dz/UniFAR-FTUs huggingface dataset:
Fractal, group theoretic, and relational structures on Cantor space

Cantor space, the set of infinite words over a finite alphabet, is a type of metric space with a ‘self-similar’ structure. This thesis explores three areas concerning Cantor space with regard to fractal geometry, group theory, and topology. We first find results on the dimension of intersections of fractal sets within the Cantor space. More specifically, we examine the intersection of a subset E of the n-ary Cantor space, Cn with the image of another subset F under a random isometry. We obtain almost sure upper bounds for the Hausdorff and upper box-counting dimensions of the intersection, and a lower bound for the essential supremum of the Hausdorff dimension. We then consider a class of groups, denoted by Vn(G), of homeomorphisms of the Cantor space built from transducers. These groups can be seen as homeomorphisms that respect the self-similar and symmetric stucture of Cn, and are supergroups of the Higman-Thompson groups Vn. We explore their isomorphism classes with our primary result being that Vn(G) is isomorphic to (and conjugate to) Vn if and only if G is a semiregular subgroup of the symmetric group on n points. Lastly, we explore invariant relations on Cantor space, which have quotients homeomorphic to fractals in many different classes. We generalize a method of describing these quotients by invariant relations as an inverse limit, before characterizing a specific class of fractals known as Sierpiński relatives as invariant factors. We then compare relations arising through edge replacement systems to invariant relations, detailing the conditions under which they are the same.
Example of a Statistical Analysis

The goal of this analysis is to see if there is a difference between the A and B samples, as well as between the C and D samples. The first step in my analysis was to make some quality control plots that can be used to check the overall quality of the raw data. Figure 1 shows the distribution of the PM probes for each chip. One of the underlying assumptions for the normalization procedure I use is that the PM probe data all come from the same distribution, with the only differences being the location and scale. Basically, this means that we want the shape of the curves to be very similar, and we want the curves to be fairly close to each other. There appears to be a large difference between the A/B and C/D samples that may have an impact on our analysis. Since we are only concerned with the comparison of A/B and C/D it might make sense to pre-process these samples separately. Figure 2 is designed to show differences between samples due to mRNA degradation or from the in vitro translation step. Any differences between samples will be indicated by a different slope. Again, the only differences are between
Role of the Thermodynamic Structure of the Inner Core in Predicting the Intensification of Hurricane Patricia (2015)

We use short‐range ensemble forecasts and ensemble clustering analysis to study the factors affecting the intensification of Hurricane Patricia (2015). Convection‐permitting ensemble forecasts are classified into two groups: 10 spin‐down (SPD) members and 10 spin‐up (SPU) members with intensification rates of <0 and >0 m s−1 for the first 6 hr, respectively. Ensemble clustering analysis found that the wind–pressure relationship was incorrect in the SPD group, indicating that the SPD issue may be partly caused by an imbalance in the initial minimum sea‐level pressure (MSLP) and the maximum wind speed (MWS). The SPD issue appears to be related to three main points: (a) a weaker upper‐level warm core; (b) a drier inner core at low levels; and (c) a dry, cold air intrusion at mid‐levels. In contrast, the SPU group has a stronger upper‐level warm core and a relatively wet inner core at lower levels, as well as a relatively strong secondary circulation. These favorable initial conditions in the SPU group, combined with a greater updraft and stronger convection around the eyewall, result in more latent heating around or in the eyewall that favors the intensification of the tropical cyclone. Comparisons between the SPD and SPU groups suggest that the analyzed ensemble could not accurately capture the relationship between the initial MSLP and MWS, which, combined with the unfavorable thermodynamic conditions at the initial time, resulted in the incorrect evolution of the intensity. Therefore, improving the initial conditions appears to be an effective way to address the SPD issue.
TDPRO: Time-Domain-Based Computing-in Memory Engine for Ultra-Low Power ECG Processor

For the wearable biomedical signal detection, both high accuracy and low-power consumption are critical requirements. Various works have employed the neural network to improve the detecting accuracy and develop the biomedical processor. However, the biomedical processor with neural network engine contains massive data movements and large data buffers. One solution is the computing-in memory (CIM) architecture, which locates more data near the computing engine to reduce data movements. In traditional CIM-based solution, the detecting accuracy and power consumption is difficult to be optimized simultaneously, where the accuracy should be satisfied for the detection. To date, the time-domain computing engine have been developed to employ both digital and time domain computation. In this work, we present a high-precision time-domain engine to perform 8-bit multiplication and addition operation for the biomedical signal detection. With the high precision time-domain engine, we develop a CIM-based neural-network processor, namely TDPRO, to perform the detection of arrhythmia. In addition, we develop TD-zero-jumping (TDJ) and idle-shutdown (ISD) techniques according to signal features and data mapping strategy, further optimizing the power consumption. Based on our evaluation, the TD-based 8-bit mulitply-accumulation operation is robust, without declining the accuracy of biomedical signal detection. We design a ECG processor with the proposed TDPRO architecture, which obtains 98.60% high accuracy and 75.7% power saving compared to the recent the state-of-the-art study.
A Sulfur Dioxide Concentration Detector for Daylily Food Safety Applications

In this work, a sulfur dioxide concentration detector is newly proposed for daylily food safety applications. The proposed detector consists of a sulfur dioxide sensor and a converter. The proposed detector can linearly convert sulfur dioxide concentration in daylily solution into output time period. By the output, sulfur dioxide concentration in daylily solution could be gotten quickly and conveniently. Furthermore, in order to be suitably used in different sulfur dioxide concentrations, the proposed converter with low range of sensing resistance was designed. The measured sensing resistance was from 0.1 to 100 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula>, and the output time period was corresponding from <inline-formula> <tex-math notation="LaTeX">$33.1 ~\mu \text{s}$ </tex-math></inline-formula> to 33.12 ms. The sensitivity was <inline-formula> <tex-math notation="LaTeX">$331.2 ~\mu \text{s}/\text{k}\Omega $ </tex-math></inline-formula>. The measured range of sulfur dioxide concentration in daylily solution was 200–1600 ppm, and the output time period was 1.17 ms–<inline-formula> <tex-math notation="LaTeX">$472 ~\mu \text{s}$ </tex-math></inline-formula>. The measured maximum linear error of 0.347% was obtained. Finally, two different sulfur dioxide concentrations in daylily solutions were experimented. By measured and experimented results, the proposed sulfur dioxide concentration detector was functioning normally for daylily food safety applications.
Your Move: An Open Access Dataset of Over 1500 Board Gamer’s Demographics, Preferences and Motivations

Intro This paper reports the demographics of a large sample of board gamers and their in-game motivations and preferences. Methods We report the specific preferences of 1603 board gamers (i.e. preferred platform, mechanics, style, theme, player count and game length) and player demographics, i.e. age, gender, education level as well as neurodivergence in this population. Participants were sampled via board game groups and game distributor mailing lists. Results Findings confirmed previous assumptions that board gamers are primarily middle-aged, university/college-educated, white males. We show that most gamers identify as mid-core/core with a preference for shorter, competitive Euro games. They tend to prefer in-person play with 3-4 players. However, a sizeable portion of the sample did not fit this description, showing a more complex picture. Discussion Results describe the population's demographics and detailed description of gamers preferred, mechanics, themes, components, preferences and motivations. An anonymised version of this data set is provided alongside this work for game designers and academics to explore further and cross-reference relationships between demographics and preferences/motivations. Considering that a quarter of the sample were neurodivergent (i.e. reported at least one mental health or neurodevelopmental condition) the dataset could also help clinicians who use board games as interventional tools. We hope this dataset (https://osf.io/tu8yq) will be helpful to board game designers, clinicians, educators, teachers, therapists and researchers interested in utilising board games to make informed choices about which games they use.
What drives the preferences for cleaner energy? Parametrizing the elasticities of environ-mental quality demand for greenhouse gases

Research background: The heterogeneity in the factors that affect demand for environmental quality implicates a diverse set of policies and actions aimed at achieving cleaner production to address the challenges posed by pollution and damage to the natural environment. Even though this topic has been widely addressed, mainly from the traditional perspective of the Environmental Kuznets Curves hypothesis (EKC), it has been assumed that the environment is a luxury good with an income elasticity greater than unity. However, it has recently been recognized that the relationship between income and demand for cleaner energy may be more complex and that further inquiry may be needed for a better understanding. Purpose of the article: This research work, employing a panel of European countries, offers direct explicit parameters for the elasticity of income-environmental quality demand for Greenhouse Gases (GHG), as well as its relationship with other important factors. It provides quantitative novel insights into the complex relationship between income and the preferences for cleaner energy. Methods: A hierarchical regression equations approach is used to analyze the evolution of the elasticity of income-environmental quality demand with the inclusion of further co-variates that are relevant for the preferences side of the EKC, such as consumption, R+D investment and BERD (Business Enterprise Research and Development). The data for the empirical study comes from a panel of 16 European countries for the period from 2010 to 2020. Findings & value added: The results show robust evidence that the elasticity of environmental quality demand, which although positive and significant, does not exceed one. To obtain an elasticity above unity, two more variables are needed, namely the R+D expenditure of business enterprises and the exposure of citizens to air pollution. These two factors have a similar or even higher effect on the preferences of agents for cleaner energy, which also means that the preferences of the citizens are endogenous to technological development. At the theoretical level, this work shows that the technological and preferences arguments are not substitute explanations of the EKC, but that technological development exerts a positive effect on the preferences of inhabitants, whose demand for environmental quality is heavily conditioned by their capabilities to see pollution, even more than by their income level. This also means that public policies directed to improve environmental awareness should be directed first towards those regions where the exposure of the citizens to pollution is lower.
Photoswitchable Molecular Units with Tunable Nonlinear Optical Activity: A Theoretical Investigation

The first-, second-, and third-order molecular nonlinear optical properties, including two-photon absorption of a series of derivatives, involving two dithienylethene (DTE) groups connected by several molecular linkers (bis(ethylene-1,2-dithiolato)Ni- (NiBDT), naphthalene, quasilinear oligothiophene chains), are investigated by employing density functional theory (DFT). These properties can be efficiently controlled by DTE switches, in connection with light of appropriate frequency. NiBDT, as a linker, is associated with a greater contrast, in comparison to naphthalene, between the first and second hyperpolarizabilities of the “open–open” and the “closed–closed” isomers. This is explained by invoking the low-lying excited states of NiBDT. It is shown that the second hyperpolarizability can be used as an index, which follows the structural changes induced by photochromism. Assuming a Förster type transfer mechanism, the intramolecular excited-state energy transfer (EET) mechanism is studied. Two important parameters related to this are computed: the electronic coupling (VDA) between the donor and acceptor fragments as well as the overlap between the absorption and emission spectra of the donor and acceptor groups. NiBDT as a linker is associated with a low electronic coupling, VDA, value. We found that VDA is affected by molecular geometry. Our results predict that the linker strongly influences the communication between the open–closed DTE groups. The sensitivity of the molecular nonlinear optical properties could assist with identification of molecular isomers.
Manifold Regularized Transfer Distance Metric Learning

The performance of many computer vision and machine learning algorithms are heavily depend on the distance metric between samples. It is necessary to exploit abundant of side information like pairwise constraints to learn a robust and reliable distance metric[2, 3]. Let D = {(xl i ,xj,yi j)} l i, j=1 denotes the labeled training set for the target task, wherein xi, x j ∈ Rd and yi j = ±1 indicates xl i and xl i are similar/dissimilar to each other. Then, a metric is usually learned to minimize the distance between the data from the same class and maximize their distance otherwise. This leads to the following loss function for learning the metric A:
Cell-specific rates of sulfate reduction and fermentation in the sub-seafloor biosphere

Microorganisms in subsurface sediments live from recalcitrant organic matter deposited thousands or millions of years ago. Their catabolic activities are low, but the deep biosphere is of global importance due to its volume. The stability of deeply buried sediments provides a natural laboratory where prokaryotic communities that live in steady state with their environments can be studied over long time scales. We tested if a balance is established between the flow of energy, the microbial community size, and the basal power requirement needed to maintain cells in sediments buried meters below the sea floor. We measured rates of carbon oxidation by sulfate reduction and counted the microbial cells throughout ten carefully selected sediment cores with ages from years to millions of years. The rates of carbon oxidation were converted to power (J s−1 i.e., Watt) using the Gibbs free energy of the anaerobic oxidation of complex organic carbon. We separated energy dissipation by fermentation from sulfate reduction. Similarly, we separated the community into sulfate reducers and non-sulfate reducers based on the dsrB gene, so that sulfate reduction could be related to sulfate reducers. We found that the per-cell sulfate reduction rate was stable near 10−2 fmol C cell−1 day−1 right below the zone of bioturbation and did not decrease with increasing depth and sediment age. The corresponding power dissipation rate was 10−17 W sulfate-reducing cell−1. The cell-specific power dissipation of sulfate reducers in old sediments was similar to the slowest growing anaerobic cultures. The energy from mineralization of organic matter that was not dissipated by sulfate reduction was distributed evenly to all cells that did not possess the dsrB gene, i.e., cells operationally defined as fermenting. In contrast to sulfate reducers, the fermenting cells had decreasing catabolism as the sediment aged. A vast difference in power requirement between fermenters and sulfate reducers caused the microbial community in old sediments to consist of a minute fraction of sulfate reducers and a vast majority of fermenters.
Multi-Feature Supervised Reinforcement Learning for Stock Trading

Deep reinforcement learning (DRL) algorithm is often used to find the best trading strategy in algorithmic trading. However, the classical DRL model is difficult to achieve rapid convergence, and the features extracted from the market data are relatively simple, resulting in incomplete DRL learning information. In this paper, we propose a supervised reinforcement learning method, a hybrid optimal investment strategy formation method consisting of long short-term memory neural network (LSTM) and deep deterministic policy gradient (DDPG). By participating in reinforcement learning in the early stage of supervised learning, agents can obtain guiding prior experience, thus reducing the cost of agent learning and accelerating convergence. In addition, multi-feature state input is added to the model to optimize the agent’s learning of the environment. Compared with DDPG algorithm, LSTM-DDPG algorithm achieves convergence faster. Experiments on three regional stock markets in China, the United States and Europe show that LSTM-DDPG algorithm has higher profit and lower risk than B&H, MACD and LSTM trading strategies.
Linear Polarization Optimization for Wideband MIMO Systems With Reconfigurable Arrays

Reconfigurable arrays mold the propagation environment to benefit wireless systems. We use single-port polarization-reconfigurable antennas in a wideband multiple-input multiple-output (MIMO) system and demonstrate the efficacy of reconfiguration techniques based on analytical channel models. We apply a double-directional channel model to show that polarization reconfiguration acts as an additional precoding step on an unpolarized channel. We use Jensen’s inequality to upper bound the spectral efficiency and leverage the relaxed objective to derive closed-form expressions for the optimal polarization angles at each antenna. We also derive upper bounds on the performance of a polarization reconfigurable system and develop an efficient procedure for polarization reconfiguration that aims to maximize these upper bounds. Numerical results show that the proposed simplified methods achieve near-optimal in wideband MIMO settings.
A Novel Nonlinear Fault Tolerant Control for Manipulator under Actuator Fault

A fault tolerant control (FTC) scheme based on adaptive sliding mode control technique is proposed for manipulator with actuator fault. Firstly, the dynamic model of manipulator is introduced and its actuator faulty model is established. Secondly, a fault tolerant controller is designed, in which both the parameters of actuator fault and external disturbance are estimated and updated by online adaptive technology. Finally, taking a two-joint manipulator as example, simulation results show that the proposed fault tolerant control scheme is effective in tolerating actuator fault; meanwhile it has strong robustness for external disturbance.
Linearizability By Construction

This paper presents a methodology for building a class of highly concurrent linked objects that are linearizable by construction, and a corresponding verification technique. The main feature these objects have in common is that they allow correct sequential search operations. We derive our design from invariants and lemmas needed to guarantee the correctness of the search operation, which can then be translated to a purely thread modular proof. We design and verify an internal-external binary search tree following this methodology as an example. Together with generalizing the Hindsight lemma, we also prove a novel Data-Extension lemma that is particularly useful for trees. We show that some advanced highly concurrent data structures such as trees can be designed from some surprisingly simple invariants.
Solving Geometrically Nonlinear Problem on Deformation of a Helical Spring through Variational Methods

Deformation of the helical springs is mostly considered as linear, due to their linearly proportionate elongation to their applied load. However, this assumption is not capable of capturing the full and accurate mechanical behavior of helical springs undergoing large deformations. Moreover, their mechanical behavior under compression is usually neglected. In this article, the problem of the tension-compression of a helical spring is solved with higher accuracy by considering a modification of its geometrical parameters. The novel solution, based on the use of variational methods, is both precise and much easier to apply than a solution based on the use of differential equations.
Identification of Eimeria tenella sporozoite immunodominant mimotopes by random phage-display peptide libraries–a proof of concept study

Introduction Coccidiosis, caused by parasites of numerous Eimeria species, has long been recognized as an economically significant disease in the chicken industry worldwide. The rise of anti-coccidian resistance has driven a search for other parasite management techniques. Recombinant antigen vaccination presents a highly feasible alternative. Properly identifying antigens that might trigger a potent immune response is one of the major obstacles to creating a viable genetically modified vaccine. Methods This study evaluated a reverse immunology approach for the identification of B-cell epitopes. Antisera from rabbits and hens inoculated with whole-sporozoites of E. tenella were used to identify Western blot antigens. The rabbit IgG fraction from the anti-sporozoite serum exhibited the highest reactogenicity; consequently, it was purified and utilized to screen two random Phage-display peptide libraries (12 mer and c7c mer). After three panning rounds, 20 clones from each library were randomly selected, their nucleotide sequences acquired, and their reactivity to anti-sporozoite E. tenella serum assessed. The selected peptide clones inferred amino acid sequences matched numerous E. tenella proteins. Results and Conclusions The extracellular domain of the epidermal growth factor-like (EGF-like) repeats, and the thrombospondin type-I (TSP-1) repeats of E. tenella micronemal protein 4 (EtMIC4) matched with the c7c mer selected clones CNTGSPYEC (2/20) and CMSTGLSSC (1/20) respectively. The clone CSISSLTHC that matched with a conserved hypothetical protein of E. tenella was widely selected (3/20). Selected clones from the 12-mer phage display library AGHTTQFNSKTT (7/20), GPNSAFWAGSER (2/20) and HFAYWWNGVRGP (8/20) showed similarities with a cullin homolog, elongation factor-2 and beta-dynein chain a putative E. tenella protein, respectively. Four immunodominant clones were previously selected and used to immunize rabbits. By ELISA and Western blot, all rabbit anti-clone serums detected E. tenella native antigens. Discussion Thus, selected phagotopes contained recombinant E. tenella antigen peptides. Using antibodies against E. tenella sporozoites, this study demonstrated the feasibility of screening Phage-display random peptide libraries for true immunotopes. In addition, this study looked at an approach for finding novel candidates that could be used as an E. tenella recombinant epitope-based vaccine.